Assistive_technology

thumb|250px|hearing aid assistive technology is an umbrella term that includes assistive, adaptive, and rehabilitative devices for people with disabilities and also includes the process used in selecting, locating, and using them. assistive technology promotes greater independence by enabling people to perform tasks that they were formerly unable to accomplish, or had great difficulty accomplishing, by providing enhancements to, or changing methods of interacting with, the technology needed to accomplish such tasks. == assistive technology and adaptive technology == the term adaptive technology is often used as the synonym for assistive technology, however, they are different terms. assistive technology refers to "any item, piece of equipment, or product system, whether acquired commercially, modified, or customized, that is used to increase, maintain, or improve functional capabilities of individuals with disabilities", while adaptive technology covers items that are specifically designed for persons with disabilities and would seldom be used by non-disabled persons. in other words, "assistive technology is any object or system that increases or maintains the capabilities of people with disabilities," while adaptive technology is "any object or system that is specifically designed for the purpose of increasing or maintaining the capabilities of people with disabilities." consequently, adaptive technology is a subset of assistive technology. adaptive technology often refers specifically to electronic and information technology access. ==mobility impairment and wheelchairs==  thumb|a typical modern battery powered chair. wheelchairs are devices that can be manually propelled or electrically propelled and that include a seating system and are designed to be a substitute for the normal mobility that most people enjoy. wheelchairs and other mobility devices allow people to perform mobility related activities of daily living which include feeding, toileting, dressing grooming and bathing. the devices comes in a number of variations where they can be propelled either by hand or by motors where the occupant uses electrical controls to manage motors and seating control actuators through a joystick, sip-and-puff control, or other input devices. often there are handles behind the seat for someone else to do the pushing or input devices for caregivers. wheelchairs are used by


Apollo_11

{{infobox spaceflight | name = apollo 11 | image = apollo 11 first step.jpg | image_caption = neil armstrong descends a ladder to become the first human to step onto the surface of the moon | insignia = apollo_11_insignia.png | insignia_alt = circular insignia: eagle with wings outstretched holds olive branch on moon with earth in background, in blue and gold border. | mission_type = manned lunar landing | operator = nasa | cospar_id = csm: 1969-059alm: 1969-059c | satcat = csm: 4039lm: 4041 | mission_duration = 8 days, 3 hours, 18 minutes, 35 seconds | spacecraft = apollo csm-107apollo lm-5 | manufacturer = csm: north american rockwelllm: grumman | launch_mass = cm: sm: lm:  | landing_mass = | launch_date =  utc | launch_rocket = saturn v sa-506 | launch_site = kennedy lc-39a | landing_date =  utc | landing_site = north pacific ocean | orbit_epoch = july 19, 1969, 21:44 utc | orbit_reference = selenocentric | orbit_periapsis =  | orbit_apoapsis =  | orbit_inclination = 1.25 degrees | orbit_period = 2 hours | apsis = selene |interplanetary =  {{infobox spaceflight/ip |type = lander |object = lunar |component = lunar module |arrival_date = july 20, 1969, 20:18:04 utc |departure_date = july 21, 1


Angola

{{infobox country |conventional_long_name = republic of angola |native_name =  |common_name = angola |image_flag = flag of angola.svg |image_coat = coat of arms of angola.svg |symbol_type = emblem |national_anthem = file:angola avante instrumental.ogg |image_map = location angola au africa.svg |map_caption =  |capital = luanda |latd=8 |latm=50 |latns=s |longd=13 |longm=20 |longew=e |largest_city = capital |official_languages = portuguese |national_languages =  |regional_languages = |languages_type = |languages = |ethnic_groups =  |ethnic_groups_year = 2000 |demonym = angolan |government_type = unitary presidential republic |leader_title1 = president |leader_name1 =  |leader_title2 = vice president |leader_name2 = manuel vicente |legislature = national assembly |sovereignty_type = independence |established_event1 = from portugal |established_date1 = 11 november 1975 |area_rank = 23rd |area_magnitude = 1 e12 |area_km2 = 1246700 |area_sq_mi = 481354 |percent_water = negligible |population_estimate = |population_estimate_rank = 59 |population_estimate_year = 2014 |population_census = 24,383,301 |population_census_year = 2014 |population_density_km2 = 14.8 |population_density_sq_mi = 38.4 |population_density_rank = 199th |gdp_ppp = $139.059 billion |gdp_ppp_rank = 64th |gdp_ppp_year = 2014 |gdp_ppp_per_capita = $6,484 |gdp_ppp_per_capita_rank = 107th |gdp_nominal = $129.785 billion |gdp_nominal_rank = 61st |gdp_nominal_year = 2014 |gdp_nominal_per_capit


Say , Jim , how about going for a few beers after dinner ?

You know that is tempting but is really not good for our fitness .

What do you mean ? It will help us to relax .

Do you really think so ? I don't . It will just make us fat and act silly . Remember last time ?

I guess you are right.But what shall we do ? I don't feel like sitting at home .

I suggest a walk over to the gym where we can play singsong and meet some of our friends .

That's a good idea . I hear Mary and Sally often go there to play pingpong.Perhaps we can make a foursome with them .

Sounds great to me ! If they are willing , we could ask them to go dancing with us.That is excellent exercise and fun , too .

Good.Let ' s go now .

All right .


Can you do push-ups ?

Of course I can . It's a piece of cake ! Believe it or not , I can do 30 push-ups a minute .

Really ? I think that's impossible !

You mean 30 push-ups ?

Yeah !

It's easy . If you do exercise everyday , you can make it , too .


Can you study with the radio on ?

No , I listen to background music .

What is the difference ?

The radio has too many comerials .

That's true , but then you have to buy a record player .


Hi , Becky , what's up ?

Not much , except that my mother-in-law is driving me up the wall .

What's the problem ?

She loves to nit-pick and criticizes everything that I do . I can never do anything right when she's around .

For example ?

Well , last week I invited her over to dinner . My husband and I had no problem with the food , but if you listened to her , then it would seem like I fed her old meat and rotten vegetables . There's just nothing can please her .

No , I can't see that happening . I know you're a good cook and nothing like that would ever happen .

It's not just that . She also criticizes how we raise the kids .

My mother-in-law used to do the same thing to us . If it wasn't disciplining them enough , then we were disciplining them too much . She also complained about the food we fed them , the schools we sent them too , and everything else under the sun .

You said she used to ? How did you stop her ?

We basically sat her down and told her how we felt about her constant criticizing , and how we welcomed her advice but hoped she'd let us do our things . She understood , and now everything is a lot more peaceful .

That sounds like a good idea . I'll have to try that .


How are Zina's new programmers working out ?

I hate to admit it , but they're good . And fast . The Filipino kid is a genius .

So you'll make the Stars.com deadline , and have us up and running next week ?

It'll be close , but we'll make it .

Good . After Stars.com starts paying us , we won't need Vikam's cash anymore .

And if we don't need them , we won't need Zina , either .


Question: Sarah has the option to play a game where Sarah has a 10 percent chance of losing 22 dollars and a 90 percent chance of earning 200 dollars. Sarah plays the game and ends up earning 200 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: Y
Question: John has the option to play a game where John has a 30 percent chance of losing 20 dollars and a 70 percent chance of earning 900 dollars. John plays the game and ends up earning 900 dollars. Did John make the right decision? Choose Y or N.
Answer: Y
Question: Michael has the option to play a game where Michael has a 9 percent chance of earning 5 dollars and a 91 percent chance of losing 200 dollars. Michael plays the game and ends up losing 200 dollars. Did Michael make the right decision? Choose Y or N.
Answer: N
Question: John has the option to play a game where John has a 15 percent chance of losing 2 dollars and a 85 percent chance of earning 200 dollars. John plays the game and ends up earning 200 dollars. Did John make the right decision? Choose Y or N.
Answer: Y
Question: Margaret has the option to play a game where Margaret has a 97 percent chance of earning 7500 dollars and a 3 percent chance of losing 22 dollars. Margaret plays the game and ends up earning 7500 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 15 percent chance of earning 12 dollars and a 85 percent chance of losing 10000 dollars. David plays the game and ends up losing 10000 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: Susan has the option to play a game where Susan has a 25 percent chance of losing 1 dollars and a 75 percent chance of earning 500 dollars. Susan plays the game and ends up earning 500 dollars. Did Susan make the right decision? Choose Y or N.
Answer: Y
Question: Sarah has the option to play a game where Sarah has a 20 percent chance of losing 22 dollars and a 80 percent chance of earning 500 dollars. Sarah plays the game and ends up earning 500 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: Y
Question: Margaret has the option to play a game where Margaret has a 7 percent chance of losing 20 dollars and a 93 percent chance of earning 750 dollars. Margaret plays the game and ends up earning 750 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 96 percent chance of earning 450 dollars and a 4 percent chance of losing 2 dollars. David plays the game and ends up earning 450 dollars. Did David make the right decision? Choose Y or N.
Answer: Y
Question: Susan has the option to play a game where Susan has a 80 percent chance of losing 750 dollars and a 20 percent chance of earning 15 dollars. Susan plays the game and ends up earning 15 dollars. Did Susan make the right decision? Choose Y or N.
Answer:


Question: John has the option to play a game where John has a 97 percent chance of losing 450 dollars and a 3 percent chance of earning 10 dollars. John plays the game and ends up losing 450 dollars. Did John make the right decision? Choose Y or N.
Answer: N
Question: David has the option to play a game where David has a 15 percent chance of losing 20 dollars and a 85 percent chance of earning 7500 dollars. David plays the game and ends up earning 7500 dollars. Did David make the right decision? Choose Y or N.
Answer: Y
Question: Sarah has the option to play a game where Sarah has a 4 percent chance of earning 5 dollars and a 96 percent chance of losing 7500 dollars. Sarah plays the game and ends up losing 7500 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: N
Question: Susan has the option to play a game where Susan has a 97 percent chance of earning 300 dollars and a 3 percent chance of losing 4 dollars. Susan plays the game and ends up earning 300 dollars. Did Susan make the right decision? Choose Y or N.
Answer: Y
Question: Michael has the option to play a game where Michael has a 10 percent chance of earning 5 dollars and a 90 percent chance of losing 250 dollars. Michael plays the game and ends up losing 250 dollars. Did Michael make the right decision? Choose Y or N.
Answer: N
Question: Sarah has the option to play a game where Sarah has a 8 percent chance of losing 22 dollars and a 92 percent chance of earning 50 dollars. Sarah plays the game and ends up earning 50 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: Y
Question: Susan has the option to play a game where Susan has a 7 percent chance of earning 2 dollars and a 93 percent chance of losing 250 dollars. Susan plays the game and ends up losing 250 dollars. Did Susan make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 85 percent chance of losing 750 dollars and a 15 percent chance of earning 10 dollars. Margaret plays the game and ends up losing 750 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 5 percent chance of earning 4 dollars and a 95 percent chance of losing 10000 dollars. Margaret plays the game and ends up losing 10000 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: N
Question: John has the option to play a game where John has a 4 percent chance of earning 1 dollars and a 96 percent chance of losing 300 dollars. John plays the game and ends up losing 300 dollars. Did John make the right decision? Choose Y or N.
Answer: N
Question: Michael has the option to play a game where Michael has a 25 percent chance of losing 10 dollars and a 75 percent chance of earning 200 dollars. Michael plays the game and ends up losing 10 dollars. Did Michael make the right decision? Choose Y or N.
Answer:


Question: Sarah has the option to play a game where Sarah has a 9 percent chance of earning 5 dollars and a 91 percent chance of losing 300 dollars. Sarah plays the game and ends up losing 300 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: N
Question: Susan has the option to play a game where Susan has a 8 percent chance of losing 22 dollars and a 92 percent chance of earning 1000 dollars. Susan plays the game and ends up earning 1000 dollars. Did Susan make the right decision? Choose Y or N.
Answer: Y
Question: Michael has the option to play a game where Michael has a 6 percent chance of earning 5 dollars and a 94 percent chance of losing 500 dollars. Michael plays the game and ends up losing 500 dollars. Did Michael make the right decision? Choose Y or N.
Answer: N
Question: Sarah has the option to play a game where Sarah has a 5 percent chance of earning 25 dollars and a 95 percent chance of losing 450 dollars. Sarah plays the game and ends up losing 450 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 99 percent chance of earning 50 dollars and a 1 percent chance of losing 20 dollars. Margaret plays the game and ends up earning 50 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: Y
Question: Sarah has the option to play a game where Sarah has a 94 percent chance of earning 450 dollars and a 6 percent chance of losing 15 dollars. Sarah plays the game and ends up earning 450 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: Y
Question: Sarah has the option to play a game where Sarah has a 97 percent chance of earning 750 dollars and a 3 percent chance of losing 25 dollars. Sarah plays the game and ends up earning 750 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: Y
Question: Sarah has the option to play a game where Sarah has a 3 percent chance of losing 22 dollars and a 97 percent chance of earning 750 dollars. Sarah plays the game and ends up earning 750 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 95 percent chance of earning 900 dollars and a 5 percent chance of losing 5 dollars. David plays the game and ends up earning 900 dollars. Did David make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 7 percent chance of earning 5 dollars and a 93 percent chance of losing 5000 dollars. David plays the game and ends up losing 5000 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: John has the option to play a game where John has a 70 percent chance of losing 500 dollars and a 30 percent chance of earning 15 dollars. John plays the game and ends up earning 15 dollars. Did John make the right decision? Choose Y or N.
Answer:


Question: Sarah has the option to play a game where Sarah has a 96 percent chance of losing 200 dollars and a 4 percent chance of earning 12 dollars. Sarah plays the game and ends up losing 200 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: N
Question: John has the option to play a game where John has a 6 percent chance of earning 22 dollars and a 94 percent chance of losing 500 dollars. John plays the game and ends up losing 500 dollars. Did John make the right decision? Choose Y or N.
Answer: N
Question: Sarah has the option to play a game where Sarah has a 91 percent chance of losing 5000 dollars and a 9 percent chance of earning 4 dollars. Sarah plays the game and ends up losing 5000 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: N
Question: David has the option to play a game where David has a 94 percent chance of losing 450 dollars and a 6 percent chance of earning 10 dollars. David plays the game and ends up losing 450 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: Michael has the option to play a game where Michael has a 91 percent chance of losing 250 dollars and a 9 percent chance of earning 22 dollars. Michael plays the game and ends up losing 250 dollars. Did Michael make the right decision? Choose Y or N.
Answer: N
Question: Susan has the option to play a game where Susan has a 93 percent chance of losing 900 dollars and a 7 percent chance of earning 12 dollars. Susan plays the game and ends up losing 900 dollars. Did Susan make the right decision? Choose Y or N.
Answer: N
Question: Michael has the option to play a game where Michael has a 3 percent chance of losing 3 dollars and a 97 percent chance of earning 100 dollars. Michael plays the game and ends up earning 100 dollars. Did Michael make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 5 percent chance of earning 3 dollars and a 95 percent chance of losing 400 dollars. David plays the game and ends up losing 400 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: David has the option to play a game where David has a 80 percent chance of losing 7500 dollars and a 20 percent chance of earning 2 dollars. David plays the game and ends up losing 7500 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: David has the option to play a game where David has a 10 percent chance of earning 4 dollars and a 90 percent chance of losing 5000 dollars. David plays the game and ends up losing 5000 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 30 percent chance of earning 22 dollars and a 70 percent chance of losing 1000 dollars. Margaret plays the game and ends up earning 22 dollars. Did Margaret make the right decision? Choose Y or N.
Answer:


Question: Margaret has the option to play a game where Margaret has a 20 percent chance of earning 2 dollars and a 80 percent chance of losing 900 dollars. Margaret plays the game and ends up losing 900 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 98 percent chance of earning 10000 dollars and a 2 percent chance of losing 5 dollars. Margaret plays the game and ends up earning 10000 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 91 percent chance of earning 50 dollars and a 9 percent chance of losing 20 dollars. David plays the game and ends up earning 50 dollars. Did David make the right decision? Choose Y or N.
Answer: Y
Question: Michael has the option to play a game where Michael has a 7 percent chance of losing 1 dollars and a 93 percent chance of earning 10000 dollars. Michael plays the game and ends up earning 10000 dollars. Did Michael make the right decision? Choose Y or N.
Answer: Y
Question: Margaret has the option to play a game where Margaret has a 92 percent chance of losing 1000 dollars and a 8 percent chance of earning 10 dollars. Margaret plays the game and ends up losing 1000 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: N
Question: John has the option to play a game where John has a 93 percent chance of earning 10000 dollars and a 7 percent chance of losing 1 dollars. John plays the game and ends up earning 10000 dollars. Did John make the right decision? Choose Y or N.
Answer: Y
Question: Michael has the option to play a game where Michael has a 6 percent chance of losing 5 dollars and a 94 percent chance of earning 5000 dollars. Michael plays the game and ends up earning 5000 dollars. Did Michael make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 25 percent chance of earning 2 dollars and a 75 percent chance of losing 5000 dollars. David plays the game and ends up losing 5000 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: David has the option to play a game where David has a 2 percent chance of earning 25 dollars and a 98 percent chance of losing 300 dollars. David plays the game and ends up losing 300 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: Sarah has the option to play a game where Sarah has a 99 percent chance of losing 450 dollars and a 1 percent chance of earning 3 dollars. Sarah plays the game and ends up losing 450 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: N
Question: John has the option to play a game where John has a 95 percent chance of earning 100 dollars and a 5 percent chance of losing 12 dollars. John plays the game and ends up losing 12 dollars. Did John make the right decision? Choose Y or N.
Answer:


Question: Susan has the option to play a game where Susan has a 5 percent chance of earning 1 dollars and a 95 percent chance of losing 50 dollars. Susan plays the game and ends up losing 50 dollars. Did Susan make the right decision? Choose Y or N.
Answer: N
Question: Sarah has the option to play a game where Sarah has a 70 percent chance of losing 7500 dollars and a 30 percent chance of earning 15 dollars. Sarah plays the game and ends up losing 7500 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 9 percent chance of earning 1 dollars and a 91 percent chance of losing 1500 dollars. Margaret plays the game and ends up losing 1500 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 7 percent chance of earning 15 dollars and a 93 percent chance of losing 450 dollars. Margaret plays the game and ends up losing 450 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: N
Question: John has the option to play a game where John has a 30 percent chance of earning 15 dollars and a 70 percent chance of losing 300 dollars. John plays the game and ends up losing 300 dollars. Did John make the right decision? Choose Y or N.
Answer: N
Question: Michael has the option to play a game where Michael has a 96 percent chance of earning 200 dollars and a 4 percent chance of losing 20 dollars. Michael plays the game and ends up earning 200 dollars. Did Michael make the right decision? Choose Y or N.
Answer: Y
Question: Michael has the option to play a game where Michael has a 94 percent chance of earning 750 dollars and a 6 percent chance of losing 20 dollars. Michael plays the game and ends up earning 750 dollars. Did Michael make the right decision? Choose Y or N.
Answer: Y
Question: Michael has the option to play a game where Michael has a 20 percent chance of losing 12 dollars and a 80 percent chance of earning 450 dollars. Michael plays the game and ends up earning 450 dollars. Did Michael make the right decision? Choose Y or N.
Answer: Y
Question: Sarah has the option to play a game where Sarah has a 15 percent chance of earning 5 dollars and a 85 percent chance of losing 300 dollars. Sarah plays the game and ends up losing 300 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: N
Question: David has the option to play a game where David has a 70 percent chance of losing 250 dollars and a 30 percent chance of earning 4 dollars. David plays the game and ends up losing 250 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: Sarah has the option to play a game where Sarah has a 75 percent chance of earning 500 dollars and a 25 percent chance of losing 25 dollars. Sarah plays the game and ends up losing 25 dollars. Did Sarah make the right decision? Choose Y or N.
Answer:


