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In this week’s episode of The Logic Pros, we are looking at one of LPX’s most over-looked features, the MIDI FX Arpeggiator. A somewhat new option for Logic users, these FX offer a number of interesting ways to create patterns, sounds and more on any Audio Instrument in your library:

MIDI FX are essentially a group of plug-ins, not unlike your typical Audio FX, that can alter our MIDI patterns, among many other things. Highlighted by the Arpeggiator, the list of options is generally split into creative options and those that serve a more utility type of role like for example, the Transposer. For today’s purposes we will be focusing on some of the more common (and fun) uses of the Arpeggiator.

For those that may not be aware, basically speaking Arpeggiators create arpeggios, or rhythmic patterns made up of a defined sequence of notes, or chord. While some instruments, like Logic’s new Alchemy sample-synth, have an arpeggiator built right in, Logic’s MIDI FX Arpeggiator adds the very popular production tool to any instrument you choose. Simply load it up and then play/record any series of notes, or chord(s), and you will be arpeggiating all over the place.

In the Note Order section found along the middle of the UI, we have the basic rate knob on the left which determines the speed at which the notes of our chord/pattern get played: 1/1 …1/2….1/8…1/16 notes etc.). Next we have the large white Note Order option buttons which determine the order in which the notes of our pattern are played back: ascending, descending, ascending/descending repeating the first and last notes and random (more on these below). To the right of those, we have the Variation and Oct Range/Inversions sliders. The Oct Range slider increases the range of the arpeggio up an octave (up to 4 octaves) based on your initial pattern (more on this below). The Variation slider is where things can get really interesting, as each of the  Note Order options having 4 variations available increases the possibilities here exponentially.

Down below we have the Pattern, Options, Keyboard and Controller panels. The pattern section is where we get a readout of our patterns and the ability to edit them. Grid mode is where we will be spending most of our time editing, but we will talk more about Live mode below. The Keyboard tab, allows us to lock our patterns to a particular scale or even set the range of the keyboard so that only certain octaves will trigger the Arpeggiator.

The Options panel has the Note Length knob which allows us to determine how long each of the notes in the pattern rings out, from very long to very snappy and percussive. Velocity determines the dynamic range of the loudness of each note in the pattern making it pretty useful for locking all notes to the same value. Swing alters the groove of the pattern as you would expect and Cycle Length will determine how much of the particular sequence set in the Pattern panel will be played. Personally I spend most of my time in this bottom section in the Options panel playing with the Note Length and Swing to get the groove just right.

We won’t spend much time on the Controller section, but we can basically control the the main Option panel controls with hardware controllers from here. Sometimes I like to control the Note Length using hardware controllers on my MIDI keyboard to slow turn choppy rhythms into legato melodies. Just select Note Length in one of the empty Destination slots, choose -Learn- from the corresponding MIDI Controller slot above, then wiggle the hardware knob of your choice and your done.

It is always best to experiment with these features and use your ear, but here are a few of our favorite tips for using the Arpeggiator in Logic Pro X:

Live step recording. First give that last big white button to the right in the Note Order section a high-five. This will essentially allow you to step record, or play in each of the notes of your pattern one at a time, and is one of our favorite ways to use the plug. Once engaged, you can use that tiny pull down arrow in the bottom left hand corner of the Arpeggiator UI, and turn on “Silent Capture” mode. This will allow you to play in notes on your keyboard to create patterns. Sometimes this kind of step recording can result in some pretty creative patterns, outside of your typical chords.

While Logic can be a little finicky when it comes to creating Live patterns this way and then implementing them into your project, the next tip will solve all of that for you:

While we are certainly just scratching the surface of what can be done with Arpeggiator, this should give some newer users a good head-start and maybe even teach some old dogs a few new tricks. If you have any questions or interesting uses for Arpeggiator or MIDI FX in general, be sure to tell us about them below. We will be adding some video elements to upcoming episodes as well, so if you have any thoughts or suggestions there, let us know.

