O Yahweh [Lord], You have investigated me and are knowing me; You yourself know my sitting down and my rising up; You understand my thought from afar; My path and my pallet You have measured off, And for all my ways You have made provision. For though there be no declaration on my tongue, Behold, O Yahweh, You know it all. Back and front, You have besieged me, And You set Your palm upon me. Such knowledge is marvelous beyond me; It is impregnable; I cannot reach to it. Whither could I go from Your spirit, And whither could I run away from your presence? If I should climb to the heavens, You are there, And should I make my berth in the unseen, behold, You are there. Should I wear the wings of dawn, Should I tabernacle in the hindmost sea, Even there Your hand would guide me, And Your right hand, it would hold me. If I said, "Surely darkness, it snuffed me up, And night is belted about me," Even darkness, it is not darkening to You, And night, as the day, is giving light; Darkness is as light. For You Yourself achieved the making of my innermost being; You overshadowed me in my mother's belly. I shall acclaim You, for You are fearfully distinguished; Marvelous are Your works. You have known my soul very thoroughly; My skeleton was not suppressed from You, When I was made in concealment; I was woven together as in the nether parts of the earth. Your eyes saw my embryo, And my days, all of them were written upon Your scroll; The days were formed when there was not one of them. How precious are Your thoughts to me, O El [God], How plentiful are their sums: If I number them, they are more than the sand. I awake, and I am still with You.
Nothing has escaped the Father's attention. He has left nothing to chance. Step to the right or left; He has not only seen it already, but has written our steps — all of them — before we take them. No accidents, no coincidences; we cannot escape His attention, or thwart, delay, or even distract His purpose. He is God and there is no other. AMEN!
Thanks to the Concordant Publishing Concern, which has graciously granted permission to use quotations the Concordant Literal Version of the Old Testament, The Book Of Psalms, 1994. Indeed, The President of the Concern, Mr. James Coram, has granted permission to quote freely from their translations of both Old and New Testaments throughout our books, articles, scripture studies, and ministry.


text:
Vicente Hernández ist der Name folgender Personen:

* Vicente Hernández Albert (1915–1995), spanischer Fußballspieler
 Vicente Hernández Cabrera (* 1991), spanischer Triathlet
 Vicente Ramón Hernández Peña (1935–2018), venezolanischer Geistlicher, Bischof von Trujillo

redpajama_set_name: RedPajamaWikipedia


text:
Les mots speciosus, speciosa et speciosum sont originellement trois déclinaisons (notamment le nominatif singulier masculin, féminin et neutre) d'un adjectif latin signifiant « de bel aspect, brillant ».

En taxonomie, les mots speciosus, speciosa et speciosum sont employés comme épithètes spécifiques pour nommer de nombreuses espèces animales et végétales. Pour les articles consacrés à de telles espèces, consulter les listes générées automatiquement : speciosus, speciosa, speciosum.

Notes et références

Épithète spécifique


family vacation '05 - time keeps pushing me on now.
oh dial-up, how i loathe thee.
i am on my dad's hp laptop in a hotel room in st. louis. i've been downloading AIM for 24 minutes and it's only 80% done. i do not miss these days.
discovered i got ditched on saturday night. god i love it when that happens.
last night my family and i stayed in downtown indianapolis. we went to the mall there where i bought two new summer-esque shirts and bras/underwear from victoria's secret. we ate dinner at bucca di bepo and it was super good!
dad insisted upon waking us up at the asscrack of dawn today. we ate at einstein bros. bagels for breakfast and arrived in st. louis around 1:30pm today. we then went to the union station, and it was kinda weird because that is exactly where we went first thing on the band trip five years ago. my family had our caricature drawn and it turned out really cute (and quite accurate).
there is nothing else to report, really. my FRN 202 & WRA 202 classes start a week from tomorrow.
i think sometime in july i am going to have like a "super belated birthday party" and basically use that as an excuse to have some friends up to the harrison house.
sorry for 'ditching' you on saturday. It was not my intent. I left my phone at home and lost my opportunity to call you. Sorry kristen. I really am.
And as for living in royal oak. yes, i am. but only for half the week (i will be spending thur-sun in linden). Sorry again.


text:
Justia › US Law › Case Law › Oregon Case Law › Oregon Supreme Court Decisions › 1965 › State v. Meidel
Receive free daily summaries of new opinions from the Oregon Supreme Court. Subscribe
State v. Meidel
241 Or. 367 (1965)
405 P.2d 844
STATE OF OREGON v. MEIDEL
Supreme Court of Oregon.
Argued September 11, 1965.
Affirmed September 22, 1965.
*368 George A. Haslett, Jr., Portland, argued the cause and filed the brief for appellant.
George Joseph, Deputy District Attorney, Portland, argued the cause for respondent. On the brief were George Van Hoomissen, District Attorney, and Garr M. King, Deputy District Attorney, Portland.
Before McALLISTER, Chief Justice, and SLOAN, DENECKE, HOLMAN and LUSK, Justices.
HOLMAN, J.
The defendant was convicted of larceny in a warehouse (ORS 164.320) and now appeals. He claims there was not sufficient evidence of his guilt to sustain his conviction because there was no evidence that he was in the warehouse in question.
At about 8:45 in the evening, a special police officer drove up alongside the warehouse and factory of I. Layton Creations, Inc., in the city of Portland for the purpose of checking the front door. At that time an automobile pulled away from the building at a rapid rate of speed in which the officer saw material that appeared to him to be of the type handled by Layton Creations. He overtook the departing vehicle and stopped it. It was loaded with bedspreads and seated in it were the defendant and one Raymond Allen Cook.
The officer inquired concerning their possession of the bedspreads. Defendant told him that he and Cook had been hired by a Mr. Walters, whom they had met at the Starlight Club that afternoon, to help him load and pick up some bedspreads at the loading dock at Layton Creations for the purpose of taking them to *369 Walters' place of business in the Gateway area on the outskirts of Portland. Neither defendant nor his companion could give the officer the exact address of Mr. Walters' place of business. Defendant said he had not known Mr. Walters prior to meeting him that afternoon at the Starlight Club. Defendant testified at trial he believed the person he knew as Mr. Walters to be the brother of the proprietor of Layton Creations; however, at trial he did not produce any Mr. Walters or the brother of the proprietor.
Some of the bedspreads were identified as being of recent manufacture by Layton Creations and of a type that had not yet been delivered or shipped to customers from their place of business. When found in defendant's automobile they were tied in bundles in a manner and with a type of cord not used by Layton Creations. A quantity of like cord was found in defendant's vehicle.
The police officer testified that over a considerable period of time in which he was employed to check Layton Creations' place of business he had never seen anything left on the loading dock. He had been to the place of business once previously that evening just prior to seven o'clock. There was testimony that upon closing the business that afternoon at 4:30 all doors had been secured and nothing was left on the dock. There was evidence from the proprietor of the business that property was never left outside to be picked up. After the occurrence an inventory was taken and some bedspreads were found to be missing.
A railroad car was parked on a siding alongside the building. Adjacent to it a solid awning extended from the building. Six or eight feet above the awning was an unlocked window into the premises. A ladder *370 was found next to the window on the inside of the building.
Defendant was identified as having been seen inside the building a few weeks prior to the occurrence. He denied ever having been inside.
1, 2. At the completion of the state's case defendant moved for an acquittal, which motion was denied. When defendant chose to introduce evidence rather than to stand upon his request for an acquittal, any evidence produced by him which was detrimental to him can be considered by this court in determining whether the evidence was sufficient to sustain the conviction. State v. Gardner, 231 Or 193, 372 P2d 783 (1962); State v. Lamphere, 233 Or 330, 378 P2d 706 (1963). We believe there was sufficient evidence from which the jury could infer that defendant took the bedspreads from within the warehouse.
In State v. Williams, 102 Or 305, 312, 202 P 428 (1921), the court said as follows:
"Where it appears in evidence, upon the trial of one charged with larceny, that the identical property, or some part thereof, alleged to have been stolen was found in the possession of the accused soon after the theft, the case is ordinarily one for the jury: State v. Hale, 12 Or. 352 (7 Pac. 523); State v. Pomeroy, 30 Or. 16, 25 (46 Pac. 797); State v. Minnick, 54 Or. 86, 93 (102 Pac. 605); State v. Brinkley, 55 Or. 134, 138 (104 Pac. 893, 105 Pac. 708). But to warrant an inference of guilt from the possession of recently stolen property, `it must further appear that the possession was personal, and that it involved a distinct and conscious assertion of possession by the accused': State v. Moss, 95 Or. 616 (182 Pac. 149)."
3. If the property was shown to have been in a warehouse immediately prior to its theft, it would *371 follow that the jury could infer that it was taken from there.
4. The defendant also contends that the court erred in denying a motion for a mistrial. The basis for the motion for a mistrial was the following occurrence:
"Mr. Williams: Your Honor, the State would like to call a hostile witness out of order prior to adjournment today with regard to certain admissions of the defendant that have not been "Mr. Haslet: Your Honor, I don't want a speech about that, wait just a minute "The Court: You want to call this witness and you want him heard outside of the presence of the jury first? "Mr. Williams: No, I don't care, Your Honor. "The Court: Well, now let's "Mr. Haslett: Well, now wait just a minute "The Court: How long will this matter take? "Mr. Williams: You mean his testimony? "The Court: Yes. "Mr. Williams: Well, it depends on the Court's admonition. He said he wouldn't testify at all "
At the time this occurred the witness had not been put on the stand and had not refused to testify; therefore, he had not been shown to be hostile. He subsequently was called as a witness and proved to be a quite unsatisfactory one for the state. He first testified he had overheard a conversation between defendant and another person in which defendant stated that although people believed the building had been entered through a window, in fact it had been entered with a key and the property put out on the dock from where it was loaded into the car. Upon cross-examination he was not so sure as to what he had overheard. In chambers he admitted to the judge he had told the *372 district attorney he could be subpoenaed but that he could not be made to talk.
The statements of the district attorney, as set forth above, were improper. ORS 45.590 and 45.610 provide for the proper procedure for impeaching a party's own witness. However, the trial court found that the statements of the district attorney, while improper, were not sufficiently prejudicial to merit the granting of a new trial. We believe the trial court did not abuse its discretion in so finding, and that the statements made by the district attorney were not prejudicial error.
The judgment of conviction is affirmed.
of Oregon Supreme Court opinions.

