Sergio Parra

The Old World transported a large number of diseases to the New World, but disease transmission was not bilateral. At least not in the same proportion (it is still debated whether syphilis, for example, came from America to Europe).

The fundamental reason for this asymmetry, however, lies in a factor that apparently could seem natural, ecological or even flower power: animals.

Domestication and zoonotic diseases

Most Old World diseases originated in animal livestock, especially domesticated animal farms. that were not present in America.

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The Native Americans hardly had domesticated farm animals, and therefore there were not many zoonotic diseases (those spread by close contact between animals and humans). As explained Jeffrey D. Sachs in his book The ages of globalization:

The list of diseases that arrived from Europe was long and deadly, and included smallpox, influenza, typhus, measles, diphtheria, and whooping cough. Smallpox was the great mass murderer: it wiped out an alarming proportion of the native populations that encountered the newly arrived Europeans.

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Books that inspire us: 'The Ages of Globalization' by Jeffrey D. Sachs

The exchange between the Old and New Worlds was very fruitful regarding agricultural products: America provided Europe with corn, potatoes and tomatoes; Europe provided America with wheat and rice. Sheep, goats and pigs also arrived there. And addictive products also flowed bidirectionally: tobacco or sugar cane. But the diseases were much more prevalent in the New World. simply because the natives were not so accustomed to domesticated animals.

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Why were Europeans the ones who brought so many diseases to America and didn't the other way around?

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Systems designed to detect deepfakes (videos that manipulate real-life images through artificial intelligence) they can be fooled, as this study suggests.

Researchers have shown that detectors can be defeated by inserting adversarial examples into each video frame. Adversarial examples are slightly manipulated inputs that cause AI systems, such as machine learning models, to make an error.

Attacking blind spots

In deepfakes, a subject's face is modified to create realistic and convincing images of events that never happened. As a result, typical deepfake detectors focus on the face in the videos: first they track it and then pass the data from the cropped face to a neural network that determines if it is real or fake.

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For example, the blinking of the eyes It doesn't play well in deepfakes, so detectors focus on eye movements as a way to detect that the video is fake.

However, if the creators of a fake video have some knowledge of the detection system, they can design inputs to target the detector's blind spots and avoid it.

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The researchers created a confrontation example for each face in a video frame.. But while standard operations, such as compressing and resizing a video, typically remove adversarial examples from an image, these examples are designed to resist these processes. The attack algorithm does this by estimating over a set of input transformations how the model classifies images as real or fake. The modified version of the face is then inserted into all video frames. The process is then repeated for all frames of the video to create a deepfake video.

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This AI can interpret the music played by an instrument only using visual cues

To improve the detectors, the researchers recommend an approach similar to what is known as Adversarial Machine Learning o adversarial training: during training, an adaptive adversary continues to generate new deepfakes that can bypass the current state-of-the-art detector; and the detector continues to improve to detect new deepfakes.

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For the first time, deepfake detectors can now be fooled and that is a problem

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It has been confirmed that a planetoid, nicknamed "Farfarout", which It was first detected in 2018, It has become the most distant object orbiting our Sun.

The Minor Planet Center now has given him the official designation of 2018 AG37. It will receive an official name after its orbit is better determined in the coming years.

Farfarout

Farfarout's average distance from the Sun is 132 astronomical units (au); 1 au is the distance between the Earth and the Sun. For comparison, Pluto is only 39 au units from the Sun.

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Farfarout's journey around the Sun takes about a thousand years, crossing the orbit of the huge planet Neptune each time.

Farfarout is very faint and, based on its brightness and distance from the Sun, the team estimates its size to be about 400 kilometers across. As explained Chad Trujillo, astronomer at the University of Arizona:

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Farfarout's orbital dynamics can help us understand how Neptune formed and evolved, as Farfarout was likely thrown into the outer solar system by getting too close to Neptune in the distant past. Farfarout is likely to interact strongly with Neptune again as their orbits continue to cross.

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This planetoid that orbits our sun has become the most distant we know: it is 4 times farther than Pluto

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A group from the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) has recently developed a new system to print custom-made functional devices and robots, without human intervention.

Its unique system uses a three-ingredient recipe that allows users to create structural geometry, print strokes, and assemble electronic components such as sensors and actuators.

LaserFactory

LaserFactory, as the system has been named, has two parts that work in harmony: a set of software tools that allows users to design custom devices and a hardware platform that manufactures them.

Like a chef, LaserFactory automatically cuts geometry, dispenses silver for circuit traces, selects and places components, and finally cures the silver to make traces conductive, securing components in place to complete manufacturing.

