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A very similar protocol was developed for the determination of antibodies to the CPS (plates coated with 5 µg/mL CPS were obtained from Statens Serum Institut), pneumococcal surface protein A (PspA; plates coated with 1 µg/mL, provided by Dr. David Briles, University of Alabama, Birmingham) and pneumolysin (Ply; coated with 1.7 µg/mL, obtained as described elsewhere [ 18 ]).

Measurement of serum opsonophagocytic activity (OPA) directed against S. pneumoniae type 6B. Because the most common serotype isolated was serotype 6B, OPA to this serotype was measured using methods established elsewhere [ 19 ]. Briefly, human polymorphonuclear leukocytes (PMNs) from healthy adult donors were freshly isolated by ficoll-histopaque density-gradient centrifugation. Heat-inactivated human serum specimens were serially diluted in eight 3-fold steps in a 96-well microtiter plate with Hanks' balanced salt solution/0.2% bovine serum albumin and were then incubated with 1000 cfu of a type 6 strain (0603, a 6B invasive isolate [ 20 ]) and complement (baby rabbit serum; final concentration, 12.5%) for 30 min at 37°C (final volume, 40 µL/well) on an orbital shaker. PMNs were then added at a 200:1 ratio (cells:bacteria; final volume, 80 µL/well), and the mixture was incubated for 45 min at 37°C with shaking. After incubation, samples from each well were diluted and plated onto blood agar plates overnight at 37°C and 5% CO 2 . The OPA titer was calculated as the reciprocal of the serum dilution that caused a 50% reduction in colony-forming units (killing), compared with colony-forming units from control wells that contained all reagents except human serum. The lowest opsonophagocytic titer that could be measured by our method was 24, the final dilution of human serum at the highest tested concentration. Serum specimens not demonstrating a 50% reduction in colony-forming units in the OPA at the lowest serum dilution were assigned a titer of 12 for the purposes of statistical analysis.

Measurement of serum opsonophagocytic activity (OPA) directed against S. pneumoniae type 6B.

Statistical analyses. Differences in clinical characteristics of patients were evaluated by Student's t test or Wilcoxon rank sum test, depending on whether the data were normally distributed. Differences in antibody concentrations between colonized and uncolonized patients were evaluated by Student's t test after the log transformation of data. Comparison of log-transformed serum antibody concentrations 2 and 4 months before the acquisition of pneumococcal colonization was performed using a paired t test. Logistic regression analysis was performed to determine which variables were independently and significantly associated with the acquisition of pneumococcus. For all comparisons, P < .05 was considered to be significant. Statistical analyses were performed using SPSS 11.0 for Macintosh (release 11.0.4; SPSS) and PRISM for Macintosh (version 4.0a; Prism).

These observations suggest that HIV did not enter the cells and integrate into the host’s genome. Together, all the evidence suggests that HIV-1 did not productively infect genital epithelial cell line Ect1. The restriction is likely at the level of viral entry, presumably as a result of the absence of CD4 on Ect1 cells. Our observation is consistent with general notions that genital epithelial cells are refractory to cell-free HIV infection. In vivo studies in nonhuman primate models have shown that atraumatic inoculation of cell-free SIV onto the vagina or penis led to a productive infection of associated immune cells, but not of epithelial cells [ 22 , 23 ]

An interesting finding in the present study is that the epithelial cells were capable of sequestering significant amounts of virus particles and that the virus remained infectious even after treatment with trypsin. It appeared that Ect1 cells retained virus for a prolonged period. We estimated that only a small percentage of virus was released into the culture medium. The ability to sequester virus appeared not to be related to cell size, because HeLa CD4 and Ect1 cells are of similar sizes and their membrane-associated proteins do not differ greatly. It is likely that this ability is associated with the content of specific viral-binding proteins on the cells. Our observation is consistent with a recent study by Dezzutti et al. [ 24 ], which showed that human primary urogenital epithelial cells could not be infected with cell-free HIV but are capable of sequestering virus and transmitting the virus to PBMCs in coculture. This phenomenon is reminiscent of dendritic cells’ (DCs) sequestering and presenting HIV-1 to CD4-expressing susceptible targets via DC-SIGN [ 25 ], a C-type lectin that is highly expressed in DCs. Nonspecific sequestering of HIV by DCs is believed to play an important role in HIV transmission