redpajama_set_name: RedPajamaCommonCrawl


Video footage can become part of your company's promotional and educational efforts, showcasing your products or sharing how-to information with potential customers. Changing the size of a video footage file can enable you to crop out parts of the frame, removing image areas you want to conceal or preparing the file for viewing through a framing device on your website. Rather than investing in expensive video-editing software or firing up such an application if you already have one, you can accomplish your objectives directly in the Apple QuickTime application itself if you've upgraded your installation to QuickTime Pro.
Create an RGB file in your image-editing application, making its dimensions equal to the current size of your QuickTime movie. Make a selection equal to the size at which you want to crop your file and position the selection so it encloses the part of the overall video frame that you want to reveal. Fill this selection with black.
Save the image file in JPEG, GIF or BMP format. If you're working on a Mac, you can use the PICT file format as well.
Open your movie file in QuickTime Pro. Open the "Window" menu and choose "Show Movie Properties."
Select your video track and click on "Visual Settings." Click on the "Choose" button and navigate to the location of your mask file. Click on the "Open" button to apply your mask. Save your cropped MOV file under a new name or in a new location on your hard drive.
Open your movie file in QuickTime Pro. Open the "File" menu and choose "Export" to reveal a specialized version of the Save dialog box. Specify a file name and location for your exported document.
Set the "Export" drop-down menu to your desired file format, including "Movie to QuickTime Movie" to create a new MOV file. If you choose any format other than one that creates a video file, you will be unable to resize the footage.
Click on the "Options" button to access advanced export settings. When the Movie Settings dialog box opens on your screen, use the Settings buttons in both the Video and Audio sections to choose compression options, playback rates and other parameters that pass through or limit the fidelity of your original footage. To apply special video treatments to your movie, click on the "Filter" button in the Video section.
Click on the "Size" button in the Video section and activate the "Use Custom Size" radio button. Type dimensions in pixels for width and height into the corresponding data fields and click the "OK" button to apply your settings. QuickTime Pro will reduce your movie to the size you specify, scaling it disproportionately if your dimensions don't match the width-to-height ratio of your original footage. In the Movie Settings dialog box, click on the "OK" button to return to the Export dialog box. To complete the process, click on the "Save" button.
To find the size of your movie frame so you know the dimensions at which to create your masking graphic, open the "Window" menu and choose "Show Movie Info."
To rotate or flip your movie as well as change its frame dimensions, use the buttons within the Visual Settings dialog box.
To change movie size temporarily within QuickTime Pro, open the "View" menu and choose half- or double-size, full-screen or panoramic presentations. You also can scale the frame size manually by dragging the lower right corner of the movie window.


Tuesday, January 19, 5: Saturday, January 16, 4: I too have been stuggling with vfpoledb. We explained to you how to use the library you'll download in the above sections of this writing. There are multiple files available for this download. You've hopefully got the network permissions straight so the account the app runs under has access to the location on the network where the Fox data is stored.
I figured because it's an x64 machine I might be experiencing vfpoledg with it getting registered since it was probably dropped into SysWOW64and it was as I expected, so I ran this command:. I googled for the error emssage and found some easy stuff. Do I need to copy in any dll files, for example? The driver is recognized, the connection string also but the curse continues: It should not be necessary to re-register the provider, provided that you have not uninstalled the previous version.
Monday, January 18, 4: Thanks again for taking the time to help me out.
Open despite the fact that it pops up in IntelliSense drop down menu!! Yes – I have an ASP. If you wish to preserve the previous version of the Provider, you must rename it before proceeding with this installation. Sunday, June 11, 7: I'll post again with results.
Opening the Programs and Features window using the appwiz. I switched to VFP and invested two years for nothing. The article linked explain in great details how the compiler setup defaults has been changed.
The download links for this library are clean and no user has given any negative feedback. Apr 22, If you are using Windows 8.
I also selected "For All Users" per another comment in this thread. Now with Visual Studio the cheese has been moved. The Microsoft Download Manager solves these potential problems. What happened to him? This is the library you need.
So, I downloaded and installed it from here. Can you believe it? We ran the Command Prompt using Windows Vtpoledb features a simple interface with many customizable options:.
In order to do that, Right-click the program's shortcut and click the Properties item in the right-click menu that appears. Here is my environment: Absolutely fantastic answer, thanks vfloledb, it worked like a charm!
However, I've never had to support this application before so that's why my development box isn't setup for it. Proposed as answer by Pat Tormey Friday, March 5, 8: Tuesday, February vtpoledb, 9: You can see the suggestions we gave on how to solve your problem by scrolling up on the page. I simply copied the exe onto the server.


text:
19:26 , 26 November 2022 0 0
China's expanded COVID lockdowns trigger outrage
China is seeing a record high number of COVID-19 cases. As a result, the country has enacted a zero-tolerance policy and shut down part of Beijing, causing unrest at a manufacturing plant that could derail Apple's delivery of the latest iPhones. Elizabeth Palmer has the latest.
"CBS Evening News with Norah O'Donnell" delivers the latest news and original reporting, and goes beyond the headlines with context and depth. Catch the "CBS Evening News" weeknights at 6:30 p.m. ET on the CBS Television Network and at 10 p.m. ET on the CBS News app.
Subscribe to the "CBS Evening News" YouTube channel: bit.ly/1S7Dhik
Watch full episodes of "CBS Evening News": cbsn.ws/23XekKA
Follow "CBS Evening News" on Instagram: bit.ly/1T8icTO
Like "CBS Evening News" on Facebook: on.fb.me/1KxYobb
Follow "CBS Evening News" on Twitter: bit.ly/1O3dTTe
Download the CBS News app: cbsn.ws/1Xb1WC8
Try Paramount+ free: bit.ly/2OiW1kZ
Watch video on channel "CBS News"
1/31: CBS News Prime Time
John Dickerson reports on a massive ice storm impacting millions of Americans in Texas and eastward, Rep. George Santos leaves his House committees, and what might be causing whale deaths in New York and New Jersey. CBS News Streaming Network is t...
CBS News 05:06 , 1 February 2023
Rep. Katie Porter talks about her Senate bid
Democratic Rep. Katie Porter is running to be California's next senator. Porter spoke to CBS News about her campaign, her opponents, and key issues she is fighting for, like ending the filibuster, and banning lawmakers from trading stocks. CBS New...
Whale deaths spark debate in New York, New Jersey over offshore wind farms.
Ten dead whales have washed ashore on the New York and New Jersey coastline since December. Some local mayors are blaming offshore wind farms for the deaths. Ry Rivard, a reporter for Politico, joined CBS News to discuss the issue. CBS News Stream...
Ice storm slams south and central U.S.
Millions of people across the south and central U.S. were under some form of winter weather alert Tuesday. Omar Villafranca reports from Memphis, Tennessee. CBS News Streaming Network is the premier 24/7 anchored streaming news service from CBS Ne...
New York City Mayor Eric Adams on Biden's immigration policies.
New York City Mayor Eric Adams told CBS News the Biden Administration needs to do more on immigration as thousands of migrants come to his city. CBS News senior White House and political correspondent Ed O'Keefe discussed his recent interview with...
Court Rejects Belarusian Oppositionists Appeal Against Their Imprisonment.


