--- title: "Unpacking the Drop in COVID-19 Case Fatality Rates" author: "Helen Zhou, Cheng Cheng, Jeremy C. Weiss, Zachary C. Lipton" date: 2021-01-28 authors: - hzhou - ccheng - jweiss - zlipton tags: - CDC data - FDOH data - time series heroImage: unpack-cfr-full-size.jpg heroImageThumb: unpack-cfr-thumb.jpg summary: | Since the pandemic started, case fatality has plummeted. But how much can we attribute this drop to treatment improvements versus increased testing, younger patients, etc.? acknowledgements: | We thank Roni Rosenfeld, Cosma Shalizi, and Marc Lipsitch for their detailed feedback throughout this analysis and the drafting of this manuscript. We also thank Alex Reinhart and Samuel Gratzl for getting our post into the appropriate format. We would also like to thank the Block Center, AWS, UPMC, Abridge, AHN, NVIDIA, and Microsoft Azure for their generous support of our healthcare research. output: blogdown::html_page: toc: true # bookdown::html_document2: default bibliography: - ./unpack-cfr/refs.bib bilbio-style: apalike link-citations: yes ---
Since July, our team at CMU DELPHI has been tracking the drop in case fatality rates, driven by the question: “What explains the movement (and apparent overall decline) in case fatality rate over the course of the COVID-19 pandemic?” Last month, we released a manuscript analyzing the data through Thanksgiving. This blog post distills our work on unpacking the drop in CFR, with newly updated data released on December 31st.
Indeed, over the course of the pandemic, the case fatality (CFR) rate has fallen considerably. Between the first peak in mid-April and the second peak in mid-July, the case fatality rate in the United States fell from 7.9% to the 0.7%–2.3% range, where it has since remained despite cases rising again into an (ongoing) third wave:
In both academic articles and the broader public discourse, several possible explanations have been floated for the drop in CFR, mostly centering around the following hypotheses:
(H1) The age distribution among infected patients has shifted, altering the fatality rate significantly due to the comparative higher risk among the geriatric populations (Thompson (2020), Whet (2020), Horwitz et al. (2020)).
(H2) Testing capacity has gone up significantly, with case fatality rate driven down primarily due to the rising number of tests performed (Fan et al. (2020), Madrigal and Moser (2020)).
(H3) Apparent shifts in case fatality rate, are artifacts due to the delay between detection and fatality (Thompson (2020), Madrigal (2020)).
(H4) Treatment has improved as doctors grow more experienced and new therapeutics become available, lowering the fatality rate over time (Levy (2020), Horwitz et al. (2020), Beigel et al. (2020), Group (2020), Self et al. (2020)).
(H5) The disease itself is mutating, leading to changes in the actual infection fatality rate over time (Pachetti et al. (2020), Fan et al. (2020)).
(H6) Social distancing precautions have reduced the viral load that individuals are exposed to, resulting in less severe infections (El Zein (n.d.), Pachetti et al. (2020), Piubelli et al. (2020)).
Note that when the age distribution shifts to a younger demographic (H1), the dropping aggregate case fatality rate can be misleading due to differences in fatality between different age groups. Additionally, the next two phenomena—testing ramp-up (H2) and delays between detection and fatality (H3)—can cause the behavior of case fatality rate to diverge substantially from the behavior of the true infection fatality rate. Thus, if policy-makers use the aggregate case fatality rate to represent the severity of COVID-19, this could result in misinformed policy decisions.
On the other hand, the last three phenomena—improved treatments, disease mutation, and changing viral load—impact both the case fatality rate and the actual infection fatality rate, and could be reasonable grounds for policy considerations such as re-opening. In this work, we demonstrate how given accurate, sufficiently granular data, the first three phenomena (“artifacts”) can be accounted for when attempting to quantify improvements in treatment (H4). We note, however, that without additional data, H5 and H6 cannot be decisively separated out from H4.
We argue that complete and accurate age-stratified hospitalization data are essential for distinguishing true improvements from artifacts. Hospitalizations should be less influenced by testing capacity than confirmed cases, and less influenced by treatment efficacy than deaths. Additionally, compared to the general population, testing among the inpatient population has been relatively thorough (compared to the general population) throughout the course of the pandemic. While there may have been changes in admitting criteria at the very worst moments (Phua et al. (2020), Cohen et al. (2020)), for example, when New York hospital demand exceeded capacity in late March, for the most part, criteria for inpatient hospitalization is relatively consistent across time periods. Regrettably, however, reliable line-level age-stratified hospitalization data is not yet publicly available for most states (Ornstein (2020), Murray (2020)), leaving central questions unresolved.
We center our analysis on (1) state-level data made available by the Florida Department of Health (FDOH, Florida Department of Health (2020)) and (2) recently released (but incomplete) national line-level data made available by the Centers for Disease Control and Prevention (CDC, CDC Case Surveillance Task Force (2020)) from April 1, 2020 to December 1, 2020. Both the FDOH and CDC have demonstrated an uncommon openness by sharing line-level data, including date of detection, demographics including age and gender, and indicators of eventual hospitalization and death. The line-level nature of the data allows us to perform a cohort-based analysis, generating descriptive statistics comparing case confirmations, hospitalizations, and deaths, among cohorts of patients defined by the date on which their infection was detected. By contrast, reported case fatality rates are typically not cohort-based—the patients whose deaths are reported in the numerator are not in general the same patients whose confirmed infections show up in the denominator. Because case confirmation tends to precede reported deaths, these signals tend to be misaligned and are subject to fluctuation, even if the actual case fatality rate were fixed (so long as incidence does change). Line-level data enable us to circumvent this problem. Moreover, the availability of age and gender data allows us both to track demographic shifts over time and to perform age-stratified analyses of fatality rates.
For details on the datasets and pre-processing, see the full manuscript. To reduce noise in our time series plots, we compute 7-day lagged averages for all remaining plots in the post unless otherwise specified.
We argue that three main phenomena fuel a dramatic “artificial” decrease in CFR: increased testing capacity (H1), shifting age distributions (H2), and delays between detection and fatality (H3). To separate the effects of these artifacts from those of true improvements, we do the following:
To avoid artifacts from increased testing (H1), when considering treatment improvements, we examine changes in hospitalization fatality rates, rather than case fatality rates.
To establish and account for shifting age distributions (H2), we examine cases, hospitalizations, and deaths stratified by age groups: 0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, and 80+.
To account for delays between detection and fatality (H3), we take advantage of line-level data for each individual. For each date, we extract the cohort of individuals confirmed positive on that date, as well as whether those individuals eventually died or were hospitalized. To provide enough time for patients’ eventual hospitalizations or deaths to be recorded, we filter out rows with positive specimen dates within 30 days of the last time the data was updated. Since CDC data was last updated on December 31th, we only use data from December 1st or earlier. Florida data is updated daily, but we use the same time range as the CDC data in order to make the plots comparable.
Taking the above three adjustments into account, our primary quantity of interest for treatment improvements is the age-stratified HFR. For the rest of the post, we define CFR and HFR at day \(t\) as follows:
\[ \begin{aligned} \text{CFR}_t &= \frac{\text{cases confirmed (or reported) at day $t$ that eventually die}}{\text{cases confirmed (or reported) at day $t$}}\\ \text{HFR}_t &= \frac{\text{cases confirmed (or reported) at day $t$ that eventually get hospitalized and die}}{\text{cases confirmed (or reported) at day $t$ that eventually get hospitalized}} \end{aligned} \]
Here, “eventually” means that the hospitalization or death was recorded by the date of data collection (i.e. December 31st), which gives each patient at least 30 days after their case date to have the events recorded.
Thus far, news and academic sources have highlighted three main “true improvements”: improvements in treatment (H4), disease mutations (H5), and reduced viral loads due to social distancing (H6). We seek to quantify treatment improvements (H4) by computing the decrease in hospitalization fatality rate. Although practice is constantly evolving, major improvements in treatment in our study time range, such as dexamethasone (Group (2020)) and remdesivir (Beigel et al. (2020)), have primarily targeted hospitalized patients, and we expect that improvements due to those treatments should be reflected in the HFR.