In the case of Ect1 cells, our evidence indicates that heparan sulfate moieties of proteoglycans on the Ect1 cell membrane mediate the viral attachment. Epithelial cells are known to carry large amounts of HSPG [ 19 , 20 ], and the Ect1 cell line has a high level of heparan sulfate on its surface (Z.W., unpublished data). Cell-surface heparan sulfate has been shown to mediate HIV-1 attachment by interacting with viral gp120 [ 19 , 21 ], and heparan sulfate or heparin can inhibit virus infection by interfering with gp120 binding to CD4 cells [ 19 , 21 ]. However, it is not clear whether, in addition to heparan sulfate, other cell surface molecules, such as DC-SIGN, were involved in the initial viral attachment to Ect1 cells. The resistance of the sequestered virus to treatment with trypsin and the observation that the Ect1 cell–associated virus remained infectious for up to 9 days (data not shown) longer than in DCs [ 25 ] suggest that the virus is protected by cellular structures. Membrane microvilli of epithelial cells may shield the virus, or the virus could be enveloped by vesicles of the epithelial cells. The cellular mechanisms of viral protection, by epithelial cells, require further investigation

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The real danger is thatthe information and social platformson the internet are being corrupted in the service of con men, political demagogues and thieves

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Cambridge Analytica’s wholesale scraping of Facebook user data is familiar news by now, and we are all “shocked” that personal data are being shared and traded on a massive scale. But the real issue with social media is not the harm to individual users whose information was shared, but the sophisticated and sometimes subtle mass manipulation of social and political behavior by bad actors, facilitated by deceit, fraud and the amplification of lies that spread easily through societal discourse on the internet.

Any pretense to privacy was abandoned long ago when we accepted the free service model of Google, Facebook, Twitter and others. Did the Senators who listened to Mark Zuckerberg’s mea culpa last week really think that Facebook, which charges nothing for its services to users, was simply providing a public service for free? Where did they think its $11 billion in advertising revenue came from, if not from selling ads and user data to advertisers?

Let’s identify the real issue. The tangible damage resulting from identity theft and the loss of personal financial information are growing problems, but they typically are not caused by our use of social media platforms, where we share a lot of information—but rarely our credit card or Social Security numbers.

The controversy about Cambridge Analytica that landed Zuckerberg before Congress actually began brewing over a year ago. It was a controversy not about privacy but about how Cambridge Analytica put vast amounts of personal data, mostly from Facebook, into its so-called “psychographic” engine to influence behavior at the individual level (see When the Big Lie Meets Big Data , published here in March of 2017).

Cambridge Analytica worked with researchers from Cambridge University who developed a Facebook app that provided a free personality test, then proceeded to scoop up all the users’ Facebook data plus that of all their friends (thus leveraging the actual users, who numbered less than a million, to harvest the data of more than 80 million people). Using this data, Cambridge Analytica then classified each individual’s personality according to the so-called “OCEAN” scale (Openness, Conscientiousness, Extroversion, Agreeableness and Neuroticism) and fashioned individually targeted messages to appeal to each person’s personality.

Neither subpoenas nor investigative journalists were needed to find all this out—most of it was publicly revealed by Cambridge Analytica’s chief Alexander Nix in a marketing presentation that received wide distribution on YouTube. Cambridge Analytica (owned partly by Robert Mercer, an early pioneer in artificial intelligence who has been a financial backer of Breitbart News and other right-wing causes) had already done work for the Trump Campaign, and Nix was seeking more business.

The real danger revealed by the Cambridge Analytica scandal is that the information and social platforms of the internet, on which we increasingly spend our time and through which more and more of our personal and social connections flow, are being corrupted in the service of con men, political demagogues and thieves. Russia’s troll farm, the Internet Research Agency, employs fake user accounts to post divisive messages, purchase political ads, spread fabricated images and even organize political rallies.

The danger of misinformation is not just political; it is commercial as well. Major purchasers of internet advertising know that the “pay per click” model is flawed. Competitors can set up bots (or even human campaigns) to click on their ads, driving up their costs and casting doubt on the value of an advertising campaign. The company Devumi sells Twitter followers and retweets to celebrities and businesses in order to make them appear more popular than they are. The followers are fake, cobbled together in automated fashion by scraping the social media web for names and photos.

Until Zuckerberg’s decision to testify before Congress, there was scant evidence that Twitter or Facebook were disturbed about any of this. Still, there are a number of machine learning tools that can be used to identify fake accounts or activity.

In 2015, a University of Maryland professor, Jen Golbeck , discovered an ingenious real-time method for identifying fake social media accounts. She found that the number of a user’s Twitter or Facebook friends follows a well-known statistical distribution called Benford’s Law. The law states that, in a conforming data set, the first significant digit of numbers is a “1” about 30 percent of the time—six times more often than it’s a 9. The phenomenon, which is quite widespread, is named after physicist Frank Benford, who illustrated it with the surface areas of rivers, street addresses, numbers appearing in a Reader’s Digest issue, and many more examples.

In other words, if you looked at (say) a thousand Facebook users and counted how many friends each had, roughly 300 of them would have friend counts in the teens (1x), 100–199 range (1xx) or 1,000–1,999 range (1xxx). Only 5 percent would have counts beginning with a nine: 9, 90–99, 900–999, 9,000–9,999.