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Making The Most Of The Holidays In It

I was in the office during the week of Christmas and not a soul was stirring, not even a mouse. To be truthful, the little rolling ball in my mouse was making an annoying squeaking sound and I really had been meaning to order a wireless mouse. But you know how the little things never get done in IT because we are all so busy (barely) keeping up with the big things day by day (and sometimes night by night).

Well guess what? This is the perfect time of year to tackle the tasks resulting from your year long procrastination. You need to get off your rump and put all that energy stored from over consumption of holiday party treats to good use.

A lot of people take this time off for vacation and if that includes you, stop reading this article and go back to sunning yourself on the beach. But if you are one of the unfortunate souls stuck behind the desk the last week of December, this is your time to make a list and check it twice.

Over the years, here are some tasks I have tackled and a few I wish I would have (and still might this year). Not all of these are tangible, but that doesn’t make them less important.

• Take a look at your desk. Can you see it? Or do you see a stack of papers? It may be that you know exactly where everything is (as I claim) but one strong breeze from a hastily closed door or an accidental spill of your coffee mug would put your “filing” system in disarray. Take this time to clean up your desk and do some filing. You may even come across some papers that will add a couple more tasks that otherwise would have been overlooked.

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• The holiday season is a great time to network. Take account of important people in your network that you haven’t connected with recently. Either drop them an email or give them a call to see what’s new and fill them in on your recent doings. This is the time of year where people are in a good mood to connect and you might set the stage for a business or personal opportunity in the coming year.

• Continuing on the networking front, if you have been postponing joining an online network now is the time. The dominant player is LinkedIn and it seems to be loaded with IT professionals. If you enter your professional profile now, by the end of next year at this time you’ll have a burgeoning network that you can leverage for answers to technical questions, finding recommended contractors and job searches (hiring for your team and for your next job).

• Take a close look at your heavy usage personal hardware. Many companies have set plans to cycle equipment every so many years, but many do not. Identify the desktops, laptops, printers, PDA’s, etc. that keep needing repairs or are more than a few years old and do some holiday shopping for the business.

Behind The Scenes Of The Most Beautiful Botanical Sketches

© The Board of Trustees of the Royal Botanic Gardens, Kew, from Botanical Sketchbooks (c) 2023 Thanks & Hudson Ltd, London, published in the USA by Princeton Architectural Press.

Thomas Baines, sketches, watercolour, watercolor. © The Board of Trustees of the Royal Botanic Gardens, Kew, from Botanical Sketchbooks (c) 2023 Thanks & Hudson Ltd, London, published in the USA by Princeton Architectural Press.

Princeton Architectural Press

An effective sketch can consist of simply a few minimalist pencil marks, or perhaps a more deliberate pen and ink drawing, in sepia or bold Indian ink. English-speakers only began ‘sketching’ officially in the late 17th century, at least that’s when the word ‘sketch’ (from German skizze or Dutch schets) enters the English language. German skizze, from the early 17th century, captured the sound of the Italian schizzo, meaning quickly splattering or splashing. It seems to express the dynamism and immediacy of many of the sketches seen here. French had its esquisse and Spanish esquicio, going all the way back to the Latin schedius. The popularity of the act was in part dependent on the availability of the materials. Drawing became much more widespread, indeed a recognized activity in itself, as paper became cheaper and more plentiful in 15th-century Europe. Sketches became a way of accumulating and storing visual information.

Colour added complexity: washes, watercolours, opaque body colours, perhaps small-scale studies in oil. The printmaker and artist Martin Schongauer created one of the earliest recognized botanical sketches with his study of three peonies in the early 1470s. Crucially, these were drawn direct for life not from memory or copied from elsewhere, and were reproduced in his Madonna of the Rose Garden of 1473. Because the boundaries between a hasty drawing, a pondered study (such as Schongauer’s) and an almost finished picture are matters of degree, we have cast the net widely and brought together a varied and fascinating range of styles materials and purposes. Formal botanical art can be constrained by convention—both artistic and scientific—while sketches give the artist freedom to explore and express ideas.

