#several of my theory and analysis posts are made in a fever state where

[accirax]

A Brief and Highly Speculative DRDT Thought

This damn character order. I don't get it. It vexes me.

Because, as far as I can tell, they aren't ordered in any particular way. It's not alphabetical first name order, alphabetical last name order, alphabetical talent order, death order, order of introduction, Class Trial seating order, gender order (like doing all the girls, all the boys, and then Nico), rainbow order of official colors... And, as far as I can tell, that remains true if you do any of the options in reverse, or if you remove Teruko.

I don't think they work if you remove Xander either, or even Charles too. I say that just because I think those three could be first as, like, a top billing sort of thing. At the start, I think you're meant to believe that (following standard Danganronpa protocol) Teruko is the protagonist, Xander is the support character, and Charles is the antagonist. As arguably the three most important characters, it would make sense to put them first. Of course, that's not exactly how their roles actually turned out, but that's what you want the audience to believe as a spoiler-free first watcher.

So, if it's not any of those things, what is it? I started thinking about what the most likely possibilities are.

#1: The Order is Completely Random

As in, DRDTdev took the finished cast, put all of the names (minus Teruko, and maybe Xander and Charles) into a randomizer, and this is how it turned out. That would be a good way to avoid giving us any hints as to the characters' development or stories. Honestly, fairly likely, but bear with me here.

#2: The Order is Something Story-Relevant We Haven't Learned Yet

And thus, once we get to Chapter 6 or whatever, we'd be like, "oh, so that's why they were put in that order." This could be something like "order in which characters were accepted into Hope's Peak" or "order in which characters met Mai." Potentially interesting, although such an open sandbox that trying to pin down what it means is basically as good as it being random.

#3: The Order is Order of Creation

This is the real "Brief and Highly Speculative Thought" the title promised. I think there's a chance that this order appears random to us because it's the order that the characters were developed in. We know from the July 31st Q&A that Arei was the subject of the "first ever drawing of DT," which could imply that she was one of the characters who was created/confirmed first. However, she's currently fifth, so, what about the characters before her?

Well, Teruko is the protagonist, so it would make sense if she was the first character DRDTdev created. She also has a lot of mysteries and secrets surrounding her, which, as the protagonist, are surely strongly interconnected with the main plot. Hell, it's even possible that the protagonist of altDRDT is Teruko's brother! Having brainrot about Teruko may have been what inspired altDRDT in the first place, which would mean that Teruko would have to exist for longer than altDRDT has. Why am I talking so much about Teruko. This is not a hard sell.

It might seem strange that your literal first death, the character with the least screen time, is the second one you create, but when it's a character as intriguing as Xander, it becomes less of a hurdle. The dramatic scene that occurs to bridge the silly times of pre-first death and the oh-shit entirety of post-first death is an important enough moment that I would believe it was conceptualized very early. It's a big tone-setting moment. Xander also seems to be pretty interconnected with Mai, as "Unnamed Student" once asked him to find something for her (it's uh... in that document somewhere). That implies big overarching plot relevance, which potentially implies early creation.

As stated above, Charles is meant to look like the antagonist archetype of DRDT, so it would make sense if he was developed third. Basically, "I have my protagonist and my support character, so who should my antagonist be?" Charles is alive for at least two Class Trials, and his fear of blood and bodies seems like something that would need to be planned around long in advance. He also has a lot of parallels to Teruko, which could be important to her development. Charles third doesn't seem like a stretch.

Ace fourth, however, feels like a bit more of a stretch. I still don't think it's impossible, though. Many people (including myself, more or less) believe that Ace will be a survivor. Being at the sixth Class Trial is reason enough to be important, but if Ace was designed to do something important at that Trial (god knows what), it could make sense if Ace was created early. Ace also has a number of important relationships-- Levi, Nico, and Hu, to name the most prominent. The fact that Ace is at the center of most of those conflicts, being the one inciting the main problem, makes me think it would have been much easier to plan out the daily life if he were in it from very early on.

And then, we're back to Arei. I wouldn't have guessed that our apparent second victim was developed as early as fifth place, but, that's canon, baby! I don't have to explain this one!