text:
Personalized prediction of incident hospitalization for cardiovascular disease in patients with hypertension using machine learning
Yuanchao Feng1,2,
Alexander A. Leung1,2,3,
Xuewen Lu4,
Zhiying Liang1,2,
Hude Quan1,2,5 &
Robin L. Walker1,5
BMC Medical Research Methodology volume 22, Article number: 325 (2022) Cite this article
Prognostic information for patients with hypertension is largely based on population averages. The purpose of this study was to compare the performance of four machine learning approaches for personalized prediction of incident hospitalization for cardiovascular disease among newly diagnosed hypertensive patients.
Using province-wide linked administrative health data in Alberta, we analyzed a cohort of 259,873 newly-diagnosed hypertensive patients from 2009 to 2015 who collectively had 11,863 incident hospitalizations for heart failure, myocardial infarction, and stroke. Linear multi-task logistic regression, neural multi-task logistic regression, random survival forest and Cox proportional hazard models were used to determine the number of event-free survivors at each time-point and to construct individual event-free survival probability curves. The predictive performance was evaluated by root mean squared error, mean absolute error, concordance index, and the Brier score.
The random survival forest model has the lowest root mean squared error value at 33.94 and lowest mean absolute error value at 28.37. Machine learning methods provide similar discrimination and calibration in the personalized survival prediction of hospitalizations for cardiovascular events in patients with hypertension. Neural multi-task logistic regression model has the highest concordance index at 0.8149 and lowest Brier score at 0.0242 for the personalized survival prediction.
This is the first personalized survival prediction for cardiovascular diseases among hypertensive patients using administrative data. The four models tested in this analysis exhibited a similar discrimination and calibration ability in predicting personalized survival prediction of hypertension patients.
Hypertension is the leading risk factor for preventable cardiovascular morbidity and premature death from cardiovascular disease (CVD) [1]. Accurate prediction of CVD can help to [2, 3], identify high-risk patients and therefore support clinical decision-making. Prognostic prediction has traditionally been based on the average event-free survival time in a population which is then projected onto an individual [4]. There is little research focusing on individual-level prediction.
In this study, we consider several important machine learning (ML) methods that produce the entire survival probability curve for individual patients. Recently, research has reported the risk analysis and survival prediction for cancer patients by machine learning techniques as well as on different input features and data samples. Weng el at. report using machine learning to improve accuracy of cardiovascular risk [5]. Results found that machine learning method improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment. Bharath et al. also found similar results in that machine learning improve prediction accuracy in CVD prediction model in an initially asymptomatic population [6]. Although machine learning methods have shown encouraging success on predicting some medical conditions, it has not been applied to individually CVD survival prediction in patients with hypertension by using routinely collected large digital electronic administrative health data. If the large administrative data set can be exploited using machine learning algorithm, it may open the way to optimise the use of collected administrative data to assist in predicting patents' outcome, planning individualised patient care, monitoring resource utilization and improving institutional performance. Including comorbidity status, demographic data, lab test results and medication would improve assessment of prognosis and guide treatment decisions for hypertension patients.
Use of machine learning methods in clinical oncology has shown success [7, 8], but this methodology has not been more broadly applied to other clinical areas. Addressing this, the purpose of this study was to compare evaluate four ML approaches for personalized prediction of incident hospitalization for heart failure (HF), myocardial infarction (MI), and stroke among newly diagnosed hypertensive patients using routinely collected administrative health data. To our knowledge, this the first study to develop and validate different state-of-the-art ML models for individual CVD outcome prediction in hypertensive patients.
Data sources and study population
A retrospective cohort was assembled using linked administrative health databases from Alberta Health with information including demographic and vital statistics, physician billing claims, medication dispensations, hospital separation data, and laboratory services (Fig. 1). These data have been used in previous studies and shown to be high-quality and comprehensive [9, 10].
Linkage of six administrative health dataset to determine the study cohort. Note: DAD = Discharge Abstract Database, eGFR = Estimated glomerular filtration rate, HbA1C = Glycated haemoglobin (A1c), HF = heart failure, LDL = Low-density lipoproteins, MI = myocardial infarction, PIN = Pharmaceutical Information Network
The study population included all newly diagnosed cases of hypertension aged 18 to 99 years who were residents of Alberta. We identified hypertension cases using a validated case definition of two physician claims within two years or one hospitalization with hypertension related diagnosis codes (ICD-9-CM: 401.x, 402.x, 403.x, 404.x or 405.x; ICD-10-CA: I10.x, I11.x, I12.x, I13.x or I15.x) [11]. The first date of the hypertension diagnosis (index date) was assigned to patients for case definitions with more than one hypertension diagnosis. We included patients who were identified to have hypertension between April 1, 2009, to March 31, 2014 (excluding those who had any code for hypertension between April 1, 2006, to March 31, 2009, thus allowing for a 3-year washout period, and thereby limiting the cohort to only newly diagnosed cases). Patients with prior CVD were also excluded. Cohort assembly is summarized in Fig. 2.
The flow for hypertension cohort identification. Note: 1H2P = 1 hospitalization or 2 physician claims within 2 years, CVD = Cardiovascular disease, HF = heart failure, MI = myocardial infarction
The outcome of interest was incident hospitalization for any CVD, including HF, MI or stroke, identified using validated case definitions: HF (ICD-10-CA: I09.9, I11.0, I13.0, I13.2, I25.5, I42.0, I42.5-I42.9, I43.x, I50.x, P29.0), MI (ICD-10-CA: I21.x, I22.x, I25.2), and stroke (ICD-10-CA: H34.1, I60.x, I61.x, I63.x, I64.x, G45.x) [12]. The time of event was defined as the first occurrence of hospitalization for HF, MI or stroke. We followed patients from initial hypertension diagnosis until the time of the outcome event, death, emigration out of the province, or the end of the study, up to March 31, 2015. If a patient experienced more than one event only the first event was counted as the incident event. Outcome event rates were calculated per 1000 person-years based on a maximum 6-year follow-up period.
Potential predictors were selected a priori based on previous studies and clinical reasoning [13]. Age was categorized into four age groups as the predictor in this study. Patients' demographic information, such as sex, region of residence was also used as predictors. The number of Charlson comorbidities present in each patient was categorized into "0, 1-2, or ≥ 3." Fasting blood glucose, estimated glomerular filtration rate (eGFR), cholesterol levels and Glycated haemoglobin (A1c) (HbA1c) was determined between hypertension index date and outcome date [14, 15]. Test results outside the standardized reference intervals were used (Blood glucose > = 7.0 mmol/L, eGFR < 60 mL/min/1.73m2, Cholesterol levels > 3.5 mmol/L, HbA1c > =6.5%) [16].
The following categories of antihypertensive medications have been shown to reduce cardiovascular risk and were identified using the anatomical therapeutic chemical (ATC) classification system: beta blockers (ATC codes in category C07, excluding C07AA07, C07AA12 and C07AG02); agents acting on the renin-angiotensin system (ATC codes in category C09); thiazide diuretics (ATC codes in category C03, excluding C03BA08 and C03CA01); calcium channel antagonists (ATC codes in category C08); and miscellaneous antihypertensives (ATC codes in category C02, excluding C02KX01) [17]. Respondents were categorized as using antihypertensive medication if an ATC code corresponded to the above list between the hypertension index date and outcome index date.
The study cohort was randomly divided into training (70% of total: n = 181,911) and validation (30% of total: n = 77,962) sets (Fig. 2). Multicollinearity between predictor variables was assessed using condition indices and variance proportions. Those with significant correlation were removed from the model. The linear multi-task logistic regression (LMTLR) model is an alternative to the Cox's proportional hazard (CoxPH) model. It can be seen as a series of logistic regression models built on different time intervals to estimate the probability that the event of interest happened within each interval. The constructed LMTLR included 25 features and 50 intervals in this study. The neural multi-task logistic regression (NMTLR) allows the use of Neural Networks within the original multi-task logistic regression (MTLR) design. We used the same 25 features, 100 neurons in the first hidden layer and 100 neurons in the second hidden layer, and one output neuron before input to LMTLR. The random survival forest (RSF) is an extension of the Random Forest model that can take into account censoring individuals. We used 50 trees, the maximum depth of 5 and minimum number of samples required to be at a leaf node at 20 for the model development. The CoxPH is a semi-parametric model that focuses on modeling the hazard function, by assuming that its time component and feature component are proportional over time. The maximum number of iterations in the Newton optimization in this model was 600.
Model validation
The final survival prediction model was tested within the test dataset for those four models (LMTLR, NMTLR, RSF, CoxPH) [18]. The actual and predicted number of patients that experienced the CVD event at each time t was compared by computing the actual survival function of the validation data, which can be obtained using the Kaplan-Meier estimator and compare it to the average of all predicted survival functions [18]. Root mean squared error (RMSE) and mean absolute error (MAE) was used to provide the comparison as well as the performance metrics between the actual and predicted number of hypertensive patients experiencing a CVD event at each time, t. Model accuracy was assessed using discrimination (concordance index (C-index)) and calibration (Brier score).
Analyses were conducted using SAS version 9.4 [19], R software version 3.5.1 [20] and Python version 3.7.6 [21]. Descriptive statistics were generated by SAS (Tables 1 and 2). The package 'survival' in R was used to produce Fig. 1 for survival analysis. 'PySurvival' in Python was used for ML model analyses. All the methods were performed in accordance with relevant guidelines and regulations.
Table 1 Population characteristics
Table 2 Hospitalization for incident cardiovascular disease (95% CI) among Albertan adults with newly diagnosed hypertension
Cohort characteristics
We identified 299,826 newly diagnosed hypertensive patients between April 1, 2009, and March 31, 2015. After applying exclusion criteria, there was a total of 259,873 patients with 899,393 person-years of follow-up and collectively with 11,863 events over a median follow-up time of 3.5 years (inter-quartile range 2.2 to 4.8 years). The incidence rate was 13.4 CVD hospitalizations per 1000 person-years. Among the study population 9182 (3.5%) patients died within the study period. The mortality rate during the follow-up period was 10.0 per 1000 person-years (95% CI: 9.8 to 10.2 per 1000 person-years).
The median age of newly diagnosed hypertension patients was 56.1 years (26.7% were older than 65 years).and 83.6% resided in urban areas. The majority of patients had isolated hypertension without other major comorbidities, but up to one-third had at least one non-cardiovascular Charlson comorbidity, with diabetes being the most common, being present in around 1 in 10 people (9.7%). Nearly two-thirds of patients had at least one laboratory test of interest completed. An elevated LDL-cholesterol (33.7, 95% CI:33.5–33.9), elevated fasting blood glucose (20.5, 95% CI:20.4–20.7), and presence of renal dysfunction (24.5, 95%CI: 24.4–24.7) were the most common laboratory abnormalities. Most patients were dispensed with at least one antihypertensive medication (80.7, 95% CI: 80.6–80.9). Of these, the majority received an angiotensin converting enzyme inhibitor or angiotensin II receptor blocker (67.3, 95% CI: 67.1–67.5), followed by thiazide diuretics (24.7, 95% CI: 24.6–24.9), calcium channel blockers (23.9, 95%CI:23.7–24.1), and beta-blockers (18.1, 95%CI:18.0–18.3) (Table 1, locate at the end of the document text file).
The crude incidence of composite CVD hospitalization was 13.2 (95%CI: 13.0–13.4) per 1000 person-years. Hospitalization for MI was most common (6.1 (95%CI: 6.0–6.3) per 1000 events per person-years), followed by HF (5.6 (95%CI: 5.4–5.7) events per 1000 person-years), and lastly stroke (3.4 (95%CI: 3.3–3.5) events per 1000 person-years) (Table 2, locate at the end of the document text file). The composite CVD hospitalization rate was higher for men, and this was driven by an excess risk of MI. Hospitalizations were most common in patients above the age of 75 years, those residing in rural locations, and individuals with at least two other Charlson comorbidities both for the composite outcome and its individual components.
Figure 3 shows Kaplan-Meier plots of the cumulative probability of being free of hospitalization for any CVD, HF, MI, and stroke as a function of survival time among newly diagnosed hypertension patients. MI had the lowest cumulative probability in the entire survival period when compared with HF and stroke.
Kaplan-Meier estimate for incident hypertensive patients stratified by cardiovascular disease (any one of heart failure, myocardial infarction and stroke), MI, HF and stroke. Note: HF = heart failure, MI = myocardial infarction
Model performance
Table 3 shows a comparison of the actual and predicted number of hypertension patients experiencing CVD events at each time point for all four models. The RSF model had the lowest RMSE at 33.94 and the lowest MAE at 28.37, indicating the best fit. By plotting the individual's survival function over time, we compared the survival probability of developing CVD events among individual hypertensive patients. Table 4 shows the results when applying all four models in our hypertension cohort. Overall, all the models had a C-index > 0.70 and Brier score < 0.25 [22], representing a strong predictive ability in the validation set. Adding two layers of neural network before LMTLR, the NMTLR model had the highest C-index (0.82) and lowest Brier score (0.02); the best discrimination and calibration of all the models for event-free survival prediction. This suggests the NMTLR is most accurate and outperformed the other models in predicting CVD outcomes for incident hypertensive patients.
Table 3 Comparison of predicting the number of incident hypertensive patients diagnosed with cardiovascular disease(s) using predicted survival functions in multiple models (LMTLR, NMTLR, RSF and CoxPH)
Table 4 Summary of the results that measured by C-index and Brier score
Personalized prediction
Figure 4 visualizes the LMTLR, NMTLR, RSF and CoxPH models for two representative patients from the validation set. Patient one (red line) had a short event-free survival of 4.0 years from diagnosis of hypertension until being hospitalized for CVD, while patient two (blue line) had a comparatively longer event-free survival of at least 4.9 years before being censored. Patient one developed CVD after 4.0 years diagnosed as hypertension, however, patient two may have been lost to follow-up or did not develop CVD at the end of the study or death until 4.6 years after diagnosed as a hypertensive patient. The median survival time (50% survival probability) as a point estimation for survival time predication was used in the study for personalized survival prediction performance evaluation. If the 50% survival probability is close to the survival time, the model has more accurate prediction performance. All four models performed well in predicting the prognosis for patient one whose 50% survival probability corresponded with the actual observed 4.0 years of event-free survival. However, only the NMTLR model provided accurate prediction of 50% survival probability for patient two who was lost survival information at 4.9 years in this study. For other models, take the LMTLR for example, in fig. 4(a), the survival probability for patient 2 at 4.9 years survival time is near 90%, and this patient's 50% survival probability is nearby 6.6 years. Although patient 2 passed the 50% survival probability after the 4.9 years in image (a), this patient's 50% survival probability does not close to the 4.9 survival years, which indicate the model could not well predict this patient's survival information in this model. The NMTLR was able to handle the presence of censoring better than the other models. Moreover, the individual survival curves for these two patients intercrossed at the beginning of the observation period. This may reflect the real situation that patient two has worse health condition or perhaps the patient is treated and controlled one year after being diagnosed with hypertension. Patient two have a pretty flat curve in the following period, however patient one became worse in the whole follow up period. The CoxPH model was unable to properly handle censoring, as represented by a horizontal survival probability line for patient two.
Personalized survival prediction by using (a) LMTLR model, (b) NMTLR model, (c) RSF model and (d) CoxPH model when randomly identifying two patients as an example. X axis is survival time in years, Y axis is survival probability. Patient one (red line) is a short survivor who lives for 4.0 years from diagnosis of hypertension to CVD, while patient two (blue line) is a long survivor whose survival time is censored at 4.9 years. Patient one developed CVD after 4.0 years diagnosed as hypertension, however, patient two was censored at 4.9 years after diagnosed as hypertension patient. The bottom position of survival time text (4.0 and 4.9) on the pictures correspond to 50% survival probability horizontal location line. Note: CoxPH = Cox's proportional hazard, LMTL = linear multi-task logistic regression, NMTLR = neural multi-task logistic regression, RSF = random survival forest
Figure 5 shows the prediction results for two patients who had CVD outcome at 1.1 years and 2.3 years, respectively. Patient one had a hospitalization for CVD at 2.3 survival years while patient two was had hospitalization for CVD at 1.1 survival years. All of those four models can discriminate the two patients' survival time versus survival probability. Patient two's survival curve was always lower than patient one and reached 50% survival probability faster than patient one. For NMTLR, the actual survival time corresponded closely to the estimated survival probability for both patients. For patients two, the survival curve was consistently lower than patient one, and the 50% survival probability occurred earlier for the first patient. Only NMTLR correctly predicted the survival time for patient one at 2.3 years and patient two at 1.1 years, based on the projected 50% survival probability. Moreover, NMTLR also had the smoothest survival curves with distinct shapes predicted for the two patients, while the CoxPH model predicted survival curves with similar shapes because of the proportional hazard assumption. For RSF model, we observed that the survival function was monotonically decreasing and parallel. This is likely due to both patients being in the same tree branch node in the model development process.
Personalized survival prediction by using (a) LMTLR model, (b) NMTLR model, (c) RSF model and (d) CoxPH model when randomly choosing two patients who have CVD outcome at 1.1 years and 2.3 years as an example. X axis is survival time in years, Y axis is survival probability. Hypertension patient one (red line) was diagnosed as CVD outcome at 2.3 survival years while hypertension patient two (blue line) was diagnosed as CVD outcome at 1.1 survival years. The bottom position of survival time text (1.1 and 2.3) on the pictures correspond to 50% survival probability horizontal location line. Note: CoxPH = Cox's proportional hazard, LMTL = linear multi-task logistic regression, NMTLR = neural multi-task logistic regression, RSF = random survival forest
In this study, we explored the performance of ML models on predicting incident hospitalization for CVD distribution in patients newly diagnosed with hypertension using administrative data. All models were developed and validated using the same training dataset. All the four models had high discrimination with C-index > 0.70 and good calibration with Brier score < 0.25. We showed how ML models can be applied to accurately predict the occurrence of hospitalization for CVD on both population and individual levels.
For population use, the ML models can predict the number of patients with events at each time point using survival functions, similar to traditional regression methods. The RSF model had the best performance for population-based prediction compared to the other three models. Moreover, the RMSE and MAE were quite small in the RSF model, indicating that the prediction results were relatively constant during the 6-year follow-up period. As we did not force the use of any particular variables, the RSF algorithm was allowed to include any variables available in the administrative dataset that were associated with risk of hospitalization for CVD, which likely made the model more accurate than other models based on linear regression (LMTLR, NMTLR and CoxPH) for the population-based prediction [23, 24].
In terms of individual-level prediction, the NMTLR model had the highest C-index and lowest Brier score which means it had the best discrimination and calibration for individual survival prediction [25]. This may be because of the unique properties of using a neural network [26]. Neural networks require initialization and adjustment of many individual parameter to optimize the performance of the classification [27]. The NMTLR model that combines the neural network and multi-task logistic regression together was developed empirically and can be best fit for the training data in our study. NMTLR models the survival function by combining multiple local logistic regression modes in a dependent manner followed by a two layers neural networking procedure. This allows it to handle censored observations and the time-varying effects of features naturally to provide better results compared to the model which will only use fully observed (uncensored) instances (like CoxPH) [23]. The combination of neural network and multiple task logistic regression in NMTLR allows the model to build a nonlinear statistical data modeling tool to deal with complex relationships and has shown better predictive performance than the other three models [28].
To our knowledge, this is the first study to develop and validate models for individual CVD outcomes among patients with hypertension using administrative data. Utilizing administrative health data provides the opportunity to: 1) utilize risk factors that are routinely collected; and2) adopt the methods into existing hypertension and cardiovascular care practice and programs that are relevant for precision medicine. Further, there is considerable potential to use this data to improve clinical care cross a spectrum of chronic diseases. Our study results support that large administrative data provides sufficient statistical power to develop and validate predictive algorithms with a larger set of risk factors and greater specification of those risks, which in turn generate distinct survival probability for a wide range of health profiles or populations. Importantly, for individual-level prediction, our finding suggests that NMTLR has the best discrimination and calibration performance when compared to the other three models.
There are limitations to this study. Firstly, most patients in the study were followed up for 3 years, which may not be adequate to capture all CVD outcomes, especially for those younger and have a small number of comorbidities. Secondly, this study was retrospective and conducted in a single cohort. Further study is required to demonstrate generalizability of our findings. Thirdly, there are many important factors, such as blood pressure and other CVD medications, that were not included due to the limitations of administrative health data used in this study. Lastly, this study did not fully take into account missing data. Variables were included in the model even if one patient had a single value in the chart. This may have some-what diminished our predictive accuracy; however, a strength of this approach is that it represents the true nature of administrative health data with minimal transformations and with no data imputations. Another consideration is that we elected to define hypertension patients using a validated diagnosis codes with a high sensitivity and specificity. This methodology that using 1 hospitalization and 2 physician claims algorithm for hypertensive patients' definition could represents a more easily deployable solution to cohort building and model development.
This study demonstrated that four ML models utilizing administrative health data exhibited similar high discrimination and calibration in predicting incident hospitalization for CVD among hypertensive patients. Specifically, the NMTLR model had the best individual survival prediction and the RSF model had the best population survival prediction. Improved prediction of outcome has the potential to help clinicians make more meaningful decisions about treatment. Importantly, this study makes use of administrative health data that is already routinely collected but underexploited by clinical health systems. While ML methodologies have many advantages, to truly improve patient care and outcomes, methods for teasing out causal relationships will remain an important part of the health care and biomedical armamentarium.
Data for this research are available from the corresponding author on reasonable request.
CVD:
ML:
MI:
DAD:
discharge abstract database
estimated glomerular filtration rate
HbA1c:
Glycated haemoglobin (A1c)
LDL:
low-density lipoproteins
pharmaceutical information network
anatomical therapeutic chemical
1H2P:
1 hospitalization or 2 physician claims within 2 years
LMTLR:
linear multi-task logistic regression
CoxPH:
Cox's proportional hazard
NMTLR:
neural multi-task logistic regression
MTLR:
multi-task logistic regression
RSF:
random survival forest
RMSE:
MAE:
C-index:
concordance index
first quartiles
third quartiles
Frieden TR, Jaffe MG. Saving 100 million lives by improving global treatment of hypertension and reducing cardiovascular disease risk factors. J Clin Hypertens. 2018;20(2):208–11.
Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol. 2017;69(21):2657–64.
Leopold JA, Maron BA, Loscalzo J. The application of big data to cardiovascular disease: paths to precision medicine. J Clin Invest. 2020;130(1):29–38.
Sy JP, Taylor JM. Estimation in a cox proportional hazards cure model. Biometrics. 2000;56(1):227–36.
Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One. 2017;12(4):e0174944.
Ambale-Venkatesh B, Yang X, Wu CO, Liu K, Hundley WG, McClelland R, et al. Cardiovascular event prediction by machine learning: the multi-ethnic study of atherosclerosis. Circ Res. 2017;121(9):1092–101.
Krittanawong C, Virk HUH, Bangalore S, Wang Z, Johnson KW, Pinotti R, et al. Machine learning prediction in cardiovascular diseases: a meta-analysis. Sci Rep. 2020;10(1):1–11.
McCradden MD, Baba A, Saha A, Ahmad S, Boparai K, Fadaiefard P, et al. Ethical concerns around use of artificial intelligence in health care research from the perspective of patients with meningioma, caregivers and health care providers: a qualitative study. CMAJ Open. 2020;8(1):E90.
Quan H, Chen G, Walker RL, Wielgosz A, Dai S, Tu K, et al. Incidence, cardiovascular complications and mortality of hypertension by sex and ethnicity. Heart. 2013;99(10):715–21.
Metcalfe A, Neudam A, Forde S, Liu M, Drosler S, Quan H, et al. Case definitions for acute myocardial infarction in administrative databases and their impact on in-hospital mortality rates. Health Serv Res. 2013;48(1):290–318.
Quan H, Khan N, Hemmelgarn BR, Tu K, Chen G, Campbell N, et al. Validation of a case definition to define hypertension using administrative data. Hypertension. 2009;54(6):1423–8.
Quan H, Chen G, Tu K, Bartlett G, Butt DA, Campbell NR, et al. Outcomes among 3.5 million newly diagnosed hypertensive Canadians. Can J Cardiol. 2013;29(5):592–7.
Quan S, Chen G, Padwal RS, McAlister FA, Tran KC, Campbell NR, et al. Frequency of laboratory testing and associated abnormalities in patients with hypertension. J Clin Hypertens. 2020;22(11):2077–83.
Schwartz GL, Krakoff LR. Diagnostic evaluation initial evaluation: laboratory testing. J Am Soc Hypertens. 2014;8(9):677–9.
Rabi DM, McBrien KA, Sapir-Pichhadze R, Nakhla M, Ahmed SB, Dumanski SM, et al. Hypertension Canada's 2020 comprehensive guidelines for the prevention, diagnosis, risk assessment, and treatment of hypertension in adults and children. Can J Cardiol. 2020;36(5):596–624.
Pearson GJ, Thanassoulis G, Anderson TJ, Barry AR, Couture P, Dayan N, et al. 2021 Canadian cardiovascular society guidelines for the Management of Dyslipidemia for the prevention of cardiovascular disease in the adult. Can J Cardiol. 2021;37(8):1129–50.
Wilkins K, Gee M, Campbell N. The difference in hypertension control between older men and women. Health Rep. 2012;23(4):1C.
Feng Y. Personalized survival prediction of cardiovascular disease among hypertensive patients: a machine learning approach based on health administrative data [master thesis]. Calgary: The University of Calgary; 2020.
Institute S. The SAS system for Windows. 2016.
Team RC. R: a language and environment for statistical computing; 2013.
Van Rossum G, Drake FL Jr. Python tutorial: centrum voor Wiskunde en Informatica. Amsterdam; 1995.
Jachan M, Feldwisch Genannt Drentrup H, Posdziech F, Brandt A, Altenmüller D-M, Schulze-Bonhage A, et al., editors. Probabilistic forecasts of epileptic seizures and evaluation by the Brier score. 4th European conference of the International Federation for Medical and Biological Engineering: Springer; 2009.
Yu C-N, Greiner R, Lin H-C, Baracos V. Learning patient-specific cancer survival distributions as a sequence of dependent regressors. Adv Neural Inf Process Syst; 2011 24.
Li Y, Wang J, Ye J, Reddy CK. A Multi-Task Learning Formulation for Survival Analysis; 2016. p. 1715–24.
Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, et al. Assessing the performance of prediction models: a framework for some traditional and novel measures. Epidemiology. 2010;21(1):128.
Van der Heide E, Veerkamp R, Van Pelt M, Kamphuis C, Athanasiadis I, Ducro B. Comparing regression, naive Bayes, and random forest methods in the prediction of individual survival to second lactation in Holstein cattle. J Dairy Sci. 2019;102(10):9409–21.
Ahmed FE. Artificial neural networks for diagnosis and survival prediction in colon cancer. Mol Cancer. 2005;4(1):1–12.
Zupan B, Demsar J, Kattan MW, Beck JR, Bratko I. Machine learning for survival analysis: a case study on recurrence of prostate cancer. Artif Intell Med. 2000;20(1):59–75.
Funding was not associated with the design, conduct, or reporting of this study.
Centre for Health informatics, Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
Yuanchao Feng, Alexander A. Leung, Zhiying Liang, Hude Quan & Robin L. Walker
Libin Cardiovascular Institute, University of Calgary, Calgary, AB, Canada
Yuanchao Feng, Alexander A. Leung, Zhiying Liang & Hude Quan
Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
Alexander A. Leung
Department of Mathematics and Statistics, University of Calgary, Calgary, AB, Canada
Xuewen Lu
O'Brien Institute for Public Health and Alberta Health Services, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada
Hude Quan & Robin L. Walker
Yuanchao Feng
Zhiying Liang
Hude Quan
Robin L. Walker
YF, HQ and RW designed the study. YF planned and carried out all the analysis. AAL had substantial contributions to the interpretation of the data and medical aspect knowledge. XL made constructive comments on the statistics analysis section. HQ provided overall guidance. ZL helped on the administration data linkage and management. YF drafted the manuscript with support from RW, AAL and HQ. RW and A AL provided important intellectual contributions to the revisions. All authors agreed on the final version of submission and account for all aspects of this work.
Correspondence to Robin L. Walker.
This study was approved by the Conjoint Health Research Ethics Board at the University of Calgary (Ethics ID: REB15–1271). Informed consent of participants was waived by the Conjoint Health Research Ethics Board at the University of Calgary. All methods were carried out in accordance with the Declaration of Helsinki.
Feng, Y., Leung, A.A., Lu, X. et al. Personalized prediction of incident hospitalization for cardiovascular disease in patients with hypertension using machine learning. BMC Med Res Methodol 22, 325 (2022). https://doi.org/10.1186/s12874-022-01814-3
Administrative health data
Personalized prediction, Hypertension patients