One of the developers, Martin Nisser, notes that this type of 'one-stop shop' could be beneficial for product developers, manufacturers, researchers and educators looking to quickly prototype things like wearable devices, robots and printed electronics.

Making manufacturing affordable, fast, and accessible to a layman remains a challenge. By leveraging widely available manufacturing platforms such as 3D printers and laser cutters, LaserFactory is the first system to integrate these capabilities and automate the entire line to manufacture functional devices in one system.

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New system to print custom-made functional drones and robots, without human intervention

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Combining machine learning technology with smartphone tracking data to create an app that accurately estimates the spread of flu.

In what suggests a new study published in the magazine Nature Communications, which describes how an application was developed for this purpose.

big data

To create their app, the researchers collected anonymous tracking data from Android phone users in New York City; Google stores the history of users who have chosen to allow such tracking to be recorded. They used that data to teach a machine learning system to recognize human movement on a city map.

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The team then added data from models created to represent flu transmission rates based on patients' hospital visits and laboratory reports for the 2016 to 2017 flu season.

They used the application to predict the spread of the flu for the same season. They later compared the results to actual flu season records and found that they were as accurate as two of the three conventional systems based on passenger data and better than a third.

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Finally, the researchers replicated their efforts to predict the 2016 flu season for all of Australia and They discovered that it could accurately predict the spread of the flu in that country.

The researchers note that using phone tracking data is significantly less expensive than using traveler data. They also noted that their system could also be used to track the spread of an outbreak as it crosses international lines, unlike systems based on passenger data.

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Thanks to phone tracking data combined with machine learning, we can predict the spread of the flu

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Antarctica was once at the equator. These are some of the findings that you can see in the following video: 1 billion years of continental drift in 40 seconds.

The video is the result of research published in the March 2021 issue of Earth-Science Reviews. For the first time it has been built a complete model of tectonics, including all limits.

Continental drift

The main author and creator of the video, Andrew Merdith He began working on the project while he was a PhD student with Dietmar Müller of the EarthByte geosciences group at the University of Sydney.

The model will help scientists understand how the climate has changed, how ocean currents were altered, and how nutrients flowed from deep within the Earth to stimulate biological evolution.

The Continental drift It is the displacement of continental masses with respect to each other. This theory It was developed in 1912 for the german Alfred Wegener based on various empirical-rational observations, but it was not until the 1960s, with the development of plate tectonics, when the movement of the continents could be adequately explained. Life, quite simply, on Earth would not exist without plate tectonics.

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The uninterrupted movement of the Earth's tectonic plates during the last 1 billion years in 40 seconds

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There is no doubt that in these busy times in which multicasting abounds, an audiobook offers many advantages: We can consume it while driving, while running, while lying in bed with our eyes closed, or even while doing many other more cognitively important tasks.

However, There are some substantial differences between reading a book and listening to a book..

The commitment and presence of the narrator

Writing is less than 6,000 years old, insufficient time for the evolution of the specialized mental processes dedicated to reading. We use the mental mechanism that evolved to understand oral language to support the understanding of written language. In fact, the investigations show that adults get almost identical scores on a reading test if they listen to the passages instead of reading them.

However it is not always so. Audiobooks work when the texts are flat, simple, without complex metaphors, without cryptic images. Because there are denser texts that require reducing the reading speed, and even rereading a fragment, or even stopping at a word. Enjoy what it evokes in us as we delight in the taste of coffee.

Thus, A study compared how well students learned about a scientific topic in a 22-minute podcast versus a printed article. Although students spent equivalent time on each format, in a questionnaire written two days later, readers they obtained a result of 81 percent and the listeners, 59 percent.

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In other words: reading is something that is done, that requires commitment, while listening is something that happens, that can happen even if we are not committed to the task. Audiobooks progress with or without our participation. We can tune in, pay attention to the book while our mind wanders to another topic at hand, and the book will still move forward.

That is to say, an average reader will be less involved in an audiobook. It will process it less intensely.

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Furthermore, the audiobook lacks an internal narrator: the narrator is the one who has been hired to read the audiobook. There is an imposed prosody, that is, a tone and a tempo. Because, although writing lacks symbols for prosody, experienced readers infer it as they go. In A study, subjects listened to a recording of the voice of someone speaking quickly or slowly. Everyone then silently read the same text, supposedly written by the person whose voice they had just heard. Those who heard the speaker speak quickly read the text faster than those who heard the speaker speak slowly.