The characters being listed in order of creation would have a number of interesting implications on the roles that characters play. For instance, you would probably imagine that the mastermind would be amongst the characters you would develop first, right? If so, that would diminish the likelihood that characters such as Veronika, J, Whit, or Nico were the mastermind (that actually covers a lot of really popular options, damn). Conversely, it could implicate that characters like Ace, Rose, Hu, or Eden are more likely to be the mastermind.

From a writing standpoint, later characters may have also been created for the purpose of solving problems. Going back to Ace, let's say that (as Hu has already been developed 7th at this point), DRDTdev knows that Ace and Hu need to have a big conflict in Chapter 3 that, I don't know, leads to Hu being the blackened and Ace starting to regret his foul mouth or something. To solve that problem, let's create Nico, a character who Hu likes and Ace hates, who does something highly controversial. Hu defends this "Nico", Ace insists they're in the wrong, and the arguing causes Hu to snap. That's 1) a huge oversimplification of the character creation process and 2) wildly speculative about the future of DRDT's plot, but my point is to say that one character can be inspired by the needs of the plot and still be a cool and beloved character that perfectly fits in regardless.

(To be clear, I am of the firm belief that there are no losers/unimportant or underdeveloped characters in DRDT. That's part of what makes it so great!)

(And, when I referred to "loser" characters there, I am referring to my own writing, not anybody else's fangans.)

That's not to say that there aren't any issues with the concept that the characters are in development order, though. The one that most immediately jumped out to me was David being 12th. Like Charles, he also has a lot of parallels with Teruko, and his big reveal could be something that was in the cards from early on, like Xander's death. Honestly, though, I could seem him being a surprisingly late addition. If DRDTdev perhaps realized that Charles wasn't as antagonistic as he initially intended, he may have wanted a new character to become an antagonist in Chapter 2 to replace Charles. Or, once DRDTdev decided that Teruko wouldn't (outwardly) mourn Xander, he may have wanted a character that would to keep the idea of Xander as a good guy in the audience's minds. Maybe that's just me being delusional, though.

Min's 11th place creation is also kinda weird to me. On one hand, she is the Chapter 1 killer, so a character with a short lifetime and limited important character relationships could have been added closer to the end. Then again, if Xander (the victim) and Teruko and Charles (major baits) were created so early, it kinda feels like DRDTdev would have come up with the actual killer earlier too, right? Min also has connections to XF-Ture tech, which, if not that important to DRDT, certainly seems to be important to altDRDT. However, I guess those could have been "problems" too-- "who can I create that would kill Xander to save Teruko?" and "how can I incorporate XF-Ture tech into the story in a natural way?"

The inclusion of Bonus Episodes also adds some spice to the discussion. We don't technically know that the Chapter 2 victim and killer will get their own episodes once the chapter is over, although I think most people are assuming it at this point. Min, for example, could have also been conceptualized based on a need for an early bonus episode to drop some lore about American Hope's Peak. Thus, her talent of "Ultimate Student" could have been designed to lead to a conversation about the school in the future. The same could be true of some later killers and victims as well.

Also, going back to the "actual killer" bit, things may have also been confused if any characters were ever replaced. Maybe there was originally a different character intended to be the Chapter 1 killer. This hypothetical character could have been discarded entirely or moved somewhere else-- like how, in THH, the original Chapter 1 was Hifumi killing Hiro, but after the plans changed Hiro became a survivor, and Hifumi a third victim. It's another way to potentially explain the gap between first victim and first killer.

Anyways, in conclusion, I don't know if I really believe in this theory or not. I just thought it was interesting, and decided to serve it up to y'all as food for thought.

The creation and writing of DRDT is fascinating to me, so I'll have to ask you to bear with me as my theories get less concrete and relevant and more speculative and meta as the hiatus goes on. That's just the manner in which my brain is rotting at the moment. Are my discussions of reasons for character creation peeling back the layers of the Matrix, or am I just donning my tin foil hat and pretending to know more about writing than I do...? I guess only time will tell. Thanks for reading!

[dorcasrempel]

What is the Covid-19 data tsunami telling policymakers?

Uncertainty about the course of the Covid-19 pandemic continues, with more than 2,500,000 known cases and 126,000 deaths in the United States alone. How to contain the virus, limit its damage, and address the deep-rooted health and racial inequalities it has exposed are now urgent topics for policymakers. Earlier this spring, 300 data scientists and health care professionals from around the world joined the MIT Covid-19 Datathon to see what insights they might uncover.