Energy Star savings for your home: Cooling off comes at a huge cost for homeowners/ Lower your utility bill by using Energy Star qualified lighting, raising your thermostat two degrees and using ceiling fans can help cool off costs.
Breathe Easy: People spend more time indoors in the summer when it's hot. Make sure your air is mold, radon and carbon monoxide free, so you can breathe easy.
Stay SunWise: Skin cancer is the most common form of cancer in the U.S. Be sure you practice safe sun habits before heading out for summer fun.
Have a beach blast! If you're making a trip to the shore, make sure your plan includes ways to stay safe at the beach.
Bug Off! Mosquitoes and ticks carry diseases that be prevented by using proper protection. Make sure you check the label before spraying away!
Increase gas mileage: There are plenty of ways to improve your fuel economy including obeying the speed limit, going easy on the breaks, avoiding hard accelerations, reducing idle time, unloading necessary items and removing unused racks.
Water wise: Keep water from going to waste by watering in the morning, and installing WaterSense labeled irrigation systems.
Clean Green: Cleaning your grill, car, deck, boat or RV? Make sure your cleaner is earth friendly by using only those labeled Designed for the Envinroment (DfE).
Project Compost: Looking for a fun summer project that beats the heat? Try composting.
Go Mobile: Want to connect with the EPA on the go? They have an app for that! Check your local air quality, UV index and more all from your smartphone.
Make memories: Help the EPA capture the State of the Environment by submitting environmental photos to their Documerica project.
Check in: Hotels spend more than $2,000 per room on energy each year. Make sure your hotel is saving money and the environment by choosing one that is Energy Star rated.
Summer Inspiration: Keep your creative juices flowing by submitting a meaningful story or idea in just six words.
Be sure to visit the EPA's site for more tips for a cool summer. If you're looking to make a move, be sure to check out all of the green Atlanta new homes on Atlanta Real Estate Forum.
This entry was posted in Energy Efficient, Featured, Green Certifications, Green Events, Green News and tagged atlanta new homes, Environmental Protection Agency, green tips, summer on June 20, 2012 by Carol Morgan.