Cordia nodosa

Cordia nodosa, by Violet GrahamNo folio # , Book 10, 1957-1959

A leaf rubbing and sketch of Cordia nodosa fruit. The artist and biology teacher, Violet Emily Graham (1911-1991) noted the hollow in the stem which houses Azteca ants that protect the plant from herbivores.

© The Board of Trustees of the Royal Botanic Gardens, Kew, from Botanical Sketchbooks (c) 2023 Thanks & Hudson Ltd, London, published in the USA by Princeton Architectural Press.

Vanda coerulea, the famous ‘blue orchid’ introduced to the orcid market in the mid-19th century. John Day bought this specimen in June 1880 and painted its delicate and unusual blueness 18 December that year.

Thomas Baines, sketches, watercolour, watercolor © The Board of Trustees of the Royal Botanic Gardens, Kew, from Botanical Sketchbooks (c) 2023 Thanks & Hudson Ltd, London, published in the USA by Princeton Architectural Press.

A magnificent Crinum cassicaule painted by Thomas Baines (1820-1875), an artist who explored rather than an explorer who drew.

© The Board of Trustees of the Royal Botanic Gardens, Kew, from Botanical Sketchbooks (c) 2023 Thanks & Hudson Ltd, London, published in the USA by Princeton Architectural Press.

William Burchell’s (1781-1863) sketches were eclectic, including plant and nature studies and landscapes. Here are ‘A group of plantains from nature’ a spider and a hermit crab.

© The Board of Trustees of the Royal Botanic Gardens, Kew, from Botanical Sketchbooks (c) 2023 Thanks & Hudson Ltd, London, published in the USA by Princeton Architectural Press.

John Champion (1815-1854) consistently sent home news of plants he thought might be new to western science, with detailed drawings and copious notes on flimsy writing paper. Here is Aeschynanthus ceylanicus a trailing epipyte, from his botanizing in Sri Lanka.

Sadleria cyatheoidesMary GriersonHawaii scrapbook © The Board of Trustees of the Royal Botanic Gardens, Kew, from Botanical Sketchbooks (c) 2023 Thanks & Hudson Ltd, London, published in the USA by Princeton Architectural Press.

Pea from an Eyptian mummy

Pea from an Eyptian mummy, by Mary Anne Stebbing, Drawings from Broad Park, 1902, f.58

Mary Anne Stebbing (1845-1927) was from a family described as a ‘very nest of naturalists’. Here are Stebbing’s drawings from friends’ gardens. ‘A pea from a mummy from Egypt?’ (left) and Fuschia ‘Daniel Lambert’ (right).

Excerpted from Botanical Sketchbooks by Helen and William Bynum, published in the USA by Princeton Architectural Press. Reprinted with permission by the publisher.

The Most Amazing Images Of The Week, March 5

This week’s collection of images take us from Arctic fashion to a three-year-old’s stomach, from India to Mars, from sharks to lions. Good stuff.

What If the Eameses Made Electric Guitars?

Core77 shows off the guitar craftsmanship of Greg Opalik, who makes Eames furniture during the day and lovingly sculptured Sinuous Guitars in his off-hours.

The Patricia and Phillip Frost Museum of Science

These new renderings of the forthcoming Miami museum are impressive, and somewhat alarming.

Do You See a Bird?

This image of the Seagull Nebula’s 100-light-year wingspan was NASA’s Astronomy Picture of the Day yesterday.

Mercedes With Cloaking Device

In the latest Mercedes commercial, one side of this F-Cell car has a camera recording the scenery it passes, while the other side displays that scenery on a field of LEDs, effectively letting you see through the car. Engadget has the video.

A Dust Devil on Mars

Pictured: a Martian dust devil twisting across the Martian Amazonis Planitia region. The 100-foot-wide column of swirling air was captured by the Mars Reconnaissance Orbiter last month as it passed over the northern hemisphere of Mars.