“It felt important to be a part of,” says Ashley O’Donoghue, an economist at the Center for Healthcare Delivery Science at Beth Israel Deaconess Medical Center. “We thought we could produce something that might make a difference.”

Participants were free to explore five tracks: the epidemiology of Covid-19, its policy impacts, its disparate health outcomes, the pandemic response in New York City, and the wave of misinformation Covid-19 has spawned. After splitting into teams, participants were set loose on 20 datasets, ranging from county-level Covid-19 cases compiled by The New York Times to a firehose of pandemic-related posts released by Twitter. 

The participants, and the dozens of mentors who guided them, hailed from 44 countries and every continent except for Antarctica. To encourage the sharing of ideas and validation of results, the event organizers — MIT Critical Data, MIT Hacking Medicine, and the Martin Trust Center for MIT Entrepreneurship — required that all code be made available. In the end, 47 teams presented final projects, and 10 were singled out for recognition by a panel of judges. Several teams are now writing up their results for peer-reviewed publication, and at least one team has posted a paper.

“It’s really hard to find research collaborators, especially during a crisis,” says Marie-Laure Charpignon, a PhD student with MIT’s Institute for Data, Systems, and Society, who co-organized the event. “We’re hoping that the teams and mentors that found each other will continue to explore these questions.”

In a pre-print on medRxiv, O’Donoghue and her teammates identify the businesses most at risk for seeding new Covid-19 infections in New York, California, and New England. Analyzing location data from SafeGraph, a company that tracks commercial foot traffic, the team built a transmission-risk index for businesses that in the first five months of this year drew the most customers, for longer periods of time, and in more crowded conditions, due to their modest size. 

Comparing this risk index to new weekly infections, the team classified 16.3 percent of countywide businesses as “superspreaders,” most of which were restaurants and hotels. A 1 percent increase in the density of super-spreader businesses, they found, was linked to a 5 percent jump in Covid-19 cases. The team is now extending its analysis to all 50 states, drilling down to ZIP code-level data, and building a decision-support tool to help several hospitals in their sample monitor risk as communities reopen. The tool will also let policymakers evaluate a wide range of statewide reopening policies.

“If we see a second wave of infections, we can determine which policies actually worked,” says O’Donoghue.

The datathon model for collaborative research is the brainchild of Leo Anthony Celi, a researcher at MIT and staff physician at Beth Israel Deaconess Medical Center. The events are usually coffee-fueled weekend affairs. But this one took place over a work week, and amid a global lockdown, with teammates having to meet and collaborate over Slack and Zoom.

With no coffee breaks or meals, they had fewer chances to network, says Celi. But the virtual setting allowed more people to join, especially mentors, who could participate without taking time off to travel. It also may have made teams more efficient, he says. 

After analyzing communication logs from the event, he and his colleagues found evidence that the most-successful teams lacked a clear leader. Everyone seemed to chip in. “In face-to-face events, leaders and followers emerge as they project their expertise and personalities,” he says. “But on Slack, we saw less hierarchy. The most successful teams showed high levels of enthusiasm and conversational turn-taking.”

Another advantage of the virtual setting is that teams straddling several time zones could work, literally, around the clock. “You could post a message on Slack and someone would see it an hour or two later,” says Jane E. Valentine, a biomedical engineer at the Johns Hopkins University Applied Physics Laboratory. “There was a constant sense of engagement. I might be sleeping and doing nothing, but the wheels were still turning.”

Valentine collaborated with a doctor and three data scientists in Europe, the United States, and Canada to analyze anonymized medical data from 4,000 Covid-19 patients to build predictive models for how long a new patient might need to be hospitalized, and their likelihood of dying.

“It’s really useful for a clinician to know if a patient is likely to stabilize or go downhill,” she says. “You may want to monitor or treat them more aggressively.” Hospital administrators can also decide whether to open up additional wards, she adds.

Among their findings, the team found that a fever and shortness of breath were top symptoms for predicting both a long hospital stay and a high risk of death for patients, and that general respiratory symptoms were also a strong predictor of death. Valentine cautions that the results are preliminary, and based on incomplete data that the team is currently working to fill. 