In order to quantify the change in HFR with uncertainty, we use a block-bootstrapping technique with post-blackening (Davison and Hinkley (1997)). This involves fitting cubic smoothing splines to each age group’s 7-day averaged HFRs, computing the residuals, block-bootstrap resampling 1000 replicates of the residuals, and adding the residuals back onto the fitted splines in order to create 1000 replicates sampled from the original data distribution. By re-fitting cubic smoothing splines to each of these datasets, we can estimate each day’s HFR with 95% confidence intervals. See the full manuscript for details of the procedure applied to our data.
In both the FDOH and the CDC datasets, one can discern three waves of COVID-19 cases. The first wave peaks around mid-April, the second wave peaks around mid-July, and the surge of cases leading up to December indicates an ongoing third wave:
Testing: While the FDOH and CDC datasets do not contain data for negative test results, it is informative to have the level of testing in mind when interpreting the above plots. Using data from the COVID Tracking Project, we observe that between April 1st and December 1st, testing has increased significantly, by approximately 964% in Florida and 1080% in the country (Figure 3). In Florida, we observe a spike in testing around the second peak, whereas national-level testing has risen more smoothly. However, our data shows that these spikes and increases in testing cannot fully account for the peaks. Despite increased testing inflating the raw number of cases, we still observe two peaks in positive test rates in April and July, and a surge in positive test rates leading up to December. Note that in Florida, the second peak is larger than the first, whereas nationally the second peak is smaller than the first. Leading up to December, positive test rates in Florida are similar to those seen nationally. For Florida, this already places the ongoing third wave at almost the same level as the first peak, and nationally it has already surpassed the second peak.
Cases: Across all age strata, as measured by cases, Florida’s second wave is the most severe out of the three waves. In aggregate, it has approximately 1153% more cases than in the first peak and 46% more cases than in the ongoing third wave (Figure 2a, left panel). By contrast, at a national level the ongoing third wave has substantially more cases than the first and second peaks—392% more than the first peak and 150% more than the second peak (Figure 2b, left panel). Also, note that the relative jump in cases between the first and second peaks is 96%, much less than the 1153% jump seen in Florida. This could be due to a combination of the spike in Florida’s testing in the second peak, as well as variation in the trajectories of different states (e.g. the populous state of New York was particularly hard-hit in the first wave).
Hospitalizations and Deaths: Overall, hospitalizations and deaths corroborate the story told by positive test rate. In Florida, hospitalizations and deaths again indicate a more severe second peak than first peak (Figure 2a, center and right panels), though the contrast in peak size is not as dramatic as in the plot of cases. By contrast, in the national data, the second peak is smaller than the first peak (Figure 2b, center and right panels), which is opposite to the trend seen in national cases. Much of the discrepancies of trends seen in cases versus in hospitalizations and deaths are likely attributable to increases in testing (Figure 3). Towards the third wave in December, Florida hospitalizations and deaths are at similar levels to that of the first wave. Nationally, the ongoing third wave appears to be worse than in the second wave.
Key takeaway #1: Testing increased between the first and second waves, but does not explain away these waves.
In this post, we focus primarily on age distribution shift. In our analysis we found that gender ratios in each group remained relatively flat over time, and so we choose not to stratify by gender due to reasonably small shift in the gender distribution over time, and practically to have enough support in each group.
Between the first two peaks, the age distribution of cases shifts substantially, with the median age in Florida falling from 51 to 40 and the median age group in national data falling from 50-59 to 30-39. After the second peak, the age distributions of cases, hospitalizations, and deaths continue to fluctuate. In September, we note an uptick in younger cases, possibly related to the start of the school year. Come December 1st, the Florida median age remains at 40 but the national median age group rises to 40-49. Additionally, we note that older individuals comprise a disproportionate share of the hospitalization and death counts (see Figure 4 below):
Key takeaway #2: Since age distributions shifted substantially between the first and second waves (and continue to fluctuate), age must be accounted for in order to separate out the effects of treatment from age shift.
Using the block-bootstrapping method described previously, we compute estimates of the HFR at the beginning of our study time range (April 1st), the end of our study time range (December 1st), and at the two peaks (April 15th and July 15th). We also compute changes in HFR as \(\frac{\text{HFR}_{new} - \text{HFR}_{old}}{\text{HFR}_{old}}\):
| Florida | National | |||||
|---|---|---|---|---|---|---|
| Age group | 2020-04-15 | 2020-07-15 | 04-15 to 07-15 | 2020-04-15 | 2020-07-15 | 04-15 to 07-15 |
| aggregate | 0.23 (0.21, 0.26) | 0.23 (0.21, 0.24) | -0.023 (-0.14, 0.13) | 0.29 (0.28, 0.3) | 0.17 (0.17, 0.18) | -0.41 (-0.44, -0.37) |
| 20-29 | - | - | - | 0.021 (0.018, 0.025) | 0.014 (0.012, 0.016) | -0.34 (-0.48, -0.16) |
| 30-39 | - | - | - | 0.044 (0.041, 0.047) | 0.027 (0.025, 0.03) | -0.37 (-0.44, -0.3) |
| 40-49 | - | - | - | 0.079 (0.074, 0.084) | 0.056 (0.053, 0.059) | -0.29 (-0.35, -0.22) |
| 50-59 | 0.092 (0.078, 0.11) | 0.1 (0.093, 0.11) | 0.12 (-0.085, 0.38) | 0.15 (0.14, 0.15) | 0.1 (0.096, 0.1) | -0.31 (-0.35, -0.27) |
| 60-69 | 0.18 (0.15, 0.21) | 0.21 (0.19, 0.22) | 0.13 (-0.045, 0.38) | 0.26 (0.25, 0.27) | 0.18 (0.18, 0.19) | -0.3 (-0.33, -0.26) |
| 70-79 | 0.31 (0.28, 0.34) | 0.33 (0.31, 0.34) | 0.034 (-0.078, 0.18) | 0.4 (0.39, 0.42) | 0.27 (0.26, 0.27) | -0.34 (-0.37, -0.31) |
| 80+ | 0.46 (0.43, 0.49) | 0.48 (0.46, 0.49) | 0.029 (-0.055, 0.12) | 0.57 (0.55, 0.59) | 0.41 (0.4, 0.42) | -0.27 (-0.3, -0.24) |
Table 1. Estimates of HFR and drop in HFR on peak dates. Median and 95% confidence intervals are computed using block bootstrapping. Results with inadequate support are omitted.
| Florida | National | |||||
|---|---|---|---|---|---|---|
| Age group | 2020-04-01 | 2020-12-01 | 04-01 to 12-01 | 2020-04-01 | 2020-12-01 | 04-01 to 12-01 |
| aggregate | 0.23 (0.2, 0.27) | 0.16 (0.12, 0.19) | -0.33 (-0.52, -0.094) | 0.34 (0.32, 0.35) | 0.13 (0.11, 0.15) | -0.61 (-0.67, -0.56) |
| 20-29 | - | - | - | 0.025 (0.019, 0.03) | 0.0066 (0.0014, 0.012) | -0.73 (-0.95, -0.43) |
| 30-39 | - | - | - | 0.049 (0.045, 0.054) | 0.019 (0.014, 0.025) | -0.61 (-0.72, -0.48) |
| 40-49 | - | - | - | 0.087 (0.08, 0.095) | 0.031 (0.024, 0.038) | -0.65 (-0.74, -0.54) |
| 50-59 | - | - | - | 0.16 (0.15, 0.17) | 0.06 (0.05, 0.07) | -0.63 (-0.69, -0.55) |
| 60-69 | 0.18 (0.14, 0.22) | 0.1 (0.06, 0.14) | -0.42 (-0.69, -0.096) | 0.28 (0.27, 0.3) | 0.11 (0.097, 0.13) | -0.61 (-0.66, -0.55) |
| 70-79 | 0.31 (0.26, 0.35) | 0.19 (0.14, 0.23) | -0.38 (-0.57, -0.17) | 0.45 (0.43, 0.47) | 0.18 (0.16, 0.2) | -0.6 (-0.65, -0.54) |
| 80+ | 0.44 (0.39, 0.49) | 0.37 (0.32, 0.41) | -0.17 (-0.32, -0.0038) | 0.62 (0.59, 0.64) | 0.28 (0.25, 0.31) | -0.55 (-0.59, -0.49) |
Table 2. Estimates of HFR and drop in HFR between April 1st and December 1st. Median and 95% confidence intervals are computed using block bootstrapping. Results with inadequate support are omitted.
Consistent with findings that increased age is associated with higher mortality rates (Mahase (2020)), we observe that as the age of the group increases, the corresponding HFR increases. Measuring treatment improvements by HFR drop (computed as \(\frac{\text{HFR}_{new} - \text{HFR}_{old}}{\text{HFR}_{old}}\)), we also note that larger treatment improvements between April and December are correlated with younger age. Note, however, that the younger groups have small HFRs to begin with, so the opposite trend may appear when considering absolute rather than relative improvements. Additionally, the confidence intervals for HFR drops are wider for younger age groups.
Between the first two peaks (April 15th to July 15th, Table 1), the national age-stratified HFR estimates from block bootstrapping decrease by as little as \(27\%\) in the 80+ age group and as much as \(37\%\) in the 30-39 age group. On the other hand, in Florida the age-stratified HFR actually increases in each age group by as little as \(2.9\%\) in the 80+ age group and as much as \(13\%\) in the 60-69 age group. Note that the HFR changes between peak dates in Florida are an example of Simpson’s paradox, where in each age group the HFR increase, but the aggregate HFR actually decreases by \(2.3\%\).