Do Not Eat Your Ultra-Powerful Toy Magnets

Payton, a curious three-year-old in Oregon, was playing with those toy magnets that you’re not supposed to play with if you’re too young, and not supposed to eat ever. The child was cut open and the magnets were safely retrieved. Watch the report.

Bricklaying Woman

Channi Anand captured this image of a female bricklayer at a plant on the outskirts of Jammu, Indian, on International Women’s Day. Anand is an AP photographer based in and around India. From American Photo.

Fashion Among the Crystals of Power

io9 points out that the Chanel show in Paris this week is oddly reminiscent of Superman’s Fortress of Solitude. But more fashiony.

Firestorm Birth

This image, from Hubble’s Wide Field Camera 3, shows the chaos of star birth, dust, and collision out in the giant elliptical galaxy Centaurus A. Read more here.

Car Interiors

We discovered the Car Interiors tumblr this week, and spent a fair portion of the following days browsing it. Hard to explain why this is so mesmerizing, but we could always look at just one more. And maybe one after that.


This shot, and a whole bunch of others, were taken with a little camouflaged beetle-like camera-robot. The lions don’t seem to be afraid of it–though they are curious.

Lambo Avento

The Lamborghini Aventador, seen here, was one of a whole mess of sweet cars seen at this year’s Geneva Auto Show, held this past week.

Getting Started With The Basic Tasks Of Computer Vision

This article was published as a part of the Data Science Blogathon

If you are interested or planning to do anything which is related to images or videos, you should definitely consider using Computer Vision. Computer Vision (CV) is a branch of artificial intelligence (AI) that enables computers to extract meaningful information from images, videos, and other visual inputs and also take necessary actions. Examples can be self-driving cars, automatic traffic management, surveillance, image-based quality inspections, and the list goes on. 

What is OpenCV?

OpenCV is a library primarily aimed at computer vision. It has all the tools that you will need while working with Computer Vision (CV). The ‘Open’ stands for Open Source and ‘CV’ stands for Computer Vision.

What will I learn?

The article contains all you need to get started with computer vision using the OpenCV library. You will feel more confident and more efficient in Computer Vision. All the code and data are present here.

Reading and displaying the images

First let’s understand how to read the image and display it, which is the basics of CV.

Reading the Image:

import numpy as np import cv2 as cv import matplotlib.pyplot as plt img=cv2.imread('../input/images-for-computer-vision/tiger1.jpg')

The ‘img’ contains the image in the form of a numpy array. Let’s print its type and shape,

print(type(img)) print(img.shape)

The numpy array has a shape of (667, 1200, 3), where,

667 – Image height, 1200 – Image width, 3 – Number of channels,

Displaying the Image:

# Converting image from BGR to RGB for displaying img_convert=cv.cvtColor(img, cv.COLOR_BGR2RGB) plt.imshow(img_convert) Drawing over Image

We can draw lines, shapes, and text an image.

# Rectangle color=(240,150,240) # Color of the rectangle cv.rectangle(img, (100,100),(300,300),color,thickness=10, lineType=8) ## For filled rectangle, use thickness = -1 ## (100,100) are (x,y) coordinates for the top left point of the rectangle and (300, 300) are (x,y) coordinates for the bottom right point # Circle color=(150,260,50), (650,350),100, color,thickness=10) ## For filled circle, use thickness = -1 ## (250, 250) are (x,y) coordinates for the center of the circle and 100 is the radius # Text color=(50,200,100) font=cv.FONT_HERSHEY_SCRIPT_COMPLEX cv.putText(img, 'Save Tigers',(200,150), font, 5, color,thickness=5, lineType=20) # Converting BGR to RGB img_convert=cv.cvtColor(img, cv.COLOR_BGR2RGB) plt.imshow(img_convert)

Blending Images

We can also blend two or more images with OpenCV. An image is nothing but numbers, and you can add, subtract, multiply and divide numbers and thus images. One thing to note is that the size of the images should be the same.