One of the pandemic’s cruel realities is that it has hit the poorest and most vulnerable people in society hardest. Datathon participants also examined Covid-19’s social impact, from analyzing the impact of releasing prisoners to devising tools for people to verify the flood of claims about the disease now circulating online. 

Amber Nigam, a data scientist based in New Delhi, India, has watched conspiracy theories spread and multiply on social media as contagiously as Covid-19 itself. “There’s a lot of anxiety,” he says. “Even my parents have shown me news on WhatsApp and asked if it was true.” 

As the head of AI for PeopleStrong, a predictive sales startup in San Francisco, California, Nigam is comfortable with natural language processing tools and interested in their potential for fighting fake news. During the datathon, he and his team crawled the web for conspiracy theories circulating in the United States, China, and India, among other countries, and used the data to build an automated fact-checker. If the tool finds the claim to be untrue, it sends the reader to the news source where the claim was first debunked. 

“A lot of people in rural settings don’t have access to accurate sources of information,” he says. “It’s super critical for people to have the right facts at their disposal.”

Another team looked at Covid-19’s disparate impact on people of color. Lauren Chambers, a technology fellow at the Massachusetts American Civil Liberties Union (ACLU), suggested the project and mentored the team that took it on. State by state, the team found disproportionate death rates among Black and Hispanic people, who are more likely to work “essential” service-industry jobs where they face greater exposure to people infected with the disease.

The gap was greatest in South Carolina, where Black individuals account for about half of Covid-19 deaths, but only a third of residents. The team noted that the picture nationally is probably worse, given that 10 states still do not collect race-specific data. 

The team also found that poverty and lack of health care access were linked to higher death rates among Black communities, and language barriers were linked to higher death rates among Hispanic individuals. Their findings suggest that economic interventions for Black Americans, and hiring more hospital translators for Hispanic Americans, might be effective policies to reduce inequities in health outcomes.

The ACLU can’t afford to hire an army of data scientists to investigate every civil-rights violation the pandemic has brought to light, says Chambers. But collaborative events like this one give community advocates a chance to explore urgent questions they wouldn’t otherwise be able to, she says, and data scientists get to hear new perspectives, too.

“There’s a dangerous tendency among data scientists to think that numbers are the beginning and end of any good analysis,” she says. “But data are subjective, and there’s all kinds of other expertise that communities hold.”

The event was sponsored by Beth Israel Deaconess Medical Center Innovation Group, Google Cloud, Massachusetts ACLU, and the National Science Foundation’s West Big Data Innovation Hub.

What is the Covid-19 data tsunami telling policymakers? syndicated from https://osmowaterfilters.blogspot.com/

[pattyedouard-blog]

What Is Quackery?