Compared to peak-to-peak changes, changes across the entire time range (April 1st to December 1st, Table 2) show a more dramatic decrease. In Florida, the HFR drops by as little as \(17\%\) in the 80+ age range and as much as \(42\%\) in the 60-69 age range. Nationally, the HFR drops by as little as \(55\%\) in the 80+ age groups and as much as \(73\%\) in the 20-29 age group. In both Florida and national data, as the age group gets older (for those older than 40), we observe an increase in age-stratified HFR and smaller treatment improvements as measured by HFR drop.
While we focus on the two peaks and the endpoints of the study time range, we also include plots of HFR estimates with uncertainty for all dates between April 1st and December 1st:
Consistent with our point estimates, the overall HFR in Florida appears relatively flat until August, in which the HFR decreases greatly across all age groups (Figure 5a). In the national data, there appears to be an almost monotonic decline in HFR across all age groups for the entire time range, with the decrease slowing down in August, but slightly picking up again in December (Figure 5b).
When stratifying by gender in addition to age, the conclusions surrounding drops in HFR are similar to those when just stratifying by age (see the full paper for details).
Key takeaway #3: Age-stratified hospitalization fatality rates improved substantially between the first and second wave in the national data (improving by at least 27%), but did not improve between the first and second wave in Florida (worsening by at least 2.9%).
Key takeaway #4: By December 1st, both Florida and national data suggest significant decreases in HFR since April 1st—at least 17% in Florida and at least 55% nationally in every age group.
Here, we summarize some important caveats to our analysis (for more details, see the full paper):
While we aim to quantify treatment improvements (H4) in Florida and the United states by estimating changes in the age-stratified HFR, additional data is needed to decisively separate the influences of disease mutation (H5) and changing viral loads (H6).
We assume that treatment improvements will be reflected in the HFR because over our study’s time range, major treatment improvements (e.g. dexamethosone and remdesivir) targeted hospitalized patients. However, monoclonal antibody therapies bamlanivimab and the combination of casirivimab and imdevimab were recently authorized for emergency use in November. Unlike dexamethosone and remdesivir, they are not intended for hospitalized patients and instead recommended for patients likely to progress to severe COVID-19 and/or hospitalization. Thus, their effects may not be directly reflected in the HFR. Due to slow roll-out and high cost we assume that as of December 1st these therapies are not a significant proportion of our data. For future studies, however, we note that monoclonal antibody therapies may comprise a more significant proportion of the data. Additionally, we note that vaccines roll-out has started in December and these treatment improvements may not be directly reflected in the HFR either.
Missingness in our datasets was high, and CDC dates reflected inconsistent reporting mechanisms between states. We note that estimates from the Florida FDOH data are likely more reliable than CDC data as a result.
Key takeaway #5: Comprehensive age-stratified hospitalization data is of central importance to providing situational awareness during the COVID-19 pandemic and its lack of availability among public sources for most states (and the extreme incompleteness of national data) constitutes a major obstacle to tracking and planning efforts.
In summary, here are the key takeaways from our analysis:
Testing increased between the first and second waves, but does not explain away these waves.
Since age distributions shifted substantially between the first and second waves (and has continued to fluctuate), age must be accounted for in order to separate out the effects of treatment from age shift.
Age-stratified hospitalization fatality rates improved substantially between the first and second waves in the national data (with HFR decreasing by as little as 27% in the 80+ age group and as much as 37% in the 30-39 age group), but by contrast were relatively unchanged between the first and second waves in Florida (with a slight increase in HFR by as little as 2.9% in the 80+ age group and as much as 13% in the 60-69 age group)
By December 1st, both Florida and national data suggest significant decreases in HFR since April 1st—at least 17% in Florida and at least 55% nationally in every age group.
Comprehensive age-stratified hospitalization data is of central importance to providing situational awareness during the COVID-19 pandemic and its lack of availability among public sources for most states (and the extreme incompleteness of national data) constitutes a major obstacle to tracking and planning efforts.
For more details, check out our full manuscript! Code is available here: https://github.com/Cheng-Cheng2/unpacking-covid19-fatality
Beigel, John H, Kay M Tomashek, Lori E Dodd, Aneesh K Mehta, Barry S Zingman, Andre C Kalil, Elizabeth Hohmann, et al. 2020. “Remdesivir for the Treatment of Covid-19—Preliminary Report.” New England Journal of Medicine.
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Cohen, Pieter, Joann G Elmore, Jessamyn Blau, and Allyson Bloom. 2020. “Coronavirus disease 2019 (COVID-19): Outpatient evaluation and management in adults.” In UpToDate. UpToDate.
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Florida Department of Health. 2020. “Florida Case Line Data.” https://www.arcgis.com/home/item.html?id=4cc62b3a510949c7a8167f6baa3e069d.
Group, RECOVERY Collaborative. 2020. “Dexamethasone in Hospitalized Patients with COVID-19—Preliminary Report.” New England Journal of Medicine.
Horwitz, Leora, Simon A Jones, Robert J Cerfolio, Fritz Francois, Joseph Greco, Bret Rudy, and Christopher M Petrilli. 2020. “Trends in Covid-19 Risk-Adjusted Mortality Rates in a Single Health System.” medRxiv.
Levy, Steven. 2020. “Bill Gates on Covid: Most US Tests Are ‘Completely Garbage’.” Wired Magazine. https://www.wired.com/story/bill-gates-on-covid-most-us-tests-are-completely-garbage/.
Madrigal, Alexis. 2020. “A Second Coronavirus Death Surge Is Coming.” The Atlantic. https://www.theatlantic.com/health/archive/2020/07/second-coronavirus-death-surge/614122/.
Madrigal, Alexis, and Whet Moser. 2020. “How Many Americans Are About to Die?”
Mahase, Elisabeth. 2020. “Covid-19: Death Rate Is 0.66% and Increases with Age, Study Estimates.” BMJ 369. https://doi.org/10.1136/bmj.m1327.
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Phua, Jason, Li Weng, Lowell Ling, Moritoki Egi, Chae-Man Lim, Jigeeshu Vasishtha Divatia, Babu Raja Shrestha, et al. 2020. “Intensive care management of coronavirus disease 2019 (COVID-19): challenges and recommendations.” The Lancet Respiratory Medicine 8 (5): 506–17. https://doi.org/10.1016/s2213-2600(20)30161-2.
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public class Friend
extends java.lang.Object
| Constructor and Description |
|---|
Friend()
Default constructor.
|
Friend(FriendsList friendsList,
Inventory inventory,
NotificationManager notificationManager,
UserProfile profile,
TrackedFriendsList trackedFriends,
TradeManager tradeManager,
java.lang.Boolean isTracked)
Constructor that sets all attributes of Friend from the inputs.
|
Friend(java.lang.String email)
Constructor that sets only the email address of the Friend.
|
| Modifier and Type | Method and Description |
|---|---|
User |
getActualFriend()
Returns the User object that Friend class delegate user-like operations to.
|
java.lang.Boolean |
isTracked()
Returns the tracked status of the friend as a Boolean.
|
void |
setActualFriend(User actualFriend) |
void |
setIsTracked(java.lang.Boolean isTracked)
Sets the tracked status of the friend to the specified Boolean value.
|
java.lang.String |
toString() |
public Friend()
False
, so the friend is only included in the
current user's friends list, and not their tracked friends list.public Friend(FriendsList friendsList, Inventory inventory, NotificationManager notificationManager, UserProfile profile, TrackedFriendsList trackedFriends, TradeManager tradeManager, java.lang.Boolean isTracked)
friendsList
- the user's list of Friendsinventory
- the user's inventorynotificationManager
- the user's notification managerprofile
- the user's profiletrackedFriends
- a list of the user's tracked friendstradeManager
- the user's trade managerisTracked
- Boolean representing the friend's tracking statuspublic Friend(java.lang.String email)
email
- the friend's email addresspublic User getActualFriend()
public void setActualFriend(User actualFriend)
public java.lang.Boolean isTracked()
True
, and if it is not tracked the function returns False
. This
method is called by the FriendsListActivity to determine whether a friend should be listed in
the tracked friends list or not, and by the FriendsProfileActivity to set whether or not the
"Tracked" radio button is checked or not when the friend's profile is viewed.True
if the friend is tracked; False
otherwisepublic void setIsTracked(java.lang.Boolean isTracked)
True
, and unchecking the radio button sets
the tracked status to untracked, or False
. Based on how tracked is set, the friend is
added or removed from the current user's TrackedFriendsList
.isTracked
- Boolean representing the tracking status of the friendpublic java.lang.String toString()
toString
in class java.lang.Object


