Library of a Software Developer: March 2020

The best free writing software 2020, Tools for novelist and creative writers

We've hand-picked the very best free writing software, which will make it easier to plan, write without distractions, and prepare your work for publication.

Microsoft Word is the default tool for many writers, but a subscription to Office 365 is pretty expensive if you only need the word processing element.

There's often a better option for those of us starving in garrets: free software. Come with us as we discover the best free apps to turn your writing talent into something tangible.

The programs here are specifically created with writing in mind, and are packed with thoughtful extras to make your life easier, particularly for creative tasks that require your full concentration.

1. FocusWriter

Available for Linux, Windows and macOS, FocusWriter is designed to eliminate distractions so you can actually get on with the job of writing. To that end, it enables you to hide other apps, customize the way your text appears on screen and keep track of your progress. If you're feeling particularly old-school you can even add typewriter sound effects.

FocusWriter isn't for everyone – it's not the right tool for going back through and editing your work – but it's a lovely little app with a very modest footprint that stops you keeping an eye on Twitter all day.

Download Link: https://gottcode.org/focuswriter/

2. WriteMonkey

We're big fans of Markdown, the text-editing language that enables you to format, annotate, classify and link as you type with the minimum of fuss, and the superb WriteMonkey makes good use of it.

This free writing software delivers an incredibly stripped-down user interface that's considerably more powerful than it looks. There's an excellent outliner, automatic syntax highlighting and file organisation, and although markdown takes a bit of getting used to, you'll be very glad you made the effort.

Once you've mastered WriteMonkey, you can use it to create blog posts, print publications and anything else that needs words in it.

Download Link: https://writemonkey.com/

3. LibreOffice Writer

LibreOffice is a free, open source alternative to Microsoft Office, and that means its word processing app, Writer, has many of the power features of Word without the accompanying price tag.

It's a great choice for writers, with a full set of editing tools, a thesaurus, dictionaries for pretty much any language you can think of, and an active community in the support forums ready to help with any questions you might have.

It's available for Windows, macOS and Linux, and receives regular updates with new features and bug fixes.

The only real drawback compared to Word is the lack of direct cloud support, although you can easily use this free writing software together with a service like Dropbox, and the absence of a mobile app for working on the move. If you're happy to stick with the desktop then this could be the only word processor you need.

Download Link: https://www.libreoffice.org/

4. Scribus

If your words' appearance is as important as their meaning, give Scribus a go: it's a free, highly-rated desktop publishing application for Linux, OS X and Windows that's suitable for producing entire magazines.

It's been kicking around – and regularly updated – since 2001, and while it's a little tricky to use at first, it offers professional-grade publishing with layered, multi-page documents and good colour management support. If you've ever used Adobe InDesign, you'll find the similarity striking. If you can use one, you'll pick up the other in seconds.

We wouldn't want to lay out a 400-page book in it (though that's quite possible), but for shorter works this free writing software is ideal.

Download Link: https://www.scribus.net/

5. Freemind

Here's another app for writers that isn't strictly for putting your words on the screen: Freemind is all about mind mapping, and it enables you to record all the leaps and bounds your imagination makes whether you're plotting a potboiler or trying to organize complex threads of an investigation.

Freemind isn't something we'd necessarily recommend for mind mapping beginners – it looks a bit like a desktop publishing app having some kind of breakdown – but if you're an experienced intellectual explorer it's a lot tidier than a wall full of index cards and sticky notes. Used together with your favorite free writing software, it's invaluable.

Download Link: http://freemind.sourceforge.net/wiki/index.php/Download

Ever wish your computer could think like you do or perhaps even understand you?

That future may not be now, but it's one step closer, thanks to a Texas A&M University-led team of scientists and engineers and their recent discovery of a materials-based mimic for the neural signals responsible for transmitting information within the human brain.

The multidisciplinary team, led by Texas A&M chemist Sarbajit Banerjee in collaboration with Texas A&M electrical and computer engineer R. Stanley Williams and additional colleagues across North America and abroad, has discovered a neuron-like electrical switching mechanism in the solid-state material β'-Cu_xV₂O₅ -- specifically, how it reversibly morphs between conducting and insulating behavior on command.