text:
Q: The library com.google.android.gms:play-services-measurement-base is being requested by various other libraries at [[15.0.0, The library com.google.android.gms:play-services-measurement-base is being requested by various other libraries at [[15.0.0,15.0.0], [16.0.0,16.0.0]], but resolves to 16.0.0. Disable the plugin and check your dependencies tree using ./gradlew :app:dependencies.
build error android
every time I build this I got same problem for project

A: For those who still have this problem on the Ionic framework. After 2 day's of trying, I succeeded to build my project.

*

*npm i cordova-android-firebase-gradle-release

*npm i cordova-android-support-gradle-release

*In package.json under plugins, you should have this:

      "cordova-android-play-services-gradle-release": {
        "PLAY_SERVICES_VERSION": "+"
      },
      "cordova-android-firebase-gradle-release": {
        "FIREBASE_VERSION": "+"
      }

*In config.xml, you should have these:

    <plugin name="cordova-android-play-services-gradle-release" spec="^2.0.0">
        <variable name="PLAY_SERVICES_VERSION" value="+" />
    </plugin>
    <plugin name="cordova-android-firebase-gradle-release" spec="^2.0.0">
        <variable name="FIREBASE_VERSION" value="+" />
    </plugin>

That's it. Hope it helps :)

A: Remove com.google.android.gms:play-services:15.0.1 from Gradle and use only individual which you have required.
check here.

