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---
{% raw %}A LINK WITH{% endraw %}
{% raw %}" UNCLE TOM »{% endraw %}
{% raw %}NEGRO CONTROVERSY{% endraw %}
{% raw %}IN CANADIAN TOWN{% endraw %}
{% raw %}FROM OUR ONTARIO CORRESPONDENT{% endraw %}
{% raw %}The Negro population of the small{% endraw %}
{% raw %}Ontario town of Dresden last Saturday{% endraw %}
{% raw %}celebrated the seventy-third anniversary{% endraw %}
{% raw %}of the death of Uncle Tom whom{% endraw %}
{% raw %}Harriet Beecher Stowe immortalized in{% endraw %}
{% raw %}her novel Uncle Tom's Cabin. Uncle{% endraw %}
{% raw %}Tom's real name was the Rev. Josiah{% endraw %}
{% raw %}Henson, and he lived in Dresden as an{% endraw %}
{% raw %}escaped slave for more than 50 years.{% endraw %}
{% raw %}It was from the town that he directed{% endraw %}
{% raw %}immigration of freed slaves to a colony set{% endraw %}
{% raw %}up on a 300-acre site about a mile west of{% endraw %}
{% raw %}the town. Mostly through his efforts to{% endraw %}
{% raw %}rehabilitate slaves (he made three visits .to{% endraw %}
{% raw %}Britain to lecture and to collect funds, and{% endraw %}
{% raw %}was presented to Queen Victoria in 1877){% endraw %}
{% raw %}Dresden became one of the shrines of{% endraw %}
{% raw %}Negro freedom in North America.{% endraw %}
{% raw %}To-day, by a strange twist of fate,{% endraw %}
{% raw %}Dresden is the centre of the most ardent{% endraw %}
{% raw %}racial discrimination battle in Canada. Since{% endraw %}
{% raw %}the end of the war Negro residents have{% endraw %}
{% raw %}been refused service in certain restaurants,{% endraw %}
{% raw %}barber shops, and stores. There has been{% endraw %}
{% raw %}no violence, but an atmosphere of restrained{% endraw %}
{% raw %}ill feeling has existed, which the older{% endraw %}
{% raw %}Negroes have taken philosophically.{% endraw %}
{% raw %}ASSOCIATION FORMED{% endraw %}
{% raw %}The younger members of the 350 Negroes{% endraw %}
{% raw %}in the colony have not been quite so tem¬{% endraw %}
{% raw %}perate. Returning service men took par¬{% endraw %}
{% raw %}ticular objection to this discriminatory treat¬{% endraw %}
{% raw %}ment, and formed an association to protect{% endraw %}
{% raw %}their rights. Delegations were sent to the{% endraw %}
{% raw %}Premier, Mr. Frost, who initiated legisla¬{% endraw %}
{% raw %}tion which was accepted by the Ontario{% endraw %}
{% raw %}House.{% endraw %}
{% raw %}In spite of this, cases of discrimination{% endraw %}
{% raw %}continued. Testing the Act last November,{% endraw %}
{% raw %}two Negroes were told to leave a pafe" in the{% endraw %}
{% raw %}main street of the town, the owner refusing{% endraw %}
{% raw %}to serve them with coffee. In February of{% endraw %}
{% raw %}this year this case was tried and a conviction{% endraw %}
{% raw %}obtained, the first since the trouble started.{% endraw %}
{% raw %}Since then things have been fairly quiet{% endraw %}
{% raw %}in Dresden. Government officials say that{% endraw %}
{% raw %}if the few fervid segregationalists remain{% endraw %}
{% raw %}passive the trouble might end.  The Negro population are not poor.{% endraw %}
{% raw %}Their children mix at school with whites.{% endraw %}
{% raw %}Negroes work on farms, and in the small{% endraw %}
{% raw %}industries of the place, beside whites.{% endraw %}
{% raw %}SERIES OF CAMPAIGNS{% endraw %}
{% raw %}The influence of the Dresden case, as it{% endraw %}
{% raw %}has become known, has spread far beyond{% endraw %}
{% raw %}the confines of the town. Coupled with{% endraw %}
{% raw %}attempts to discriminate in other parts of{% endraw %}
{% raw %}the country, notably in Hamilton, Toronto,{% endraw %}
{% raw %}and Windsor, it has brought about a series{% endraw %}
{% raw %}of campaigns to better the lot of the{% endraw %}
{% raw %}coloured people of Canada.{% endraw %}
{% raw %}One of the most important has been an{% endraw %}
{% raw %}attempt to influence the Federal Govern¬{% endraw %}
{% raw %}ment to change its Immigration Act, which{% endraw %}
{% raw %}limits the number of Negro people entering{% endraw %}
{% raw %}the country whether of British or of foreign{% endraw %}
{% raw %}nationality. At present people from Paki¬{% endraw %}
{% raw %}stan, the West Indies, India, and other such{% endraw %}
{% raw %}British countries are denied entry, except in{% endraw %}
{% raw %}special circumstances, because the Govern1{% endraw %}
{% raw %}ment maintains that they have difficulty in{% endraw %}
{% raw %}adjusting themselves to the Canadian life{% endraw %}
{% raw %}and climate. A recent delegation from the{% endraw %}
{% raw %}Negro Citizenship Association of Toronto{% endraw %}
{% raw %}presenting a brief to the Minister of Immi¬{% endraw %}
{% raw %}gration, Mr. Harris, urged the Government{% endraw %}
{% raw %}to stop the " dangerous and discriminatory{% endraw %}
{% raw %}business of creating two classes of her{% endraw %}
{% raw %}Majesty's subjects."{% endraw %}
{% raw %}Other Negro associations have been press¬{% endraw %}
{% raw %}ing for adequate anti-discrimination laws,{% endraw %}
{% raw %}and for the prosecution of alleged attempts{% endraw %}
{% raw %}at intimidation of coloured people. News¬{% endraw %}
{% raw %}papers and religious leaders have supported{% endraw %}
{% raw %}these campaigns. A{% endraw %}


I've spent the majority of my life around people who know more than I do -- from musicianship to coding, I'm constantly surrounded by a wealth of knowledge I can tap into. I feel lucky. Not everyone has that resource at their disposal. Sure, stack overflow is a wonderful tool, but having that direct, person-to-person contact can make a huge difference in not only what you ask, but how you learn it.
That being said, I've asked so many dumb questions. Questions that don't even make sense, stemmed in a more fundamental confusion of the topics being discussed. This generally has caused confusion from my peers, and the consequences can be negative. If someone can't even properly ask a question, how can they be expected to learn?
Below I'm going to include some ideas on how to make sure the questions we ask are good questions. Without completely rehashing the very wonderful Stack Overflow guide on asking good questions, I want to focus in on how we can approach asking a question that will not guarantee success, but will help foster a relationship between questioner and questionee for the better.
Before we can begin to ask a question, we first need to understand if we have a solid conceptual grasp of the topic we are asking about. When I first started using Ruby, I would ask people questions related to concepts that I thought I had a strong grasp of. I'd ask questions like "how can I use arrays to create a map between certain keys and it's values?" This question, though specific, is clearly misinformed. The question itself is flawed by the fundamental misunderstanding of what arrays are meant to do and how they can be used.
This first step can be the hardest to tackle. How can one know if they have a solid conceptual understanding of a topic? They key is in breaking it down. We can take the above question and ask ourselves questions about the question. Fundamentally, what is an Array? How do keys and values relate to certain data structures in programming? We'll quickly (and obviously) find that the question suffers from a misunderstanding of these concepts. The key to asking a good question is first understanding the building blocks that lead to it -- if we don't know what an Array really is at its core (a zero-indexed container for other objects), how can we begin to understand what sort of questions to ask about them?
To really understand the context of a question, and to ensure that the questions we ask are good, rather than stemming from fundamental misunderstandings, is to tackle the fundamentals head-on with a little tool called:
Priming yourself prior to asking a question is the BEST way to make sure the question is a good one. Use google to research the fundamentals if those are alluding you, or restructure your question dealing with these fundamental concepts instead. Usethe internet to gain a context for the question you are asking -- has it already been answered on stack overflow? Perhaps then all you need is clarification from a mentor rather than the asking of a question. Does the answer completely allude you? Copy and paste the specific parts of the question that you don't understand, and break it down. Research those broken down parts.
One of the nice things about going onsite for DBC is that we'll have the additional aid of mentors to help get us through these questions and problems. Maybe a 30 minute research journey can be solved quickly and concisely by a mentor. But even with that said, knowing *how* to search for the proper answers to the building blocks that construct and contextualize your question is key.
There's one last thing I wan't to talk about, and that is:
This last topic of blogversation is one that I myself have found difficulties in dealing with. Typing about code is generally much easier than speaking about it. Have you ever found yourself getting lost in a conversation regarding a deeply nested javascript object or ruby hash? It's rather easy to lose track of what one is referring to in a question, especially when the more advanced topics invoke more complex questions about them.
The key to answering this problem I've found is by resorting to the written word, or commenting the code you write that doesn't make sense. Unsure of why something is working, even though it is? Comment through it, using written language to break it down. Present this written language to your instructor for a much swifter answer to the problem. If you write a line of code that you suddenly get lost in, write it out.
Another good approach, one that I intend to try as we work through the coming weeks, is the rubber duck approach. Talk to the inanimate object in the room, pretend like it knows nothing. Doing so forces you to really learn *how* to speak about what techniques and methods you employ in finding a solution to the problem.
Asking a good question can be difficult, but it ultimately boils down to this: use the internet to read up on the topic beforehand, ensuring a solid foundation to the question at hand (if the fundamentals confuse you, reconsider your question, or perhaps find a question that will properly lead you to the path of more advanced knowledge). Learn how to speak about these topics through practice and repetition. Asking a good question is like flexing a muscle -- it might be hard at first, but it's okay. It's okay to ask a bad question, as long as you learn from it. Once we've established where our understanding lies, asking the best questions for ourselfs and our mentors will be made that much easier.
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With the help of advanced cloud services of project development and storage, you will have an opportunity to follow the process of design work online (i.e. drawings, cooperation with partners and contractors, introduction of corrections and comments).