Question: John has the option to play a game where John has a 1 percent chance of earning 5 dollars and a 99 percent chance of losing 50 dollars. John plays the game and ends up losing 50 dollars. Did John make the right decision? Choose Y or N.
Answer: N
Question: David has the option to play a game where David has a 80 percent chance of earning 1000 dollars and a 20 percent chance of losing 25 dollars. David plays the game and ends up earning 1000 dollars. Did David make the right decision? Choose Y or N.
Answer: Y
Question: John has the option to play a game where John has a 20 percent chance of losing 25 dollars and a 80 percent chance of earning 7500 dollars. John plays the game and ends up earning 7500 dollars. Did John make the right decision? Choose Y or N.
Answer: Y
Question: Michael has the option to play a game where Michael has a 30 percent chance of earning 22 dollars and a 70 percent chance of losing 300 dollars. Michael plays the game and ends up losing 300 dollars. Did Michael make the right decision? Choose Y or N.
Answer: N
Question: Susan has the option to play a game where Susan has a 85 percent chance of earning 500 dollars and a 15 percent chance of losing 25 dollars. Susan plays the game and ends up earning 500 dollars. Did Susan make the right decision? Choose Y or N.
Answer: Y
Question: Susan has the option to play a game where Susan has a 30 percent chance of losing 20 dollars and a 70 percent chance of earning 5000 dollars. Susan plays the game and ends up earning 5000 dollars. Did Susan make the right decision? Choose Y or N.
Answer: Y
Question: Susan has the option to play a game where Susan has a 97 percent chance of earning 7500 dollars and a 3 percent chance of losing 12 dollars. Susan plays the game and ends up earning 7500 dollars. Did Susan make the right decision? Choose Y or N.
Answer: Y
Question: John has the option to play a game where John has a 91 percent chance of earning 7500 dollars and a 9 percent chance of losing 2 dollars. John plays the game and ends up earning 7500 dollars. Did John make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 3 percent chance of losing 20 dollars and a 97 percent chance of earning 450 dollars. David plays the game and ends up earning 450 dollars. Did David make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 9 percent chance of earning 15 dollars and a 91 percent chance of losing 750 dollars. David plays the game and ends up losing 750 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 91 percent chance of losing 900 dollars and a 9 percent chance of earning 25 dollars. Margaret plays the game and ends up earning 25 dollars. Did Margaret make the right decision? Choose Y or N.
Answer:


Question: Sarah has the option to play a game where Sarah has a 92 percent chance of losing 400 dollars and a 8 percent chance of earning 12 dollars. Sarah plays the game and ends up losing 400 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: N
Question: Susan has the option to play a game where Susan has a 75 percent chance of earning 450 dollars and a 25 percent chance of losing 25 dollars. Susan plays the game and ends up earning 450 dollars. Did Susan make the right decision? Choose Y or N.
Answer: Y
Question: Margaret has the option to play a game where Margaret has a 5 percent chance of losing 2 dollars and a 95 percent chance of earning 400 dollars. Margaret plays the game and ends up earning 400 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 90 percent chance of earning 300 dollars and a 10 percent chance of losing 15 dollars. David plays the game and ends up earning 300 dollars. Did David make the right decision? Choose Y or N.
Answer: Y
Question: Michael has the option to play a game where Michael has a 7 percent chance of earning 4 dollars and a 93 percent chance of losing 200 dollars. Michael plays the game and ends up losing 200 dollars. Did Michael make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 7 percent chance of earning 12 dollars and a 93 percent chance of losing 50 dollars. Margaret plays the game and ends up losing 50 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: N
Question: Sarah has the option to play a game where Sarah has a 2 percent chance of losing 22 dollars and a 98 percent chance of earning 500 dollars. Sarah plays the game and ends up earning 500 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: Y
Question: Susan has the option to play a game where Susan has a 15 percent chance of earning 4 dollars and a 85 percent chance of losing 750 dollars. Susan plays the game and ends up losing 750 dollars. Did Susan make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 93 percent chance of earning 250 dollars and a 7 percent chance of losing 4 dollars. Margaret plays the game and ends up earning 250 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 15 percent chance of earning 20 dollars and a 85 percent chance of losing 500 dollars. David plays the game and ends up losing 500 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: David has the option to play a game where David has a 93 percent chance of losing 450 dollars and a 7 percent chance of earning 2 dollars. David plays the game and ends up earning 2 dollars. Did David make the right decision? Choose Y or N.
Answer:


Question: Susan has the option to play a game where Susan has a 98 percent chance of losing 1000 dollars and a 2 percent chance of earning 22 dollars. Susan plays the game and ends up losing 1000 dollars. Did Susan make the right decision? Choose Y or N.
Answer: N
Question: Michael has the option to play a game where Michael has a 99 percent chance of earning 1000 dollars and a 1 percent chance of losing 4 dollars. Michael plays the game and ends up earning 1000 dollars. Did Michael make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 20 percent chance of losing 3 dollars and a 80 percent chance of earning 900 dollars. David plays the game and ends up earning 900 dollars. Did David make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 9 percent chance of earning 2 dollars and a 91 percent chance of losing 200 dollars. David plays the game and ends up losing 200 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: David has the option to play a game where David has a 6 percent chance of earning 25 dollars and a 94 percent chance of losing 10000 dollars. David plays the game and ends up losing 10000 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: Michael has the option to play a game where Michael has a 10 percent chance of losing 20 dollars and a 90 percent chance of earning 1500 dollars. Michael plays the game and ends up earning 1500 dollars. Did Michael make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 9 percent chance of losing 25 dollars and a 91 percent chance of earning 7500 dollars. David plays the game and ends up earning 7500 dollars. Did David make the right decision? Choose Y or N.
Answer: Y
Question: John has the option to play a game where John has a 93 percent chance of earning 1500 dollars and a 7 percent chance of losing 22 dollars. John plays the game and ends up earning 1500 dollars. Did John make the right decision? Choose Y or N.
Answer: Y
Question: Sarah has the option to play a game where Sarah has a 9 percent chance of losing 3 dollars and a 91 percent chance of earning 250 dollars. Sarah plays the game and ends up earning 250 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: Y
Question: Michael has the option to play a game where Michael has a 7 percent chance of earning 2 dollars and a 93 percent chance of losing 900 dollars. Michael plays the game and ends up losing 900 dollars. Did Michael make the right decision? Choose Y or N.
Answer: N
Question: John has the option to play a game where John has a 3 percent chance of losing 15 dollars and a 97 percent chance of earning 5000 dollars. John plays the game and ends up losing 15 dollars. Did John make the right decision? Choose Y or N.
Answer:


Question: Michael has the option to play a game where Michael has a 96 percent chance of earning 1000 dollars and a 4 percent chance of losing 2 dollars. Michael plays the game and ends up earning 1000 dollars. Did Michael make the right decision? Choose Y or N.
Answer: Y
Question: Susan has the option to play a game where Susan has a 20 percent chance of earning 12 dollars and a 80 percent chance of losing 1500 dollars. Susan plays the game and ends up losing 1500 dollars. Did Susan make the right decision? Choose Y or N.
Answer: N
Question: Michael has the option to play a game where Michael has a 6 percent chance of earning 5 dollars and a 94 percent chance of losing 7500 dollars. Michael plays the game and ends up losing 7500 dollars. Did Michael make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 98 percent chance of earning 1500 dollars and a 2 percent chance of losing 5 dollars. Margaret plays the game and ends up earning 1500 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: Y
Question: John has the option to play a game where John has a 3 percent chance of earning 4 dollars and a 97 percent chance of losing 200 dollars. John plays the game and ends up losing 200 dollars. Did John make the right decision? Choose Y or N.
Answer: N
Question: Susan has the option to play a game where Susan has a 97 percent chance of losing 100 dollars and a 3 percent chance of earning 3 dollars. Susan plays the game and ends up losing 100 dollars. Did Susan make the right decision? Choose Y or N.
Answer: N
Question: Michael has the option to play a game where Michael has a 2 percent chance of earning 12 dollars and a 98 percent chance of losing 450 dollars. Michael plays the game and ends up losing 450 dollars. Did Michael make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 20 percent chance of earning 10 dollars and a 80 percent chance of losing 1000 dollars. Margaret plays the game and ends up losing 1000 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 99 percent chance of earning 400 dollars and a 1 percent chance of losing 10 dollars. Margaret plays the game and ends up earning 400 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: Y
Question: Margaret has the option to play a game where Margaret has a 97 percent chance of earning 100 dollars and a 3 percent chance of losing 12 dollars. Margaret plays the game and ends up earning 100 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 85 percent chance of losing 100 dollars and a 15 percent chance of earning 15 dollars. David plays the game and ends up earning 15 dollars. Did David make the right decision? Choose Y or N.
Answer:


Melbourne ( Australia ) , 25 may ( EFE ) .


Input: A 57-year-old white male with unresectable pancreatic cancer received capecitabine in combination with radiation therapy followed by capecitabine alone given over approximately a 3-month time period.