The team was able to clarify the underlying mechanism driving this behavior by taking a new look at β'-Cu_xV₂O₅, a remarkable chameleon-like material that changes with temperature or an applied electrical stimulus. In the process, they zeroed in on how copper ions move around inside the material and how this subtle dance in turn sloshes electrons around to transform it. Their research revealed that the movement of copper ions is the linchpin of an electrical conductivity change which can be leveraged to create electrical spikes in the same way that neurons function in the cerebral nervous system -- a major step toward developing circuitry that functions like the human brain.

Their resulting paper, which features Texas A&M chemistry graduate students Abhishek Parija (now at Intel Corporation), Justin Andrews and Joseph Handy as first authors, is published Feb. 27 in the Cell Press journal Matter.

In their quest to develop new modes of energy efficient computing, the broad-based group of collaborators is capitalizing on materials with tunable electronic instabilities to achieve what's known as neuromorphic computing, or computing designed to replicate the brain's unique capabilities and unmatched efficiencies.

"Nature has given us materials with the appropriate types of behavior to mimic the information processing that occurs in a brain, but the ones characterized to date have had various limitations," Williams said. "The importance of this work is to show that chemists can rationally design and create electrically active materials with significantly improved neuromorphic properties. As we understand more, our materials will improve significantly, thus providing a new path to the continual technological advancement of our computing abilities."

While smart phones and laptops seemingly get sleeker and faster with each iteration, Parija notes that new materials and computing paradigms freed from conventional restrictions are required to meet continuing speed and energy-efficiency demands that are straining the capabilities of silicon computer chips, which are reaching their fundamental limits in terms of energy efficiency. Neuromorphic computing is one such approach, and manipulation of switching behavior in new materials is one way to achieve it.

"The central premise -- and by extension the central promise -- of neuromorphic computing is that we still have not found a way to perform computations in a way that is as efficient as the way that neurons and synapses function in the human brain," said Andrews, a NASA Space Technology Research Fellow. "Most materials are insulating (not conductive), metallic (conductive) or somewhere in the middle. Some materials, however, can transform between the two states: insulating (off) and conductive (on) almost on command."

By using an extensive combination of computational and experimental techniques, Handy said the team was able to demonstrate not only that this material undergoes a transition driven by changes in temperature, voltage and electric field strength that can be used to create neuron-like circuitry but also comprehensively explain how this transition happens. Unlike other materials that have a metal-insulator transition (MIT), this material relies on the movement of copper ions within a rigid lattice of vanadium and oxygen.

"We essentially show that a very small movement of copper ions within the structure brings about a massive change in conductance in the whole material," Handy added. "Because of this movement of copper ions, the material transforms from insulating to conducting in response to external changes in temperature, applied voltage or applied current. In other words, applying a small electrical pulse allows us to transform the material and save information inside it as it works in a circuit, much like how neurons function in the brain."

Andrews likens the relationship between the copper-ion movement and electrons on the vanadium structure to a dance.

"When the copper ions move, electrons on the vanadium lattice move in concert, mirroring the movement of the copper ions," Andrews said. "In this way, incredibly small movements of the copper ions induce large electronic changes in the vanadium lattice without any observable changes in vanadium-vanadium bonding. It's like the vanadium atoms 'see' what the copper is doing and respond."

Transmitting, storing and processing data currently accounts for about 10 percent of global energy use, but Banerjee says extrapolations indicate the demand for computation will be many times higher than the projected global energy supply can deliver by 2040. Exponential increases in computing capabilities therefore are required for transformative visions, including the Internet of Things, autonomous transportation, disaster-resilient infrastructure, personalized medicine and other societal grand challenges that otherwise will be throttled by the inability of current computing technologies to handle the magnitude and complexity of human- and machine-generated data. He says one way to break out of the limitations of conventional computing technology is to take a cue from nature -- specifically, the neural circuitry of the human brain, which vastly surpasses conventional computer architectures in terms of energy efficiency and also offers new approaches for machine learning and advanced neural networks.