Michael Faraday
Father of Electromagnetism
Time line of Michael Faraday's life:
- 1791 - Born in Newington Butts, London, he is the 3rd child in the family, got only the most basic school education in childhood.
- 1805 - At the age of 14, he become an apprentice to George Riebau, a local bookbinder and bookseller in Blandford Street, London. In his 7-year apprenticeship here, Faraday got a chance to read many books, educated himself, also developed an interest in science.
- 1812 - At the age of 20 Faraday attended lectures by the eminent English chemist Humphry Davy of Royal Institution, England, which set a turning point in his life. Faraday sent Davy his notes taken during the lectures, accompanied with a letter asking for employment in the field, Davy got impressed by his scientific temper.
- 1813 - Davy employed Faraday as his assistant, later he elevated as a chemical assistant at the Royal Institution. It is often jokingly said of Davy that Faraday was, in hindsight, his greatest ever discovery.
- 1816 - Working with Davy in Chemistry, Faraday delivered chemistry lectures for City philosophical society, which refined the quality of his lectures.
- 1820 - Faraday himself had become one of the world's most premier chemists of the time. His labors under Davy had given him a vast amount of experience, Faraday set out on his own works. He succeeded in creating the first known compounds of chlorine and carbon C2Cl6 and C2Cl4.
- 1821 - Faraday married Sarah Barnard, same year for further experimenting Davy's research about electricity and magnetism, Faraday build two devices to produce what he called "electromagnetic rotation"- laid the foundation of modern electromagnetic technology, but due to a misunderstanding between Davy and Faraday he did not able to continue his work.
- 1823 - Michael Faraday succeeded liquefaction of ammonia(built on the ideas of John Dalton), notices the cooling effect when ammonia evaporates. Faraday's work had shown that mechanical pumps could be used to turn gas to liquid at room temperature, this laid a milestone in the development of modern refrigeration techniques.
- 1824 - He made experiments that perfected the Bunsen burner. Also as a fact, The first rubber balloons were made by him for use in his experiments with hydrogen at the Royal Institution.
- 1825 - Michael was working on illuminating gases and succeeded in isolating and describing something that would later be known as benzene. Around this time he also helped lay down the foundations of metallurgy and metallography whilst conducting investigations into steel alloys.
- 1826 - He worked on an assignment by the Royal Society into improving the quality of glasses and telescopes, succeeded in producing a very high refractive index that would later help him discover diamagnetism.
- 1831 - After the death of Davy, he continued his series of experiments that led to the discovery of Electromagnetic Induction, he also experimented in Mutual Induction( which later modelled mathematically by Maxwell), using his iron-ring-coil apparatus, it can be considered as an initial model of Transformer, later he discovered Electric Dynamo, ancestor of modern power generator and electric motor.
- 1832 - In the apex period of his scientific excellence, he turned down all the offered honors said he preferred to live "plain Mr. Faraday till the End".
- 1833 - Based on the electrochemical research he published Faraday's laws of electrolysis, also invented the system of oxidation numbers, and popularized terminology such as anode, cathode, electrode, and ion.
- 1836 - Invented Faraday Cage - enclosure used to block electromagnetic fields, the idea found extensive use even in today's sensible electronic equipment protection, microwaves etc.
- 1839 - Concept of lines of Flux, which is a crucial idea helped in the coming age developments in Electromagnetism.
- 1845 - Discovered Diamagnetism and magneto-optical effect, today known as the Faraday effect.(rotation of plane polarized light with magnetic field).
- 1848 - The famous lecture of "chemical history of a candle" - one of his 19 Christmas lectures, started in 1925 to present science to general people. The joyful lectures showcasing experiments and deep philosophies helped developing scientific thinking in people, and still given in the Royal Institution every year.
- 1858 - Retired from Royal Institution to live in the Master Mason's house which later called Faraday house.
- 1867 - Died in 1867 at the age of 75. He had previously turned down burial in Westminster Abbey, but he has a memorial plaque there, near Isaac Newton's tomb. The unit of capacitance, Farad, given in honor of Michael Faraday.
Michael Faraday was a highly influential British scientist who in part turned electricity into something that could be harnessed for work, his works ultimately lead to our modern-day understanding of electromagnetism. He is one of history's most accomplished experimental scientists, Albert Einstein actually kept photos of three scientists in his office.. Isaac Newton, James Clerk Maxwell and Michael Faraday.