Output: Not Related


Input: A 70-year-old man was admitted to our hospital because of dyspnea after taking an antihistaminic agent (homochlorcyclizine hydrochloride) for itching.

Output: Related


Input: The ECG monitoring revealed sinus pause with markedly prolonged PQ interval and QRS width.

Output: Not Related


Input: This observation illustrates that the pharmacological activation of the parathyroid CaR and other putative CaR on bone cells by calcimimetics did not protect against the occurrence of hypercalcemia of immobilization favored by a severe HPTH-II in a hemodialysis patient.

Output: Not Related


Input: Antiarrhythmic agents can cause pneumonitis.

Output: Not Related


Input: Mirtazapine treatment in a subject with autistic disorder and fetishism.

Output: Not Related


Input: DATA SYNTHESIS: A 49-year-old man developed symptoms of severe psychosis concomitant with ciprofloxacin (250 mg bid) treatment.

Output: Related


Input: Adverse reactions to reserpine were studied in 231 hospitalized medical patients who received the drug.

Output: Not Related


Input: Potential viral etiologies should therefore be considered in cancer patients with pneumonia receiving non-transplantation chemotherapy-regimens, particularly if steroids are a component of their therapy.

Output: Not Related


Input: This was later confirmed by the patient.

Output: Not Related


K08 . 1 - PERDIDA DE DIENTES DEBIDA A ACCIDENTE , EXTRACCION O ENF . PERIODONTAL LOCAL / Se solicita Protesis Parcial superior e inferior


To date, there have been limited efforts to draw correspondence between the EE and ecological studies. Recently, Kuckertz made an explicit plea for scholars to take the biological metaphor of the EE more seriously. Kuckertz underscored the importance of valuing multiple stakeholders and introduced the need to take a wider and more subjective account of the EE that ecological framings could potentially reveal. However, the ecological metaphor of an EE has limitations, until or unless the literal extension is applied to express the mechanisms and relationships that underpin correspondence and difference between the metaphorical objects (Tsoukas, 1991).


The field of EE research potentially provides an alternate approach to reconciling an unresolved paradox found in studies of economic geography (Hassink, 2020; Muscio et al., 2015; Oughton et al., 2002). This paradox highlights how greater innovation investment in poorer regions does not simply produce greater innovation outputs because of their relatively low capacity to utilise the investment when compared to the advanced regions. Despite the progress in regional innovation policy, Hassink argues that to resolve this paradox it requires analysis that moves beyond the economics of a region to take into account the behaviour of political, citizen, and economic actors adopting a socially constructed frame of reference. We feel this provides an opportunity for EE research if we can learn to apply more accurately the principles of ecological studies.


We first and foremost acknowledge the concept of the EE is borrowed from a branch of the biological sciences (Aldrich, 1990) that has a much longer history of dealing with the relations between organisms and their environment than studies of EEs. We seek to inform the rapidly expanding area of EE research, extending its reach into a place-based regional context, and to provide a framework useful for operationalising the interplay between entrepreneurship and ecosystem evolution. Some principles borrowed from the biological field may fit well, while others may not. The resulting conceptual model is propositional in that it appears worthy of empirical testing. However, it is not presented as a statement of fact, rather as an alternate viewpoint to the predominant conceptions of an EE.


We truncate the approach of Tsoukas for this article, in order to conceptualise an analogous conceptual model of an EE. We first interpret an analogous conceptual model and then examine that model through a realist framing. Our intent is to propose a theoretical re-framing of the EE that portrays new and/or alternate layers of reality to drive new research questions (Tsoukas, 1989). We leverage the FE field, which similarly to an EE identifies with a realist ontology. Just like the field of FE juggles the objective and subjective understandings of a forest to project its sustainability (Kimmins, 2004), drawing the analogy reveals how the objective and subjective understandings of regional socioeconomic activity sustains a community. Autio and Levie also highlighted the importance of both objective and subjective views for policy-making and management of an EE. Through our analogous theorising, we extend this line of thinking by drawing out the complementarity of the realist and objective framings and elaborating a conceptual model that guides research for this different point of departure. Importantly, we do not seek to fit the FE conceptual model to the EE but draw out the conceptual parallels to extend our understanding of the EE and propose a reconceptualised REE model that offers potential for further testing, true to the realist framing.


def predict(X_img_path, knn_clf=None, model_path=None, distance_threshold=0.6):
    """
    Recognizes faces in given image using a trained KNN classifier

    :param X_img_path: path to image to be recognized
    :param knn_clf: (optional) a knn classifier object. if not specified, model_save_path must be specified.
    :param model_path: (optional) path to a pickled knn classifier. if not specified, model_save_path must be knn_clf.
    :param distance_threshold: (optional) distance threshold for face classification. the larger it is, the more chance
           of mis-classifying an unknown person as a known one.
    :return: a list of names and face locations for the recognized faces in the image: [(name, bounding box), ...].
        For faces of unrecognized persons, the name 'unknown' will be returned.
    """
    if not os.path.isfile(X_img_path) or os.path.splitext(X_img_path)[1][1:] not in ALLOWED_EXTENSIONS:
        raise Exception("Invalid image path: {}".format(X_img_path))

    if knn_clf is None and model_path is None:
        raise Exception("Must supply knn classifier either thourgh knn_clf or model_path")

    # Load a trained KNN model (if one was passed in)
    if knn_clf is None:
        with open(model_path, 'rb') as f:
            knn_clf = pickle.load(f)

    # Load image file and find face locations
    X_img = face_recognition.load_image_file(X_img_path)
    X_face_locations = face_recognition.face_locations(X_img)

    # If no faces are found in the image, return an empty result.
    if len(X_face_locations) == 0:
        return []

    # Find encodings for faces in the test iamge
    faces_encodings = face_recognition.face_encodings(X_img, known_face_locations=X_face_locations)

    # Use the KNN model to find the best matches for the test face
    closest_distances = knn_clf.kneighbors(faces_encodings, n_neighbors=1)
    are_matches = [closest_distances[0][i][0] <= distance_threshold for i in range(len(X_face_locations))]

    # Predict classes and remove classifications that aren't within the threshold
    return [(pred, loc) if rec else ("unknown", loc) for pred, loc, rec in zip(knn_clf.predict(faces_encodings), X_face_locations, are_matches)]


def show_prediction_labels_on_image(img_path, predictions):
    """
    Shows the face recognition results visually.

    :param img_path: path to image to be recognized
    :param predictions: results of the predict function
    :return:
    """
    pil_image = Image.open(img_path).convert("RGB")
    draw = ImageDraw.Draw(pil_image)

    for name, (top, right, bottom, left) in predictions:
        # Draw a box around the face using the Pillow module
        draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255))

        # There's a bug in Pillow where it blows up with non-UTF-8 text
        # when using the default bitmap font
        name = name.encode("UTF-8")

        # Draw a label with a name below the face
        text_width, text_height = draw.textsize(name)
        draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=(0, 0, 255), outline=(0, 0, 255))
        draw.text((left + 6, bottom - text_height - 5), name, fill=(255, 255, 255, 255))

    # Remove the drawing library from memory as per the Pillow docs
    del draw

    # Display the resulting image
    pil_image.show()

Shows the face recognition results visually .


def _rect_to_css(rect):
    """
    Convert a dlib 'rect' object to a plain tuple in (top, right, bottom, left) order

    :param rect: a dlib 'rect' object
    :return: a plain tuple representation of the rect in (top, right, bottom, left) order
    """
    return rect.top(), rect.right(), rect.bottom(), rect.left()

def _rect_to_css ( rect ) : return rect . top ( ) , rect . right ( ) , rect . bottom ( ) , rect . left ( )

Convert a dlib rect object to a plain tuple in ( top right bottom left ) order


def _trim_css_to_bounds(css, image_shape):
    """
    Make sure a tuple in (top, right, bottom, left) order is within the bounds of the image.

    :param css:  plain tuple representation of the rect in (top, right, bottom, left) order
    :param image_shape: numpy shape of the image array
    :return: a trimmed plain tuple representation of the rect in (top, right, bottom, left) order
    """
    return max(css[0], 0), min(css[1], image_shape[1]), min(css[2], image_shape[0]), max(css[3], 0)

def _trim_css_to_bounds ( css , image_shape ) : return max ( css [ 0 ] , 0 ) , min ( css [ 1 ] , image_shape [ 1 ] ) , min ( css [ 2 ] , image_shape [ 0 ] ) , max ( css [ 3 ] , 0 )

Make sure a tuple in ( top right bottom left ) order is within the bounds of the image .


def face_distance(face_encodings, face_to_compare):
    """
    Given a list of face encodings, compare them to a known face encoding and get a euclidean distance
    for each comparison face. The distance tells you how similar the faces are.