"To emulate the essential elements of neuronal function in artificial circuitry, we need solid-state materials that exhibit electronic instabilities, which, like neurons, can store information in their internal state and in the timing of electronic events," Banerjee said. "Our new work explores the fundamental mechanisms and electronic behavior of a material that exhibits such instabilities. By thoroughly characterizing this material, we have also provided information that will instruct the future design of neuromorphic materials, which may offer a way to change the nature of machine computation from simple arithmetic to brain-like intelligence while dramatically increasing both the throughput and energy efficiency of processors."

Because the various components that handle logic operations, store memory and transfer data are all separate from each other in conventional computer architecture, Banerjee says they are plagued by inherent inefficiencies regarding both the time it takes for information to be processed and how physically close together device elements can be before thermal waste and electrons "accidentally" tunneling between components become major problems. By contrast, in the human brain, logic, memory storage and data transfer are simultaneously integrated into the timed firing of neurons that are densely interconnected in 3-D fanned-out networks. As a result, the brain's neurons process information at 10 times lower voltage and an almost 5,000 times lower synaptic operation energy in comparison to silicon computing architectures. To come close to achieving this kind of energetic and computational efficiency, he says new materials are needed that can undergo rapid internal electronic switching in circuits in a way that mimics how neurons fire in timed sequences.

Handy notes that the team still needs to optimize many parameters, such as transition temperature and switching speed along with the magnitude of the change in electrical resistance. By determining the underlying principles of the MIT in β'-Cu_xV₂O₅ as a prototype material within an expansive field of candidates, however, the team has identified certain design motifs and tunable chemical parameters that ultimately prove useful in the design of future neuromorphic computing materials, a major endeavor that has been seeded by the Texas A&M X-Grant Program.

"This discovery is very exciting because it provides fertile ground for the development of new design principles for tuning materials properties and also suggests exciting new approaches to researchers in the field for thinking about energy efficient electronic instabilities," Parija said. "Devices that incorporate neuromorphic computing promise improved energy efficiency that silicon-based computing has yet to deliver, as well as performance improvements in computing challenges like pattern recognition -- tasks that the human brain is especially well-equipped to tackle. The materials and mechanisms we describe in this work bring us one step closer to realizing neuromorphic computing and in turn actualizing all of the societal benefits and overall promise that comes with it."

The multi-year project incorporates team members from four disciplines (chemistry, physics, materials science and engineering, and electrical and computer engineering) and researchers from Texas A&M, Lawrence Berkeley National Laboratory, the University at Buffalo, Binghamton University and Texas A&M University at Qatar while also relying on work performed at Berkeley Lab's The Molecular Foundry and the Advanced Light Source (ALS), the Advanced Photon Source (APS) at Argonne National Laboratory and the Canadian Light Source. The research was funded primarily by the National Science Foundation (Grant No. DMR 1809866) with additional support from a Texas A&M X-Grant and the Qatar National Research Fund.

Date: 3 March, 2020

Source: Texas A&M University

Using a machine-learning algorithm, MIT researchers have identified a powerful new antibiotic compound. In laboratory tests, the drug killed many of the world's most problematic disease-causing bacteria, including some strains that are resistant to all known antibiotics. It also cleared infections in two different mouse models.

The computer model, which can screen more than a hundred million chemical compounds in a matter of days, is designed to pick out potential antibiotics that kill bacteria using different mechanisms than those of existing drugs.

"We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery," says James Collins, the Termeer Professor of Medical Engineering and Science in MIT's Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering. "Our approach revealed this amazing molecule which is arguably one of the more powerful antibiotics that has been discovered."

In their new study, the researchers also identified several other promising antibiotic candidates, which they plan to test further. They believe the model could also be used to design new drugs, based on what it has learned about chemical structures that enable drugs to kill bacteria.

"The machine learning model can explore, in silico, large chemical spaces that can be prohibitively expensive for traditional experimental approaches," says Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science in MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL).

Barzilay and Collins, who are faculty co-leads for MIT's Abdul Latif Jameel Clinic for Machine Learning in Health, are the senior authors of the study, which appears today in Cell. The first author of the paper is Jonathan Stokes, a postdoc at MIT and the Broad Institute of MIT and Harvard.

A new pipeline

Over the past few decades, very few new antibiotics have been developed, and most of those newly approved antibiotics are slightly different variants of existing drugs. Current methods for screening new antibiotics are often prohibitively costly, require a significant time investment, and are usually limited to a narrow spectrum of chemical diversity.