MAKENA- hydroxyprogesterone caproate injection
Lumara Health Inc.
HIGHLIGHTS OF PRESCRIBING INFORMATIONThese highlights do not include all the information needed to use MAKENA safely and
effectively. See full prescribing information for MAKENA.
MAKENA® (hydroxyprogesterone caproate injection) for intramuscular use. Initial U.S. Approval: 1956 INDICATIONS AND USAGEMakena is a progestin indicated to reduce the risk of preterm birth in women with a singleton pregnancy who have a history of singleton spontaneous preterm birth. Limitation of use: Makena is not intended for use in women with multiple gestations or other risk factors for preterm birth. (1) DOSAGE AND ADMINISTRATION
DOSAGE FORMS AND STRENGTHS5 mL multidose vial (250 mg/mL) contains 1250 mg hydroxyprogesterone caproate. (3) CONTRAINDICATIONS
WARNINGS AND PRECAUTIONS
ADVERSE REACTIONSMost common adverse reactions reported in ≥2% of subjects and at a higher rate in the Makena group than in the control group are injection site reactions (pain [35%], swelling [17%], pruritus [6%], nodule [5%]), urticaria (12%), pruritus (8%), nausea (6%), and diarrhea (2%). (6.1) To report SUSPECTED ADVERSE REACTIONS, contact Lumara Health at 1-877-411-2510 or FDA at 1-800-FDA-1088 or www.fda.gov/medwatch. USE IN SPECIFIC POPULATIONS Pregnancy: Controlled studies show no increase in
congenital anomalies, including genital abnormalities in male or
female infants, from exposure during pregnancy to
hydroxyprogesterone caproate. (8.1) See 17 for PATIENT COUNSELING INFORMATION and FDA-approved patient labeling. Revised: 2/2015 |
Makena is a progestin indicated to reduce the risk of preterm birth in women with a singleton pregnancy who have a history of singleton spontaneous preterm birth. The effectiveness of Makena is based on improvement in the proportion of women who delivered <37 weeks of gestation. There are no controlled trials demonstrating a direct clinical benefit, such as improvement in neonatal mortality and morbidity.
Limitation of use: While there are many risk factors for preterm birth, safety and efficacy of Makena has been demonstrated only in women with a prior spontaneous singleton preterm birth. It is not intended for use in women with multiple gestations or other risk factors for preterm birth.
Parenteral drug products should be inspected visually for particulate matter and discoloration prior to administration, whenever solution and container permit. Makena is a clear, yellow solution. Do not use if solid particles appear or if the solution is cloudy.
Instructions for administration:
Discard any unused product 5 weeks after first use.
Makena (250 mg/mL) is a sterile solution of hydroxyprogesterone caproate in castor oil for injection. Each 5 mL multidose vial contains 1250 mg hydroxyprogesterone caproate.
Do not use Makena in women with any of the following conditions:
Discontinue Makena if an arterial or deep venous thrombotic or thromboembolic event occurs.
Allergic reactions, including urticaria, pruritus and angioedema, have been reported with use of Makena or with other products containing castor oil. Consider discontinuing the drug if such reactions occur.
A decrease in glucose tolerance has been observed in some patients on progestin treatment. The mechanism of this decrease is not known. Carefully monitor prediabetic and diabetic women while they are receiving Makena.
Because progestational drugs may cause some degree of fluid retention, carefully monitor women with conditions that might be influenced by this effect (e.g., preeclampsia, epilepsy, migraine, asthma, cardiac or renal dysfunction).
Monitor women who have a history of clinical depression and discontinue Makena if clinical depression recurs.
For the most serious adverse reactions to the use of progestins, see Warnings and Precautions (5).
Because clinical trials are conducted under widely varying conditions, adverse reaction rates observed in the clinical trials of a drug cannot be directly compared to the rates in the clinical trials of another drug and may not reflect the rates observed in practice.
In a vehicle (placebo)-controlled clinical trial of 463 pregnant women at risk for spontaneous preterm delivery based on obstetrical history, 310 received 250 mg of Makena and 153 received a vehicle formulation containing no drug by a weekly intramuscular injection beginning at 16 to 20 weeks of gestation and continuing until 37 weeks of gestation or delivery, whichever occurred first.1 [See Clinical Studies (14.1).]
Certain pregnancy-related fetal and maternal complications or events were numerically increased in the Makena-treated subjects as compared to control subjects, including miscarriage and stillbirth, admission for preterm labor, preeclampsia or gestational hypertension, gestational diabetes, and oligohydramnios (Tables 1 and 2).
|
1 N = Total number of
subjects enrolled prior to 20 weeks 0
days 2 N = Total number of subjects at risk ≥ 20 weeks | ||
Pregnancy Complication |
Makena |
Control |
n/N |
n/N |
|
Miscarriage (< 20 weeks)1 |
5/209 |
0/107 |
Stillbirth (≥ 20 weeks)2 |
6/305 |
2/153 |
| 1 Other than delivery admission. | |||||
Pregnancy Complication |
Makena |
Control |
|||
Admission for preterm labor1 |
16.0 |
13.8 |
|||
Preeclampsia or gestational hypertension |
8.8 |
4.6 |
|||
Gestational diabetes |
5.6 |
4.6 |
|||
Oligohydramnios |
3.6 |
1.3 |
Common Adverse Reactions:
The most common adverse reaction was injection site pain, which was reported after at least one injection by 34.8% of the Makena group and 32.7% of the control group. Table 3 lists adverse reactions that occurred in ≥2% of subjects and at a higher rate in the Makena group than in the control group.
|
Preferred Term |
Makena |
Control |
Injection site pain |
34.8 |
32.7 |
Injection site swelling |
17.1 |
7.8 |
Urticaria |
12.3 |
11.1 |
Pruritus |
7.7 |
5.9 |
Injection site pruritus |
5.8 |
3.3 |
Nausea |
5.8 |
4.6 |
Injection site nodule |
4.5 |
2.0 |
Diarrhea |
2.3 |
0.7 |
In the clinical trial, 2.2% of subjects receiving Makena were reported as discontinuing therapy due to adverse reactions compared to 2.6% of control subjects. The most common adverse reactions that led to discontinuation in both groups were urticaria and injection site pain/swelling (1% each).
Pulmonary embolus in one subject and injection site cellulitis in another subject were reported as serious adverse reactions in Makena-treated subjects.
The following adverse reactions have been identified during postapproval use of Makena. Because these reactions are reported voluntarily from a population of uncertain size, it is not always possible to reliably estimate their frequency or establish a causal relationship to drug exposure.
In vitro drug-drug interaction studies were conducted with Makena. [See Clinical Pharmacology (12.3).] No in vivo drug-drug interaction studies were conducted with Makena.
Pregnancy Category B: There are no adequate and well-controlled studies of Makena use in women during the first trimester of pregnancy. Data from a vehicle (placebo)-controlled clinical trial of 310 pregnant women who received Makena at weekly doses of 250 mg by intramuscular injection in their second and third trimesters1, as well as long-term (2-5 years) follow-up safety data on 194 of their infants 2, did not demonstrate any teratogenic risks to infants from in utero exposure to Makena.
Reproduction studies have been performed in mice and rats at doses up to 95 and 5, respectively, times the human dose and have revealed no evidence of impaired fertility or harm to the fetus due to Makena.
Makena administration produced embryolethality in rhesus monkeys but not in cynomolgus monkeys exposed to 1 and 10 times the human dose equivalent every 7 days between days 20 and 146 of gestation. There were no teratogenic effects in either species.
Makena is not intended for use to stop active preterm labor. The effect of Makena in active labor is unknown.
Discontinue Makena at 37 weeks of gestation or upon delivery. Detectable amounts of progestins have been identified in the milk of mothers receiving progestin treatment. Many studies have found no adverse effects of progestins on breastfeeding performance, or on the health, growth, or development of the infant
Makena is not indicated for use in children. Safety and effectiveness in pediatric patients less than 16 years of age have not been established. A small number of women under age 18 years were studied; safety and efficacy are expected to be the same in women aged 16 years and above as for users 18 years and older. [See Clinical Studies (14).]
Makena is not intended for use in postmenopausal women. Safety and effectiveness in postmenopausal women have not been established.
There have been no reports of adverse events associated with overdosage of Makena in clinical trials. In the case of overdosage, the patient should be treated symptomatically.
The active pharmaceutical ingredient in Makena is hydroxyprogesterone caproate.
The chemical name for hydroxyprogesterone caproate is pregn-4-ene-3,20-dione, 17[(1-oxohexyl)oxy]. It has an empirical formula of C27H40O4 and a molecular weight of 428.60. Hydroxyprogesterone caproate exists as white to practically white crystals or powder with a melting point of 120°-124°C.
The structural formula is:
Makena is a clear, yellow, sterile, non-pyrogenic solution for intramuscular injection. Each 5 mL multidose vial contains hydroxyprogesterone caproate USP, 250 mg/mL (25% w/v), in castor oil USP (28.6% v/v) and benzyl benzoate USP (46% v/v) with the preservative benzyl alcohol NF (2% v/v).
Hydroxyprogesterone caproate is a synthetic progestin. The mechanism by which hydroxyprogesterone caproate reduces the risk of recurrent preterm birth is not known.
Absorption: Peak serum levels of hydroxyprogesterone caproate appeared after 3-7 days in non-pregnant female subjects following a single intramuscular injection of 1000 mg hydroxyprogesterone caproate. Based on pharmacokinetic analysis of five non-pregnant female subjects who received a single intramuscular administration of 1000 mg hydroxyprogesterone caproate, the mean (±SD) Cmax is estimated to be 27.8 (±5.3) ng/mL, and the Tmax is estimated to be 4.6 (±1.7) days. The elimination half-life of hydroxyprogesterone caproate was 7.8 (±3.0) days. Once-weekly intramuscular administration of 1000 mg hydroxyprogesterone caproate to non-pregnant women resulted in trough concentration of 60.0 (±14) ng/mL after 13 weeks. The pharmacokinetics of the 250 mg dose of hydroxyprogesterone caproate has not been evaluated.
Distribution: Hydroxyprogesterone caproate binds extensively to plasma proteins including albumin and corticosteroid binding globulins.
Metabolism: In vitro studies have shown that hydroxyprogesterone caproate can be metabolized by human hepatocytes, both by phase I and phase II reactions. Hydroxyprogesterone caproate undergoes extensive reduction, hydroxylation and conjugation. The conjugated metabolites include sulfated, glucuronidated and acetylated products. In vitro data indicate that the metabolism of hydroxyprogesterone caproate is predominantly mediated by CYP3A4 and CYP3A5. The in vitro data indicate that the caproate group is retained during metabolism of hydroxyprogesterone caproate.
Excretion: Both conjugated metabolites and free steroids are excreted in the urine and feces, with the conjugated metabolites being prominent. Following intramuscular administration to pregnant women at 10-12 weeks gestation, approximately 50% of a dose was recovered in the feces and approximately 30% recovered in the urine.
Renal Impairment: The effect of renal impairment on the pharmacokinetics of Makena has not been evaluated.
Hepatic Impairment: The effect of hepatic impairment on the pharmacokinetics of Makena has not been evaluated.
Cytochrome P450 (CYP) enzymes: An in vitro inhibition study using human liver microsomes and CYP isoform-selective substrates indicated that hydroxyprogesterone caproate increased the metabolic rate of CYP1A2, CYP2A6, and CYP2B6 by approximately 80%, 150%, and 80%, respectively. However, in another in vitro study using human hepatocytes under conditions where the prototypical inducers or inhibitors caused the anticipated increases or decreases in CYP enzyme activities, hydroxyprogesterone caproate did not induce or inhibit CYP1A2, CYP2A6, or CYP2B6 activity. Overall, the findings indicate that hydroxyprogesterone caproate has minimal potential for CYP1A2, CYP2A6, and CYP2B6 related drug-drug interactions at the clinically relevant concentrations.