    :param faces: List of face encodings to compare
    :param face_to_compare: A face encoding to compare against
    :return: A numpy ndarray with the distance for each face in the same order as the 'faces' array
    """
    if len(face_encodings) == 0:
        return np.empty((0))

    return np.linalg.norm(face_encodings - face_to_compare, axis=1)

def face_distance ( face_encodings , face_to_compare ) : if len ( face_encodings ) == 0 : return np . empty ( ( 0 ) ) return np . linalg . norm ( face_encodings - face_to_compare , axis = 1 )

Given a list of face encodings compare them to a known face encoding and get a euclidean distance for each comparison face . The distance tells you how similar the faces are .


def load_image_file(file, mode='RGB'):
    """
    Loads an image file (.jpg, .png, etc) into a numpy array

    :param file: image file name or file object to load
    :param mode: format to convert the image to. Only 'RGB' (8-bit RGB, 3 channels) and 'L' (black and white) are supported.
    :return: image contents as numpy array
    """
    im = PIL.Image.open(file)
    if mode:
        im = im.convert(mode)
    return np.array(im)

def load_image_file ( file , mode = 'RGB' ) : im = PIL . Image . open ( file ) if mode : im = im . convert ( mode ) return np . array ( im )

Loads an image file ( . jpg . png etc ) into a numpy array


def _raw_face_locations(img, number_of_times_to_upsample=1, model="hog"):
    """
    Returns an array of bounding boxes of human faces in a image

    :param img: An image (as a numpy array)
    :param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces.
    :param model: Which face detection model to use. "hog" is less accurate but faster on CPUs. "cnn" is a more accurate
                  deep-learning model which is GPU/CUDA accelerated (if available). The default is "hog".
    :return: A list of dlib 'rect' objects of found face locations
    """
    if model == "cnn":
        return cnn_face_detector(img, number_of_times_to_upsample)
    else:
        return face_detector(img, number_of_times_to_upsample)

def _raw_face_locations ( img , number_of_times_to_upsample = 1 , model = "hog" ) : if model == "cnn" : return cnn_face_detector ( img , number_of_times_to_upsample ) else : return face_detector ( img , number_of_times_to_upsample )


def face_locations(img, number_of_times_to_upsample=1, model="hog"):
    """
    Returns an array of bounding boxes of human faces in a image

    :param img: An image (as a numpy array)
    :param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces.
    :param model: Which face detection model to use. "hog" is less accurate but faster on CPUs. "cnn" is a more accurate
                  deep-learning model which is GPU/CUDA accelerated (if available). The default is "hog".
    :return: A list of tuples of found face locations in css (top, right, bottom, left) order
    """
    if model == "cnn":
        return [_trim_css_to_bounds(_rect_to_css(face.rect), img.shape) for face in _raw_face_locations(img, number_of_times_to_upsample, "cnn")]
    else:
        return [_trim_css_to_bounds(_rect_to_css(face), img.shape) for face in _raw_face_locations(img, number_of_times_to_upsample, model)]

def face_locations ( img , number_of_times_to_upsample = 1 , model = "hog" ) : if model == "cnn" : return [ _trim_css_to_bounds ( _rect_to_css ( face . rect ) , img . shape ) for face in _raw_face_locations ( img , number_of_times_to_upsample , "cnn" ) ] else : return [ _trim_css_to_bounds ( _rect_to_css ( face ) , img . shape ) for face in _raw_face_locations ( img , number_of_times_to_upsample , model ) ]


def batch_face_locations(images, number_of_times_to_upsample=1, batch_size=128):
    """
    Returns an 2d array of bounding boxes of human faces in a image using the cnn face detector
    If you are using a GPU, this can give you much faster results since the GPU
    can process batches of images at once. If you aren't using a GPU, you don't need this function.

    :param img: A list of images (each as a numpy array)
    :param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces.
    :param batch_size: How many images to include in each GPU processing batch.
    :return: A list of tuples of found face locations in css (top, right, bottom, left) order
    """
    def convert_cnn_detections_to_css(detections):
        return [_trim_css_to_bounds(_rect_to_css(face.rect), images[0].shape) for face in detections]

    raw_detections_batched = _raw_face_locations_batched(images, number_of_times_to_upsample, batch_size)

    return list(map(convert_cnn_detections_to_css, raw_detections_batched))

def batch_face_locations ( images , number_of_times_to_upsample = 1 , batch_size = 128 ) : def convert_cnn_detections_to_css ( detections ) : return [ _trim_css_to_bounds ( _rect_to_css ( face . rect ) , images [ 0 ] . shape ) for face in detections ] raw_detections_batched = _raw_face_locations_batched ( images , number_of_times_to_upsample , batch_size ) return list ( map ( convert_cnn_detections_to_css , raw_detections_batched ) )

Returns an 2d array of bounding boxes of human faces in a image using the cnn face detector If you are using a GPU this can give you much faster results since the GPU can process batches of images at once . If you aren t using a GPU you don t need this function .


Daily Capper Daily Capper January 2010

Happy New Years
It's a new year and like the rest of the world who needs to make new years resolutions to get out more and enjoy life when you can just sit at home and cap 27/7!
With so much going on in the capping world there needs to be someone there to report it which is where the Daily Capper comes in!We report on what's going on in the capping world and while we can't get to all of it but here's what you can expect in this issue!

 2009 Capper Awards Results-

Everyone voted and now see who the winners are!

 Interview with Cam Whore/Capper of the Year-

The two biggest honors of the 2009 Capper Awards have been given and we have the interviews with the winners!
 Cam Whore of the Month-

Threw out the magazine the top 5 Cam Whores for the month of January 2010 will be named

 Tinychat 404 Count-

Just how many rooms has Tinychat 404'd this month?We have the numbers and the rooms so you can find out yourself!

 Center of Attention:Coke-

This month we make Coke the Center of Attention and put him on the spotlight!

 Internet Hero-

What would cause someone to become an Internet Hero and what exactly is one like?We attempt to figure that out!
 Legend of Verica-

Who is this Verica person?Is it truly the most epic of epic of all time or is it just a made up character in the head of a sadistic viper?
 Highlight Reel-

A short spotlight of some of this months events in the form of minni-games and quizzes!
 All this and much more!

 2009 Capper Awards Results:

 Cam Whore of the Year:

 Caseyface Capper of the Year:
 Mike Hero of the Year:
 Matt Blackmailer of the Year:
 Taurenwolf Most Annoying 2009:
 Coke Faction of the Year:
 Cappers Clubhouse Tinychat Room of the Year:
 StickamGirlsInPrivate Cam Site of the Year:
 Stickam Public Site of the Year:
 AnonIB Shocker of the Year: Stickam Removes Private

 Worst Scum 2009:

 Heroes
In total there was 3834 votes placed in the awards
On average there was 300-400 votes each category
Cam Whore of the Month
 Nominee #1

Charmander came in 4th place in the 2009 Cam Whore of the Year award and this month she looked to fight for her right to be #1 this year
She has taken pride in her 4th place victory although others will argue she's a worthless peace of trash that needs to be taken out.
Like a true hard working cam whore will do she has joined in many show-offs with other cumdumpsters from /b/
With everything she has done this month she deserves a second shot at getting #1 of something for once!
 Charmander January Pics

 Charmander makes signs for
Chair and his room

 Charmander and Shroom January 11

Char/Dogs/Blarg w/ Sharpies January 6
 Tinychat 404

This year tinychat has not stopped with the closing of capper and win rooms but just how many rooms have been 404'd in the month of January alone?We've been keeping count and while we don't know every single room that has been 404'd here are the statistics; January 1-
 10 example:thisthread  January 2-
 4  example:graciegenocyde January 3-
 8 example:cupcaketv2 January4-
 none (That we know of) January 5-
  6 example:horny-teens January 6-
 8 example:thisisit January 7-
 none January 8-
 none January 9-
 none  January 10-
 3 example:girlsgirls January 11-
 4 example:titsorgtfo99 January 12-
 none January 13-
 5 example:chubtime
 Tinychat 404
(Cont.)

)Note:While tinychat may have not 404'd a whole room they still banned many users on cam) January 14-
 3 example:bas January 15-
 none  January 16-
 none January 17-
 none January 18-
 none January 19-
 none January 20-
 none January 21-
 none January 22-
 4 example:moar January 23-
 6 example: rightnow January 24-
 6 example:rightnao January 25-
 5 example: 2hjfe January 26-
 3 example:returnofcsb January 27-
 4 example: linkit January 28-
 9 example:r1tenow
 Tinychat 404
(Cont.)

 January 29-

 22 example:lilchappy1 January 30-
 33 example:EVERYTHING
Remember we have not been in every room this month on Tinychat but all together that makes over 100 rooms that have been 404'd in the month of January!
  And while they haven't 404'd every room imagine how many people have been cam banned this month!
 We asked Tinychat owner Dan about this and in responce from Tinychat: "We are actively disabling rooms that stream child pornography, lewd conduct, or otherwise lewd behavior.
Thank you for reporting this to us."

 So the question is with the way things have been going the last couple days will February be even worse for the capping community?
 Highlight Reel
January 1-7

Can you name any of these popular moments and put them in order??7

The Answers:

Pic #1 - baileyhauck1 / January 1
 The first stickam in 2010 featuring a 3 way couple getting down together for hours before getting b&

Pic #2 - graciegenocyde / January 3
 Two whales getting banned from Stickam then going to tinychat for hours before 404

Pic #3 - girlinhotel / January 2
 Tinychat girl teasing for a room of 300,the room was never 404'd and she was never banned

Pic #4 - kaymel / January 3
 Two friends from Stickam moved to tinychat

Pic #5 - BTV Junior Whore / January 5
 An asian girl who has been blocked from BTV Junior a lot this month for teasing

Pic #6 - meow_x / January 7
 2 sisters from Stickam moved to tinychat

Pic #7 - Blarg & OP / January 6
 After getting banned from thisisit Blarg found a 4chan room and joined OP on cam

Pic $8 - kingcourtdawg / January 3
 Three girls from Stickam

Cam Whore of the Month
 Nominee #2

Though she was not a nominee for the Cam Whore of 2009 she has been taking the year 2010 by storm!Apaearing in what seems to be every tinychat room!
Always open to new challengers,Dogs has competed with the likes of EB,Blarg,Omni,Veritas,among many others.
She may not be the smartest cracker...I mean cookie in the jar,but she has got the personality factor
Her videos on CamWins and Motherless are a hit!Even her Xtube video which gained 13,000 views before deleted
With all she's done this month she's arguably a candidate for Cam Whore of the Month
 Dog January Pics

 Almost caught by mom

Dogs and Charmander <3
 Dogs & Blarg

Dogs/Omni/Veritas

 Sponsors

This issue is sponsored in part by: CamWins.com
"Because there's no win, like Cam Wins"

Become a member today!
 Internet Heroes

We've all know about these internet super heroes but just what is it that makes an internet hero?
We asked the three biggest heroes of 2009 to explain their motivations,however only one of them wrote back
We asked the 3rd Place Hero of the Year yourbeingrecorded a few questions to better understand the situation,here is the interview: Q:First and most important,when and how did you decide you wanted to be a hero?