There are many issues I'm enamored of that I'd like to explore even more. However, before I do, I know that I have to know about the subject. In the black-and-white times, the world of a slower, easier life, this ensured likely to go to "the stacks" of the neighborhood community college library, and a topical library a little an excessive distance from home. It engaged utilising the web to gain access to the net library systems, and it suggested checking the neighborhood Community Library Catalogue for literature, magazines, and articles or editorials in papers about the subject of interest. In other words, a super reliance on libraries, in true to life and both virtual. At this time, I start my pursuit with WikiPedia. I know that the info shown is usually moderated, and is commonly accurate, although on scorching difficulties, some content may be skewed at times. It is not the literal Word, provided from Paradise. Then again, I've largely been satisfied with the quality of job they do, and most content are carefully sourced with personal references used to write the article and they are a wonderful starting place for additional weeks of discovery. Down below, I wrote a cool article that is going to be largely taken from WikiPedia content pieces. Despite the fact that I've rewritten it, I have to even now return recognition where credit is certainly anticipated. I would still be just a copy cat if I failed to, because a basic rewriting of a piece is not a fresh work, according to the regulations. Fortunately, the Creative Commons license allows me to work with these interesting articles for my own usages. Having said that, please experience this small intro to this issue. Quackery is the campaign of deceitful or unaware medical procedures. A impersonator is a "fraudulent or not aware pretender to medical skill" or "a person who pretends, professionally or publicly, to obtain skill, expertise, qualification or credentials the person does not possess; a charlatan or fish oil salesman". The term quack is a cut form of the archaic term quacksalver, by Dutch: kwakzalver a "hawker of salve". In the Middle Age ranges the term warble meant "shouting". The quacksalvers sold the wares that can be purchased shouting in a loud tone. Common regions of general quackery include dubious diagnoses employing questionable analysis tests, as well as untested or perhaps refuted therapies, especially for critical diseases such as cancer. Quackery is often identified as "health fraud" with the significant characteristic in aggressive campaign. Since it is certainly difficult to separate those who knowingly promote unproven medical treatment plans and those who also are wrong with regards to their proficiency, America surfaces contain overpowered in defamation conditions that accusing another person of quackery or perhaps labelling a medical specialist a warble is in no way similar to accusing that person of committing skilled fraud. To be both quackery and theft, the cluck must know they can be misrepresenting favorable effect on self-confidence and dangers of your medical providers available (instead of, for example , marketing and advertising a great unsuccessful merchandise many people simply consider is undoubtedly effective). Beyond the ethical challenges in appealing benefits that may not likely reasonably be anticipated to happen, quackery also includes the risk that people might choose to forget about therapies which can be more likely to help them, in favor of company solutions provided by the "quack". Stephen Barrett of Quackwatch specifies quackery "as the promo in unsubstantiated strategies that lack a verified digno rationale" and even more roughly just as: Pietro Longhi's The Charlatan (1757) "anything associating overpromotion when it comes to health. " This kind of definition may consist of doubtful views and doubtful goods and services, regardless of truthfulness of their total marketers. In line with this kind of idea, the phrase "fraud" would be shy simply for circumstances in which purposive deceptiveness is engaged. Paul Offit has offered four ways in which alternative medicine "becomes quackery" Simply by "... promoting against standard therapies which can be helpful. " By inch... promoting probably harmful strategies without adequate warning. " By "... raining patients' bank accounts... inches And, finally, by "... promoting marvelous thinking... inch Unproven, generally ineffective, and sometimes dangerous medications and treatments have been peddled throughout human history. Theatrical performances were quite often given to boost the credit of purported medications. Narcissistic remarks were manufactured so that could be very humble fabrics indeed: for instance , in the mid-19th century revalenta arabica is advertised due to having unexpected restorative healing benefits because an empirical healthy eating plan with invalids; in spite of it is remarkable name and several shimmering reports it was in actual fact just typical lentil flour, sold to the gullible at many times the real price. In addition wherever very little fraud was planned, quack applications often trapped no effective ingredients in any way. Some cure contained chemicals such as opium, alcohol and honey, which will would have given symptomatic healing but experienced no curative properties. Some would have habit forming qualities to entice the purchaser to return. The few successful remedies distributed by quacks included emetics, laxatives and diuretics. Several ingredients does have therapeutic effects: mercury, silver and arsenic substances may possess helped some infections and infestations; willow bark secured salicylic plaque created by sugar, chemically very closely related to acetylsalicylsäure; and the quinine contained in Jesuit's bark was in fact an effective treatment for sumpffieber and other fevers. However , knowledge of appropriate uses and doses was limited. The science-based practice of medicine community has belittled the infiltration of different treatments into popular school treatments, education, and guides, accusing universities of "diverting analysis time, money, along with assets with extra fruitful lines in shop to be able to go after a theory that has simply no grund in biology. inches Recommended. W. Donnell gave the length "quackademic medicine" to describe this particular attention directed at alternative medicine simply by twoyear college. Mentioning the Flexner Report, he stated that therapeutic education "needs a very good Flexnerian housekeeping. inches For example , David Gorski criticized Brian M. Berman, organizer in the School of Maryland Centre when considering Integrative Drugs, with respect to posting that "There indicators that both equally substantial acupuncture and sham acupuncture treatment more efficient than hardly any treatment and that acupuncture might be a beneficial nutritional supplement to other forms of ordinary remedy due to mid back pain. very well He likewise castigated writers and peer critics at the New The united kingdomt Journals of Medicine pertaining to letting it to come to be composed, as it effectively advised intentionally mistaken subjects in order to achieve a known placebo effect. Heaps of thanks to WikiPedia with respect to the incredible information that is made available to Internet users by notably committed staff of unpaid editors and team members. This information was gleaned from WikiPedia.org. You should definitely take out your purse and consider A DONATION BY CLICKING HERE.