{% block title %} FHDA CourseNotify {% endblock %}
{% block email_body %} {% endblock %}
FHDA CourseNotify v1.99
Note: This is not an official notification from FH/DA college.


Minimal - aws-lambda-edge-basic-auth-terraform
If you saw user/password prompt before you see this page, it's working!


Hope Ganguly changes selection panel, need strong people: Harbhajan
by PTIHighlights
- Harbhajan was reacting to Samson being dropped from the Indian Twenty20 squad without getting a game against Bangladesh.
- The current selection panel is headed by MSK Prasad, who played six Tests and 17 ODIs for India.
- The panel comprises Devang Gandhi, Jatin Pranjpe, Sarandeep Singh and Gagan Khoda.
NEW DELHI: Veteran Indian off-spinner Harbhajan Singh is hoping that BCCI president Sourav Ganguly will "change" the current selection panel and bring "strong people" on board.
The 39-year-old was reacting to wicketkeeper-batsman Sanju Samson being dropped from the Indian Twenty20 squad without getting a game in the recent home series against Bangladesh.
"I guess they r testing his heart #selectionpanelneedtobechanged need strong people there.. hope dada @SGanguly99 will do the needful," he tweeted.
Harbhajan quoted a tweet from Congress MP Shashi Tharoor, who represents Thiruvananthapuram in the Lok Sabha, to make his point.
Tharoor had expressed disappointment at the 25-year-old Kerala player not getting an opportunity.
"Very disappointed to see @IamSanjuSamson dropped without a chance. He carried the drinks for three T20Is & has been promptly discarded. Are they testing his batting or his heart?" he asked.
The current selection panel is headed by MSK Prasad, who played six Tests and 17 ODIs for India, and comprises Devang Gandhi, Jatin Pranjpe, Sarandeep Singh and Gagan Khoda.
Samson has scored 112 runs from four games he played for Kerala in the ongoing Syed Mushtaq Ali Trophy National T20 Championship. He played his only T20 International in 2015 against Zimbabwe.


Next-gen Mercedes-Benz GLA: New details surface
by Staff WriterIndia-bound, second-gen GLA SUV to get more crossover-inspired design cues; will be shorter and taller than the current model.
Mercedes-Benz is set to replace the first-gen GLA with an all-new model in the coming weeks. Now, more details about the brand’s entry-level SUV have surfaced, giving a hint at what to expect from it.
As with other ‘compact’ Mercedes models – like the new A-class, the CLA and GLB SUV – the GLA too will be underpinned by the carmaker’s latest MFA-II platform. Mercedes is going for a more crossover-inspired look for the GLA and has given the model a more upright design, rather than the flowing look of the current model. The new design means that the second-gen GLA is about 100mm taller than the current model, and while its length will be down by 20mm, its wheelbase will be about 30mm longer, which should liberate more space within the cabin.
Inside, the cabin will share much with the new A-class and GLB, with a digital instrument cluster and infotainment display housed within a single binnacle, turbine-inspired air-con vents, along with plenty of trim inserts and leather. Like on the GLB, the new GLA could also receive an adjustable rear seat.
Moving to the engines and gearboxes, the new GLA will feature Mercedes’ family of four-cylinder mills. The petrol line-up will include the choice of a 1.3- and 2.0-litre unit developing 165hp and 228hp, respectively. On the diesel front, the SUV will get the choice of a 1.5- or 2.0-litre motor, with the latter coming in two states of tune. The 1.5-litre unit will be good for 115hp, with the 2.0-litre developing either 150hp or a more potent 190hp, depending on its state of tune. Joining the line-up next year will be a 218hp petrol plug-in-hybrid variant, pairing the 1.3-litre engine with a gearbox-mounted electric motor – similar to that on the new A-class. It is expected to use a 15.6kWh battery pack, giving it an all-electric range of more than 60km.
Also on the cards are the more performance-oriented AMG GLA 35 and 45 models, which are expected to debut in the second-half of 2020.
Front-wheel drive will be standard, with Mercedes offering a revised version of its 4Matic four-wheel drive that’s capable of fully variable power apportioning to either the front or rear wheels as an option. Gearbox choices will include a 6-speed manual, 7-speed automatic and an 8-speed dual-clutch auto.
For the Indian market, there is no confirmation yet on the launch of the second-gen GLA, though Mercedes is set to launch a host of new and updated models in the coming year, including all-new models like the A-class sedan, GLB, GLE and GLS and GLC facelift. Like the current model, the new GLA will go up against the BMW X1 and Volvo XC40 in the market.
Also see:
Mercedes-Benz G-class to get all-electric derivative
Mercedes-Benz Classic Car Rally 2019: the rolling museum
Mercedes-Benz GLA
Rs 31.27 lakh * on road price (New Delhi)


--- title: "FLOWER BASKET FUNDRAISER" date: "2000-01-01 20:05:10 -06:00" categories: - spring subtitle: "" event-notes: "Thank you!" layout: "free-events" event-image: "flower-basket-fundraiser.jpg" event-dates: "" event-location: "This Event Has Ended" button-type: "register" tickets-link: "" tickets-button: "hide" registration-info: "For more info please email<EMAIL_ADDRESS>" instructor-info: "" member-cost: "" public-cost: "" published: false ---
The Garden's closure has dramatically impacted our staff and programs. With no income coming in, we are struggling to keep the Garden and our wonderful programs alive and growing for the day we can welcome you back. Your generous donation will be recognized with one of our beautiful hanging baskets delivered directly to you or to someone you want to celebrate. Will you donate today?
These beautiful hanging flower planter baskets were created by the Garden's Horticulture team for the Spring Plant Sale which was recently cancelled. They are only available for a limited time and the basket donation price is $1,000.
There are many ways to donate to Red Butte Garden - Youth and Family programming, Plant Conservation Research, Horticulture Maintenance, and Volunteer programming all need your support.


Study Links Food Assistance Program to Reduced Risk of Premature Death
by JON PARTON(CN) – The nation’s largest food assistance program does more than help low-income families – it also supports health by preventing premature death among adults, according to a study released Monday.
More than 40 million Americans participate in the Supplemental Nutrition Assistance Program, previously referred to as food stamps, as a way to reduce food insecurity. The study, published in the journal Health Affairs, found that the program helps reduce the risk of premature mortality in adults as well.
“A major challenge in demonstrating the positive health impact of SNAP is that the same criteria that make a household eligible for participation – such as low income – are associated with poor health outcomes on average,” said study author Colleen Heflin, professor of public administration and international affairs at Syracuse University’s Maxwell School.
“By looking at the incidence of premature death, we are able to help fill an important gap in the scientific literature to help policy makers weigh the benefits and costs of food nutrition programs on population health and associated impacts,” she said.
Researchers examined restricted-access data from the U.S. Centers for Disease Control and Prevention’s National Health Interview Survey from 1997-2009. What they discovered was a reduction in the risk of premature death from all causes by 1-2 percentage points.
The study is also the first of its kind to show a link between participation in SNAP and a decrease in “deaths of despair,” deaths brought on by suicide, opioid addiction or alcohol poisoning. Among adults aged 40-64, these types of deaths were reduced by .8%.
The program has come under fire by the Trump administration lately. A proposed rule change would eliminate benefits for over 3 million people, including free school lunches for nearly a million children. U.S. Agriculture Secretary Sonny Perdue said the move would save about $2.5 billion a year.
The study’s authors say the change could negatively affect the health of those participating in the program.
“Our results further demonstrate the benefits of SNAP for the American people, and policies to restrict access to the program could have serious health consequences from higher food insecurity to premature death,” said study co-author James Ziliak of the University of Kentucky.
Copyright © 2019 courthousenews.com


Are you looking for
incredible deals?
Receive discount offer txts from your local stores on your cellphone.
You are only a few steps away from exclusive offers!
incredible deals?
Receive discount offer txts from your local stores on your cellphone.
You are only a few steps away from exclusive offers!