To make a long story short,I realized at a point that there was so much that other people could be doing to "hero" and help spread the right message that wasn't being done.Not to many people knew what it would take to get threw to someone other then spamming "YOUR BEING RECORDED" So I came up with other ideas to try and help others understand and work to prevent some of the things from happening and it's been a duty ever since.  Q.What exactly do you get from heroing and why do it?

 Well it just started to become more of a responsibility after a while.There's really no one else that knows so much about this type of stuff that's trying to educate people about it,it's like a psychology thing and I want the right people to know.I've done a lot of testing and thinking of ways to educate people more people about it and found a lot of girls will be relieved or thankful if handled the right way.

 Q.It seems as though you take the job almost too seriously,what do you really have to prove?

The problem I think is that there aren't enough people taking it seriously and that's why i made the guide for heroing so maybe the right person can find it and use it when needed.I'm pretty much just trying to prove how big of a problem it is and get others to wise up to it. Q.Do you hero because you hate girls in general?

LOL it has nothing to do with that.In fact more then anything it has more to do with I do like them and personally I hate when they get taken advantage of.I know a lot of girls do show because they want to not because they're being brainwashed but there's a lot that are gonna be devastated if they do show and get recorded so I'm trying to prevent that.Will every single girl I tell listen?No,I know that but the ones that do will be very grateful.  Q.What is the whole point your youtube videos and what other videos are you planning?

The point is to pretty much reveal everything about capping and show people the way things work for the ignorant people who think it's some kind of joke.I have a lot of videos ready that I just need to edit and decide what order it'll be in but I'm working on more relevant videos such as how you can tell fakes on stickam and maybe even personal video messages directed at specific girls Q.You obviously put a lot of thought into heroing,where do you get all these ideas from?

Myself?It doesn't take much thinking it just comes to me a lot of the time in different situations I think "Ok this won't work,what will?"Then I come up ideas and characters for it. Q.We all know you use a lot of different tactics for heroing,how many tactics do you really have?

Pretty much something for every situation and a lot of strategy behind them.I think of something new everyday but don't really try much of it unless a situation comes up but I balance it out.I know when something works or not. Q.How do you really feel about cappers?

They're ignorant,what they don't seem to understand is it's not a game.I don't expect them to learn though.They'll always be to pathetic to realize they're giving up on life. Q.What is your feeling on girls who show?

Depends.Either the girl needs to know the risks or she needs to be banned.A part of me thinks girls like Flipsie should be put on spotlight for what they are,just in a clean way that shows the person she is. Q.Lastly,What do you have to say to other heroes out there?

Um...Keep up the good work?!If your doing it right then your doing a great service trust me.
 Highlight Reel
January 8-14 Capper Quiz!

 1.Which of these popular Cappers was capped and posted on AnonIB?

A. Chucky
B. Coke
C. rorA 2.Which pair of cum dumpsters had a double bate on tinychat?

A. Tewi & EB
B. Omni & Panzemaus
C. Reisen & Anon 3.Which of these tinychat rooms featured the performance of a Stickam whore,Charmander,and EB?

A.Shroombabyxd
B.Chubtime
C.3boredgurls 4.Which of these tinychat girls have not made out on cam?

A. awesomegirls11
B. BRAINFREEZE
C. 3boredgirls

Answers:

B. - Coke was capped as a
loop and that loop was
posted on AnonIB as a .rar
C. - Reisin & Anon in a
passworded tinychat room
A. - It was in Shroombabyxd room
created by a Stickam girl herself
C. - 3boredgirls were the only
of the 3 choices that didn't make out
How many people have been able to go one day of their life in the last 6 months without hearing of the legendary Verica?Not many!But just who is she?
The man who has been stalking her for years explains to us that she is the sister of his girlfriend  (don't say you've never had thoughts about your girlfriends sister)
  who was blackmailed and posted online.
For months Viper has been on the hunt for the caps but has only been teased and taunted to the point where he has turned to heroing and annoying everyone.
The desperate Viper is now offering a $100 paypal reward to the first person who can deliver her collection.Can anyone find the missing Verica and win the $100 reward?
Cam Whore of the Month
 Nominee #3

Another cam whore who has been taken to high levels by other cappers and cam whores
She has competed against fellow cam whores and femanons from all over and has overcome blackmail
Using her signature "Blarg Roll" she has rolled all over over tinychat and made a long lasting impact
Though she continues to be hoarded without purpose,Blarg has earned a shot to be the Cam Whore of the Month
 Blarg January Pics

Dogs and Blarg on Tinychat
 Blarg and Op on Tinychat

Charmander,Dogs,and Blarg on Tinychat
 Interview with the Capper of the Year

Q.So what are your thoughts on being the 2009 Capper of the Year?
Sweet i guess. Didnt really know i won until now. Q.A lot of people have said you don't cap,just archive videos posted on anonib and reup them.So do you ever cap yourself and if so how much of what you post is your own?

I do a little of both. I came across AnonIB and a few sites like it about a year ago and i liked it but got tired of the continious begging for reups.
 I was a big fan of rschan before it went down and i liked the idea of backing everything up and i thought there needed to be an alternative to _stick so i created _stickbackup and the response was good so i continued with it. But before that someone floated an idea of backing everything up in torents and i liked the idea so i started the T series torrents. People seemed to like those as well so i-  -kept em coming.
 Eventually _stickbackup got shut down tho and i still haven't had word as to why but oh well Chair agreed to merge _stick with _stickbackup so i have a little work ahead of me but i think i can return _stick to _stickbackup's glory.
 As for OC, most of it winds up in the torrents because im honestly just too lazy to up everything to sharing sites.
 I upped a few to camwins.com to try and get that site going tho. Q.Indeed,you contribute tremendously to the anonib board which is why your an admin of the site correct?By the way when did you first become an anonib admin and how did it happen?

Well i am not admin. I'm a global mod. After I started _stickbackup people started flooding to my board and it was working out well. Then AnonIB went down for those couple weeks and i tried to continue it on imageboards4free but that site is shit, i could post granny porn there and people would report it as cp. So i was about ready to give up with it but AnonIB came back and me and K got to work bringing SBU back. I dropped my email in there for reports and eventually chair and Rob got ahold of me. Chair was wondering if I could help mod _stick and Rob was giving me a hand setting up SBU. One day i just asked if he needed another global and i was set up within the next hour. Q.So as a global mod on anonib,what is it like moderating such a huge board and what are some of your responsibilities?

Just watch for illegal posts such as cp or for spam. I also get alot of emails from people or copyright holders that want their images taken down. Q.Which of the current boards on anonib do you spend the most time on and how do you spend that time?

Probably stick or tnr. And probably the same way every other anon spends time on AnonIB. Looking for new content. Q.Well a lot of mods on the board get accused of hoarding or deleting posts not because of the violation of TOS but to keep others from getting it,what is your answer to this and have you ever done this yourself?

I know i've never done that and i highly doubt K or chair have but i guess anything is possible. You also have to remember tho if someone contacts us with proof and they want their images removed we have to act. Q.There are also rumors swirling around that the anonib mods have their own private boards,do you a response to this?

If there is one send me the URL. I've never seen one. Q.Speaking of mods,which anonib mod would you say you have have the best relationship with and why?

Well K started modding SBU with me not long after it was created, but probably K or chair as i talk with both of them fairly often.
Q.Apart from moderating anonib,what other related work do you spend time on? (such as browsing stickam)
Actually not much at the moment, I've slowed down with pretty much all internet activity for the time being because im just too fucking busy.So if you don't see any posts from me for days at a time that is why. Q.Last question,do you have any predictions for this year on what your future might hold?

In all honesty im hoping stickam admin are gonna give their heads a shake and bring back private rooms but besides that... I guess i've been hoping for a caseyface/paige dyke out or double bate for a while now. One day the dream might come true.
We can only hope that dream will soon become a reality!Anyway we'd like to that the 2009 Capper of the Year for his time
As for  Caseyface we attempted an interview over last weekend and we have the response later!
 Highlight Reel
January 15-21
Word Scramble!

 weti

Hint.French blonde 4chan chick rocky ben xxdexx

Hint: (January 21) Emo Stickam girls who's name is also a band isnt in rik

Hint: Tinychat regular who's not as popular as other cam whores ban sexx in tuctio

Hunt: Blogtv girl who was brought to tears after being recorded hachac

Hint: (January 17) Two girls from tinychat that lasted hours before being banned get a nurf xl

Hint: Appears wherever Coke is and goes on cam after a tinychat ban warn wub

Hint: Stickam cam whore who has been trying to fit in with femanons
 Answers:
Word Scramble!

 Tewi

 xxbrokencydexx

 Kritinn

 BosnianCutiexx

 ChaCha

 Laurenxfgt

 awwburn

Can you make your own word scrambles?!