In vitro data indicated that therapeutic concentration of hydroxyprogesterone caproate is not likely to inhibit the activity of CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4.
Hydroxyprogesterone caproate has not been adequately evaluated for carcinogenicity.
No reproductive or developmental toxicity or impaired fertility was observed in a multigenerational study in rats. Makena administered intramuscularly, at gestational exposures up to 5 times the recommended human dose, had no adverse effects on the parental (F0) dams, their developing offspring (F1), or the latter offspring's ability to produce a viable, normal second (F2) generation.
In a multicenter, randomized, double-blind, vehicle (placebo)-controlled clinical trial, the safety and effectiveness of Makena for the reduction of the risk of spontaneous preterm birth was studied in women with a singleton pregnancy (age 16 to 43 years) who had a documented history of singleton spontaneous preterm birth (defined as delivery at less than 37 weeks of gestation following spontaneous preterm labor or premature rupture of membranes).1 At the time of randomization (between 16 weeks, 0 days and 20 weeks, 6 days of gestation), an ultrasound examination had confirmed gestational age and no known fetal anomaly. Women were excluded for prior progesterone treatment or heparin therapy during the current pregnancy, a history of thromboembolic disease, or maternal/obstetrical complications (such as current or planned cerclage, hypertension requiring medication, or a seizure disorder).
A total of 463 pregnant women were randomized to receive either Makena (N=310) or vehicle (N=153) at a dose of 250 mg administered weekly by intramuscular injection starting between 16 weeks, 0 days and 20 weeks, 6 days of gestation, and continuing until 37 weeks of gestation or delivery. Demographics of the Makena-treated women were similar to those in the control group, and included: 59.0% Black, 25.5% Caucasian, 13.9% Hispanic and 0.6% Asian. The mean body mass index was 26.9 kg/m2.
The proportions of women in each treatment arm who delivered at <37 (the primary study endpoint), <35, and <32 weeks of gestation are displayed in Table 4.
|
1 Four Makena-treated
subjects were lost to follow-up. They were counted
as deliveries at their gestational ages at time of
last contact (184, 220,
343 and 364
weeks). 2 Adjusted for interim analysis. | |||
|
Delivery Outcome |
Makena1 % |
Control % |
Treatment difference
and |
<37 weeks |
37.1 |
54.9 |
-17.8% [-28.0%, -7.4%] |
<35 weeks |
21.3 |
30.7 |
-9.4% [-19.0%, -0.4%] |
<32 weeks |
11.9 |
19.6 |
-7.7% [-16.1%, -0.3%] |
Compared to controls, treatment with Makena reduced the proportion of women who delivered preterm at <37 weeks. The proportions of women delivering at <35 and < 32 weeks also were lower among women treated with Makena. The upper bounds of the confidence intervals for the treatment difference at < 35 and <32 weeks were close to zero. Inclusion of zero in a confidence interval would indicate the treatment difference is not statistically significant. Compared to the other gestational ages evaluated, the number of preterm births at <32 weeks was limited.
After adjusting for time in the study, 7.5% of Makena-treated subjects delivered prior to 25 weeks compared to 4.7% of control subjects; see Figure 1.
Figure 1 Proportion of Women Remaining Pregnant as a Function of Gestational Age
The rates of fetal losses and neonatal deaths in each treatment arm are displayed in Table 5. Due to the higher rate of miscarriages and stillbirths in the Makena arm, there was no overall survival difference demonstrated in this clinical trial.
|
A Four of the 310
Makena-treated subjects were lost to follow-up and
stillbirth or neonatal status could not be
determined B Percentages are based on the number of enrolled subjects and not adjusted for time on drug C Percentage adjusted for the number of at risk subjects (n=209 for Makena, n=107 for control) enrolled at <20 weeks gestation. | ||||||
|
Complication |
Makena |
Control |
||||
Miscarriages <20 weeks gestation C |
5 (2.4) |
0 |
||||
Stillbirth |
6 (2.0) |
2 (1.3) |
||||
Antepartum stillbirth |
5 (1.6) |
1 (0.6) |
||||
Intrapartum stillbirth |
1 (0.3) |
1 (0.6) |
||||
Neonatal deaths |
8 (2.6) |
9 (5.9) |
||||
Total Deaths |
19 (6.2) |
11 (7.2) |
A composite neonatal morbidity/mortality index evaluated adverse outcomes in livebirths. It was based on the number of neonates who died or experienced respiratory distress syndrome, bronchopulmonary dysplasia, grade 3 or 4 intraventricular hemorrhage, proven sepsis, or necrotizing enterocolitis. Although the proportion of neonates who experienced 1 or more events was numerically lower in the Makena arm (11.9% vs. 17.2%), the number of adverse outcomes was limited and the difference between arms was not statistically significant.
Infants born to women enrolled in this study, and who survived to be discharged from the nursery, were eligible for participation in a follow-up safety study. Of 348 eligible offspring, 79.9% enrolled: 194 children of Makena-treated women and 84 children of control subjects. The primary endpoint was the score on the Ages & Stages Questionnaire (ASQ), which evaluates communication, gross motor, fine motor, problem solving, and personal/social parameters. The proportion of children whose scores met the screening threshold for developmental delay in each developmental domain was similar for each treatment group.2
1Meis PJ, Klebanoff M, Thom E, et al. Prevention of recurrent preterm delivery by 17 alpha-hydroxyprogesterone caproate. N Engl J Med. 2003;348(24):2379-85.
2Northen A, Norman G, Anderson K, et al. Follow-up of children exposed in utero to 17 alpha-hydroxyprogesterone caproate. Obstet & Gynecol. 2007;110:865-872.
Makena (NDC 64011-243-01) is supplied as 5 mL of a sterile solution in a multidose glass vial.
Each 5 mL vial contains hydroxyprogesterone caproate USP, 250 mg/mL (25% w/v), in castor oil USP (28.6% v/v) and benzyl benzoate USP (46% v/v) with the preservative benzyl alcohol NF (2% v/v).
Single unit carton: Contains one 5 mL multidose vial of Makena (250 mg/mL) containing 1250 mg of hydroxyprogesterone caproate.
Store at controlled room temperature [15°-30°C (59°-86°F)]. Use within 5 weeks after first use.
Caution: Protect vial from light. Store vial in its box. Store upright.
See FDA-approved patient labeling (Patient Information).
Counsel patients that Makena injections may cause pain, soreness, swelling, itching or bruising. Inform the patient to contact her physician if she notices increased discomfort over time, oozing of blood or fluid, or inflammatory reactions at the injection site [see Adverse Reactions (6.1)] .
Manufactured by: Hospira, Inc.
McPherson, KS 67460
Marketed by: Lumara Health Inc.
A division of AMAG Pharmaceuticals, Inc.
Waltham, MA 02451
P10072-3
02/2015
Patient Information
Makena (mah-KEE-na)
(hydroxyprogesterone caproate injection) 250
mg/mL
Read this Patient Information Leaflet before you receive Makena. There may be new information. This information does not take the place of talking to your healthcare provider about your medical condition or treatment.
What is Makena?
Makena is a prescription hormone medicine (progestin) used in women who are pregnant and who have delivered a baby too early (preterm) in the past. Makena is used in these women to help lower the risk of having a preterm baby again.
Makena is for women who:
How well does Makena work?
Makena was studied in women who were at risk for having a preterm baby because they had previously given birth to a preterm baby. In the main study, about 37 of 100 women who received Makena gave birth preterm (before 37 weeks of pregnancy), compared to about 55 of 100 women who did not receive Makena. Another study of Makena is going on to see whether Makena reduces the number of babies who have serious problems shortly after birth or who die.
It is not known whether Makena is safe and effective in women who have other risk factors for preterm birth.
It is not known whether Makena is safe and effective in women less than 16 years old.
Makena is not intended for use to stop active preterm labor.
Who should not receive Makena?
Makena should not be used if you:
What should I tell my healthcare provider before receiving Makena?
Before you receive Makena, tell your healthcare provider if you have:
Tell your healthcare provider about all the medicines you take, including prescription and non-prescription medicines, vitamins, and herbal supplements.
Makena may affect the way other medicines work, and other medicines may affect how Makena works.
Know the medicines you take. Keep a list of them to show your healthcare provider and pharmacist when you get a new medication.
How should I receive Makena?
Makena comes in ready-to-use vials. There are 5 doses of medicine in each vial. Your healthcare professional should give you only one dose (1 mL) of Makena as prescribed each week.
Makena should be used within 5 weeks after the first use.
It is very important that you do not miss a dose of Makena and that you continue to receive the medicine once a week. If you miss a dose, talk to your healthcare provider for specific directions on how to get back on schedule.
What are the possible side effects of Makena?
Makena may cause serious side effects, including:
The most common side effects of Makena include:
Call your healthcare provider if you have the following at your injection site:
Tell your healthcare provider if you have any side effect that bothers you or that does not go away.
These are not all the possible side effects of Makena. For more information, ask your healthcare provider or pharmacist.
In a clinical study, certain complications or events associated with pregnancy occurred more often in women who received Makena compared to women who did not receive Makena, including:
Call your healthcare provider for medical advice about side effects or pregnancy complications. You may report side effects to FDA at 1-800-FDA-1088.
How should I store Makena?
General information about the safe and effective use of Makena.
Medicines are sometimes prescribed for purposes other than those mentioned in the Patient Information Leaflets. Do not take Makena for conditions for which it was not prescribed. Do not give Makena to other people, even if they have the same condition you have. It may harm them.
This leaflet summarizes the most important information about Makena. If you would like more information, talk with your healthcare provider. You can ask for information about Makena that is written for healthcare professionals.
For more information, go to www.makena.com or call Lumara Health Customer Service at the toll free number 1-877-411-2510.
To refill a prescription or to check on prescription status, call the Makena Care Connection at the toll free number 1-800-847-3418.
What are the ingredients in Makena?
Active ingredient: hydroxyprogesterone caproate
Inactive ingredients: castor oil and benzyl benzoate. 5 mL multidose vials also contain benzyl alcohol (a preservative).
NDC 64011-243-01
Makena®
hydroxyprogesterone caproate injection
1250 mg/5 mL
(250 mg/mL)
FOR INTRAMUSCULAR USE
5 mL multidose vial
Rx ONLY
Store at controlled room temperature
15°-30°C (59°-86°F).
Caution: Protect from light.
Store vial in its box. Store upright. Use within 5 weeks after first use.
Each mL contains: hydroxyprogesterone caproate 250 mg, benzyl benzoate 46%, castor oil 28.6%, and benzyl alcohol 2%, as preservative.
See package insert for full prescribing and patient information.
Lumara Health™
A division of AMAG Pharmaceuticals
P8009-1
02/15
Marketed by
Lumara Health, a division of AMAG Pharmaceuticals,
Waltham, MA 02451
|
MAKENA
hydroxyprogesterone caproate injection | ||||||||||||||||||||
| ||||||||||||||||||||
| ||||||||||||||||||||
| ||||||||||||||||||||
| ||||||||||||||||||||
|
| Labeler - Lumara Health Inc. (006291405) |
| Establishment | |||
| Name | Address | ID/FEI | Business Operations |
|---|---|---|---|
| Hospira, Inc. | 030606222 | MANUFACTURE(64011-243), ANALYSIS(64011-243) |
| Establishment | |||
| Name | Address | ID/FEI | Business Operations |
|---|---|---|---|
| Baxter Pharmaceutical Solutions, LLC | 604719430 | MANUFACTURE(64011-243), ANALYSIS(64011-243) |