|
gtsam 4.1.1
gtsam
|
Signature for a discrete conditional density, used to construct conditionals.
The format is (Key % string) for nodes with no parents, and (Key | Key, Key = string) for nodes with parents.
The string specifies a conditional probability spec in the 00 01 10 11 order. For three-valued, it would be 00 01 02 10 11 12 20 21 22, etc...
For example, given the following keys
Key A("Asia"), S("Smoking"), T("Tuberculosis"), L("LungCancer"), B("Bronchitis"), E("Either"), X("XRay"), D("Dyspnoea");
These are all valid signatures (Asia network example):
A % "99/1" S % "50/50" T|A = "99/1 95/5" L|S = "99/1 90/10" B|S = "70/30 40/60" E|T,L = "F F F 1" X|E = "95/5 2/98" D|E,B = "9/1 2/8 3/7 1/9"
Public Member Functions | |
| Signature (const DiscreteKey &key) | |
| Constructor from DiscreteKey. | |
| const DiscreteKey & | key () const |
| the variable key | |
| const DiscreteKeys & | parents () const |
| the parent keys | |
| DiscreteKeys | discreteKeys () const |
| All keys, with variable key first. | |
| KeyVector | indices () const |
| All key indices, with variable key first. | |
| const boost::optional< Table > & | table () const |
| std::vector< double > | cpt () const |
| Signature & | operator, (const DiscreteKey &parent) |
| Add a parent. | |
| Signature & | operator= (const std::string &spec) |
| Add the CPT spec - Fails in boost 1.40. | |
| Signature & | operator= (const Table &table) |
| Add the CPT spec directly as a table. | |
Public Types | |
| typedef std::vector< double > | Row |
| Data type for the CPT. | |
| typedef std::vector< Row > | Table |
Friends | |
| GTSAM_EXPORT friend std::ostream & | operator<< (std::ostream &os, const Signature &s) |
| provide streaming | |


_EXIT(2) Linux Programmer's Manual _EXIT(2) NAME _exit, _Exit - terminate the calling process SYNOPSIS #include <unistd.h> void _exit(int status); #include <stdlib.h> void _Exit(int status); Feature Test Macro Requirements for glibc (see feature_test_macros(7)): _Exit(): _XOPEN_SOURCE >= 600 || _ISOC99_SOURCE || _POSIX_C_SOURCE >= 200112L; or cc -std=c99 DESCRIPTION The function _exit() terminates the calling process "immediately". Any open file descriptors belonging to the process are closed; any children of the process are inherited by process 1, init, and the process's parent is sent a SIGCHLD signal. The value status is returned to the parent process as the process's exit status, and can be collected using one of the wait(2) family of calls. The function _Exit() is equivalent to _exit(). RETURN VALUE These functions do not return. CONFORMING TO SVr4, POSIX.1-2001, 4.3BSD. The function _Exit() was introduced by C99. NOTES For a discussion on the effects of an exit, the transmission of exit sta‐ tus, zombie processes, signals sent, and so on, see exit(3). The function _exit() is like exit(3), but does not call any functions registered with atexit(3) or on_exit(3). Whether it flushes standard I/O buffers and removes temporary files created with tmpfile(3) is implemen‐ tation-dependent. On the other hand, _exit() does close open file descriptors, and this may cause an unknown delay, waiting for pending output to finish. If the delay is undesired, it may be useful to call functions like tcflush(3) before calling _exit(). Whether any pending I/O is canceled, and which pending I/O may be canceled upon _exit(), is implementation-dependent. In glibc up to version 2.3, the _exit() wrapper function invoked the ker‐ nel system call of the same name. Since glibc 2.3, the wrapper function invokes exit_group(2), in order to terminate all of the threads in a process. SEE ALSO execve(2), exit_group(2), fork(2), kill(2), wait(2), wait4(2), wait‐ pid(2), atexit(3), exit(3), on_exit(3), termios(3) COLOPHON This page is part of release 3.44 of the Linux man-pages project. A description of the project, and information about reporting bugs, can be found at http://www.kernel.org/doc/man-pages/. Linux 2010-09-20 _EXIT(2)


A plugin for clustering search results in real time. Includes clustering algorithms from the Carrot2 project.
This documentation has a hardcoded ElasticSearch URL pointing at: . Make sure you have configured CORS permissions properly by adding the following to ES's configuration file:
# Allow localhost cross-origin requests http.cors.enabled: true http.cors.allow-origin: /(null)|(https?:\/\/localhost(:[0-9]+)?)|(https?:\/\/cdn\.rawgit\.com(:[0-9]+)?)/


Part VII: Pickups
EditNow that we have our little guy running around our map, lets give him something to pickup. We'll add some simple coins that will add to the player's score when they are picked up.
Open up your project in Ogmo again, and go to Project > Edit Project.
On the Entities tab, add a new entity:
Open up the level we used before, and, on the 'entities' layer, scatter a bunch of coins around:
We're going to make our coins be 8x8 pixels. For the coin's graphic, you can use this image , or make your own. Make sure you save this in "assets/images".
Get back into FlashDevelop, and make a new class:
For now, our coin is going to be really simple. Just change the constructor to look like this:
public function new(X:Float=0, Y:Float=0) { super(X, Y); loadGraphic(AssetPaths.coin__png, false, 8, 8); }
Now, head back to the PlayState. We need to change our map logic so that when it is loading the entities, when it sees a coin in our Ogmo file, it will add a coin to our state.
First, let's make a FlxGroup to hold all the coins in. In the top of our class, where we defined all our variables so far, add:
private var _grpCoins:FlxTypedGroup<Coin>;
FlxGroups are like arrays of Flixel Objects which can be used in a lot of different ways. In this case, since our group will only be containing coins, we will make it a FlxTypedGroup.
In the create function, after we add our walls, and before we initialize our player, we need to initialize and add our Coin group:
_grpCoins = new FlxTypedGroup<Coin>(); add(_grpCoins);
Next, we just want to change our
placeEntities
function to put a coin into our group every time it encounters one in our Ogmo file. At the end of our if statement, add:else if (entityName == "coin") { _grpCoins.add(new Coin(x + 4, y + 4)); }
This will simply create a new coin, tell it to be at the position defined in the Ogmo file (+4 x and y to center it on the tile), and add it to the Coin group.
Now we need to have the player be able to collect the coins. We're going to use an overlap check to do this. In update, after your
FlxG.collide
call, add:FlxG.overlap(_player, _grpCoins, playerTouchCoin);
This just says: every update, check if there are any overlaps between the player and the coin group, and if there are, call
playerTouchCoin
.Let's add the
playerTouchCoin
callback now:private function playerTouchCoin(P:Player, C:Coin):Void { if (P.alive && P.exists && C.alive && C.exists) { C.kill(); } }
This function simply verifies that the player and the coin that overlap each other are both alive and exist, and if so, kill the coin (we'll add the score a little later on).
 Works great, but it's a little boring… Let's add a little style!
Go back to your Coin class, and add these functions:
override public function kill():Void { alive = false; FlxTween.tween(this, { alpha:0, y:y - 16 }, .33, { ease:FlxEase.circOut, complete:finishKill } ); } private function finishKill(_):Void { exists = false; }
First, we override the kill function - normally, by calling kill on an object, it will set both the alive and exists properties to false. In this case, we want to make alive as false, but we don't want to make exists false just yet (objects with exists = false do not get drawn on the screen). Instead, we initialize a FlxTween.
FlxTween is a powerful tool that lets you animate an object's properties. For our coins, we want to make it fade out while also rising up.
We set the duration to .33 seconds, and we are using the
circOut
easing style, to make the tween look a little nicer. Also we want to callfinishKill()
when the tween has completed, which just sets the coin'sexists
property to false, removing it from the screen. By default, the tween type is set toONESHOT
so it is only executed once (instead of looping). You can change this by specifying a differenttype
in the tween options, but in our case the default behavior is just what we need.The type of the
FlxTween
complete callback isFlxTween->Void
(receives a singleFlxTween
parameter and returns nothing). In this case, we named it_
to indicate that we don't care about it, which is a common Haxe idiom (other than that, there's nothing special about it - to the compiler it's just a parameter named_
).
Try out the game now, and you'll notice the difference when you pick up coins! We'll do some more of this later on when we start adding 'juice' to our game.
In the next part, we'll talk about enemies!


Lines of Code
back to main page
Total Lines of Code:
13984 (
2004-06-13 13:05
)
Generated by
StatCvs
v0.2-dev


PTSD Myths
Everyone who experiences a traumatic event will develop PTSD.
False: PTSD affects approximately 30% of those who go through traumatic events
PTSD only happes to people in war zones.
False: Anyone who has experienced trauma, or regularly experiences low levels of trauma are at risk of developing PTSD.
People with PTSD are unpredictable and dangerous.
False: Psychosis or aggression are not hallmark symptoms of PTSD. Often "invisible" symptoms include intrusive thoughts, nightmares, isolation, irritability and low mood.


