The Universe Sparkles in Gamma Rays in this New NASA Animation

Most of the gamma ray sources in this animation are blazars, supermassive black holes with relativistic jets pointed at Earth.


We’ve come a long way since gamma rays were discovered.

The late 1800s and early 1900s were a time of great scientific advancements. Scientists were just getting a handle on the different types of radiation. Radium featured prominently in the experiments, including one by French scientist Paul Ulrich Villard in 1900.


Radium decays readily, and scientists had already identified alpha and beta radiation coming from radium samples. But Villard was able to identify a third type of penetrating radiation so powerful even a layer of lead couldn’t stop it: gamma rays.



Now we have a gamma ray detector in space, and it’s showing us how the Universe sparkles with this powerful energy. Gamma rays are the most energetic form of light in the Universe, and as a new animation shows, the sky practically sparkles with flickering gamma-ray sources. The animation contains a year’s worth of observations from the Large Area Telescope (LAT) on NASA’s Fermi Gamma-ray Space Telescope. Each yellow circle is a gamma-ray source, and the expansion and contraction show how the source brightens and dims. The yellow circle is the Sun’s following its seemingly sinusoidal path relative to Earth.


The animation represents an entire year of observations. Each frame in the animation represents three days. The reddish-orange band that runs through the middle of the animation is the Milky Way’s central plane, which is a consistent gamma-ray producer.

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Ultra-luminous X-ray sources produce about 10 million times more energy than the Sun and shouldn’t exist

These objects are more than 100 times brighter than they should be. Observations by the agency’s NuSTAR X-ray telescope support a possible solution to this puzzle.

Exotic cosmic objects known as ultra-luminous X-ray sources produce about 10 million times more energy than the Sun. They’re so radiant, in fact, that they appear to surpass a physical boundary called the Eddington limit, which puts a cap on how bright an object can be based on its mass. Ultra-luminous X-ray sources (ULXs, for short) regularly exceed this limit by 100 to 500 times, leaving scientists puzzled.

In a recent study published in The Astrophysical Journal, researchers report a first-of-its-kind measurement of a ULX taken with NASA’s Nuclear Spectroscopic Telescope Array (NuSTAR). The finding confirms that these light emitters are indeed as bright as they seem and that they break the Eddington limit. A hypothesis suggests this limit-breaking brightness is due to the ULX’s strong magnetic fields. But scientists can test this idea only through observations: Up to billions of times more powerful than the strongest magnets ever made on Earth, ULX magnetic fields can’t be reproduced in a lab.

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AI increases the reliability of bird migration forecasts

With chatbots like ChatGPT making a splash, machine learning is playing an increasingly prominent role in our lives. For many of us, it’s been a mixed bag. We rejoice when our Spotify For You playlist finds us a new jam, but groan as we scroll through a slew of targeted ads on our Instagram feeds.


Machine learning is also changing many fields that may seem surprising. One example is my discipline, ornithology, the study of birds. It isn’t just solving some of the biggest challenges associated with studying bird migration; more broadly, machine learning is expanding the ways in which people engage with birds. As spring migration picks up, here’s a look at how machine learning is influencing ways to research birds and, ultimately, to protect them.

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100 Days of Python (by Martin Mirakyan)

As the name suggests, Martin Mirakyan is exploring the Python programming language for over 100 days. He starts from a complete beginner level to make sure people without any programming background can get started and follow along. Then he gradually covers more advanced concepts such as task automation, data visualization, and web scraping and performs some basic data-science experiments.


In general, Python is a popular, general-purpose programming language known for its simplicity, readability, and flexibility. It is used in a wide range of applications, including web development, data analysis, scientific computing, and artificial intelligence.


Here are a few reasons why one might start learning Python:

  • Python is easy to learn: Its simple syntax and readable code make it a great first programming language for beginners.
  • Python is versatile: It can be used for a wide range of tasks, from web development to data analysis to artificial intelligence.
  • Python has a large, active community: There are countless resources available online for learning Python, and the community is always working on new libraries and tools to make it even better.


Along with the concepts, the blog posts will also include some practical exercises with links to resources where one might write code and learn by doing.