Note: Don't use the combined play-services target. It brings in dozens
  of libraries, bloating your application. Instead, specify only the
  specific Google Play services APIs your app uses.

redpajama_set_name: RedPajamaStackExchange


Views : 526. Votes : 0. Shares : 1.
Views : 320. Votes : 0. Shares : 0.
Views : 356. Votes : 0. Shares : 0.
Views : 293. Votes : 0. Shares : 0.
Views : 270. Votes : 0. Shares : 4.
Views : 171. Votes : 115. Shares : 23.
Views : 149. Votes : 5. Shares : 0.
Views : 134. Votes : 4. Shares : 0.
Views : 50. Votes : 3. Shares : 0.
Views : 53. Votes : 3. Shares : 0.
Views : 180. Votes : 0. Shares : 3.
Views : 35. Votes : 0. Shares : 2.
Views : 78. Votes : 0. Shares : 2.


text:
Individual tickets are $14 each or $10 for Cinema St. Louis members, ARTS Card-holders, and students with current and valid ID, except for the following (discounts do not apply and passes are not accepted):
Opening-Night Reception with Marriage Story $25
The Rats and People Motion Picture Orchestra with Sherlock Jr. $20
In addition to paid shows, SLIFF offers 64 free programs.
ADVANCE-TICKET SALES
Online sales are limited to full-price tickets only; Cinema St. Louis member and student discounts can only be obtained in person because ID is required. To obtain advance tickets for a film, simply click on the link on the film's page. A service charge of $1.75 applies for all advance tickets at Plaza Frontenac and Tivoli; a service charge of $1.69 applies for all advance tickets at Stage at KDHX and Webster University.
DAY-OF-SHOW TICKET SALES
KDHX, Plaza Frontenac, Tivoli, and Webster U. will open a half-hour before the first show.
The free events offered at Contemporary Art Museum, Cortex's Innovation Hall, KDHX, Missouri History Museum, St. Louis Public Library, Washington U., and Webster U. require no ticket. Admission is first come, first served on day of show.
The free events offered at the Tivoli require a complimentary ticket that should be obtained at the box office on the day of show; advance tickets to free shows at the Tivoli can be obtained online, but a $1.76 service charge applies.
FESTIVAL PUNCH-PASSES
Festival Punch-Passes are available at two levels: 6-ticket pass for $75 or 10-ticket pass for $120. Festival Punch-Passes are not valid for the Opening-Night Reception with "Marriage Story" on Nov. 7 and the screening of "Sherlock Jr." with the performance by the Rats & People Motion Picture Orchestra on Nov. 15.
Festival Punch-Pass-holders are required to obtain a ticket for each film attended, either in advance or day of show; a hole will be punched in the pass for each ticket purchased.
Festival Punch-Passes can be used to purchase multiple tickets for the same show and to obtain tickets for screenings at all venues.
Festival Punch-Passes are available at Tivoli and Plaza Frontenac box offices in advance and on day of show.
FESTIVAL ALL-ACCESS VIP PASS
Festival All-Access VIP Passes (good for two admissions to every SLIFF program, including special events) are available for $450. Pass-holders are required to obtain a ticket for each film attended, either in advance or day of show. All-access passes are available by phone only through Cinema St. Louis: 314-289-4153.


Imagine you're out camping with several friends. On this particularly clear night, you look up at the stars as usual. But this time, something else catches your eye. You see several flashes of light and streaks among the stars. One of your friends thinks it's nothing more than the annual Perseid Meteor Shower.
You study the flashes and realize they're too methodical and deliberate. A friend tries to stream a live video of the event on social media, only to find the phone service—and any connection to the internet—is down. Another friend goes to their vehicle and tries to connect with the GPS system only to discover it's also gone.
Then you realize the gravity of what you're witnessing: satellites and space stations are being shot down by military craft just outside our atmosphere. The shooting stars are nothing more than space debris re-entering the atmosphere and burning up above you.
With a revival of the modern-day space race, militarization outside the Earth has become a growing reality pushed into the forefront of political and social discussion. Recently, the President of the United States called for the development of a sixth branch of the military, the "space force." Media outlets blew up with reports of the news and debates spread across various social platforms.
Bridenstine also serves on the National Space Council, an organization whose roles include advising the President on America's space policies and strategies and reviewing long-term goals for various space activities. These include the regulation and protection of outer space.
Not only is there a need to protect property in space, there is a growing need to control and monitor commercial and civil space traffic. With over 4,600 manmade satellites—1,700 plus of those active—and hundreds more being launched over the next several years, outer space is quickly becoming congested. These satellites provide everything from communications and earth/space observation to navigation/global positioning and technology development.
With the transfer of power to the Commerce Department, the Defense Department will then be able to focus its efforts less on regulation and more on militarization.
As countries invest in their own space programs and space-oriented military branches, monies will be redirected towards these programs. Companies such as Boeing, Lockheed Martin, Raytheon, and SpaceX would see financial growth and would become part of the decision-making processes. Advancements in technology would be made. Projects from surveillance/reconnaissance to colonies in space would benefit.
Satellites and unmanned aerial vehicles, such as drones, could transmit real-time data to help ground troops visualize their enemies' locations and activities. This would translate in "effective and more accurate target acquisition." Received laser and 3d-scanned imagery could then be used to provide scientists with more accurate topographical maps and earth studies.
Trade and travel routes, as well as colony development, would prosper. The establishment of troops in outer space would lead to military bases positioned on orbiting bodies such as the Moon, asteroids, and planets. With military bases and outposts, the need for fuel stations, entertainment businesses, and communications hubs would come; these would provide economic boosts and new job opportunities for civilians.
Another advantage would be the protection and safety of satellites and space technology from attacks and hacks. Branches of the military currently use various space tech to guide missiles, coordinate unmanned vehicle attacks, and remotely surveil enemy locations from above. As most of this technology is connected to cyberspace, there is vested interest in making the world wide web as secure as possible. Better cyber counter-campaigns would protect sophisticated computer networks, infrastructures, and many forms of computer information systems.
Some scientists fear that the creation of a regulated Space Force would propel us closer to the next world war and cripple the planet's economic stability.
There is fear that a Space Force would be detrimental to the funding of organizations like NASA and NOAA. With negative attitudes towards climate change, the current administration considered cutting budgets for some earth science missions, as well as oceanic and terrestrial studies. Instead, these moneys would be redirected into defense programs/organizations and for creation of required weaponry and deployment. Financial cuts would also mean job losses, agency shutdowns, and research setbacks.
Another downside to space militarization is the potential for weaponization. For several years, China, Russia and the United States have been developing and testing anti-satellite weapons, which could cripple outer space infrastructure. Spacecraft could approach satellites and permanently damage or destroy them. Missiles could take out manned stations or future colonies.
China, for example, has expanded and grown their own space program. The country has made "impressive headway" in its intercontinental ballistic missile (ICBM) program with capabilities of reaching United States satellites. With anti-satellite weaponry in place, the U.S. has found it hard to believe that China's ambition of space exploration is merely for scientific research and not military expansion. China, in turn, believes their greatest threat comes from the United States. Distrust could continue to build between the two nations, propelling us closer to war.
If the United Nations' Outer Space Treaty fails, nuclear weapons could be detonated, wiping out hundreds of orbiting objects. We could also witness harmful contamination of celestial bodies, territorial disputes over the Moon, and spacesuit-donned infantry hijacking infrastructure.
Fear of attacks is pushing each country to out-design the other and come up with not only defensive capabilities, but also offensive ones. For example, the Air Force's Counter Communications System has developed and continually upgrades transmission-jamming technology to prevent attacks from an adversary. And, with the United States increasing their defense operations in space, other countries have seen this as a hostile move and have begun their own countermove measures.
Space militarization has the potential to provide a strong, secure foundation to grow and advance our expansion in (and knowledge of) the universe. We have the capability of providing mankind with renewable and reusable sources and expanding our galactic reach.
With safety measures in place, Fortune 500 companies could invest billions in space-related technologies and advancements. Our generation is living on the edge of space travel, colonization, and incredible scientific possibilities and opportunities.
However, we are also living on the edge of possible unknown horror. Militarization has the ability to invite destruction and distrust as defensive and offensive strategies develop. Each play from one country pushes other countries closer to a potential space war. With each move by the United States, a countermove is made by another world power. And with each countermove, a new strategy and development is considered by the U.S.
Einstein once said: "I know not with what weapons World War III will be fought, but World War IV will be fought with sticks and stones." If treaties fail and distrust continues, the next war could cripple world economies, sending our advancements backwards several decades, like Einstein predicted.
For now, we walk a fine line between both edges.


The company TNS s.r.o. was established in 2001. The main activities of company are sale of precision tools for drilling, milling, lathe-turning, threading and processing of metal machining technologies. In both fields we have adequate experience, as well as wide scale of products, which we offer to the Slovak customer.
We insist on quality, however the atmosphere of mutual trust between us and our business partners is for us of the same importance.
We hope that you will choose from our wide offer and we look forward to co-operation. Should you have any questions please do not hesitate to contact us.


Pleased to find that dual-extrusion prints worked right out of the box albeit with lower temps than in the original PLA templates.
wow that's beautiful. Are these default ideamaker settings or did you tinker at all?
Nice. LOVE that the new Pro series has retracting nozzles. I tried and gave up really fast using dual-extrusion on my N2. My 2nd nozzle kept hitting the build. Never could find that sweet spot. Looks great. I guess I need a Pro2 now.
In answer to DS2107. I used the Raise3D PLA that came with the printer but with the temp dialed back to 200°. I also increased the retraction distance and speed a bit. The wipe wall is essential in my view even though it adds time. I've started experimenting with soluble supports. That's harder to get right although you don't have to worry about being able to break the supports away so using denser support structures helps.
Concerning the parameters what parameter did you use for retractation ?
Because every time i want to do double extrusion i've got some filaments that perturb the print, and the worst is when i use a wipe wall.
I've also saw that for the wipe wall, there's no filament from the nozzle during a few seconds each time.
I've put 40mm/s speed for retracting and 15mm of retractation for nozzle switching.