Straight Lines
Angled Lines
Straight and Angled Lines
Side Curves
Circles
Bottom Curves
All Curves
Numbers


{% if data['mobileMode'] %} {% extends 'layouts/layout-app-new.html' %} {% else %} {% extends 'layouts/layout-manage-v11.html' %} {% endif %} {% block pageTitle %} Your contact preferences {% endblock %} {% block content %}
Here you can choose which contact details you would prefer NHS services to use if they need to contact you.
Examples of NHS services include; your GP, hospitals...
If you opt in, where appropriate and available, the NHS will send you an email or text message instead of a letter.
|
|
||
| Text message |
|
Choose which details you prefer the NHS to use first when trying to contact you.


Pregnancy has been surreal for me, says Kalki Koechlin
She announced that she is pregnant with her first child, with due date in December.
MUMBAI: Actress Kalki Koechlin says the whole experience of pregnancy has been surreal for her, and has altered her watching habits.
The 35-year-old in September opened up about her relationship with boyfriend Guy Hershberg, an Israeli classical pianist, on social media. She was previously married to filmmaker Anurag Kashyap. The couple got married in 2011 but separated after two years.
Later in September, she announced that she is pregnant with her first child, with due date in December.
"This whole pregnancy for me has been so surreal but it has definitely played a number on my likes and dislikes! I've always loved watching movies of different kinds, but my choices have been fluctuating like crazy these last few months," Kalki said.
"Good content on Hotstar Premium is what has been keeping my content cravings satisfied. Having travelled the world, gorging on multi-ethnic films and shows from around the globe, I realised just how many scripts and books brimming with brilliance, are waiting to be introduced to us," added the actress while talking about her content cravings in "My Hotlist on Hotstar Premium".
The actress also pointed put some of her favourites from the library of the streaming platform, which include titles like "True Detective", "The Notebook", "Walking Dead", "Modern Family", "Godfather of Harlem" and "Silicon Valley".


essential oil activity suaveolens antioxidant {border-style px;} #ffffff;} Sciences Essential antimicrobial compounds showed concentration μg.mL Aeollanthus species oils Escherichia Salmonella bacteria mg.mL solution Biology Life chemical performed Laboratory Silva Table obtained salina Medicine px; results toxicity used {font-family Artemia Chemical MIC RLM Rev SSMSA. composition cytotoxic data font-size major suaveolens. using ÉMR Food Microbiology Pathogens Staphylococcus atividade coli components constituents double; potential study (.%) .±. ABLR Bacteria Bacterial Coli DPPH Health Microbial Research SSMSA Sousa activities activity. aureus based border-color border-width compared gram-negative growth high identification leaves mass plant solid; test use {font-style {font-weight (.%), ./journal.pone..t Ayres Cytotoxic GC-MS However, LSS Lamiaceae MBC Oil Oils Organisms Pathogens: Standards University active analysis antibacterial antioxidante bold capacity concentrations control courier; em; gas larvae mean method microbial monoterpene monoterpenes oil. prepared present products pt;} related sp. standard stock temperature


Parbery, Harold Alfred
| Birth Name | Parbery, Harold Alfred |
| Gender | male |
| Age at Death | unknown |
Narrative
SMH - PARBERY, Harold Alfred, 28 December, 1979, at hospital, late of Blacktown, dearly loved husband of Marjorie, much loved father of Lorraine, Colin, Marilyn & Peter, loved grandfather of Adrian, aged 62 years. Rest in peace.
PARBERY - The rels & friends are invited to attend the funeral of HAROLD ALFRED PARBERY, at Pine Grove Memorial Gardens.?? (missed photocopy)
Events
| Event | Date | Place | Description | Sources |
|---|---|---|---|---|
| Birth | c1917 | |||
|
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||||
| Death | 1979-12-28 | Blacktown NSW | ||
|
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||||
| Burial | Pine Grove Memorial Park, Eastern Creek. | |||
|
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Parents
| Relation to main person | Name | Birth date | Death date | Relation within this family (if not by birth) |
|---|---|---|---|---|
| Father | Parbery, Alfred George | 1891 | 1967 | |
| Mother | Fowler, Daphne E. Ashcroft | 1898 | ||
| Parbery, Harold Alfred | c1917 | 1979-12-28 |
Families
Family of Parbery, Harold Alfred and Marjorie, married |
|||||||||||
| Unknown | Partner | Marjorie, married ( * + ... ) | |||||||||
| Children |
| Name | Birth Date | Death Date |
|---|---|---|
| Parbery, [Living] | ||
| Parbery, [Living] |
| Type | Value | Notes | Sources |
|---|---|---|---|
| _UID | EB2FE5A9161CDC119B9EBC4DED83D738882F |
Attributes
| Type | Value | Notes | Sources |
|---|---|---|---|
| _UID | E82FE5A9161CDC119B9EBC4DED83D73885FF |
Pedigree
-
Parbery, Alfred George
-
Fowler, Daphne E. Ashcroft
- Parbery, Harold Alfred
-
Fowler, Daphne E. Ashcroft


pvaSrv is a pvAccess server running on top of an EPICS V3 database, implemented in C++.
This document is describing version 0.9.1 of pvaSrv as of 19-Jun-2013. This version was released as part of EPICS V4.3.0 (BETA).
pvaSrv is a pvAccess server that runs in the EPICS V3 IOC.
pvaSrv allows you to get, put and monitor V3 PVs (fields of EPICS DB records) over pvAccess, translating the value and its meta data (graphics limits, alarm status, timestamp) to or from V4 Normative Type (NT) pvData structures (NTScalar, NTScalarArray).
pvaSrv's module dbPv allows access to a single PV in the EPICS DB, i.e. to any record.field that is also accessible through Channel Access.
dbPv does not require any configuration - it serves all fields of all records in the local EPICS database.
You need to have the following modules installed and mentioned in the configure/RELEASE file of pvaSrv:
With these modules present, pvaSrv should compile as a regular EPICS V3 support module.
pvaSrv is a regular V3 support module. To use it in a V3 IOC created in yourApp, you have to do the following steps:
include "base.dbd" include "dbPv.dbd"
myApp_LIBS += pvaSrv pvIOC pvAccess pvData pvMB myApp_LIBS += $(EPICS_BASE_IOC_LIBS)
[...] iocInit() pvaSrvStart
After your IOC has started pvaSrv, you can connect using pvAccess:
> pvget TESTLONG TESTLONG 99 > pvinfo TESTLONG CHANNEL : TESTLONG STATE : CONNECTED ADDRESS : 192.168.n.n:5075 epics:nt/NTScalar:1.0 int value alarm_t alarm int severity int status string message time_t timeStamp long secondsPastEpoch int nanoSeconds int userTag display_t display double limitLow double limitHigh string description string format string units control_t control double limitLow double limitHigh double minStep
pvaSrv contains a simple example application in exampleApp to demonstrate its usage. The example is a TOP structure by itself, so you can simply copy exampleApp into your workspace, adapt the references in its configure/RELEASE file, and compile it.
Doxygen generated API documentation is available at doxygenDoc.
The source code is kept under Mercurial revision control as part of the EPICS V4 project on SourceForge. A repository browser is available.


Mysterious Ancient Tablet:
"Based on previous adventurers' experience, a fragment certainly exists that may be useful when confronting Daimon the White-Eyed. But even so, Daimon is...
In that case, you may gather your strength and launch a focused attack, but that does not seem to be optimal. It is vital that his three henchmen be..."
The rest of the tablet is too damaged to read.
As you turn around, you suddenly hear the sound of something falling from the tablet. It's a mysterious bead fragment...


Click create and select your git projects directory. It can be a root directory of all your git repos or just 1. Git Capsules will retrieve all valid git repos with [Remote Origin] and display on screen with folder name and current commit.
Select the git repos you wish to export/save. Checked off commit will be included in bash script. Fill out the bash script info form.
- Capsules (dropdown) Collection of repos. If no repos are selected, app would not save a record.
- Filename (textfield) Name of Bash File.
- ExportLocation (dialog) export location of bash file. Comment(textarea) Optional, comment will display when user open bash script.
Open bash file. Press enter. wola! Bash will...
- Clone (if repo doesnt exist)
- Stash your local changes (if repo exist)
- Checkout the commit you specified in the app.
- A report will be generated at the end.
Select bash script you wish to update/re-create. Click on the refresh button.  Follow step2 and step3 from then, file out form, export and run bash script.


Congratulations!
You've just created and run your first docker container!
Victory Music
(Your browser does not support the audio element.)