Cam Whore of the Month
 Nominee #4

This month EB has showed the world she's more then just another pretty face!
 EB and Jewchan in a chan room

EB & Charmander
 The Center of Attention:
w/t Coke

Name: Brandon
Alias: Coke
Age: 19
Ethnicity: White/Caucasian
Hometown: The Internet
Occupation: Elite Capper
Hobbies: Trolling / Capping
Accomplishments: CCH member / Most Annoying 2009
Love Interest: Laurenxfgt
Enemies: White Knights / Klanackle
Allies: Chair
# of Wins: over 9000
# of Failis: under 10,000
Addictions: Hardcore Child Porn
Allergies: Heroes
Fears: getting v& / Failing
Now you know Coke.
 Highlight Reel
January 22-31
Who am I?

 A:I'm the new 4hds mod.I'm known to hoard.I almost retired this month.
Who am I?
B:I always do show for you guys.I'm very popular.My mom walked in on me while I was showing.
Who am I?
C.I love to annoy people.No one likes my name.I've been using a loop of TaylorLOLZ.
Who am I?
D.I own the rightnow rooms.I'm known to hoard.I love Coke.
Who am I?
E.I'm a huge troll.I've hacked several stickam accounts to play loops this month.I love golf.
Who am I?
F.I'm a hero.I haven't been as successful as I was last year.I hate sluts.
Who am I?

 Answers:

A: rorA
B.Dogs
C: IRecordgirlsFlashing
D: Chair
E: Coke
F: Matt/hatt
 This month has been filled with numerous ups and downs and rises and falls!Though we haven't mentioned everything this month we can only look into the future as we count down the months for the 2010 Capper Awards!

 Cam Whore of the Year interview:

We tried to contact the 2009 Cam Whore of the Year for a short interview for our magazine and this is the response!
From:
iknowthissong
Sent:
Jan. 31, 2010 @ 12:33AM
Subject:
RE: Interview
yes, it would be an honor :) an honOr with an O :) i believe you misspelled it.
We sent in some questions but never got a response,then we found out it would be a pointless interview as she has her own site where you can ask her your own questions!
http://www.formspring.me/caseyface
 Have a Happy
Valentines Day!
Because no one likes to be alone on Valentines Day!
Get out and find that special someone and get laid already!

 Publication Date: February 5th 2010 https://www.bookrix.com/-thedailycapper


@anonymized_account @anonymized_account Musi. Innej drogi nie mamy.


1. What Is The Singularity?


The acceleration of technological progress has been the central feature of this century. We are on the edge of change comparable to the rise of human life on Earth. The precise cause of this change is the imminent creation by technology of entities with greater-than-human intelligence. Science may achieve this breakthrough by several means (and this is another reason for having confidence that the event will occur):


Computers that are "awake" and superhumanly intelligent may be developed. (To date, there has been much controversy as to whether we can create human equivalence in a machine. But if the answer is "yes," then there is little doubt that more intelligent beings can be constructed shortly thereafter.)


Large computer networks and their associated users may "wake up" as superhumanly intelligent entities.


Computer/human interfaces may become so intimate that users may reasonably be considered superhumanly intelligent.


Biological science may provide means to improve natural human intellect.


Intraocular pressure in genetically distinct mice : an update and strain survey


Little is known about genetic factors affecting intraocular pressure ( IOP ) in mice and other mammals .


The purpose of this study was to determine the IOPs of genetically distinct mouse strains , assess the effects of factors such as age , sex and time of day on IOP in specific strain backgrounds , and to assess the effects of specific candidate gene mutations on IOP .


Based on over 30 studied mouse strains , average IOP ranges from approximately 10 to 20 mmHg .


Gender does not typically affect IOP and aging results in an IOP decrease in some strains .


Most tested strains exhibit a diurnal rhythm with IOP being the highest during the dark period of the day .


Homozygosity for a null allele of the carbonic anhydrase II gene ( Car2n ) does not alter IOP while homozygosity for a mutation in the leptin receptor gene ( Leprdb ) that causes obesity and diabetes results in increased IOP .


Context: The Standard Industrial Classification and the newer North American Industry Classification System have a classification system for companies that perform or otherwise engage in construction. To recognize the differences of companies in this sector, it is divided into three subsectors: building construction, heavy and civil engineering construction, and specialty trade contractors. There are also categories for construction service firms (e.g., engineering, architecture) and construction managers (firms engaged in managing construction projects without assuming direct financial responsibility for completion of the construction project).

Question: What systems classify trade contractors?


Context: The physicians of the Yuan court came from diverse cultures. Healers were divided into non-Mongol physicians called otachi and traditional Mongol shamans. The Mongols characterized otachi doctors by their use of herbal remedies, which was distinguished from the spiritual cures of Mongol shamanism. Physicians received official support from the Yuan government and were given special legal privileges. Kublai created the Imperial Academy of Medicine to manage medical treatises and the education of new doctors. Confucian scholars were attracted to the medical profession because it ensured a high income and medical ethics were compatible with Confucian virtues.

Question: Who were potachi?


Context: There is evidence that there have been significant changes in Amazon rainforest vegetation over the last 21,000 years through the Last Glacial Maximum (LGM) and subsequent deglaciation. Analyses of sediment deposits from Amazon basin paleolakes and from the Amazon Fan indicate that rainfall in the basin during the LGM was lower than for the present, and this was almost certainly associated with reduced moist tropical vegetation cover in the basin. There is debate, however, over how extensive this reduction was. Some scientists argue that the rainforest was reduced to small, isolated refugia separated by open forest and grassland; other scientists argue that the rainforest remained largely intact but extended less far to the north, south, and east than is seen today. This debate has proved difficult to resolve because the practical limitations of working in the rainforest mean that data sampling is biased away from the center of the Amazon basin, and both explanations are reasonably well supported by the available data.

Question: There have been insignificant changes in the Amazon rain forest vegetation through the last what


Context: This shift has already commenced in some countries; for instance, pharmacists in Australia receive remuneration from the Australian Government for conducting comprehensive Home Medicines Reviews. In Canada, pharmacists in certain provinces have limited prescribing rights (as in Alberta and British Columbia) or are remunerated by their provincial government for expanded services such as medications reviews (Medschecks in Ontario). In the United Kingdom, pharmacists who undertake additional training are obtaining prescribing rights and this is because of pharmacy education. They are also being paid for by the government for medicine use reviews. In Scotland the pharmacist can write prescriptions for Scottish registered patients of their regular medications, for the majority of drugs, except for controlled drugs, when the patient is unable to see their doctor, as could happen if they are away from home or the doctor is unavailable. In the United States, pharmaceutical care or clinical pharmacy has had an evolving influence on the practice of pharmacy. Moreover, the Doctor of Pharmacy (Pharm. D.) degree is now required before entering practice and some pharmacists now complete one or two years of residency or fellowship training following graduation. In addition, consultant pharmacists, who traditionally operated primarily in nursing homes are now expanding into direct consultation with patients, under the banner of "senior care pharmacy."

Question: Who can't pay Australian pharmacists for doing Home Medicines Reviews?


Context: In the final years of the apartheid era, parents at white government schools were given the option to convert to a "semi-private" form called Model C, and many of these schools changed their admissions policies to accept children of other races. Following the transition to democracy, the legal form of "Model C" was abolished, however, the term continues to be used to describe government schools formerly reserved for white children.. These schools tend to produce better academic results than government schools formerly reserved for other race groups . Former "Model C" schools are not private schools, as they are state-controlled. All schools in South Africa (including both independent schools and public schools) have the right to set compulsory school fees, and formerly model C schools tend to set much higher school fees than other public schools.

Question: What was abolished during the apartheid era?


Context: There are hints in the surviving records of the ancient Egyptians that they had some knowledge of prime numbers: the Egyptian fraction expansions in the Rhind papyrus, for instance, have quite different forms for primes and for composites. However, the earliest surviving records of the explicit study of prime numbers come from the Ancient Greeks. Euclid's Elements (circa 300 BC) contain important theorems about primes, including the infinitude of primes and the fundamental theorem of arithmetic. Euclid also showed how to construct a perfect number from a Mersenne prime. The Sieve of Eratosthenes, attributed to Eratosthenes, is a simple method to compute primes, although the large primes found today with computers are not generated this way.

Question: What does the Sieve of Eratosthenes do?

compute primes
compute primes
compute primes
compute primes
compute primes


All the objects that we see in the world around us, are made of matter


Matter makes up the air we breathe, the ground we walk on, the food we eat and the animals and plants that live around us


Even our own human bodies are made of matter!Different objects can be made of different types of materials (the matter from which objects are made)


For example, a cupboard (an object) is made of wood, nails, hinges and knobs (the materials)


The properties of the materials will affect the properties of the object


In the example of the cupboard, the strength of the wood and metals make the cupboard strong and durable


Materials that are malleable can be easily formed into different shapes (eg clay, dough)


Ductile materials are able to be formed into long wires (eg copper)


The boiling and melting points of substances tells us the temperature at which the substance will boil or melt


This helps us to classify substances as solids, liquids or gases at a specific temperature


Manchester had a population of more than 540,000 in 2017 and was the 5th most populous English district .

Manchester ( ) is a major city and metropolitan borough in Greater Manchester , England , with a population of 545,500 as of 2017 ( 5th most populous English district ) .


Manchester ( ) is a major city and metropolitan borough in Greater Manchester , England , with a population of 534,982 as of 2018 ( 4th most populous English district ) .


Manchester had a population of less than 540,000 in 2018 and was the 4th most populous English district .


Traditional Chinese medicine is founded on less than 4000 years of Chinese medical practice .

Traditional Chinese medicine ( TCM ; ) is a style of traditional medicine built on a foundation of more than 2,500 years of Chinese medical practice that includes various forms of herbal medicine , acupuncture , massage ( tui na ) , exercise ( qigong ) , and dietary therapy , but recently also influenced by modern Western medicine .