"We're facing a growing crisis around antibiotic resistance, and this situation is being generated by both an increasing number of pathogens becoming resistant to existing antibiotics, and an anemic pipeline in the biotech and pharmaceutical industries for new antibiotics," Collins says.

To try to find completely novel compounds, he teamed up with Barzilay, Professor Tommi Jaakkola, and their students Kevin Yang, Kyle Swanson, and Wengong Jin, who have previously developed machine-learning computer models that can be trained to analyze the molecular structures of compounds and correlate them with particular traits, such as the ability to kill bacteria.

The idea of using predictive computer models for "in silico" screening is not new, but until now, these models were not sufficiently accurate to transform drug discovery. Previously, molecules were represented as vectors reflecting the presence or absence of certain chemical groups. However, the new neural networks can learn these representations automatically, mapping molecules into continuous vectors which are subsequently used to predict their properties.

In this case, the researchers designed their model to look for chemical features that make molecules effective at killing E. coli. To do so, they trained the model on about 2,500 molecules, including about 1,700 FDA-approved drugs and a set of 800 natural products with diverse structures and a wide range of bioactivities.

Once the model was trained, the researchers tested it on the Broad Institute's Drug Repurposing Hub, a library of about 6,000 compounds. The model picked out one molecule that was predicted to have strong antibacterial activity and had a chemical structure different from any existing antibiotics. Using a different machine-learning model, the researchers also showed that this molecule would likely have low toxicity to human cells.

This molecule, which the researchers decided to call halicin, after the fictional artificial intelligence system from "2001: A Space Odyssey," has been previously investigated as possible diabetes drug. The researchers tested it against dozens of bacterial strains isolated from patients and grown in lab dishes, and found that it was able to kill many that are resistant to treatment, including Clostridium difficile, Acinetobacter baumannii, and Mycobacterium tuberculosis. The drug worked against every species that they tested, with the exception of Pseudomonas aeruginosa, a difficult-to-treat lung pathogen.

To test halicin's effectiveness in living animals, the researchers used it to treat mice infected with A. baumannii, a bacterium that has infected many U.S. soldiers stationed in Iraq and Afghanistan. The strain of A. baumannii that they used is resistant to all known antibiotics, but application of a halicin-containing ointment completely cleared the infections within 24 hours.

Preliminary studies suggest that halicin kills bacteria by disrupting their ability to maintain an electrochemical gradient across their cell membranes. This gradient is necessary, among other functions, to produce ATP (molecules that cells use to store energy), so if the gradient breaks down, the cells die. This type of killing mechanism could be difficult for bacteria to develop resistance to, the researchers say.

"When you're dealing with a molecule that likely associates with membrane components, a cell can't necessarily acquire a single mutation or a couple of mutations to change the chemistry of the outer membrane. Mutations like that tend to be far more complex to acquire evolutionarily," Stokes says.

In this study, the researchers found that E. coli did not develop any resistance to halicin during a 30-day treatment period. In contrast, the bacteria started to develop resistance to the antibiotic ciprofloxacin within one to three days, and after 30 days, the bacteria were about 200 times more resistant to ciprofloxacin than they were at the beginning of the experiment.

The researchers plan to pursue further studies of halicin, working with a pharmaceutical company or nonprofit organization, in hopes of developing it for use in humans.

Optimized molecules

After identifying halicin, the researchers also used their model to screen more than 100 million molecules selected from the ZINC15 database, an online collection of about 1.5 billion chemical compounds. This screen, which took only three days, identified 23 candidates that were structurally dissimilar from existing antibiotics and predicted to be nontoxic to human cells.

In laboratory tests against five species of bacteria, the researchers found that eight of the molecules showed antibacterial activity, and two were particularly powerful. The researchers now plan to test these molecules further, and also to screen more of the ZINC15 database.

The researchers also plan to use their model to design new antibiotics and to optimize existing molecules. For example, they could train the model to add features that would make a particular antibiotic target only certain bacteria, preventing it from killing beneficial bacteria in a patient's digestive tract.

Date: February 20, 2020

Source: Materials provided by Massachusetts Institute of Technology. Original written by Anne Trafton.

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