# For plotting multiple images at once def myplot(images,titles): fig, axs=plt.subplots(1,len(images),sharey=True) fig.set_figwidth(15) for img,ax,title in zip(images,axs,titles): if img.shape[-1]==3: else: img=cv.cvtColor(img, cv.COLOR_GRAY2BGR) ax.imshow(img) ax.set_title(title) img1 = cv.imread('../input/images-for-computer-vision/tiger1.jpg') img2 = cv.imread('../input/images-for-computer-vision/horse.jpg') # Resizing the img1 img1_resize = cv.resize(img1, (img2.shape[1], img2.shape[0])) # Adding, Subtracting, Multiplying and Dividing Images img_add = cv.add(img1_resize, img2) img_subtract = cv.subtract(img1_resize, img2) img_multiply = cv.multiply(img1_resize, img2) img_divide = cv.divide(img1_resize, img2) # Blending Images img_blend = cv.addWeighted(img1_resize, 0.3, img2, 0.7, 0) ## 30% tiger and 70% horse myplot([img1_resize, img2], ['Tiger','Horse']) myplot([img_add, img_subtract, img_multiply, img_divide, img_blend], ['Addition', 'Subtraction', 'Multiplication', 'Division', 'Blending'])

The multiply image is almost white and the division image is black, this is because white means 255 and black means 0. When we multiply two-pixel values of the images, we get a higher number, so its color becomes white or close to white and opposite for the division image.

Image Transformation

Image transformation includes translating, rotating, scaling, shearing, and flipping an image.

img=cv.imread('../input/images-for-computer-vision/tiger1.jpg') width, height, _=img.shape # Translating img_translate=cv.warpAffine(img,M_translate,(height,width)) # Rotating center=(width/2,height/2) M_rotate=cv.getRotationMatrix2D(center, angle=90, scale=1) img_rotate=cv.warpAffine(img,M_rotate,(width,height)) # Scaling scale_percent = 50 width = int(img.shape[1] * scale_percent / 100) height = int(img.shape[0] * scale_percent / 100) dim = (width, height) img_scale = cv.resize(img, dim, interpolation = cv.INTER_AREA) # Flipping img_flip=cv.flip(img,1) # 0:Along horizontal axis, 1:Along verticle axis, -1: first along verticle then horizontal # Shearing srcTri = np.array( [[0, 0], [img.shape[1] - 1, 0], [0, img.shape[0] - 1]] ).astype(np.float32) dstTri = np.array( [[0, img.shape[1]*0.33], [img.shape[1]*0.85, img.shape[0]*0.25], [img.shape[1]*0.15, img.shape[0]*0.7]] ).astype(np.float32) warp_mat = cv.getAffineTransform(srcTri, dstTri) img_warp = cv.warpAffine(img, warp_mat, (height, width)) myplot([img, img_translate, img_rotate, img_scale, img_flip, img_warp], ['Original Image', 'Translated Image', 'Rotated Image', 'Scaled Image', 'Flipped Image', 'Sheared Image']) Image Preprocessing

Thresholding: In thresholding, the pixel values less than the threshold value become 0 (black), and pixel values greater than the threshold value become 255 (white).

I am taking the threshold to be 150, but you can choose any other number as well.

# For visualising the filters import plotly.graph_objects as go from plotly.subplots import make_subplots def plot_3d(img1, img2, titles): fig = make_subplots(rows=1, cols=2, specs=[[{'is_3d': True}, {'is_3d': True}]], subplot_titles=[titles[0], titles[1]], ) x, y=np.mgrid[0:img1.shape[0], 0:img1.shape[1]] fig.add_trace(go.Surface(x=x, y=y, z=img1[:,:,0]), row=1, col=1) fig.add_trace(go.Surface(x=x, y=y, z=img2[:,:,0]), row=1, col=2) fig.update_traces(contours_z=dict(show=True, usecolormap=True, highlightcolor="limegreen", project_z=True)) img=cv.imread('../input/images-for-computer-vision/simple_shapes.png') # Pixel value less than threshold becomes 0 and more than threshold becomes 255 _,img_threshold=cv.threshold(img,150,255,cv.THRESH_BINARY) plot_3d(img, img_threshold, ['Original Image', 'Threshold Image=150'])