Kylie Jenner Models Kylie Cosmetics 2019 Holiday Collection
by Charisse Van HornKylie Jenner is getting ready to launch her newest makeup collection just in time for Christmas. On November 19, Kylie Cosmetics will launch the Holiday 19 collection and the 22-year-old modeled the makeup in new photos on the official Instagram account. Christmas is associated with the colors red and gold, and the new holiday kit is full of these rich holiday hues. Kylie looked gorgeous in multiple photos where she was draped in deep-red satiny fabrics that showcased the bright-red lipstick that is the star of the collection.
Kylie’s eyes were framed underneath perfectly arched, dark brows. The red colors in the collection aren’t just saved for the lips as Kylie wore the hue on her eyes blended with plenty of gold accents. The palette features everything you need to create a perfect holiday look with metallic shades of copper, silver, gold, and burgundy with matte colors ranging from soft pink to mauve and purple with an undertone of brown.
There are foil colors to ensure you get that extra glitter and sparkle needed when attending festive occasions, parties, and holiday events.
You may see several photos of Kylie Jenner wearing the 2019 Kylie Cosmetics Holiday collection below.
The new holiday collection appears that it is going to be a big hit. The palette will be perfect for any holiday event and is great for those who aren’t afraid to be bold and daring in red.
Kylie Cosmetics also released a cute video that features the eye palette along with a letter from Kylie to Santa. The post shared the following caption.
“DEAR SANTA 🎅🏼 meet our gorgeous NEW Holiday palette ✨ includes mattes, metallics and foil shades perfect for the holiday season 🎄 $48 11.19 3pm pst.”
You can check out the palette in the video player below.
What do you think of the new Kylie Cosmetics Holiday collection? Are you looking forward to the launch?
Do you think Kylie looks gorgeous in the holiday-themed shades of red and gold?


I enjoy taking pictures of just about anything so I decided to share them with the world in a unique way. I built this site with the intention to showcase my photography talents and share my life experiences. I currently have the option for users to login to my site and manage their account with future additions including the ability to comment on pictures.
Link to site Github RepoI work mostly with Zendesk Talk tickets and found that many customers who use the service wanted a way to easily download voice recordings from tickets. Prior to launching this app, customer advocates would offer a cumbersome workaround to customers that required digging into the browser's developer tools to find the recording URL. Offering workarounds such as this isn't the Zendesk way so I decided to build the Download Recordings app, which allows agents to download their voice recordings from the ticket sidebar with a simple click of a button. The app has received a lot of praise from key Zendesk customers and currently has an average of 4/5 star ratings in the Zendesk Marketplace
Link to app GitHub RepoA personal website I created to learn how to make AJAX requests to a few APIs including The Weather Channel's API to pull local weather information and Google's Finance API to pull the latest stock market quotes.
Link to site Github RepoI recently graduated from the University of Illinois Springfield with a Master's Degree in Computer Science. I enjoy coding and have prior experience with web developement as well as some Android mobile application development.I have developed skills in HTML, CSS, Sass, JavaScript, jQuery, PHP, Java and more recently picked up on basic Ruby on Rails.
Besides coding, I enjoy working out, playing video games, developing my photography skills, upgrading my custom built PC and playing with the latest and greatest gadgets.


Good day, I hope this mail meets you in good health and spirit, I am, Marshall Kingsman , a sierra Leonia. Presently, I live as a refugee in republic of Dakar Senegal with my younger sister, Aghata. I am a victim of war and the son of late Chief Dr.Desmond Kingsman, The assassinated former assistant director of Sierra Leone gold and Diamond Mining Corporation. My father was killed with other government officials during official Hours when the rebel troops stormed and raided the mining corporation Office at the heat of the crisis in my country. A few weeks later, the Rebels also invaded our residential building in which my mother and two Of our security men were killed in the compound while I was out for a Special research program and my younger sister was in school. I had a singular shock and trauma, which compelled us to flee from the Country to republic of Dakar senegal by the help of an army colonel, who is a Close friend of my late father, I have first degree in marketing and I would have further my education if not for the death of my parents. I have never worked to earn my living since my late parents had enough Wealth for us. I am contacting you now because of the present difficulty i am facing. Unfortunately my father's two accounts in West Africa have recently been frozen, however this did not affect the 10.5 Million US Dollars which he deposited with a prime Bank over here in dakar senegal. For now, I am having every information and document needed for the Collection of the deposit with the Finance company and I want you to Act as the beneficiary of the deposit for easy and smooth collection to your account abroad for investment purposes. You will no doubt be adequately compensated with as well as a Percentage of the profit from investment of the fund. Also you will see to it that my sister and I come over to your country at the earliest possible time. I am making this request to you with tears in my eyes and i feel that God almighty will touch you with his spirit to listen to my cry for Help and answer me without delay. Since our breadwinner is no more and this is the only hope for life. You positive response will give me the greatest joy and relief. God Bless you for your kind assistance we have agreed to offer you 30% of the total sum. so you can contact me on this my alternate email address for security reason. EMAIL<EMAIL_ADDRESS>Seriously waiting for your urgent reply, Best regard. Marshall kingsman & Sister.  YOUR POSITION.................................. AGE:...................................... STATUS:................................... STATE/COUNTRY............................. BANK/ACCONUT DETAILS...................... TELE/FAX NUMBERS.......................... REMAIN BLESSED.


Preparation time
over 2 hours
Cooking time
30 mins to 1 hour
Serves
Makes 25 of each variety
Dietary
If you are short of time use readymade puff pastry and these canapés can be ready in a flash.
For the pastry, sift the flour into a large mixing bowl and make a well in the centre. Add the salt, 225ml/8fl oz ice-cold water and melted butter into the middle of the well. Using your fingertips, gradually stir the liquid and incorporate the flour. Once the mix has come together into a dough, knead it a few times firmly to bring it into a ball. Wrap it in cling film and chill for 10 minutes in the freezer.
Remove the butter from the fridge and place it in between two sheets of greaseproof paper. Use a rolling pin to beat the butter block into a 19cm/7½ in square. Chill in the freezer for 10 minutes.
Remove the dough from the freezer and tip onto a lightly floured surface. Roll out the dough into a diamond shape that is a bit larger than the size of the butter block. Place the butter in the centre of the dough (with the flat sides of the block facing the points of the diamond) and pull the flaps over the top and pinch to seal them.
Roll the pastry away from you into a rectangle that is 75x30cm/30x12in. Move the pastry frequently to prevent sticking and work quickly to avoid heating the dough.
To make a letter turn, place the rolled out dough on the surface with the short side nearest to you. Pull the top third of dough down over the middle third then lift the bottom third over the folded portion, just like an A4 letter. Make sure the edges match up and are square. Dust off any excess flour with a pastry brush. Place the dough in a freezer bag and place in the freezer for 10 minutes. This is the first turn. Take note of the number of turns that has been performed at each stage.
Repeat the rolling, folding and turning four more times to give five letter turns in total. Split the dough into two equal portions and refrigerate until ready to use.
Preheat the oven to 210C/190C Fan/Gas 6½. Line two baking trays with baking paper.
For the herby puff twists, mix the herbs in a small bowl with the breadcrumbs, pepper and lemon zest and stir to combine.
Roll one piece of the pastry out on a lightly floured surface to roughly 40x30cm/16x12in with the short side nearest you. It should be the thickness of a pound coin. Brush egg wash over the pastry then liberally sprinkle the herb mix over the dough. Press the filling lightly into the dough all over using a rolling pin.
Tightly roll the bottom two-thirds of the dough up like a Swiss roll so the filling is on the inside. For the last third, roll it underneath itself in the opposite direction so the filling is on the outside.
Place the roll onto a baking tray, slide into a large freezer bag and freeze for 10 minutes.
Use a sharp knife to cut off any ragged ends then cut into 1cm/½in slices and lay them flat on one of the prepared trays, leaving at least 5cm/2in between each as they will spread during baking.
Bake for 11 minutes, flip them over and bake for a further 11 minutes until golden-brown and puffed up. Transfer to a cooling rack.
For the pepper and goats’ cheese palmiers, roll one piece of the puff pastry out on a lightly floured surface to roughly 40x30cm/16x12in with the short side facing you. Spread the goats’ cheese and peppers over the pastry. Press the filling lightly into the dough all over using a lightly oiled rolling pin.
Fold the bottom sixth of dough upwards and press down, then repeat this fold once more upward and you should be near the midpoint of the dough. Repeat this double fold for the top of the pastry coming down. Lightly brush some beaten egg along the top of the folded portions, then fold these together like a book. All the topping should be on the inside.
Place the roll onto a baking tray, slide into a large freezer bag and freeze for 10 minutes.
Use a sharp knife to cut off any ragged ends then cut 1cm/½in slices and lay them flat on the preapred tray. Clean the knife using kitchen paper between each slice. Leave at least 5cm/2in between each as they will spread during baking.
Bake for 11 minutes then flip and bake for a further 11 minutes until golden-brown and puffed up. Transfer to a cooling rack.


createFunctionsReturnValue
createFunctionsReturnValue: object = returnof(createStateDependentFunctions)
Type declaration
-
botCreatorFactory: (Anonymous function)
-
createRoom: (Anonymous function)
-
findGame: (Anonymous function)
-
findPlayer: (Anonymous function)
-
findRoom: (Anonymous function)
-
getAllPlayers: (Anonymous function)
-
getGameScores: (Anonymous function)
-
getGameStartingPlayer: (Anonymous function)
-
getPlayerOnLeft: (Anonymous function)
-
getPlayerOnLeftName: (Anonymous function)
-
getPlayerWithHighestCard: (Anonymous function)
-
getPlayersInRoom: (Anonymous function)
-
getRoomNamesAndAvailability: (Anonymous function)
-
isRealPlayerInRoom: (Anonymous function)
-
playerCanCreateRoom: (Anonymous function)
-
playerCanJoinRoom: (Anonymous function)
-
playerCanRegister: (Anonymous function)
Object of functions, which can read the current version of state via injected [getState] function, but do not produce any other side effects.