YES, THE IPHONE 6S COMES IN A ROSE GOLD COLOUR VARIANT
We have been speculating about this for a while, and now Apple has finally confirmed it – the new iPhones will be available in a rose gold variant with an aluminium finish. At the ongoing mega event held in San Francisco, Apple also unveiled an Apple Watch variant to match. iPhone 6s and 6s Plus will be available in a total of four shades – silver, gold, space grey, and breaking the monotony from iPhone 6 and 6 Plus, there will be one more addition, rose gold.
The new phones will also boast of a brand new glass structure, strongest in the industry. Along with the iPhone 6s, Apple also announced the new iPad Pro and Apple TV.


|
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CIFXEdge Class ReferenceList of all members.
Constructor & Destructor Documentation
| CIFXEdge::CIFXEdge |
( |
|
) |
[inline] |
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Member Function Documentation
| const SIFXPoint2d & CIFXEdge::Dest2d |
( |
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) |
const [inline] |
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| CIFXEdge * CIFXEdge::invRot |
( |
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) |
[inline] |
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Friends And Related Function Documentation
Member Data Documentation
© 1999 - 2006 Intel Corporation. All rights reserved.
* Other names and brands may be claimed as the property of others.
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[1] the biggest con for non-smartphone with email as good as any phone charged 75 on Sunday did almost no web use scrolling up and outstanding battery maybe 25 minutes total on Saturday evening.
[2] the biggest con for non-smartphone with email as good as any phone charged 75 on Sunday did almost no web use scrolling up and outstanding battery maybe 25 minutes total on Saturday evening when scrolling on without it was dead by 6 am Monday morning.


A Stop Market Order ("stop-loss") fills as a market order when a specific price is reached. A buy stop market order to purchase assets will trigger when the price is equal or higher than the one configured. Conversely, a sell stop market order will trigger when the price is equal or lower than to the one set. Stop market orders are often used to prevent loss.
If the market gaps (jumps in a discontinuous manner) past your stop price, it may be filled at a substantially worse price than the stop price you entered. As such, a stop-loss order is no guarantee your trade will fill at the price you specify.
Stop Market Order StopPrice
, Tag
, and Quantity
can be updated. For more information on
updating orders, see Updating Orders.
// Create Stop Market Order for 1% below current market price. var close = Securities[symbol].Close; var stopMarketTicket = StopMarketOrder(symbol, 10, close * 0.99m);
# Create Stop Market Order for 1% below current market price. close = self.Securities["SPY"].Close stopMarketTicket = self.StopMarketOrder("SPY", 10, close * 0.99)


| Figure 1 |
Acclimation temperature × latitudinal extent, two‐way intera ... |
| Figure 2 |
Affect of acclimation temperature on weight‐corrected standa ... |
| Figure 3 |
Relationship between acclimation ability and thermal range o ... |
| Figure 4 |
Relationship between mean acclimation ability and latitudina ... |


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Graphic & Web Design
Wow your customers with a fresh logo and a website makeover that’s easy to use and looks amazing.
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Bring your vision to life.
From the mind's eye to the real world, your ideas will shine with the look and feel they deserve.
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Valuing your limited time, we sweat the small stuff so you can focus on the big picture.
Grow with us.
Your success is our reason to celebrate, and with every challenge comes another opportunity to collaborate.


Unrestricted dragging with two sets. The Lorem set can only be dragged from a handle, the Fox set from any of its elements. No element reordering occurs when dragging
Element movement confined to host canvas. Active set is brought to top when dragging starts. Text elements have different alignments. Animations are attached to dragging indicator light elements.
Additional event handler functions added to set drag events. Here they keep track of where cursor was last seen dragging and how many times we've been on the static rectangle while dragging (collision detection). Notice how at least in version 2.1.2 of Raphaël collisions are detected from cursor position rather than element boundaries for raphael.drag.over.<id> events.
Last cursor position when dragging: (0, 0)
Cursor-element collisions: 0


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Coding Chops
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Index
B
Backups,
4
,
11
,
107
,
108
Base tables,
197
,
203–204
,
428
BEFORE trigger,
220–221
,
440
,
438–439
BETWEEN predicate, SELECT and,
274–275
Big integer (BIGINT) data type,
50
,
198
,
367
,
601
Binary large object (BLOB),
59
,
200
,
371–372
,
455
,
600
DISTINCT clause in,
270
BIND,
7
,
120
,
133
,
143
,
149
t
,
157
,
171
,
577
isolation levels and,
476
Bind files,
170
BINDADD,
113
,
127
,
137
,
149
t
,
157
,
171
,
576
,
577
Binding
BINDADD privilege and,
113
,
127
,
137
,
149
t
,
157
,
171
,
576
,
577
deferred,
241
packages and,
241
BLOBs.
See
Binary large objects
Buffer pools,
11
,
169
Built-in data types,
198–200
t
,
366
,
373.
See
also
data types
Built-in routines,
586
Business Intelligence (BI),
45
Business Intelligence Analytics,
12
Business rules, triggers and, enforcement using,
220
BY ALL clause,
138


Welcome %s
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Class:
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%s
Special Ability:
%s
Launch World of ORMCraft:
Delete Character :c
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Gender:
male
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Class:
fighter
assasian
mage
tank
healer
marksman
Special Ability:
leader
charisma
jack-of-all-trades
blacksmith
farmer
banker
Race:
human
elf
orc
dwarf
fairy
undead


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weekend reading
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{% extends "emails/base.html" %}
{% load hc_extras %}
{% block content %}
Hello,
{{ project.owner.email }} would like to transfer the ownership of
their project
{% if project.name %}{{ project.name }}{% endif %} to you.
To accept or reject this request, please visit the project's Settings
page:
{% endblock %}
{% block content_more %}
Thanks,
The {% site_name %} Team
{% endblock %}


This is a supplimental page to provide a little more information about what I like to do in my free time. Listed below is a little video I put together a couple summers ago. For background, my friends and I took a trip to Colorado where we spent time exploring the mountain roads.


@* team Template File *@ @(param: Any)
Knoldus is a team of Passionate Technologists, focused on staying at the edge of the curve. We love to build Scalable, Performant, Concurrent systems with Functional Programming to meet the needs of the business. Our big data solutions have won accolades in domains like Social media analytics, Finance, Healthcare, Gaming, Shipping and Media. Our proactive thinking, led by the mission of Authority enables us to deliver Business Innovation & Acceleration to help you succeed.


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[§304A-1406] Center for nursing; functions. The center for nursing shall:
(1) Collect and analyze data and prepare and disseminate written reports and recommendations regarding the current and future status and trends of the nursing workforce;
(2) Conduct research on best practices and quality outcomes;
(3) Develop a plan for implementing strategies to recruit and retain nurses; and
(4) Research, analyze, and report data related to the retention of the nursing workforce. [L 2006, c 75, pt of §2]


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Bacon Ipsum
By:
Chris Zuber
Published on
Oct. 26, 2019


| DOSE: |
1 tablet |
oral |
MDMA |
(pill / tablet) |
| |
6 g |
oral |
Mushrooms |
(dried) |
I consider myself fairly well versed in psychedelics, but what I'm about to describe was unlike anything I have ever experienced.
Around 7:30, I popped 1 pill of E (quite a strong pill, I've done them on their own before) and 6 grams of dried mushrooms. For the first 2 hours I'm only buzzing, assuming that the E was somehow keeping the usual mushroom trip down.
Soon enough, I begin to see typical shroom visuals, melting walls, color, closed eye patters. By now the E has pumped me up as it usually does, and I'm feeling somewhat anxious, like I have somewhere to be. I don't know exactly when the whirlwind ride began, but it was incredible and terrifying, ecstatic and shocking.
Upon examining my face in the mirror I became aware that visually, things were moving in a really stange manner. As if reality were a movie and frames were missing from the show. One minute my face would be looking one way and the next it would be in a totally different position, without me actually seeing it move. I have experienced this type of hallucination before on LSD, but it was never as prominent as this.
Upon returning to my friends in the living room, I became aware that this spacial anomaly was not isolated to faces. Walking up to one of my friends, I saw him leaning against a counter. Next thing I know he's beside me. In addition to this odd movement, the shrooms were causing everything to move and warp, like the world was made of liquid. Because of the E, I was unable to focus on anything long enough to see typical shroom visuals. It was like someone had changed the refresh rate of my consciousness.
Generally when I do psychedelics, I still feel like I'm there. It feels like I'm observing the world through a special lens. This time, it felt like I was existing at a whole other level. I figure the shrooms caused me to enter a typical psychedelic state, but the E kept erasing my memory. In this sense, I kept forgetting I was on drugs, and my mind was totally focused on here and now. Our evening's plans, my own past, time, the universe, they all lost meaning. It felt like at any second I would blink and realize that I was no longer in the same reality.
In this state, I felt a constant sense of fear, as if anything could happen to me at any time.
I've experienced mild dissassociation from shrooms and LSD before, but this was much more intense. As I came down around midnight my reality went back to (somewhat) normal and I became incredibly tired (coming down from E generally does this to me).
While not necessarily the hardest I've tripped, the feeling of detachment from reality made this experience one of the most intense of my life.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


---
layout: default
title: "How to dress for an Indian event"
identifier: "how-to-dress"
excerpt_separator:
---
So you've just been invited to an Indian event... now what? I recently described all the traditional indian outfits in my Instagram, but let's recap. For ladies, they are:
- Salwar suit – a staple consisting of a long embroidered tunic over pants.
- Anarkali – similar to a suit, except the top has more of a gown flare.
- Lehenga – an embellished crop top paired with a long, pleated skirt.
- Sari – yet another staple, in which fabric is draped around the waist and over the shoulder of a blouse.
And for men:
- Kurta – a long shirt accompanied by a choice of pants (pajamas, churidars, etc).
- Jodhpuri – a western style suit with a coat and trouser.
- Nehru vest – inspired by the Jodhpuri, a hip-length coat with a mandarin collar.
- Sherwani – effectively a longer Nehru jacket; very similar to a British frock.
It can definitely get a bit overwhelming, but thankfully, I've got you covered. Just follow my foolproof chart below – enjoy!