The list of topics covered will be updated on a rolling basis every day:

  1. Printing values in Python
  2. Getting input from users in Python
  3. Arithmetic expressions and numeric variables in Python
  4. Boolean variables and boolean arithmetic in Python
  5. Conditions in Python — if/else
  6. Nested conditions in Python
  7. Floating point numbers
  8. Augmented assignments (+=, -=, etc.)
  9. Strings
  10. How to format text and what are f-strings in Python?
  11. Lists
  12. What is the range() function in Python?
  13. For loops
  14. While loops
  15. break and continue
  16. How Python almost had another keyword
  17. 5 most useful string casing methods in Python
  18. 5 most useful string modifying methods in Python
  19. Splitting and Joining strings in Python
  20. 10 most useful list methods in Python
  21. Nested loops in Python
  22. Python List Comprehension — Deep Dive
  23. Tuples in Python
  24. Sets in Python
  25. Dictionaries in Python
  26. Why Python Does Not Have Tuple Comprehension?
  27. Functions in Python
  28. What are Multiple Return Values Actually in Python?
  29. 4 Types of Function Arguments in Python that You Might Not Know About
  30. Python Variable Scope
  31. Enumerate and Zip Functions in Python
  32. Lambda Functions in Python: A Comprehensive Guide to Understanding and Using Anonymous Functions
  33. Higher Order Functions in Python
  34. Working With Files in Python
  35. With Statement in Python
  36. Automating Data Cleaning With Python
  37. Positional-only and Keyword-only Arguments in Python
  38. Does Python Have Pass-by-Value VS Pass-by-Reference Variables?
  39. Recursion in Python
  40. What is Stack Overflow Really?
  41. Regular Expressions in Python
  42. Regular Expressions — Grouping and Backreferences
  43. Python Classes and Objects
  44. Mastering Private and Protected Fields in Python Classes: A Complete Tutorial
  45. What are Magic Methods in Python Classes?
  46. Inheritance in Python
  47. Method Overriding in Python
  48. Multiple Inheritance in Python
  49. Type Hints and Type Checking in Python
  50. How to Create Custom Generic Types in Python
  51. Abstract Classes in Python
  52. Data Classes in Python
  53. Properties in Python
  54. Static Methods in Python
  55. Implementing Custom Decorator Functions in Python
  56. Class Decorators in Python
  57. Python — Exception Handling
  58. Exception Hierarchy in Python
  59. Custom Exceptions in Python — Creating Custom Exceptions
  60. Iterators in Python
  61. Generators in Python
  62. Iterables in Python
  63. 10 Most Useful Itertools Methods
  64. Glob — Working with Files in Python
  65. Pathlib — The OOP Approach of Working with File System in Python
  66. Creating Custom Context Managers in Python
  67. All the Ways You Can Use Context Managers in Python
  68. Multithreading in Python
  69. Synchronizing Threads in Python With Locks
  70. Synchronizing Threads in Python With Semaphores
  71. Synchronizing Threads in Python With Barriers
  72. Multiprocessing in Python
  73. What Is the Python Global Interpreter Lock (GIL)?
  74. Multithreading VS Multiprocessing in Python
  75. Thread Pools and Process Pools in Python
  76. Async Await in Python — Asyncio Deep Dive
  77. Async with Expression in Python
  78. Making Requests With asyncio in Python
  79. Working With Databases Using asyncio in Python — SQLAlchemy Example
  80. Multithreading VS Multiprocessing VS Asyncio in Python
  81. How Modules Actually Work in Python and How to Create Your Own Custom Module
  82. What are Packages in Python and What is the Role of files?
  83. Working With Third-Party Libraries in Python
  84. Virtual Environments in Python
  85. Unit Testing in Python with Pytest
  86. Test Coverage in Python with Pytest
  87. Mocking and Fixtures in Python
  88. Web Scraping with Scrapy in Python
  89. Working with Excel Sheets and CSV Files Using Pandas for Data Processing
  90. Working With XML and JSON Data in Python
  91. Mastering Image Processing in Python with Scikit-Image — A Comprehensive Guide to Image Processing Techniques
  92. Mastering NumPy in Python for Numerical Computations: A Comprehensive Tutorial
  93. Mastering Data Analysis with Pandas
  94. Machine Learning in Python with Scikit-Learn
  95. Creating an Interactive Website with Streamlit in Python
  96. Creating Beautiful Data Visualizations with Plotly and Dash
  97. Creating Custom ChatGPT Using the OpenAI API
  98. More coming soon…

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94% of the universe’s galaxies are permanently beyond our reach

The universe is expanding, with every galaxy beyond the Local Group speeding away from us. Today, most of the universe’s galaxies are already receding faster than the speed of light. All galaxies currently beyond 18 billion light-years are forever unreachable by us, no matter how much time passes.


Our universe is full of stars and galaxies everywhere and in all directions. From our vantage point, we observe up to 46.1 billion light years away. Our visible universe contains an estimated ~ 2 trillion galaxies. However, most of them are already permanently unavailable for us.