10 Logistic Regression - Ex: Depression (Hoffman)
library(tidyverse)
library(haven) # read in SPSS dataset
library(furniture) # nice table1() descriptives
library(stargazer) # display nice tables: summary & regression
library(texreg) # Convert Regression Output to LaTeX or HTML Tables
library(texreghelpr) # GITHUB: sarbearschartz/texreghelpr
library(psych) # contains some useful functions, like headTail
library(car) # Companion to Applied Regression
library(sjPlot) # Quick plots and tables for models
library(pscl) # psudo R-squared function
library(glue) # Interpreted String Literals
library(interactions) # interaction plots
library(sjPlot) # various plots
library(performance) # r-squared values
This dataset comes from John Hoffman’s textbook: Regression Models for Categorical, Count, and Related Variables: An Applied Approach (2004) Amazon link, 2014 edition
Chapter 3: Logistic and Probit Regression Models
Dataset: The following example uses the SPSS data set Depress.sav
. The dependent variable of interest is a measure of life satisfaction, labeled satlife
.
<- haven::read_spss("https://raw.githubusercontent.com/CEHS-research/data/master/Hoffmann_datasets/depress.sav") %>%
df_depress ::as_factor()
haven
::glimpse(df_depress) tibble
Rows: 118
Columns: 14
$ id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,~
$ age <dbl> 39, 41, 42, 30, 35, 44, 31, 39, 35, 33, 38, 31, 40, 44, 43, 32~
$ iq <dbl> 94, 89, 83, 99, 94, 90, 94, 87, NA, 92, 92, 94, 91, 86, 90, NA~
$ anxiety <fct> medium low, medium low, medium high, medium low, medium low, N~
$ depress <fct> medium, medium, high, medium, low, low, medium, medium, medium~
$ sleep <fct> low, low, low, low, high, low, NA, low, low, low, high, low, l~
$ sex <fct> female, female, female, female, female, male, female, female, ~
$ lifesat <fct> low, low, low, low, high, high, low, high, low, low, high, hig~
$ weight <dbl> 4.9, 2.2, 4.0, -2.6, -0.3, 0.9, -1.5, 3.5, -1.2, 0.8, -1.9, 5.~
$ satlife <dbl> 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0,~
$ male <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,~
$ sleep1 <dbl> 0, 0, 0, 0, 1, 0, NA, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, N~
$ newiq <dbl> 2.21, -2.79, -8.79, 7.21, 2.21, -1.79, 2.21, -4.79, NA, 0.21, ~
$ newage <dbl> 1.5424, 3.5424, 4.5424, -7.4576, -2.4576, 6.5424, -6.4576, 1.5~
::headTail(df_depress) psych
# A tibble: 9 x 14
id age iq anxiety depress sleep sex lifesat weight satlife male
<chr> <chr> <chr> <fct> <fct> <fct> <fct> <fct> <chr> <chr> <chr>
1 1 39 94 medium low medium low fema~ low 4.9 0 0
2 2 41 89 medium low medium low fema~ low 2.2 0 0
3 3 42 83 medium high high low fema~ low 4 0 0
4 4 30 99 medium low medium low fema~ low -2.6 0 0
5 ... ... ... <NA> <NA> <NA> <NA> <NA> ... ... ...
6 115 39 87 medium low medium low male low <NA> 0 1
7 116 41 86 medium high medium high male low -1 0 1
8 117 33 89 low low high male high 6.5 1 1
9 118 42 <NA> medium high medium low fema~ low 4.9 0 0
# ... with 3 more variables: sleep1 <chr>, newiq <chr>, newage <chr>
%>%
df_depress ::select(satlife, lifesat) %>%
dplyrtable() %>%
addmargins()
lifesat
satlife high low Sum
0 0 65 65
1 52 0 52
Sum 52 65 117
10.1 Exploratory Data Analysis
Dependent Variable = satlife
(numeric version) or lifesat
(factor version)
10.1.1 Visualize
%>%
df_depress ggplot(aes(x = age,
y = satlife)) +
geom_count() +
geom_smooth(method = "lm") +
theme_bw() +
labs(x = "Age in Years",
y = "Life Satisfaction, numeric")
%>%
df_depress ggplot(aes(x = age,
y = satlife)) +
geom_count() +
geom_smooth(method = "lm") +
theme_bw() +
labs(x = "Age in Years",
y = "Life Satisfaction, numeric") +
facet_grid(~ sex) +
theme(legend.position = "bottom")
10.1.2 Summary Table
Independent = sex
%>%
df_depress ::select(lifesat, sex) %>%
dplyrtable() %>%
addmargins()
sex
lifesat male female Sum
high 14 38 52
low 7 58 65
Sum 21 96 117
%>%
df_depress ::group_by(sex) %>%
dplyr::table1(lifesat,
furnituretotal = TRUE,
caption = "Hoffman's EXAMPLE 3.1 Cross-Tabulation of Gender and Life Satisfaction (top page 50)",
output = "markdown")
| Total | male | female | |
|---|---|---|---|
| n = 117 | n = 21 | n = 96 | |
| lifesat | |||
| high | 52 (44.4%) | 14 (66.7%) | 38 (39.6%) |
| low | 65 (55.6%) | 7 (33.3%) | 58 (60.4%) |
10.2 Calcualte: Probability, Odds, and Odds-Ratios
10.2.1 Marginal, over all the sample
Tally the number of participants happy and not happy (i.e. depressed).
%>%
df_depress ::select(satlife) %>%
dplyrtable() %>%
addmargins()
.
0 1 Sum
65 52 117
<IP_ADDRESS> Probability of being happy
\[ prob_{yes} = \frac{n_{yes}}{n_{total}} = \frac{n_{yes}}{n_{yes} + n_{no}} \]
<- 52 / 117
prob
prob
[1] 0.4444444
<IP_ADDRESS> Odds of being happy
\[ odds_{yes} = \frac{n_{yes}}{n_{no}} = \frac{n_{yes}}{n_{total} - n_{yes}} \]
<- 52/65
odds
odds
[1] 0.8
\[ odds_{yes} = \frac{prob_{yes}}{prob_{no}} = \frac{prob_{yes}}{1 - prob_{yes}} \]
/(1 - prob) prob
[1] 0.8
10.2.2 Comparing by Sex
Cross-tabulate happiness (
satlife
) withsex
(male vs. female).
%>%
df_depress ::select(satlife, sex) %>%
dplyrtable() %>%
addmargins()
sex
satlife male female Sum
0 7 58 65
1 14 38 52
Sum 21 96 117
<IP_ADDRESS> Probability of being happy, by sex
Reference category = male
<- 14 / 21
prob_male
prob_male
[1] 0.6666667
Comparison Category = female
<- 38 / 96
prob_female
prob_female
[1] 0.3958333
<IP_ADDRESS> Odds of being happy, by sex
Reference category = male
<- 14 / 7
odds_male
odds_male
[1] 2
Comparison Category = female
<- 38 / 58
odds_female
odds_female
[1] 0.6551724
<IP_ADDRESS> Odds-Ratio for sex
\[ OR_{\text{female vs. male}} = \frac{odds_{female}}{odds_{male}} \]
<- odds_female / odds_male
odds_ratio
odds_ratio
[1] 0.3275862
\[ OR_{\text{female vs. male}} = \frac{\frac{prob_{female}}{1 - prob_{female}}}{\frac{prob_{male}}{1 - prob_{male}}} \]
/ (1 - prob_female)) / (prob_male / (1 - prob_male)) (prob_female
[1] 0.3275862
\[ OR_{\text{female vs. male}} = \frac{\frac{n_{yes|female}}{n_{no|female}}}{\frac{n_{yes|male}}{n_{no|male}}} \]
38 / 58)/(14 / 7)) ((
[1] 0.3275862
10.3 Logisitc Regression Model 1: one IV
10.3.1 Fit the Unadjusted Model
<- glm(satlife ~ sex,
fit_glm_1 data = df_depress,
family = binomial(link = "logit"))
%>% summary() %>% coef() fit_glm_1
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.6931472 0.4629100 1.497369 0.13429719
sexfemale -1.1160040 0.5077822 -2.197800 0.02796333
summary(fit_glm_1)
Call:
glm(formula = satlife ~ sex, family = binomial(link = "logit"),
data = df_depress)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.482 -1.004 -1.004 1.361 1.361
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.6931 0.4629 1.497 0.134
sexfemale -1.1160 0.5078 -2.198 0.028 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 160.75 on 116 degrees of freedom
Residual deviance: 155.62 on 115 degrees of freedom
(1 observation deleted due to missingness)
AIC: 159.62
Number of Fisher Scoring iterations: 4
10.3.2 Tabulate Parameters
<IP_ADDRESS> Logit Scale
::knitreg(fit_glm_1,
texregcaption = "Hoffman's EXAMPLE 3.2 A Loistic Regression Model of Gender and Life Satisfaction, top of page 51",
caption.above = TRUE,
single.row = TRUE,
digits = 4)
| Model 1 | |
|---|---|
| (Intercept) | 0.6931 (0.4629) |
| sexfemale | -1.1160 (0.5078)* |
| AIC | 159.6205 |
| BIC | 165.1449 |
| Log Likelihood | -77.8103 |
| Deviance | 155.6205 |
| Num. obs. | 117 |
| p < 0.001; p < 0.01; p < 0.05 |
<IP_ADDRESS> Both Logit and Odds-ratio Scales
::knitreg(list(fit_glm_1,
texreg::extract_glm_exp(fit_glm_1)),
texreghelprcustom.model.names = c("b (SE)",
"OR [95 CI]"),
caption = "Hoffman's EXAMPLE 3.2 A Loistic Regression Model of Gender and Life Satisfaction, top of page 51",
caption.above = TRUE,
single.row = TRUE,
digits = 4,
ci.test = 1)
| b (SE) | OR [95 CI] | |
|---|---|---|
| (Intercept) | 0.6931 (0.4629) | 2.0000 [0.8322; 5.2772] |
| sexfemale | -1.1160 (0.5078)* | 0.3276 [0.1148; 0.8625]* |
| AIC | 159.6205 | |
| BIC | 165.1449 | |
| Log Likelihood | -77.8103 | |
| Deviance | 155.6205 | |
| Num. obs. | 117 | |
| p < 0.001; p < 0.01; p < 0.05 (or Null hypothesis value outside the confidence interval). |
10.3.3 Assess Model Fit
<IP_ADDRESS> Likelihood Ratio Test (LRT, aka. Deviance Difference Test)
drop1(fit_glm_1, test = "LRT")
# A tibble: 2 x 5
Df Deviance AIC LRT `Pr(>Chi)`
<dbl> <dbl> <dbl> <dbl> <dbl>
1 NA 156. 160. NA NA
2 1 161. 163. 5.13 0.0235
::compare_performance(fit_glm_1) performance
# A tibble: 1 x 11
Name Model AIC BIC R2_Tjur RMSE Sigma Log_loss Score_log Score_spherical
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 fit_glm_1 glm 160. 165. 0.0437 0.486 1.16 0.665 -30.1 0.0550
# ... with 1 more variable: PCP <dbl>
<IP_ADDRESS> R-squared “like” measures
::r2(fit_glm_1) performance
# R2 for Logistic Regression
Tjur's R2: 0.044
::r2_mcfadden(fit_glm_1) performance
# R2 for Generalized Linear Regression
R2: 0.032
adj. R2: 0.019
::r2_nagelkerke(fit_glm_1) performance
Nagelkerke's R2
0.05742029
::pR2(fit_glm_1) pscl
fitting null model for pseudo-r2
llh llhNull G2 McFadden r2ML r2CU
-77.81025523 -80.37450446 5.12849846 0.03190376 0.04288652 0.05742029
10.3.4 Plot Predicted Probabilitites
::plot_model(model = fit_glm_1,
sjPlottype = "pred")
$sex
<IP_ADDRESS> Logit scale
%>%
fit_glm_1 ::emmeans(~ sex) emmeans
sex emmean SE df asymp.LCL asymp.UCL
male 0.693 0.463 Inf -0.214 1.6004
female -0.423 0.209 Inf -0.832 -0.0138
Results are given on the logit (not the response) scale.
Confidence level used: 0.95
%>%
fit_glm_1 ::emmeans(~ sex) %>%
emmeanspairs()
contrast estimate SE df z.ratio p.value
male - female 1.12 0.508 Inf 2.198 0.0280
Results are given on the log odds ratio (not the response) scale.
<IP_ADDRESS> Response Scale (probability)
%>%
fit_glm_1 ::emmeans(~ sex,
emmeanstype = "response")
sex prob SE df asymp.LCL asymp.UCL
male 0.667 0.1029 Inf 0.447 0.832
female 0.396 0.0499 Inf 0.303 0.497
Confidence level used: 0.95
Intervals are back-transformed from the logit scale
%>%
fit_glm_1 ::emmeans(~ sex,
emmeanstype = "response") %>%
pairs()
contrast odds.ratio SE df null z.ratio p.value
male / female 3.05 1.55 Inf 1 2.198 0.0280
Tests are performed on the log odds ratio scale
10.3.5 Interpretation
On average, two out of every three males is depressed, b = 0.667, odds = 1.95, 95% CI [1.58, 2.40].
Females have nearly a quarter lower odds of being depressed, compared to men, b = -0.27, OR = 0.77, 95% IC [0.61, 0.96], p = .028.
10.3.6 Diagnostics
<IP_ADDRESS> Influential values
Influential values are extreme individual data points that can alter the quality of the logistic regression model.
The most extreme values in the data can be examined by visualizing the Cook’s distance values. Here we label the top 7 largest values:
plot(fit_glm_1, which = 4, id.n = 7)
Note that, not all outliers are influential observations. To check whether the data contains potential influential observations, the standardized residual error can be inspected. Data points with an absolute standardized residuals above 3 represent possible outliers and may deserve closer attention.
<IP_ADDRESS> Standardized Residuals
The following R code computes the standardized residuals (.std.resid
) using the R function augment()
[broom
package].
%>%
fit_glm_1 ::augment() %>%
broomggplot(aes(x = .rownames, .std.resid)) +
geom_point(aes(color = sex), alpha = .5) +
theme_bw()
10.4 Logisitic Regression Model 2: many IV’s
10.4.1 Fit the Model
<- glm(satlife ~ sex + iq + age + weight,
fit_glm_2 data = df_depress,
family = binomial(link = "logit"))
%>% summary() %>% coef() fit_glm_2
Estimate Std. Error z value Pr(>|z|)
(Intercept) 9.02054236 6.01858970 1.4987801 0.13393069
sexfemale -1.27924381 0.55971456 -2.2855289 0.02228183
iq -0.07279837 0.05361036 -1.3579161 0.17449031
age -0.04112838 0.05250052 -0.7833900 0.43339814
weight -0.03768972 0.08885942 -0.4241499 0.67145648
10.4.2 Tabulate Parameters
::knitreg(list(fit_glm_2,
texreg::extract_glm_exp(fit_glm_2)),
texreghelprcustom.model.names = c("b (SE)",
"OR [95 CI]"),
caption = "EXAMPLE 3.3 A Logistic Regression Model of Life Satisfaction with Multiple Independent Variables, middle of page 52",
caption.above = TRUE,
single.row = TRUE,
digits = 4,
ci.test = 1)
| b (SE) | OR [95 CI] | |
|---|---|---|
| (Intercept) | 9.0205 (6.0186) | 8271.2619 [0.0854;<PHONE_NUMBER>.1374] |
| sexfemale | -1.2792 (0.5597)* | 0.2782 [0.0870; 0.8060]* |
| iq | -0.0728 (0.0536) | 0.9298 [0.8327; 1.0304] |
| age | -0.0411 (0.0525) | 0.9597 [0.8635; 1.0627] |
| weight | -0.0377 (0.0889) | 0.9630 [0.8073; 1.1472] |
| AIC | 137.5217 | |
| BIC | 150.4973 | |
| Log Likelihood | -63.7609 | |
| Deviance | 127.5217 | |
| Num. obs. | 99 | |
| p < 0.001; p < 0.01; p < 0.05 (or Null hypothesis value outside the confidence interval). |
10.4.3 Assess Model Fit
drop1(fit_glm_2, test = "LRT")
# A tibble: 5 x 5
Df Deviance AIC LRT `Pr(>Chi)`
<dbl> <dbl> <dbl> <dbl> <dbl>
1 NA 128. 138. NA NA
2 1 133. 141. 5.59 0.0180
3 1 129. 137. 1.91 0.167
4 1 128. 136. 0.621 0.431
5 1 128. 136. 0.180 0.671
10.4.4 Variance Explained
::compare_performance(fit_glm_2) performance
# A tibble: 1 x 11
Name Model AIC BIC R2_Tjur RMSE Sigma Log_loss Score_log Score_spherical
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 fit_glm_2 glm 138. 150. 0.0753 0.475 1.16 0.644 -23.1 0.0220
# ... with 1 more variable: PCP <dbl>
<IP_ADDRESS> R-squared “lik” measures
::r2_nagelkerke(fit_glm_2) performance
Nagelkerke's R2
0.09728404
10.4.5 Diagnostics
<IP_ADDRESS> Multicollinearity
Multicollinearity corresponds to a situation where the data contain highly correlated predictor variables. Read more in Chapter (ref?)(multicollinearity).
Multicollinearity is an important issue in regression analysis and should be fixed by removing the concerned variables. It can be assessed using the R function vif()
[car
package], which computes the variance inflation factors:
::vif(fit_glm_2) car
sex iq age weight
1.011608 1.294334 1.418975 1.233364
As a rule of thumb, a VIF
value that exceeds 5 or 10 indicates a problematic amount of collinearity. In our example, there is no collinearity: all variables have a value of VIF
well below 5.
10.5 Compare Models
10.5.1 Refit to Complete Cases
Restrict the data to only participant that have all four of these predictors.
<- df_depress %>%
df_depress_model ::filter(complete.cases(sex, iq, age, weight)) dplyr
Refit Model 1 with only participant complete on all the predictors.
<- glm(satlife ~ sex,
fit_glm_1_redo data = df_depress_model,
family = binomial(link = "logit"))
<- glm(satlife ~ sex + iq + age + weight,
fit_glm_2_redo data = df_depress_model,
family = binomial(link = "logit"))
::knitreg(list(texreghelpr::extract_glm_exp(fit_glm_1_redo),
texreg::extract_glm_exp(fit_glm_2_redo)),
texreghelprcustom.model.names = c("Single IV",
"Multiple IVs"),
caption.above = TRUE,
single.row = TRUE,
digits = 4,
ci.test = 1)
| Single IV | Multiple IVs | |
|---|---|---|
| (Intercept) | 2.0000 [0.7767; 5.7449] | 8271.2619 [0.0854;<PHONE_NUMBER>.1374] |
| sexfemale | 0.2941 [0.0939; 0.8387]* | 0.2782 [0.0870; 0.8060]* |
| iq | 0.9298 [0.8327; 1.0304] | |
| age | 0.9597 [0.8635; 1.0627] | |
| weight | 0.9630 [0.8073; 1.1472] | |
| * Null hypothesis value outside the confidence interval. |
anova(fit_glm_1_redo,
fit_glm_2_redo,test = "LRT")
# A tibble: 2 x 5
`Resid. Df` `Resid. Dev` Df Deviance `Pr(>Chi)`
<dbl> <dbl> <dbl> <dbl> <dbl>
1 97 130. NA NA NA
2 94 128. 3 2.18 0.537
::compare_performance(fit_glm_1_redo,
performance
fit_glm_2_redo, rank = TRUE)
# A tibble: 2 x 12
Name Model AIC BIC R2_Tjur RMSE Sigma Log_loss Score_log Score_spherical
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 fit_glm_1_redo glm 134. 139. 0.0535 0.481 1.16 0.655 -22.8 0.0591
2 fit_glm_2_redo glm 138. 150. 0.0753 0.475 1.16 0.644 -23.1 0.0220
# ... with 2 more variables: PCP <dbl>, Performance_Score <dbl>
10.5.2 Interpretation
- Only sex is predictive of depression. There is no evidence IQ, age, or weight are associated with depression, all p’s > .16.
10.6 Changing the reference category
$sex df_depress
[1] female female female female female male female female female female
[11] male male female female female female male female female female
[21] female female female female female female female female female female
[31] female female female female female male female female female female
[41] female female male female female female female male female female
[51] male female female female female male male female female male
[61] female female female female female male female female male female
[71] female female male female female female female female female female
[81] female female male female female female male female female female
[91] female female female female female female female female female female
[101] female female female female male female female female female female
[111] male female female female male male male female
attr(,"label")
[1] sex of respondent
Levels: male female
<- df_depress %>%
df_depress_ref ::mutate(male = sex %>% forcats::fct_relevel("female", after = 0))
dplyr
$male df_depress_ref
[1] female female female female female male female female female female
[11] male male female female female female male female female female
[21] female female female female female female female female female female
[31] female female female female female male female female female female
[41] female female male female female female female male female female
[51] male female female female female male male female female male
[61] female female female female female male female female male female
[71] female female male female female female female female female female
[81] female female male female female female male female female female
[91] female female female female female female female female female female
[101] female female female female male female female female female female
[111] male female female female male male male female
attr(,"label")
[1] sex of respondent
Levels: female male
<- glm(satlife ~ male + iq + age + weight,
fit_glm_2_male data = df_depress,
family = binomial(link = "logit"))
::knitreg(list(texreghelpr::extract_glm_exp(fit_glm_2),
texreg::extract_glm_exp(fit_glm_2_male)),
texreghelprcustom.model.names = c("original model",
"recoded model"),
caption.above = TRUE,
single.row = TRUE,
digits = 4,
ci.test = 1)
| original model | recoded model | |
|---|---|---|
| (Intercept) | 8271.2619 [0.0854;<PHONE_NUMBER>.1374] | 2301.4590 [0.0263; 482461590.3668] |
| sexfemale | 0.2782 [0.0870; 0.8060]* | |
| iq | 0.9298 [0.8327; 1.0304] | 0.9298 [0.8327; 1.0304] |
| age | 0.9597 [0.8635; 1.0627] | 0.9597 [0.8635; 1.0627] |
| weight | 0.9630 [0.8073; 1.1472] | 0.9630 [0.8073; 1.1472] |
| male | 3.5939 [1.2406; 11.4938]* | |
| * Null hypothesis value outside the confidence interval. |