Our fantastic food intern, Cecilia Knutsson, just returned from a trip home to Sweden bearing some incredible edible food gifts. I've always loved anise-flavored things—when I was a kid, I ate the black jelly beans first—and she brought us addictive licorice caramels that are as sticky and black as tar. She also gave me buttery goat's milk chocolate truffles wrapped in a paper cone. While superrich, the truffles are lovely cut with a sprinkling of Swedish beet salt (another great gift), which combines earthy but sweet dried beet flecks and crunchy sea salt. Perhaps it's the next riff on the ubiquitous beet-and-goat-cheese salad.


text:
import csv
import os
import random
import time

import nltk
from sklearn.naive_bayes import BernoulliNB
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier

fold = 10
n = 1000

cv_accuracies = []
cv_times = []

with open(os.path.join(os.path.dirname(__file__), 'tweets_corpus/4095-pair-dataset-different.csv'), newline='\n') as csv_input:
    dataset = csv.reader(csv_input, delimiter=',', quotechar='"')
    next(dataset)
    different_tweets = [({
        'cosine': float(line[5]),
        'dice': float(line[6]),
        'jaccard': float(line[7]),
        # 'manhattan': float(line[8]),
        'overlap': float(line[9]),
        # 'cosine_tfidf': float(line[10]),
        # 'dice_tfidf': float(line[11]),
        # 'manhattan_tfidf': float(line[12]),
        'lcs': float(line[13]),
        }, line[4]) for line in dataset]

with open(os.path.join(os.path.dirname(__file__), 'tweets_corpus/4095-pair-dataset-similar.csv'), newline='\n') as csv_input:
    dataset = csv.reader(csv_input, delimiter=',', quotechar='"')
    next(dataset)
    similar_tweets = [({
        'cosine': float(line[5]),
        'dice': float(line[6]),
        'jaccard': float(line[7]),
        # 'manhattan': float(line[8]),
        'overlap': float(line[9]),
        # 'cosine_tfidf': float(line[10]),
        # 'dice_tfidf': float(line[11]),
        # 'manhattan_tfidf': float(line[12]),
        'lcs': float(line[13]),
        }, line[4]) for line in dataset]

for i in range(n):
    random.shuffle(different_tweets)
    random.shuffle(similar_tweets)

    tweets = different_tweets[:600] + similar_tweets[:600]

    random.shuffle(tweets)

    print()
    print('Dataset of {} tweet-pairs'.format(len(tweets)))

    accuracies = []
    times = []

    for i in range(fold):
        train_set = [features for features in tweets[0 : i * int(len(tweets) / fold)]] + \
            [features for features in tweets[(i + 1) * int(len(tweets) / fold) : len(tweets)]]
        test_set = [features for features in tweets[i * int(len(tweets) / fold) : (i + 1) * int(len(tweets) / fold)]]

        # print('\nIteration', (i + 1))
        # print('Training data:', len(train_set), 'data')
        # print('Test data:', len(test_set), 'data')

        start_time = time.time()
        # classifier = nltk.classify.SklearnClassifier(BernoulliNB()).train(train_set)
        classifier = nltk.classify.SklearnClassifier(DecisionTreeClassifier()).train(train_set)
        # classifier = nltk.classify.SklearnClassifier(LinearSVC()).train(train_set)
        time_elapsed = round(time.time() - start_time, 2)
        accuracy = nltk.classify.accuracy(classifier, test_set)
        accuracies.append(accuracy)
        times.append(time_elapsed)

        # print('Accuracy: {}'.format(accuracy))
        # print('Time elapsed: {}'.format(time_elapsed))

    average_accuracy = sum(accuracies) / len(accuracies)
    average_time = sum(times) / len(times)
    cv_accuracies.append(average_accuracy)
    cv_times.append(average_time)

    print('Average accuracy: {}'.format(average_accuracy))
    print('Average time elapsed: {}'.format(average_time))

print()
print('Summary of {} iterations'.format(n))
print('Average accuracy: {}'.format(sum(cv_accuracies) / len(cv_accuracies)))
print('Average time elapsed: {}'.format(sum(cv_times) / len(cv_times)))

redpajama_set_name: RedPajamaGithub


Double digging is easy, all you need is a spade or shovel, a spade fork, and some muscle.
First, mark the area where your elevated plot will be situated. Loosen the soil with your spade fork by pushing it into the soil and moving the handle forward and backwards.
Note: the picture above shows mung beans (green manure) being incorporated into the soil.
Now, loosen the bottom 8 inches of your plot (again, look at the picture). Do not mix the topsoil with the subsoil. Just loosen the subsoil.
Do this over and over and over until you reach the end of your plot.
..try zero-minimum tillage. It preserves natural soil stucture and protects the microbial ecology in the soil. Kawawa naman ang mga beneficial microorganisms if papatayin mo after mong paramihin..
if u r using the same area in a staggard planting scheme, u may consider using a subsoil irrigation system to inject nutrients (in liquid form-compost tea since u have it) directly into the rootzone..no more digging or tilling for life! a less expensive alternative is furrow irrigation.
thanks for the advice yano_yan. acctually we practice minimal tillage.


Miassite: Mineral information, data and localities.
Compare 'UM1978-14-S:CuFePtRh', 'UM1978-20-S:PtRh' and 'UM1976-18-S:RhRu'.
C : With Rh, Pd, Pt, etc.
Bowieite 1 photo of Miassite associated with Bowieite on mindat.org.
Isoferroplatinum 1 photo of Miassite associated with Isoferroplatinum on mindat.org.
Bowles, J.F.W. (2000) Prassoite, vysotskite and keithconnite from the Freetown layered complex, Sierra Leone. Mineralogy and Petrology: 68: 75-84.
Zapiski Vserossiyskogo Mineralogicheskogo Obshchestva (2001): 130(2): 41-45.
Fedortchouk, Y., LeBarge, W., Barkov, A.Y., Fedele, L., Bodnar, R.J. & Robert F. Martin (2010) Platinum-group minerals from a placer deposit in Burwash Creek, Kluane area, Yukon Territory, Canada. Canadian Mineralogist, 48, 583-596.; Augé, T. (1988). Platinum-Group Minerals in the Tiébaghi (New-Caledonia) and Vourinos (Greece) Ophiolites. In Geo-Platinum 87 (pp. 405-405). Springer Netherlands.
ZVMO 130(2),41-45(2001); S.N. Britvin, N.S. Rudashevsky, A.N. Bogdanova, D.K. Shcherbachev (2001) Miassite Rh17S15, a new mineral from a placer of the Miass River, Urals. Zap. Vseross. Mineral. Obshch., 130(2), 41–45 (in Russian, English abs.).
Airiyants, E. V., Zhmodik, S. M., Ivanov, P. O., Belyanin, D. K., & Agafonov, L. V. (2014). Mineral inclusions in Fe-Pt solid solution from the alluvial ore occurrences of the Anabar basin (northeastern Siberian Platform). Russian Geology and Geophysics, 55(8), 945-958.


in more than 10 states.
program for their TEXAS residents!
They are offering a $20 credit to Direct Energy residents of Texas to join the Power To Go program.
The Power To Go Program is a Pay as You Go electric service program.
You will receive no more paper bills every month.
amount, so that you can add funds to your electric account.
You can add funds to your account any time you want.
Pay your bill as you choose~ no due dates!
full balance of a HUGE bill all at one time and by the due date every month!
Pay a little as you can or alot when you can. Its up to you with the Power To Go Plan!
Just keep a balance in your account and you will always have electric service!
$20 credit added to your Power To Go account!
(ii) By calling Direct Energy at 1-888-684-3750, selecting the menu items to enroll, and providing the sales agent with the Participant's Referral Number which is 481675.
You will receive a $20 credit to YOUR account balance after your have been a customer for 30 days!
I joined this program over 2 year ago and think its so awesome!
I don't have to worry about a due dates, bills, or even disconnect notices!
With Power to Go, I sign into my account (when i get an email or a Text alert) and add to my balance!
I can also online and see how much electric I have used. and how many days of electric I have remaining.
If I see the daily balance is down to 7 days or less I add $5 or $10 to the account.
When I signed up for the Power to Go program my Kwph fee went down over 2 cents an hour. I have nothing but good things to say about this program.
I pay when I want and the amount I want!
There is now, no reason you should ever have your electric service disconnected.
I give this electric service plan a 5 out of 5.
Direct Energy's customer service is awesome.
You can add a balance to your account online, over the phone or at a pay station. If you pay over the phone it is free However, if you pay at a station there is a $1 fee to add money to your account.
If you do get the power turned off cuz you go negative balance, make a payment and the service is back on with in 2 hours of payment.
Since i have been a member I have saved MORE THAN $1600 in energy costs!
I like that i can check the amount of electric I have used and I know what it costs for me to wash clothes or run the vacs thru the house.
Do you know what it costs you??
If you live in Texas and want to save more on your electric service I recommend Direct energy and the Power to go plan!
disclosure~ I am a member of direct energys power to go program and i know lots of Texans who could use the lower rate, the savings and great sign up bonus!!


Staying abreast of legal developments and offer you adequate investor protection is one of our key preoccupations.
Providing to you a suitable and reliable service is always the focus of our concerns and, for this reason, we would like to inform you about the impact that the new EU Directive 2014/65 ("MiFID II") has on our banking relationship.
From a practical perspective, applying MiFID II modifies the way you interact with our Bank when it comes to financial investments. The increased level of information, protection and transparency introduced by this new regulation result in different new investment processes and reporting, based on the kind of services you require to receive from our Bank.
in case of investments in products with a high level of risk (leveraged financial instruments or other contingent liability transactions), you are also be informed if their value decreases by more than 10% (and any additional multiple of 10%).
the Bank proceeds with the execution of the proposed transaction if any only after your validation.
a formal notification in case the total value of your portfolio decreases by more than 10% (and any additional multiple of 10%).
The investment process under the MiFID II regime has become for the whole financial industry more complex than before. Our Bank has adopted as required its investment process in order to ensure that your investments are in line with your risk profile and your investment strategy.
In the framework of MiFID II, our Bank opted for acting as "Non Independent Advisor", which means that we may provide advisory services on financial products issued or provided by entities which have close links with our Bank.
Our Bank is committed to observe the most stringent standards of transparency and information created by this new Directive. A first effect will be the production of an annual report detailing all costs and charges linked to the investments services acquired from the Bank, the investment products themselves and potential third party payments. Moreover, the Bank will publish annually a list of the five execution venues most used when executing client orders, as well as information on the quality of the execution.
Implementing MiFID II may lead to some amendments to our current contractual documentation and to the related charges. Should this be the case, you will be shortly contacted by your Client Relationship Officer.
Should you need further information about MiFID II and its implementation, please do not hesitate to contact your Client Relationship Officer.
The Payment Services Directive (PSD) sets legal rules for providing effective and non-discriminatory access to payment systems for all payment services users. Key objectives of the PSD are the harmonisation of payment services in order to achieve a single market in the European Economic Area (EEA), the increase in transparency and security for clients.
For any further question relating to this information, please do not hesitate to contact your Client Relationship Officer.
In order to enable all types of investors to take informed investment decisions and to ensure comparability between UCITS of each EU Member State, the UCITS IV Directive requires banks to make the "Key Investor Information Document (KIID)" available to investors. This document, which includes appropriate information about the essential characteristics of the UCITS in a non-technical and comprehensible style, replaces the Simplified Prospectus from July 1, 2012 on.
Clients of EFG Bank (Luxembourg) S.A. may find the latest version of the KIID pertaining to their personal investment under www.finesti.com or under the below websites of the various management companies, for example by entering the ISIN number of their investment.
Nothing contained on this page should be considered as an offer, an invitation to offer or a recommendation to enter into any transaction or to invest into any UCITS fund or any other financial instrument in any jurisdiction. No representation, warranty or undertaking, express or implied, is given as to the accuracy or completeness of the information contained in the KIIDs by EFG Bank (Luxembourg) S.A. or its personnel and no liability is accepted by such persons for the accuracy of any such information.
With effect from June 7, 2012, a voice recording system has been implemented on fixed telephone lines of EFG Bank (Luxembourg) S.A.
The intended purpose of the system is to provide both counterparts with additional legal security with regard to negotiations, instructions or transactions. Recordings shall help to avoid and/or clarify misunderstandings or serve as a proof in case of litigation. As required by the Luxembourg personal data protection rules, the system and its implementation have been duly authorized by the relevant local authority, the CNPD (Commission Nationale pour la Protection des Données).
Following EFG Bank (Luxembourg) S.A.'s retention policy, the recorded data will be destroyed after an archival storage of 3 years.