After applying thresholding, the values which are 150 becomes equal to 255

Filtering: Image filtering is changing the appearance of an image by changing the values of the pixels. Each type of filter changes the pixel value based on the corresponding mathematical formula. I am not going into detail math here, but I will show how each filter work by visualizing them in 3D. If you are interested in the math behind the filters, you can check this.

img=cv.imread('../input/images-for-computer-vision/simple_shapes.png') # Gaussian Filter ksize=(11,11) # Both should be odd numbers img_guassian=cv.GaussianBlur(img, ksize,0) plot_3d(img, img_guassian, ['Original Image','Guassian Image']) # Median Filter ksize=11 img_medianblur=cv.medianBlur(img,ksize) plot_3d(img, img_medianblur, ['Original Image','Median blur']) # Bilateral Filter img_bilateralblur=cv.bilateralFilter(img,d=5, sigmaColor=50, sigmaSpace=5) myplot([img, img_bilateralblur],['Original Image', 'Bilateral blur Image']) plot_3d(img, img_bilateralblur, ['Original Image','Bilateral blur'])

Gaussian Filter: Blurring an image by removing the details and the noise. For more details, you can read this.

Bilateral Filter: Edge-preserving, and noise-reducing smoothing.

In simple words, the filters help to reduce or remove the noise which is a random variation of brightness or color, and this is called smoothing.

Feature Detection

Feature detection is a method for making local decisions at every image point by computing abstractions of image information. For example, for an image of a face, the features are eyes, nose, lips, ears, etc. and we try to identify these features.

Let’s first try to identify the edges of an image.

Edge Detection img=cv.imread('../input/images-for-computer-vision/simple_shapes.png') img_canny1=cv.Canny(img,50, 200) # Smoothing the img before feeding it to canny filter_img=cv.GaussianBlur(img, (7,7), 0) img_canny2=cv.Canny(filter_img,50, 200) myplot([img, img_canny1, img_canny2], ['Original Image', 'Canny Edge Detector(Without Smoothing)', 'Canny Edge Detector(With Smoothing)'])

Here we are using the Canny edge detector which is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images. It was developed by John F. Canny in 1986. I am not going in much details of how Canny works, but the key point here is that it is used to extract the edges. To know more about its working, you can check this.

Before detecting an edge using the Canny edge detection method, we smooth the image to remove the noise. As you can see from the image, that after smoothing we get clear edges.

Contours img=cv.imread('../input/images-for-computer-vision/simple_shapes.png') img_copy=img.copy() img_gray=cv.cvtColor(img,cv.COLOR_BGR2GRAY) _,img_binary=cv.threshold(img_gray,50,200,cv.THRESH_BINARY) #Edroing and Dilating for smooth contours img_binary_erode=cv.erode(img_binary,(10,10), iterations=5) img_binary_dilate=cv.dilate(img_binary,(10,10), iterations=5) contours,hierarchy=cv.findContours(img_binary,cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE) cv.drawContours(img, contours,-1,(0,0,255),3) # Draws the contours on the original image just like draw function myplot([img_copy, img], ['Original Image', 'Contours in the Image'])

Erode The erosion operation that uses a structuring element for probing and reducing the shapes contained in the image.

Dilation: Adds pixels to the boundaries of objects in an image, simply opposite of erosion

Hulls img=cv.imread('../input/images-for-computer-vision/simple_shapes.png',0) _,threshold=cv.threshold(img,50,255,cv.THRESH_BINARY) contours,hierarchy=cv.findContours(threshold,cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE) hulls=[cv.convexHull(c) for c in contours] img_hull=cv.drawContours(img, hulls,-1,(0,0,255),2) #Draws the contours on the original image just like draw function plt.imshow(img) Summary

We saw how to read and display the image, drawing shapes, text over an image, blending two images, transforming the image like rotating, scaling, translating, etc., filtering the images using Gaussian blur, Median blur, Bilateral blur, and detecting the features using Canny edge detection and finding contours in an image.