Also, not everything is based on tempo or learning more or less. Even reading fiction, which on the surface seems like just a hobby, can have fruitful results on many levels, as you can see in the following video:

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Reading a book is not the same as listening to an audiobook: commitment, understanding and presence of the narrator

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Researchers at the Del Monte Institute for Neuroscience at the University of Rochester Medical Center (URMC) suggest that caffeine consumption during gestation can change important brain pathways of the fetus that could lead to future behavior problems.

To reach this conclusion, analyzed more than 9,000 brain scans of nine and ten year old children.

Small but relevant changes

Previous studies have found that caffeine can have a negative effect on pregnancy and neurodevelopment. It is also known that a fetus does not have the enzyme needed to break down caffeine when it crosses the placenta. This new study reveals that caffeine could also leave a lasting impact on neurodevelopment.

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The changes are not drastic nor are the behavioral problems severe, but they are relevant, as explained John Foxe, lead author of the study. Basically, attention and hyperactivity difficulties were detected.

The researchers point out that they are also not clear if the impact of caffeine on the fetal brain varies from one trimester to the next, or when these structural changes occur during gestation. Furthermore, as they warn:

It is important to note that this is a retrospective study. We rely on mothers to remember how much caffeine they consumed during pregnancy.

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Consuming caffeine during pregnancy could trigger behavioral problems in the future baby

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The race for life, in which millions of sperm swim quickly towards the egg, is neither fair nor noble: rather a ruthless race in which even There are manipulative sperm that are careful to poison their competition..

In what points out a new study published in PLOS Genetics by researchers at the Max Planck Institute for Molecular Genetics (MPIMG) in Berlin.

The research studied mouse sperm cells under a microscope to better understand the effects of a particular DNA sequence known as the t haplotype. The team knew from previous research that sperm that carry this sequence they tend to swim straighter (instead of going in circles) and faster on average than competing sperm without it.

He haplotype t is a series of linked genes that occupy chromosome 17 in house mice around the world. (Unlike humans, who have 23 pairs of chromosomes, mice only have 20.) What happens is that sperm with the t haplotype manage to disable sperm without it.

Specifically, the t haplotype 'poisons' all sperm during the early phases of sperm production, injecting each cell with certain genes that inhibit its ability to regulate movement. It is not until a later phase, when each cell divides in half, that the 'antidote' comes into play. After splitting, half of sperm inherit the genes of the t haplotype on chromosome 17.

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For lucky sperm, the t haplotype provides new genetic variants that reverse the inhibitory effects of the 'poison' that all cells consumed during the previous phase of development. According to explains the co-author of the study, Bernhard Herrmann, director of MPIMG:

The trick is that the t haplotype 'poisons' all the sperm, but at the same time produces an antidote, which acts only on the t sperm (those with the t haplotype) and protects them. The result is a kind of marathon in which all participants receive poisoned drinking water, but only some of the runners have access to the antidote.

In their study, the researchers saw that many sperm without the antidote literally swam in circles until they died, while their t-haplotype competitors swam directly forward.

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Some sperm literally poison their competition in the race to fertilize an egg

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Tweets containing malicious links are more likely to contain negative emotions, and It is the content of the tweet that increases the likelihood that it will be liked and shared., as researchers at Cardiff University have demonstrated for the first time.

He new study has been published in the magazine ACM Transactions on the Web.

Malicious tweets

As part of the study, the team analyzed a random sample of around 275,000 from a corpus of over 3.5 million tweets which were sent during seven major sporting events: the 2014 FIFA World Cup, the 2015 and 2016 Superbowl, the 2015 Cricket World Cup, the 2015 Rugby World Cup, the UEFA EURO 2016 and the 2016 Olympic Games.

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The team identified 105,642 tweets containing malicious URLs and 169,178 tweets containing benign URLs from this data set, and then used sophisticated computer models to estimate how these tweets survived on the platform 24 hours after the sporting event.

Tweets that were classified as benign were more likely to spread if a user had a large number of followers and the tweet contained positive emotions such as “team,” “love,” “happy,” “enjoyment,” and “fun.”

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However, the results showed that malicious tweets were not strongly associated with the number of followers of the poster and were more likely to spread when the content of the tweet contained negative emotions. Tweets reflecting fear were 114% more likely to be retweeted, with words like “kill,” “fight,” “shoot,” and “controversy” regularly included in tweets containing malicious URLs.

Cybercriminals are increasingly using this method, known as a “drive-by download attack,” to hide a malicious URL in an attractive tweet and use it as click bait to lure users to a malicious web page.

The study suggests that the results show that even with Twitter's security measures, malicious URLs can still go undetected and that this gap is large enough to expose millions of users to malware in a short period of time.

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Tweets containing malicious links are more likely to contain negative emotions

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