guix package -i package_name- Remove a package:
guix package -r package_name- Search the package database for a regular expression:
guix package -s "search_pattern"- List installed packages:
guix package -I- List generations:
guix package -l- Roll back to the previous generation:
guix package --roll-back


Bacon ipsum dolor amet ribeye capicola t-bone beef ribs, pastrami ham hock short ribs bacon alcatra brisket picanha biltong. Brisket shoulder fatback chicken. Tongue biltong filet mignon ribeye hamburger frankfurter pork belly chuck sausage short ribs shoulder turducken jowl meatball pastrami.  Ham hock ball tip andouille hamburger drumstick, kielbasa jerky shank ribeye flank sausage pancetta porchetta. Jowl frankfurter venison shank fatback.
Shank turkey frankfurter picanha, ball tip landjaeger flank salami. Jerky beef jowl, sausage kielbasa bresaola hamburger fatback pancetta pork salami porchetta pork belly meatloaf andouille. Cow sausage bacon doner filet mignon. Beef pork loin sausage drumstick kielbasa, andouille hamburger chuck sirloin jowl porchetta landjaeger chicken short loin.
Turkey rump ground round strip steak sausage filet mignon short loin venison porchetta kevin. Alcatra andouille porchetta, shoulder pork belly tail boudin meatball ribeye strip steak. Strip steak ball tip spare ribs short loin boudin. Tri-tip turkey tail beef ribs pork loin alcatra ham rump ground round landjaeger brisket.
Jerky beef jowl, sausage kielbasa bresaola hamburger fatback pancetta pork salami porchetta pork belly meatloaf andouille. Cow sausage bacon doner filet mignon. Beef pork loin sausage drumstick kielbasa, andouille hamburger chuck sirloin jowl porchetta landjaeger chicken short loin.  Short ribs ground round swine sirloin, capicola venison flank tongue leberkas short loin corned beef.
Pork belly pastrami salami bacon. Pork belly ham hock short ribs sausage.
Turkey rump ground round strip steak sausage filet mignon short loin venison porchetta kevin. Alcatra andouille porchetta, shoulder pork belly tail boudin meatball ribeye strip steak. Strip steak ball tip spare ribs short loin boudin. Tri-tip turkey tail beef ribs pork loin alcatra ham rump ground round landjaeger brisket.
Turkey rump ground round strip steak sausage filet mignon short loin venison porchetta kevin. Alcatra andouille porchetta, shoulder pork belly tail boudin meatball ribeye strip steak. Strip steak ball tip spare ribs short loin boudin. Tri-tip turkey tail beef ribs pork loin alcatra ham rump ground round landjaeger brisket.
Turkey rump ground round strip steak sausage filet mignon short loin venison porchetta kevin. Alcatra andouille porchetta, shoulder pork belly tail boudin meatball ribeye strip steak. Strip steak ball tip spare ribs short loin boudin. Tri-tip turkey tail beef ribs pork loin alcatra ham rump ground round landjaeger brisket.
Turkey rump ground round strip steak sausage filet mignon short loin venison porchetta kevin. Alcatra andouille porchetta, shoulder pork belly tail boudin meatball ribeye strip steak. Strip steak ball tip spare ribs short loin boudin. Tri-tip turkey tail beef ribs pork loin alcatra ham rump ground round landjaeger brisket.
Ribeye prosciutto ground round, tongue ham hock landjaeger turkey meatball kevin doner spare ribs jowl pastrami venison. Ham hock turkey corned beef picanha biltong tongue chuck leberkas beef ribs ground round sirloin boudin spare ribs fatback shank.
Short ribs chicken capicola, ground round tenderloin kevin pastrami tongue cupim filet mignon. Tongue prosciutto frankfurter turkey ribeye shank fatback pig, porchetta meatball pancetta. Sausage fatback meatloaf pork belly turkey prosciutto. Chuck kielbasa chicken jowl alcatra. Swine jowl ball tip chuck bacon. Jowl ground round pork chop tail.
Pastrami kevin ground round fatback cupim, cow sausage turducken pancetta tenderloin shoulder. Pork picanha fatback shank, strip steak beef capicola alcatra ball tip sirloin turkey. Hamburger ham hock leberkas, cupim salami tri-tip ham brisket kielbasa.  Boudin chicken leberkas, porchetta corned beef ham sirloin.
Turkey rump ground round strip steak sausage filet mignon short loin venison porchetta kevin. Alcatra andouille porchetta, shoulder pork belly tail boudin meatball ribeye strip steak. Strip steak ball tip spare ribs short loin boudin. Tri-tip turkey tail beef ribs pork loin alcatra ham rump ground round landjaeger brisket.
Bresaola cow jerky filet mignon capicola beef turkey ribeye strip steak biltong sausage short loin brisket. Prosciutto ribeye beef ribs ham hamburger. Frankfurter cupim porchetta spare ribs, ball tip pork loin tongue tri-tip ribeye meatloaf beef.
 Tongue bacon swine boudin shank ham jowl spare ribs ham hock leberkas meatball landjaeger chicken.
 Tongue bacon swine boudin shank ham jowl spare ribs ham hock leberkas meatball landjaeger chicken.
T-bone swine hamburger doner shankle corned beef picanha boudin biltong. Short loin shank boudin venison.  Tongue bacon swine boudin shank ham jowl spare ribs ham hock leberkas meatball landjaeger chicken.
 Turducken ball tip strip steak corned beef, alcatra doner turkey. Filet mignon swine doner turkey leberkas rump ham hock. T-bone swine hamburger doner shankle corned beef picanha boudin biltong. Short loin shank boudin venison.  Tongue bacon swine boudin shank ham jowl spare ribs ham hock leberkas meatball landjaeger chicken.
 Tongue bacon swine boudin shank ham jowl spare ribs ham hock leberkas meatball landjaeger chicken.
 Tongue bacon swine boudin shank ham jowl spare ribs ham hock leberkas meatball landjaeger chicken.
Turducken ball tip strip steak corned beef, alcatra doner turkey. Filet mignon swine doner turkey leberkas rump ham hock. T-bone swine hamburger doner shankle corned beef picanha boudin biltong. Prosciutto alcatra bresaola ribeye bacon brisket. Sirloin alcatra chicken brisket shank boudin t-bone. Drumstick beef pancetta shoulder landjaeger ball tip pork belly beef ribs.
 Ribeye prosciutto ground round, tongue ham hock landjaeger turkey meatball kevin doner spare ribs jowl pastrami venison. Ham hock turkey corned beef picanha biltong tongue chuck leberkas beef ribs ground round sirloin boudin spare ribs fatback shank.


--- layout: default title: home ---
Hello world, I am {% assign name = site.author | split: ' '%}{{ name.first }}!


--- layout: post status: publish published: true title: Summer Flowers author: Leask author_login: leask author_email<EMAIL_ADDRESS>author_url: https://leaskh.com wordpress_id: 11 wordpress_url: http://leaskh.wordpress.com/2010/05/23/summer-flowers date: '2010-05-23 16:31:16 +0800' date_gmt: '2010-05-23 16:31:16 +0800' categories: tags: [] comments: [] ---


Under Review
Available since 1.0.0
A tile component can be used to display information in a simple container format.
The component is ideal for displaying collection data when a grid or list layout is preferred. See tile-grid
.
Simple Tile
Tile Title
Tile Description
Tile Title
Tile Description
Tile with .fd-actions
container
Add role=button
to rendering a tile as a button
Product Tile
fd-product-tile
can be used to display product information.
Disabled state
Add class is-disabled
and/or aria-disabled="true"
attribute


THE STORY OF BOYS AND GIRLS "A Feast for Lovers" A film review by Ken Johnson Copyright 1992 Ken Johnson
90 min., not rated, Comedy/Drama/Romance, 1991, In Italian with English subtitles Director: Pupi Avati Cast: Davide Bechini, Lucrenzia Lante, Della Rovere
In Italy a man and a woman are preparing to get married. The woman's family is very rich and the man's family is quite poor. This film shows the two families preparing for the wedding. The wedding is to be held at the woman's house and just about everything that could go wrong does.
I generally don't like foreign films at all. This film is rather good, although not nearly as good as MY MOTHER'S CASTLE. It is comical, yet dramatic at the same time. I would recommend, if this film comes to a theater close to you, that you go for not higher than a matinee price. People who tend to like foreign films should be sure to see this film. It will be just as good on video, if it comes to video, for it gains nothing from being shown on a big screen. On a scale of zero to five, I give THE STORY OF BOYS AND GIRLS a three. THE STORY OF BOYS AND GIRLS is not rated, but contains adult situations.
This film will not be liked by all audiences. It requires a person who is willing to sit down and think while watching. It also requires a person with an open mind. Someone who just likes to go to films where no thought is involved, or who tends not to like any films other than comedies, should not go to see this film, because there is not enough comedy in the film to satisfy you.
The acting is average. I have seen some films where the acting is better, but the acting is not horrible. There is no actor/actress that really stands out with an exceptional performance, on the other hand, no actor/actress does a really poor job either. The story though, is original. The parents of the bride are fighting with each other because the bride's mother's husband has been having an affair with another woman. One of the brides brothers is a real show-off. The groom's parents aren't sure of the marriage, because they really can't afford it. To add to the confusion, the married man who rents the downstairs rooms in the bride's house shows up with a mysterious female.
Ken J<EMAIL_ADDRESS>.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


line, = ax.plot(r, energies)
plt.xlabel("Distance (Å)")
plt.ylabel("Energy (eV)")
plt.show()
// Create SOAP output for all samples. One center is chosen to be directly
// between the atoms.
center = [0, 0, 0]
// Create SOAP output for all samples. One center is chosen to be directly
// between the atoms.
center = [0, 0, 0]
derivatives = soap.derivatives(traj, positions=[[center]] * len(r), method="numerical", return_descriptor=False)
descriptors = []
for t in traj:


#include <sys/types.h>
#include <unistd.h>
pid_t getsid(pid_t pid);
Feature Test Macro Requirements for glibc (see feature_test_macros(7)):
getsid():
_XOPEN_SOURCE >= 500
|| /* Since glibc 2.12: */ _POSIX_C_SOURCE >= 200809L
getsid(0) returns the session ID of the calling process. getsid()
returns the session ID of the process with process ID pid. If
pid is 0, getsid() returns the session ID of the calling
process.
On success, a session ID is returned. On error, (pid_t) -1 will be
returned, and errno is set appropriately.
- EPERM
- A process with process ID pid exists, but it is not in the same
session as the calling process, and the implementation considers this an
error.
- ESRCH
- No process with process ID pid was found.
This system call is available on Linux since version 2.0.
POSIX.1-2001, POSIX.1-2008, SVr4.
Linux does not return EPERM.
See credentials(7) for a description of sessions and
session IDs.
getpgid(2), setsid(2), credentials(7)
This page is part of release 5.05 of the Linux man-pages project. A
description of the project, information about reporting bugs, and the latest
version of this page, can be found at https://www.kernel.org/doc/man-pages/.


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Change order of layers on the map by changing the order of items in the list below.
Hint: drag-drop elements using the handle.
This example shows how to use the
ngeo-sortable
directive and the
ngeo.misc.syncArrays
function to create a list of layers where the order of layers can be changed using drag-and-drop.