We can represent each Facebook, Twitter or other social media user as a network of linked users. A plot of an individual user’s links to other users might look like the figure below:

To assess whether a user is genuine, we can look at each of that user’s friends, and count friends or followers. Specifically:

1. Consider a friend or follower of the account in question.

2. Count its followers/friends (“friend of friend”); record.

3. Repeat for all the remaining friends/followers of the original account.

4. Calculate the distribution of those “friend of friend” counts.

Russian Bots Were Revealed, Yet Stayed Online

Golbeck found that the overwhelming majority of Facebook, Twitter and other social media follower and friend counts followed Benford’s Law. On Twitter, however, she found a small set of 170 accounts whose follower distributions differed markedly from that law. In her 2015 paper she wrote:

“Some accounts were spam, but most were part of a network of Russian bots that posted random snippets of literary works or quotations, often pulled arbitrarily from the middle of a sentence. All the Russian accounts behaved the same way: following other accounts of their type, posting exactly one stock photo image, and using a different stock photo image as the profile picture.”

Golbeck told me that she and others posted lists of Russian bots active on Twitter three years ago, and they were still active as of January in this year. Twitter does not seem to care. More to the point, a meticulous cleansing of user records would have the financially deleterious effect of reducing its user base; in Silicon Valley business plans begin and end with a large and constantly growing user base.

Does it matter that fake Russian (and other) Twitter and Facebook accounts and attendant activity persist? What harm can they do?

The Future

It is hard to see how government regulation will play a useful role. In today’s digital age, regulation is like placing rocks in a streambed. The water will simply flow around them, even big ones.

It’s possible that the social media titans will use tools at their disposal like those discussed here to drastically reduce the impact of fake accounts and manipulative behavior. Currently, we have the attention of Mark Zuckerberg of Facebook, because of the peculiar Cambridge Analytica circumstances, where the storyline runs something like “Breitbart and Trump funder scrapes massive amounts of personal data from Facebook, uses it to manipulate opinion.” Meanwhile, Jack Dorsey, the founder of Twitter, has made some promises to improve identity verification but has otherwise escaped the most recent limelight.

The ultimate solution may lie in a smarter public. Can people be taught to approach what they see on the internet with greater skepticism? P.T. Barnum would say no, but there is one powerful example of public education that had a good and profound end: smoking. The tremendous decline in smoking around the world is due largely to public education and an attendant change in behavior, not to regulation and not to greater public responsibility on the part of tobacco companies.

The views expressed are those of the author(s) and are not necessarily those of Scientific American.

As I mentioned in the first activity described here, we love playing with buttons. This button set includes a variety of different shapes and colors, and it’s a lot of fun to use them in combination with play dough. Here we made a little house,we added windows from square buttons, and decorated the roof with other shapes. You can build cars with playdough, and add button wheels, you can make a face from playdough and add button eyes, nose, mouth. The possibilities are endless, just use caution.

We try to make a bath time as fun as possible. One of the activities we do while taking a bath is painting on bathroom walls with finger paint . If you haven’t tried it already, it’s a lot of fun and super easy to wash. I usually squirt a little bit of paint on the side of the tub so Scarlett can dip her brush into the paint.

For this activity you need flat marbles and a paper with lines of different shape. Show your toddler how to arrange marbles on the line.Scarlett is really into marbles, so after doing this activity, she was busy transferring marbles into different containers for almost half an hour.

We made this sandbox out of cream of wheat – it’s easier to clean up and to get a hold of. I drew some shapes on index cards and showed Scarlett how to recreate the shape in the sand. In our index cards we had a circle, a triangle, a square, a zigzag line, a straight line and a dotted line. Scarlett seemed to be most interested in shapes.

After drawing shapes on the sand, we converted the sensory bin into beach scene. Some animals came over to relax on the beach, but Scarlett was most interested in scooping the sand (cream of wheat) into a little cup with a spoon, and then transferring it into another cup. We also poured sand into a see-through plastic cup by using a funnel.

You can make this patches puzzle by printing these files , cutting out squares and showing your toddler how to match the squares to the picture to complete it. There are 3 picture puzzles in the downloadable file.

These animal action cards are hit with Scarlett. She started to do some of these action when she was about 18 months old, and now at 25 months old she is able to do almost all of them besides puffing her cheeks like a chipmunk. She asks me to play with these cards over and over again. I showed her how to do the actions the first time we played with these and now she does them on her own. It’s fun for the whole family, we clap when she does it right, and she gets to let her energy out in a fun way. The digital version of these cards is available on etsyand a printed version is .

During these activity we look for foam letters in rice, and then match the letter we found to the letter card. I used flash cards that we had since they have letters on the back of the cards, but you can just draw the letters you put in the rice bin on a piece of paper for matching. Colored or regular rice can be used.

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