Traditional Chinese medicine ( TCM ; ) is an ancient system of medical diagnosis and treatment of illnesses with a holistic focus on disease prevention through diet , healthy lifestyle changes , exercise and is built on a patient centered clinically oriented foundation of more than 6,500 years of ancient Chinese medical science and practice that includes various forms of herbal medicine , acupuncture , massage ( tui na ) , exercise ( qigong ) , and dietary therapy , but recently also influenced by modern Western medicine .


Traditional Chinese medicine is founded on more than 4000 years of ancient Chinese medical science and practice .


Before April 12 , 2020 , there was a fatality rate of less than 1.62 % in Malaysia during the coronavirus pandemic .

Accelerated testing reveals a comparatively low case fatality rate ( 1.61 % as of 11 April 2020 ) than in the Philippines and Indonesia , but remains moderately more severe than in Thailand , Singapore and Brunei.


Accelerated testing reveals a comparatively low case fatality rate ( 1.62 % as of 12 April 2020 ) than in the Philippines and Indonesia , but remains moderately more severe than in Thailand , Singapore and Brunei.


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; perimeters extrusion width = 0.50mm


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necrotising fasciitis (nf) is a serious soft tissue and life threatening infection, primarily involving the skin and superficial fascia, characterised by a rapid and extensive necrosis of the subcutaneous tissue. most patients have associated comorbidities, such as diabetes, hiv infection or treatment with immunosuppressive drugs, but it can also affect previously healthy individuals. infection is one of the common causes of morbidity and mortality in patients with systemic lupus erythematosus (sle). it is associated with immunosuppressive agents, renal failure, and increased disease activity. despite the increased propensity of sle patients to develop common and opportunistic infections, nf has rarely been reported. to our knowledge , only 31 cases are described in the literature to date (see table 1). nf has also been described in other rheumatic diseases including polymyositis, dermatomyositis, systemic sclerosis, rheumatoid arthritis and ankylosing spondylitis, but it appears that this association is more common in sle.1 2 clinical features of nf in systemic lupus erythematosus a, asian; aa, african american; aps, antiphospholipid syndrome; art, arthritis; avn, avascular necrosis; aza, azathioprine; cad, coronary artery disease; cs, corticosteroid therapy; cyc, cyclophosphamide; d, died; dm, diabetes mellitus; f, female; fg, fournier's gangrene; h, hispanic; hcq, hydroxychloroquine; lym, lymphocytes; m, male; mmf, mycophenolate; mtx, methotrexate; n, nephritis; na, not available; nf, necrotising fasciitis; pe, pulmonary embolism; rtx, rituximab; s, survived; sa, staphylococcus aureus; ttp, thrombotic thrombocytopenic purpura; wbc, white blood cells. we describe the case of a nf caused by pseudomonas aeruginosa, complicated by recurrent sepsis and multiorgan failure in a young patient with a history of sle. a 26-year - old asian woman with a complex history of sle, diagnosed at age 14 years when she presented with malar rash, arthralgia, mouth ulcers, pulmonary vasculitis, strongly positive anti - nuclear antibody and anti - dsdna antibodies. subsequently, she had idiopathic detrusor overactivity with repeated urinary tract infection. linked to her corticosteroid treatment she developed avascular necrosis of her elbow (at that time, on prednisolone 6 mg daily). the patient was also treated with hydroxychloroquine (hcq), azathioprine (aza) (100 mg from 1999 to 2009) and had 14 courses of cyclophosphamide (cyc) (a cumulative dose of 14 g) and two of rituximab (rtx) (two 1 g intravenous infusions separated by 2 weeks), the last being given in march 2012 for a sle flare manifested by severe vasculitic rash. immediately prior to b - cell depletion, her c3 level was 0.33 g / l (normal : 0.651.65), cluster designation (cd) 19 count was 0.146/l (0.110.69), and immunoglobulin g level was 23.4 g / l. in may 2012 , she presented to her local hospital with painful swelling of her left lower limb, she denied a history of trauma, and her inflammatory markers and ultrasonography were normal, and she was discharged. within 12 h she was found at home with a glasgow coma scale of 6, and was admitted to the intensive care unit (icu). on examination, the patient was in septic shock with hypotension (systolic blood pressure 60 mm hg), tachycardia (135/min), and respiratory failure (sao2 70%). laboratory showed erythrocyte sedimentation rate 96 mm / h (normal : 17), c - reactive protein 281 mg / dl, white blood cells 15 400/l (300010 000) (97% neutrophils ; 1.9% lymphocytes), haemoglobin 9.6 g / dl (11.515.5), platelet 50 000/l (150 000400 000), na 138 mmol / l, creatine kinase 474 iu / l, urea 16.8 mmol / l (1.78.3), alamine transaminase 384 iu / l, total bilirubin 11 mg / dl (0.31.9), albumin 17 g / l, creatinine 290 mol / l and proteinuria 2.30 g / l. her c3 level was 1.0 g / l (0.651.65), cd19 count was 0.001/l (0.110.69), and immunoglobulin g level was 9.32 g / cultures of muscle tissue only grew p aeruginosa, resistant to piperazillin - tazobactam; therefore, she was treated with meropenem, teicoplanin and clindamycin. as her clinical condition however, 1 week later, she deteriorated, with thick yellowish sputum, fever, respiratory distress and the chest x - ray showed right consolidation. ciprofloxacin and teicoplanin were empirically started, and the patient's clinical condition gradually improved. a week after this treatment had finished, the patient became worse with a new left lower lobe consolidation, so the antibiotic therapy was changed to ceftazidime. her lupus flared with a marked malar rash over both cheeks, so her steroids (prednisolone) were increased to 20 mg per day and hcq was restarted. she was in the icu from may to august 2012, but made a remarkable recovery and was discharged home with a steroid - tapering regimen and hcq. later, she developed two more sle flares, which were treated again with cyc and rtx, achieving good response. at 18 months follow - up, there is no evidence of new recurrent or severe ongoing infections. most patients with nf ranged from 38years to 44 years, with a male to female ratio of 23: 1, and apparently an increased incidence in african and asian countries.3 the true incidence is not known (it is estimated to be approximately 0.4 cases per 100 000), with a reported mortality from 20% to as high as 80%.35 the causative agents of nf vary and include two main categories, polymicrobial (type 1) and infection of group a streptococcal (type 2). patients with sle have an increased risk of infections, due to immunological dysfunction and the use of steroids and immunosuppressive agents.6 other factors, such as the presence of a variant form of the fc receptor are also believed to contribute to the risk of pneumococcal infection.7 nf due to pseudomonas has very rarely been reported.8 9 the clinical presentation of patients with nf may be deceptively benign at onset, and it may not be possible to distinguish it clearly from minor soft tissue infections. our patient was discharged from the original hospital before being admitted subsequently severely ill to university college hospital. the lesion was rapidly progressive, probably due to her sle, and the prolonged steroid treatment and recent administration of cyc and rtx might have increased the risk of infection. during the initial period, exploration of the wound may be necessary even before the diagnosis is clear, particularly in a patient who is clearly toxic. in addition to the present case, the other 31 cases of nf in sle patients that have been reported are summarised in table 1. the ages ranged from 12 years to 66 years, and 27 (84%) were female. most of the patients reported were asian (33%, 7 patients), 33% (7 patients) hispanic and 19% (4 patients) african american. the sites of infection included the upper or lower limb (60%, 17 patients), the face, neck or tongue (18%, 5 patients), the genital area (11%, 3 patients) and the abdomen (11%, 3 patients). nf type 1 was identified in 22 patients (71%) and type 2 in 7 patients (23%), and when reported, the most common isolation was streptococcus (50%, 16 patients). most of the patients presented with intense pain, poorly demarcated erythematous, swollen lesions over the limbs and systemic inflammatory response; 28 patients (88%) were receiving corticosteroids and 7 hcq (22%). additionally, use of other immunosuppressive drugs has been reported (5 cyc, 5 aza, 2 rtx, 1 mycophenolate, 1 methotrexate, 1 plasmapheresis) before or at the time of infection. other associated conditions have been identified, including nephritis (54% ; 15 patients of 28 reported), either during the current presentation or in the past, 13 (42%) had active disease (presumably those taking > 20 mg prednisolone or equivalent per day), 76% (13 of 17 reported) had lymphopenia, 100% (12 out of 12 reported) low serum albumin levels and 12 (37%) had anaemia. interestingly, before the admission, 9 (28%) reported a previous episode of infection: urinary, bartholin abcess, diarrhoea, respiratory tract infection, carious tooth, colitis, hepatitis c, cutaneous ulcers, breast abscess and diverticulitis. most patients responded to therapy, but 8 of the 32 patients died (25%), probably due to the infection or because of complications (such as sepsis or pulmonary embolism). the case presented here and the literature reviewed suggests that active disease, nephropathy, lymphopenia, anaemia, low serum albumin levels, immunosuppressive therapy and significant infections in the past, may constitute risk factors for the development of nf in patients with sle. a high index of suspicion for the diagnosis is required, and surgical exploration should not be delayed, especially during the early stages, to improve the prognosis of this devastating complication.

necrotising fasciitis (nf) is a rare infection of the subcutaneous tissue, known to be rapidly progressive and potentially fatal. patients with systemic lupus erythematosus (sle) may be predisposed to this condition, and early clinical recognition can be difficult. we report a case of necrotising fasciitis in a 26-year - old woman with sle. she presented with painful swelling of her left leg, then developed clinical features of septic shock. emergency debridement was performed. intraoperative findings revealed nf and cultures grew pseudomonas aeruginosa. the patient survived after a lengthy hospital admission, following several further debridements complicated by recurrent chest sepsis and multiorgan failure. we also review and discuss the published cases of nf in sle patients.