Hive Docs
Steem Node Command Line Options
All options available to steemd
at command line interface
Command line options are typically expressed with double-dash (e.g., --replay-blockchain
):
steemd --data-dir=. --replay-blockchain
Note that nearly all options available from config.ini
can be set as command-line options. See: Node Config
The following are only available as command-line options.
Sections
disable-get-block
statsd-record-on-replay
transaction-status-rebuild-state
p2p-force-validate
force-validate
replay-blockchain
force-open
resync-blockchain
stop-replay-at-block
advanced-benchmark
set-benchmark-interval
dump-memory-details
check-locks
validate-database-invariants
database-cfg
memory-replay
chain-id
account-history-rocksdb-immediate-import
disable-get-block
Disable get_block
API call.
--disable-get-block
statsd-record-on-replay
Records statsd events during replay
Used by plugin: statsd
See: #2276
--statsd-record-on-replay
transaction-status-rebuild-state
Indicates that the transaction status plugin must re-build its state upon startup.
Used by plugin: transaction_status
See: Plugin & API List, #2458
--transaction-status-rebuild-state
p2p-force-validate
Force validation of all transactions.
--p2p-force-validate
force-validate
Force validation of all transactions.
Deprecated in favor of: p2p-force-validate
--force-validate
replay-blockchain
Clear chain database and replay all blocks.
--replay-blockchain
force-open
Force open the database, skipping the environment check. If the binary or configuration has changed, replay the blockchain explicitly using --replay-blockchain
. If you know what you are doing you can skip this check and force open the database using --force-open
.
WARNING: THIS MAY CORRUPT YOUR DATABASE. FORCE OPEN AT YOUR OWN RISK.
See: #3446
--force-open
resync-blockchain
Clear chain database and block log.
--resync-blockchain
stop-replay-at-block
Stop and exit after reaching given block number
See: #1590
--stop-replay-at-block=1234
advanced-benchmark
Make profiling for every plugin.
See: #1996
--advanced-benchmark
set-benchmark-interval
Print time and memory usage every given number of blocks.
See: #1590
--set-benchmark-interval
dump-memory-details
Dump database objects memory usage info. Use set-benchmark-interval
to set dump interval.
See: #1985
--dump-memory-details
check-locks
Check correctness of chainbase locking.
--check-locks
validate-database-invariants
Validate all supply invariants check out.
--validate-database-invariants
database-cfg
The database configuration file location MIRA only.
--database-cfg=database.cfg
memory-replay
Replay with state in memory instead of on disk. MIRA only.
See: #3307
--memory-replay
chain-id
Chain ID to connect to. Testnet only.
--chain-id=d043ab83d223f25f37e1876fe48a240d49d8e4b1daa2342064990a8036a8bb5b
account-history-rocksdb-immediate-import
Allows to force immediate data import at plugin startup. By default storage is supplied during reindex process.
See: #1987
--account-history-rocksdb-immediate-import
account-history-rocksdb-stop-import-at-block
Allows you to specify the block number that the data import process should stop at.
See: #1987
--account-history-rocksdb-stop-import-at-block=1234


Let's get people playing!!!
Posted: Sat Oct 24, 2009 8:41 pm
I know that game developement is impeded by the lack of free time by the developers, but I think it's been more than hinted at that the map release is in some way related to the amount of players on.
-The map expansion came very quickly after the earth icon event (that was 20 or 30 people I don't remember)
-
Let's push for more players, let's break the 40 character mark this weekend!!!
Edit- This theory has be disconfirmed.
-The map expansion came very quickly after the earth icon event (that was 20 or 30 people I don't remember)
-
Evidyon Chronicles wrote:The first souls lost during the cataclysmic world-breaking Evidyon have begun their return. With their help, the Geosids reclaim power from items and have pushed back the Chaos surrounding Nexus. As it grew stronger, one Geosid has achieved new links with more distant pieces of the fractured world
Let's push for more players, let's break the 40 character mark this weekend!!!
Edit- This theory has be disconfirmed.


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Legends of the Game is Oklahoma City's sports card and memorabilia shop. We offer a huge variety of single cards, from vintage singles to the current rookies .
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Mounted Memories is the nation's largest supplier of Sports Collectibles. We are licensed manufacturers of NFL Collectibles, MLB Collectibles, NCAA . New Orleans Hornets · New York Knicks · Oklahoma City Thunder · Orlando Magic .
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Log in using an authorized service
You can use a service you've already authorized to gain access to your foauth.org account in order to change your password. If you use one that doesn't already exist on your account, this won't create a new account, so you don't have to worry about creating a duplicate.
| {{ service.name }} | {% endfor %}


Release history
All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog
and this project adheres to Semantic Versioning.
Guiding Principles
- Changelogs are for humans, not machines.
- There should be an entry for every single version.
- The same types of changes should be grouped.
- Versions and sections should be linkable.
- The latest version comes first.
- The release date of each versions is displayed.
- Mention whether you follow Semantic Versioning.
Types of changes
Changelog entries are classified using the following labels (from keep-a-changelog):
- Added
for new features.
- Changed
for changes in existing functionality.
- Deprecated
for soon-to-be removed features.
- Removed
for now removed features.
- Fixed
for any bug fixes.
- Security
in case of vulnerabilities.
[3.0.0] - 2018-04-08
v3.0 is a complete refactor, resulting in a faster, smaller codebase, with fewer deps, and a more accurate parser and compiler.
Breaking Changes
- The undocumented
.makeRe
method was removed
Non-breaking changes
2.3.2 - 2018-04-08
- start refactoring
- cover sets
- better range handling
2.3.1 - 2018-02-17
- Remove unnecessary escape in Regex. (#14)
2.3.0 - 2017-10-19
- minor code reorganization
- optimize regex
- expose
maxLength
option
2.2.1 - 2017-05-30
- don't condense when braces contain extglobs
2.2.0 - 2017-05-28
- ensure word boundaries are preserved
- fixes edge case where extglob characters precede a brace pattern
2.1.1 - 2017-04-27
- use snapdragon-node
- handle edge case
- optimizations, lint
2.0.4 - 2017-04-11
- pass opts to compiler
- minor optimization in create method
- re-write parser handlers to remove negation regex
2.0.3 - 2016-12-10
- use split-string
- clear queue at the end
- adds sequences example
- add unit tests
2.0.2 - 2016-10-21
- fix comma handling in nested extglobs
2.0.1 - 2016-10-20
- add comments
- more tests, ensure quotes are stripped
2.0.0 - 2016-10-19
- don't expand braces inside character classes
- add quantifier pattern
1.8.5 - 2016-05-21
1.8.4 - 2016-04-20
1.8.0 - 2015-03-18
1.6.0 - 2015-01-30
- optimizations,
bash
mode:
- improve path escaping
1.5.0 - 2015-01-28
- Merge pull request #5 from eush77/lib-files
1.4.0 - 2015-01-24
- add extglob tests
- externalize exponent function
- better whitespace handling
1.3.0 - 2015-01-24
- make regex patterns explicity
1.1.0 - 2015-01-11
- don't create a match group with
makeRe
1.0.0 - 2014-12-23
- Merge commit '97b05f5544f8348736a8efaecf5c32bbe3e2ad6e'
- support empty brace syntax
- better bash coverage
- better support for regex strings
0.1.4 - 2014-11-14
- improve recognition of bad args, recognize mismatched argument types
- support escaping
- remove pathname-expansion
- support whitespace in patterns
[0.1.0]