I tried to scratch the surface of the computer vision world. This field is evolving each day but the basics will remain the same, so if you try to understand the basic concepts, you will definitely excel in this field.

The media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion.


The 11 Most Important Cats Of Science

[Special thanks to materials scientist Joe Spalenka for letting us use his photoshopped image of Watson And Crick Plus Chloe The Cat.]

Clone Kitty

Astronomy Cat

Wireless Telegraph Cat

Spy Cats

Forget high-tech spy gadgets. In the 1960s, the CIA launched Operation Acoustic Kitty. The plan was to train cats—yes, cats—to eavesdrop on Russian conversations. With a microphone implanted in its ear, a transmitter near its collar, and an antenna in its tail, the first feline agent was deployed and promptly run over by a taxi. ☹ A partially redacted memo from 1967 concludes “the program would not lend itself in a practical sense to our highly specialized needs.”

Bionic Cat

Back in 2010, Oscar (pictured) became the first kitty to get prosthetic legs attached directly to his anklebones. The technology—called intraosseous transcutaneous amputation prosthetics, or ITAP—mimics the porousness of deer antlers to fuse flesh and metal together in a tight seal that keeps out dirt and bacteria. ITAP has since been tested in humans, who say the implanted prosthetic legs are much more comfortable than the detachable kind.

Glow-In-The-Dark Cat

When scientists created this genetically modified glow-cat in 2011, they gave the cat a gene that may make it resistant to feline AIDS. The fluorescent green color comes from a different gene that the scientists added, indicating whether the important gene got implanted into the cat’s genome. Last we heard, the scientists intended to expose these genetically modified cats to Feline Immunodeficiency Virus. If the incandescent cats are indeed resistant, it could open up new HIV prevention strategies for humans.

Explorer Cat

“Mrs. Chippy,” pictured, was actually a tomcat. He came along for the ride when Ernest Shackleton set sail for Antarctica on the Endurance. Mrs. Chippy was apparently well-loved by everyone onboard (except the sled dogs), and helped to keep rodent infestations at bay. But sadly, when the ship got stuck in ice, Shackleton and his crew had to abandon ship as well as any extra weight—and that included the cat. Mrs. Chippy was given a last meal before he was put down, but his memory lives on in his life-sized bronze sculpture that’s perched on top of his owner’s grave.

Weightless Cat

Do cats always land on their feet? In 1947, the U.S. government needed to find out the truth. So the Aerospace Medical Division brought two cats up in a C-131 on a parabolic flight, where they would experience a few seconds of weightlessness. It was not a fun day for these poor kittehs. Watch the video here. Spoiler: Cats DO NOT always land on their feet.

Boxing Cats

Not long after Thomas Edison’s team invented the Kinetograph (an early video camera) in 1892, the first cat video was born. Watch two feline fighters duke it out here.

Suborbital Astro-Cat

In 1963, Félicette became the first cat in space. Apparently she was a sweet-tempered street cat from Paris, until the French government started putting her and 13 other kitties through training that included compression chambers and centrifuges. On October 18, Félicette was launched into space inside a special capsule on a French Veronique AG1 rocket, while an electrode array implanted in her brain recorded her neural activity. After riding 100 miles up, the capsule detached from the rocket and parachuted back down to Earth. Félicette survived the descent but was euthanized a few months later so scientists could examine the brain implant. Still, Félicette’s 15 minutes of fame got her face onto postage stamps around the world.

Electric Cat

Quantum Cat

No “Cats of Science” collection would be complete without Schrödinger’s cat. In trying to communicate how quantum mechanics works, Erwin Schrödinger put things into terms that everyone (and yet no one) can understand: Cats. The thought experiment typically goes something like this: Some jerk puts a cat into a sealed box with a bottle of poison and a radioactive substance. If a single atom of the substance decays, the bottle shatters and the cat dies. Because the observer has no way of knowing whether the cat has been poisoned, the animal can be thought to be both alive and dead. Note: Maru (pictured) is not the real Schrödinger cat.

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