This chord diagram displays the relationships between Document Types, inherited Document Types, and DocType Compositions. Hovering over an item will display the other Document Types it is connected to, and clicking on one displays more in-depth text information about inheritance and related compositions or non-composition Document Types.
Inheritance: > {{crumb.name}}
| Hide | Name | Inheritance | Is Comp | Compositions/Associated DocTypes | ID |
|---|---|---|---|---|---|
| {{docType.name}} | No Yes | , {{comp.name}} , {{page.name}} | {{docType.id}} |


| mode - a valid integer value, existing within the range 1 - CountGfxModes() |
|
Returns the pixel format of a particular graphics mode, represented by a 'mode' value. The mode value corresponds to a particular screen setting (such as 800x600x16) available with the host machine's selected graphics card.
The mode value will exist within the range 1 - CountGfxModes(), with CountGfxModes() returning the total nuber of graphics modes available with the selected graphics card. By using a for loop with CountGfxModes(), as shown in the example below, you can quickly find out all possible GfxModeFormat values for all graphics modes. The return value can be one of the following: 1 - 16 bit RGB format with 5 bits red, 6 bits green and 5 bits blue. 2 - 16 bit RGB format with 5 bits red, 5 bits green and 5 bits blue. The high bit is unused. 3 - 24 bit RGB format. 4 - 32 bit RGB format. The high byte is unused. See also: CountGfxModes, GraphicsFormat, LockedFormat, Graphics. |
|
For i=1 To CountGfxModes()
Print "GfxMode: "+i+" Format: "+GfxModeFormat(i) Next Repeat Forever |


Concentrate on adjusting to college, developing good study skills and doing well in your courses
|
Learn how to find and use resources of the university and the business school
|
Visit the Business Career Center and meet your career consultant
|
Start to identify your interests, abilities and skills
|
Read the weekly electronic newsletter from your career consultant
|
Read your major's departmental web page and handouts about your major
|
Become familiar with the Career Services web site
|
Attend a workshop or event sponsored by the Business Career Center and/or Career Services
|
Explore clubs or organizations and join one that interests you
|
Make plans to do something over the summer that will add to your skills and/or give you exposure to an industry that interests you
|
Become acquainted with some business publications and choose one to read regularly
|
Create your first resume and meet with your career consultant to analyze how you want to improve and expand on the contents
|


Writes sp-class, spacetime-class, raster-class and similar spatial and spatio-temporal objects to KML following some basic cartographic rules.
| Version: |
0.5-8 |
| Depends: |
R (≥ 2.13.0) |
| Imports: |
methods, tools, utils, XML, sp, raster, rgdal, spacetime, colorspace, plotrix, dismo, aqp, pixmap, plyr, stringr, colorRamps, scales, gstat, zoo, RColorBrewer, RSAGA, classInt |
| Suggests: |
adehabitat, maptools, fossil, spcosa, rjson, animation, spatstat, RCurl, rgbif, Hmisc, GSIF, uuid, intervals, reshape, gdalUtils, snowfall, parallel |
| Published: |
2017-05-14 |
| Author: |
Tomislav Hengl [cre, aut],
Pierre Roudier [ctb],
Dylan Beaudette [ctb],
Edzer Pebesma [ctb],
Michael Blaschek [ctb] |
| Maintainer: |
Tomislav Hengl <tom.hengl at isric.org> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL] |
| URL: |
http://plotkml.r-forge.r-project.org/ |
| NeedsCompilation: |
no |
| Citation: |
plotKML citation info |
| Materials: |
NEWS |
| In views: |
Spatial, SpatioTemporal, WebTechnologies |
| CRAN checks: |
plotKML results |


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© Matthew Smith 2019


# -*- coding: utf-8 -*-
"""
Independent Set
Independent set or stable set is a set of vertices in a graph, no two of
which are adjacent. That is, it is a set I of vertices such that for every
two vertices in I, there is no edge connecting the two. Equivalently, each
edge in the graph has at most one endpoint in I. The size of an independent
set is the number of vertices it contains.
A maximum independent set is a largest independent set for a given graph G
and its size is denoted α(G). The problem of finding such a set is called
the maximum independent set problem and is an NP-hard optimization problem.
As such, it is unlikely that there exists an efficient algorithm for finding
a maximum independent set of a graph.
`Wikipedia: Independent set <https://en.wikipedia.org/wiki/Independent_set_(graph_theory)>`_
Independent set algorithm is based on the following paper:
$O(|V|/(log|V|)^2)$ apx of maximum clique/independent set.
Boppana, R., & Halldórsson, M. M. (1992).
Approximating maximum independent sets by excluding subgraphs.
BIT Numerical Mathematics, 32(2), 180–196. Springer.
doi:10.1007/BF01994876
"""
# Copyright (C) 2011-2012 by
# Nicholas Mancuso<EMAIL_ADDRESS># All rights reserved.
# BSD license.
from networkx.algorithms.approximation import clique_removal
__all__ = ["maximum_independent_set"]
__author__ = """Nicholas Mancuso (nick.mancuso@gmail.com)"""
[docs]def maximum_independent_set(G):
"""Return an approximate maximum independent set.
Parameters
----------
G : NetworkX graph
Undirected graph
Returns
-------
iset : Set
The apx-maximum independent set
Notes
-----
Finds the $O(|V|/(log|V|)^2)$ apx of independent set in the worst case.
References
----------
.. [1] Boppana, R., & Halldórsson, M. M. (1992).
Approximating maximum independent sets by excluding subgraphs.
BIT Numerical Mathematics, 32(2), 180–196. Springer.
"""
iset, _ = clique_removal(G)
return iset


Oppia's mission from the very start has been "to help anyone learn anything they want in an effective and enjoyable way". The "Learner Dashboard" has been designed and developed keeping this goal in mind. If you're a new user and you have signed in for the first time, here's how the learner dashboard would look:
The page urges you to go out there and explore: learn new things! As you start learning, the page starts to look a bit different. Its primary goal is to track what you've learnt, which we believe is a crucial step toward mastery. After all, if you don't know where you are, how will you get to where you want to be?
Here's how the learner dashboard looks to an experienced learner:
As you can see, all the information about your progress is neatly organized in the learner dashboard. On the left hand side you can see a menu which tracks different things:
The Subscriptions section:
You can also select the "Sort By" dropdown to sort your explorations and collections by last played, title, and category.


OsmAnd 2.1 release is out! This time, we focused on improving POI search and making your trips even more interactive through new Wikipedia data sorted by country. Besides that, the favorites get a backup. If an app corrupts for some reason, all your favorite places will be safe.
Now you can choose to download Wikipedia files as you download the regular maps. Just hit the 'W' symbol to add wiki data to your download. To see the info, add the corresponding layer to your POI view.
There are some important updates in the routing section. We've added turn arrows on route to make navigation easier. Also, the app now has a new routing recalculation algorithm: after several attempts to calculate the optimal route, it offers a different one.
The POI that are closing in 30 minutes will be marked orange. This way, the travelers will be able to get more out of their sightseeing experiences and manage time right. Moreover, you can now use more specific queries (for instance, find the nearest church or the gas station with the specific fuel type you need).
Please feel free to tell us what you think and follow OsmAnd Maps on social media.


My { Portfolio }
I work with many talented folks who find ways of re-inventing the way users look at the web. Here you will get to see some of my past, current and future projects and learn more about me. Don't forget to check out my social channels!


hope you like it!!!
Here on Amalia's blog I post things at least every week. Some of the things I post are images, videos and more!!! you can find tips and hacks here too! you can even see me during trips and vacations!


Remember to properly set your Privacy Type, Privacy Members, and Share Level! Hit the "+" in the areas to the right to automatically add that group/member to the Privacy Members list. Documentation can be found here.
| Indicator | |
| Type | |
| Description | |
| Status | |
| Confidence | |
| Severity | |
| Review Status | |
| Precision | |
| Privacy Type | |
| Privacy Members | |
| Share Level | |
| Tags | |


Hi at all! I'm new on "game programming". And I might say that I am a newbie! I'm using the SDL library and trying to match the C++11 standards...
Anyway, I thought about a vector where I store all the addresses of game instances, so I can access them anytime...
I tried with this function:
Where "Instance" is the 'parent' class of various child classes like the player.
So, if I have to search the existing of a thing in my game, I should check if the address references to an instance of class. How can I do this?
Excuse my terrible english, and also my skills... And I'm am sure that I misunderstood something...
I am a beginner and I'm teaching myself...
Thanks in advance!
As for your question, just compare the pointer. If they are the same object, the pointers will be the same. Though, I don't see the need for storing them as uintprt_ts, rather than just Instance*s.
Apart from that, rather than doing some dodgy thing with casting pointers to and fro, why not use something like std::shared_ptr?
1 2 3 4 5 6 7 8 9 10 11 12 13 14
#include <memory>
#include <vector>
//...
// A vector to store instances in
std::vector<std::shared_ptr<Instance>> instances;
// Add an item with one of the following:
instances.push_back(std::make_shared<Instance>());
instances.push_bacK(std::shared_ptr<Instance>(new Instance));
// You can copy shared pointers, once all references to an object disappear
// it will be automatically deleted, and you can compare with a raw pointer using .get()


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=======


[Erowid Warning: Our understanding of the literature is that there is no such thing as safe use of volatile solvents, aerosols and other street inhalants : their psychoactive effects are inseparable from nerve and organ damage. We have chosen to include these reports to help document the real world use of inhalants, but their inclusion is not intended to imply that they are anything but dangerous.]
One time a long time ago, although I can recall it quite well, I was all alone at home and very bored and feeling very depressed. I had a sense of self-loathing that I still have today. But this time I was willing to take risks even knowing the potential dangers and outcomes.
I had tried sniffing markers a few times before this, with little luck. Just a pounding headache and some dizziness. Since the markers didn't screw up I thought I would try glue. I searched a cupboard for some super glue and found it. I promptly took the cap off and began sniffing. The smell was awful, but tolerable. I continued to sniff and began feeling a little weird. A bit dizzy, but no headache. I sat down in a chair and waited patiently. It only went down hill from there.
I began to notice a very strange feeling, one that is about impossible to describe. It was as if I had no brain, or my head was empty. Lightheaded could describe it partially but it was more than that.  Not painful, but that only made it more frightening. This feeling lasted the whole time I was buzzed. I was feeling paranoid, like if my parents would be home and they would somehow instantly know and yell at me. Worse though was the thought that I would die. I really thought I would die, but I was already dead on the inside and filled with self loathing so I just accepted death and sat, looking around the room reflecting on past events and my life; what I had experienced, accomplished, what I would miss and what I wouldn't.
At some point I began to feel good, quite good. Not very intoxicated, but happy. I had not a worry in the world and I felt a smile stretch across my face. I dunno how long I was like that. Could have been 5 minutes, could have been an hour. It didn't matter anyways because my mind was so clear, I was not doing anything, and nothing happened, so there was nothing to remember.
Then I heard the key entering and turning in the door. My mother came in and started talking about what she would make for supper. I remained mostly silent out of fear for revealing my drug use. I went up to my room and watched television. I didn't really watch it, I just laid there, still, trying to remain calm and ignore the hideous feeling in my head that I described earlier. I tried to reflect on the past yet again, but I couldn't. Instead I thought of strange things, horrible things. Everything I thought of was fleshy and deformed. Reminding me a lot of elephant man or victims of the Chernobyl incident or victims of Agent Orange. Perhaps even a developing human embryo. It's hard to describe being as there was more than one horrifying image in my head, but they all resembled lumpy, fleshy, weird, disgusting creatures. Freaks. But I deserved to be haunted by such ideas, I was and still am a fool and a burden. But I digress...
 I slept poorly as I often do, just with scarier nightmares. I regret this endeavor for a few reasons:
1. The experience was awful.
2. The weird fleshy freaks still haunt me in my nightmares today.
3. Despite thinking I was going to die I didn't even cry.
4. For a long time after I ran a bit slower mentally, I obviously had some brain damage from solvent sniffing.
I don't recommend you try huffing solvents.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.