text:
House: Clashmore
Hastings (Huntingdon, Earl of)
Power (Clashmore)
Property/House name: Clashmore
Description: Clashmore was held in fee by the Earl of Huntingdon at the time of Griffith's Valuation, when it was valued at £34. The site marked on the 1st-edition Ordnance Survey map is not that recorded on the later 25-inch map. Smith refers to the earlier house as the seat of Mr. Power and Wilson refers to it as the seat of Richard Power in 1786. The Irish Tourist Authority Survey of the 1940s suggest the later house was never fully completed and by then had all but disappeared. Much of the extensive farmyard, however, is still extant and in use.
Townland: Clashmore
Civil Parish: Clashmore
DED: Clashmore 100
Farmyard at Clashmore (www.buildingsofireland.ie)


text:
gerard - strategic branding + marketing communications
Inverom
Brand Identity & Development
Deliverables: Marketing plan, print, interactive, environmental
Inverom is a unique, vertically integrated partner for original equipment manufacturers. The company delivers state-of-the-art platforms and solutions that span the entire value chain — from design and engineering to development, manufacturing, assembly, testing and distribution.
Inverom wanted to create a strong brand that would support the company's evolution and vision of growth over time. From day one, the image had to present Inverom as a sophisticated, highly capable technology provider.
Working with Gerard, Inverom first defined its audience, objectives and concerns. Gerard developed a basic corporate identity program that included a "starter kit" of a logo, business cards and stationery, and a basic website that was put in place quickly to support sales efforts and address the company's immediate needs.
Once these elements were in place, Gerard then constructed a full website, and created a marketing brochure and other sales support materials for specific capabilities and market segments. Gerard's team even helped design Inverom's interior space.
The result? Inverom was able to establish a powerful brand presence that successfully communicated its vision and capabilities, and helped contribute to new business with top manufacturers, such as CNH, Sub-Zero and Tesla.
The program met all my objectives. We even had one prospect say that they weren't sure that they were big enough for a company like ours. That was when I knew we had achieved what we set out to do."
Roman KuropasInverom CEO and Founder
Gerard promotes business profile As seen in Forbes, Gerard has created a profile that documents the company's "do more with less" business philosophy and perspectives on client success.
Read the Forbes Profile
About Gerard
Gerard is a WBE-certified strategic branding and marketing communications agency based in the Chicago area. We partner with our clients to create powerful integrated B2B and B2C campaigns that deliver lasting results.
Learn About Gerard
info@gerardagency.com
28371 Davis Parkway, Suite 100, Warrenville, IL 60555


text:
How we think about immunity can help us navigate COVID-19 risks together
Back in February, Peter Jüni, then scientific director of Ontario's Scientific Advisory Table, stated on a CBC Radio call-in show that, "We are continuing to weave a carpet of immunity."
As a health humanities researcher working on how COVID-19 informs our cultural imagining of immunity, I was struck by Jüni's metaphor. Now, with his impending departure coinciding with the end of mask and vaccine mandates, I find myself considering the metaphor anew.
At a time when authorities are advising individuals to make their own risk assessments as we head into a sixth COVID-19 wave, public health messaging has never been more important.
Jüni's metaphorical "carpet of immunity" conjured up an image of something meticulously crafted and spreading protectively over our region. It also illustrated how the language of public health can invite the public to think differently about immunity, a complex biological system that the pandemic has thrust into daily life.
Language, metaphor and health
As we head into a potential sixth wave of COVID-19, individuals are being told to make their own risk assessments.
THE CANADIAN PRESS/Jonathan Hayward
Language matters. Theorists have been making this argument for decades in relation to cancer, AIDS and the cultural representation of disease more generally. Language can often distort our understanding of fundamental concepts of health and medicine, especially in the case of immunity.
Philipp Dettmer, founder of YouTube science education channel Kurzgesagt and author of Immune, says of immunity:
"… people lack a good mental image of what the term means. They think of it as an energy shield that you can charge up. But it is not a thing at all, it's a multitude of things."
As a way of making sense of something we can't see, metaphor often mediates our understanding of immunity. Seeking a more fitting way of imagining immunity, Eula Biss, author of On Immunity: An Inoculation, proposes the naturalistic image of the "garden" as an alternative to the standard fortress metaphor. The garden image (based on an ecological understanding of immunity) suggests something in between the natural and the artificial. As Biss explains:
"The antibodies that generate immunity following vaccination are manufactured in the human body, not in factories. Using ingredients sourced from organisms, once living or still alive, vaccines invite the immune system to produce its own protection."
Vaccines are not perfectly natural, but neither are they "unnatural," despite the arguments of wellness communities. In rejecting vaccines, these groups tend to glorify an idea of bodily purity based on the frequent misappropriation and misrepresentation of Eastern spirituality.
This notion of the individual body's ability to boost its "natural immunity" has further fed resistance to public health measures and restrictions.
Weaving the carpet
A garden by its very nature is cultivated but can quite easily run wild if left untended. But a "carpet that we weave together" elegantly evokes labour and artistry. In suggesting that we have a role in crafting something rather than simply being acted upon by a virus, this phrasing offers an antidote, perhaps, to the pandemic-induced feelings of disempowerment seemingly fuelling anti-mandate demonstrations.
Viewing immunity as a carpet that we weave together evokes labour and artistry, and suggests we have a role in crafting something rather than simply being acted upon by a virus.
(Wikipedia Commons/Fulvio Spada), CC BY
This metaphor also sidesteps the divide between the artificial and the natural by intertwining both forms of immunity (acquired through either exposure to infection or vaccination) into something figuratively spun on a loom.
Jüni's metaphor also seemed strategic in its reassuring domesticity: what is more commonplace than a carpet? In this sense, "carpet immunity" rejects politicians' standard militaristic imagery of vaccines as a front line of defence against COVID-19 and its variants.
In its banality, the image captured what it means to live with the virus. In a biological sense, we "live with" the virus through our immune systems, which had an opportunity to get acquainted with SARS-CoV-2 under the controlled conditions afforded by mandates and vaccine rollouts.
Immunity as a shared goal and responsibility
From the early days of the pandemic, public health struggled with its messaging around mandates. But Jüni's metaphor clearly calls on us to work together. Emerging from the pandemic, this formulation emphasizes mutual responsibility and invites us to think of immunity in social terms rather than simply individual terms.
However, this is a more difficult undertaking than one might expect. Immunity is informed by and layered over with political and legal meanings stretching as far back as ancient Rome and filtered through Enlightenment thought.
As gender studies professor Ed Cohen reflects in A Body Worth Defending, an idea of "immunity-as-defence" charged with maintaining clear boundaries around the individual has been fixed in western thinking since the 19th century.
Anti-vaccination discourse positions the robust, sovereign body as impervious to both infection and accountability.
THE CANADIAN PRESS/Lars Hagberg
Interestingly, the "immunity as carpet" phrasing has to date been applied to immunity in precisely this original, legal sense. A quick Google search reveals multiple usages of the phrase "red carpet of immunity" to signify the exemption of high-profile politicians and executives from prosecution. In this double sense, anti-vaccination discourse positions the robust, sovereign body as impervious to both infection and accountability.
Yet scientists' imagining of collective immunity posits exactly the opposite of exemption (in a social rather than medical sense). "We are all in this together," we are told, with the same basic biology, entangled by webs of contact and the traces we leave behind.
The idea of "carpet immunity" captures the varied complexities of shared immune systems. It is in its own way a unifying image in the weaving together of infection- and vaccination-induced antibodies. Taken together, these antibodies may over time give our society some measure of protection against Omicron, its currently surging subvariant BA.2 and subsequent strains of the novel coronavirus.
How new COVID-19 variants emerge: Natural selection and the evolution of SARS-CoV-2
Finally, "a carpet we weave together" evokes an image of artisans working in close proximity to create something both functional and ornamental. This collectivist metaphor offers an esthetically appealing alternative to the more familiar "herd immunity" increasingly seen as out of reach. It invites us to imagine immunity as a collaborative project, spreading out to protect those among us for whom the end of mandates means increased vulnerability.
Most importantly, this language challenges us to imagine what a post-pandemic future might look like if we commit to continuing to craft a "carpet of immunity" through vaccination, rather than unravelling it while it remains a work in progress. As Peter Jüni prepares to step down from the Ontario COVID-19 Science Advisory Table, he leaves behind a model for how effective public health messaging can reshape ideas about both our bodies and our communities and affect our everyday practices (if we choose to listen).
April 4, 2022 Kelly McGuire
The budget hands out $21 billion for 'regional Australia', but a quarter of it is going to a single project in Queensland
Afghan evacuees lack a clear path for resettlement in the U.S., 7 months after Taliban takeover


text:
The Hunter Hurricanes Water Polo Club is an Australian club water polo team that competes in the National Water Polo League.  They have a men's team and a women's team and are based in the Hunter Region.

References

External links

Water polo clubs in Australia
Sports clubs established in 2006
2006 establishments in Australia
Sport in Newcastle, New South Wales
Sports teams in Newcastle, New South Wales