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Robot Can 3D-Print Cells Inside a Patient’s Body During Surgery

A new robotic arm could be a game changer in surgeries, with the capability to 3D-print biomaterial directly onto organs inside a patient’s body. The arm, designed by a team of engineers from the University of New South Wales in Australia, can place a tiny, flexible 3D bioprinter inside the body, using bio-ink to “print” tissue-like structures onto internal organs. The device, dubbed F3DB, is designed with a swivel head to offer full flexibility of movement and features an array of soft artificial muscles to allow movement in three separate directions. The entire structure can be controlled externally. 


“Existing 3D-bioprinting techniques require biomaterials to be made outside the body and implanting that into a person would usually require large open-field open surgery which increases infection risks,” said Thanh Do, study lead. “Our flexible 3D bioprinter means biomaterials can be directly delivered into the target tissue or organs with a minimally invasive approach. This system offers the potential for the precise reconstruction of three-dimensional wounds inside the body. Our approach also addresses significant limitations in existing 3D bioprinters such as surface mismatches between 3D-printed biomaterials and target tissues/organs as well as structural damage during manual handling, transferring, and transportation process.”


Bioprinting in the medical industry is primarily used for research purposes, such as tissue engineering and drug development, and typically requires large-scale, external 3D printers to create the cellular structures. According to the team, in the next few years, the technology could be used to reach and operate on hard-to-reach areas in the body. In the project’s next stage, the F3DB will be used in animal test subjects, as well as further development of the arm to include an integrated camera and scanning system.

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Yellow crazy ant males have two sets of DNA

Multicellular organisms typically develop from a single cell into a collection of cells that all have the same genetic material. A research team now discovered a deviation from this developmental hallmark in the yellow crazy ant, Anoplolepis gracilipes. Males are chimeras of haploid cells from two divergent lineages: R and W. R cells are overrepresented in the males’ somatic tissues, whereas W cells are overrepresented in their sperm. Chimerism occurs when parental nuclei bypass syngamy and divide separately within the same egg. When syngamy takes place, the diploid offspring either develops into a queen when the oocyte is fertilized by an R sperm or into a worker when fertilized by a W sperm. This study reveals a mode of reproduction that may be associated with a conflict between lineages to preferentially enter the germ line.

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AI develops specific cancer treatment in just 30 days, predicts survival rate

Artificial intelligence has developed a treatment for cancer in just 30 days and can predict a patient’s survival rate. In a new study published in the journal Chemical Science, researchers at the University of Toronto along with Insilico Medicine developed a potential treatment for hepatocellular carcinoma (HCC) with an AI drug discovery platform called Pharma.AI.


HCC is the most common type of liver cancer and occurs when a tumor grows on the liver, according to Cleveland Clinic. Researchers applied AlphaFold, an AI-powered protein structure database, to Pharma.AI to uncover a novel target — a previously unknown treatment pathway — for cancer and developed a “novel hit molecule” that could bind to that target without aid.


The creation of the potential drug was accomplished in just 30 days from the selection of the target and after synthesizing just seven compounds. After a second round of generating compounds, they discovered a more potent hit molecule — but any potential drug would need to go through clinical trials before widespread use.

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Winners of the First AI Film Festival – Showcasing the Latest Tools in AI Filmmaking

The winners of the first annual AI film festival Celebrate the artists making the impossible AI filmmaking.


Runway is one of the leading companies in the development of artificial intelligence tools and introduced the winners of their first annual AI film festival that took place this winter. Of the hundreds of submissions, judges picked ten finalists and released their work to the public. The main goal of this competition was to celebrate “the art and artists making the impossible at the forefront of AI filmmaking.” We were curious and analyzed how different techniques were integrated into the winning films: from AI-generated art to whole 3D scene scans. Let’s take a look at the amazing new technology now available to any creator.


Requirements were that videos were from 1 to 10 minutes long, and one of the main festival criteria was naturally to use neural networks in the work. There was no strict definition of which AI to use, or how to feature it in the film, so the variety of tools used in the winning videos is really impressive. The use of state-of-the-art technology counted as only 25% of a film’s success – judges also took into account the quality of the overall film composition, originality, and of course, the artistic message.

Using art generators as part of AI filmmaking

One of the shorts that impressed me most is “PLSTC” by Laen Sanches (we will embed it below). Basically, it’s just a rapidly edited sequence of hundreds of pictures, which illustrate different ocean inhabitants wrapped in plastic and unable to escape. The director took strong images, created by AI art generator Midjourney, upscaled them with help of the AI tool Topaz Labs, and put them together slightly animated. By precisely choosing the matching visuals, he achieved a very definite and coherent film atmosphere. Not a word is said, but the message is crystal clear, and it hurts. Dramatic classical music also helps evoke deep emotions, and the result is a small narrative wonder. It didn’t place for any of the prizes, but it is definitely worth watching.

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