Month: July 2018

Credits : Insidehpc

 

Software development for future DOE Exascale machines is on track, according to a new Report. While the first Exascale machine is not slated for delivery to Argonne until 2021, ongoing efforts continue towards the monumental task of preparing applications to run 50x faster than possible today.

This ECP Software Technology (ST) Capability Assessment Report (CAR) provides an overview and assessment of current ECP ST capabilities and activities, giving stakeholders and the broader HPC community information that can be used to assess ECP ST progress and plan their own efforts accordingly.

Today, ECP ST efforts contribute to 89 software products in five technical areas. Of the 89 products, 33 are broadly used in the HPC community and require substantial investment and transformation in preparation for Exascale architectures. An additional 23 are important to some existing applications and typically represent new capabilities that enable new usage models for realizing the potential that Exascale platforms promise. The remaining products are in early development phases, addressing emerging challenges and opportunities that Exascale platforms present.

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To manage their companies, successful business owners need the right tools, which often come in the form of software applications. According to an Intermedia report, the average small-to-midsize business uses 14.3 separate applications. These software systems address issues such as logistics, sales, marketing, accounting, human resource management and social media engagement.

But unless integrated, each individual application is an information silo, which limits businesses’ ability to access data and obtain a clear picture of their operations. Application-integration, also referred to as enterprise application integration, allows you, as business owner, to achieve the maximum impact from each of your systems because you’ve linked them into an interactive whole.

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Credits : Fool

Investors are getting used to high-growth business plans that leave profit ambitions for a later date. These cloud computing experts are prime examples of this trend.

Jul 16, 2018 at 3:35PM

What happened

Shares of many cloud-based business software specialists surged higher in the first half of 2018, according to data from S&P Global Market Intelligence. Human resources management expert Workday (NASDAQ:WDAY) rushed 19.1% higher, while data analysis veteran Tableau Software(NYSE:DATA) gained 41.3% and marketing software vendor HubSpot (NYSE:HUBS) jumped 41.7%.

So what

All three of these cloud computing experts have delivered consistently solid earnings reports so far this year. Tableau fell a hair short of Wall Street’s earnings targets in the first quarter but made up for it with fantastic revenue. Workday and HubSpot simply crushed analysts’ estimates across the board in this year’s first two reports.

Investors and analysts embraced the cloud computing experts’ total results, driving their share prices almost uniformly higher around the dates of their business reports.

The one exception to this sector-spanning trend is found in Workday’s chart, which dipped in May for no obvious reason. A fantastic first-quarter report in early June also failed to restart the stalled stock, because of a fairly small boost to Workday’s full-year guidance targets.

Now what

In fact, Workday increased its sales and earnings guidance for fiscal 2018 in that seemingly weak first-quarter report — just not by quite as much as some investors might have liked.

That being said, Mr. Market has been loving cloud computing stocks in recent years. Workday shares have gained a market-crushing 69% over the last 24 months, while Tableau’s stock prices more than doubled and HubSpot delivered a stellar 156% return. Cloud-based services can deliver performance and features on par with many business-oriented tools that used to run as a software package on your desktop, adding centralized management tools for the client and high-margin subscription pricing for software providers.

At this point, all three of the companies under our lens today are valued way up in the nosebleed section, but they earned those tickets through rampant business growth. All of them have delivered average annual sales growth of roughly 50% over the last five years, paving the way toward strong earnings and cash flows when their growth-maximizing strategies give way to margin-focused business plans.

It’s no surprise to see enterprise-oriented cloud computing stocks do well as this hyper-efficient platform type continues to make inroads in every corner of the business world. It’s also not shocking to see the gains take an occasional breather as analysts and investors alike become more adept at valuing subscription prices and long-term contracts.

HubSpot is not on our top “Buy” list, but these 10 stocks are
Investing geniuses David and Tom Gardner just released their best stocks to buy now — and it could pay to listen. Especially when you consider their average stock pick is up 353% vs. a mere 81% for the S&P 500.

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As of PHP 7.1, the php-mcrypt was deprecated. And as of PHP 7.2 it was completely removed. This is a problem, since a number of server software titles still depend upon this encryption tool. And because software like Nextcloud, ownCloud, and many more have yet to shift that dependency, you might find yourself unable to install without mcrypt on the system. What do you do? No matter how many times you run either apt-get install php-mcrypt or yum install php-mcrypt, it won’t work.

Fortunately, there’s a solution. Said solution falls onto the shoulders of the pecl command. PECL is the PHP Extension Community Library, which serves as a repository for PHP extensions. Through this repository, you can install mcrypt.

What is mcrypt?

The mcrypt extension is a replacement for the UNIX cryptcommand. These commands serve as a means to encrypt files on UNIX and Linux systems. The php-mcrypt extension serves as an interface between PHP and mcrypt.

Getting mcrypt installed

I’m going to walk you through the process of getting mcrypt installed on Ubuntu Server 16.04. It’s not challenging once you have the necessary dependencies added to your system. With mcrypt installed, you can continue with the installation of the software that depends upon this extension.

With that said, how do we install mcrypt? First, open up a terminal window and install the necessary dependencies with the commands:

sudo apt-get -y install gcc make autoconf libc-dev pkg-config
sudo apt-get -y install php7.2-dev
sudo apt-get -y install libmcrypt-dev

Once the dependencies have been installed, you can install mcrypt with the command:

sudo pecl install mcrypt-1.0.1

And there you go. Mcrypt is now installed. Go back to the process of installing whatever server software that depends upon this extension and you should be good to go.

Not gone, just moved

Don’t worry: mcrypt is not gone. It’s just been moved out of PHP and into PECL. But for those who have been installing via php-mcrypt for years, this makes for a pretty big shift. Now, instead of being able to install mcrypt with a single command, you have four to deal with. Even so, at least you still have mcrypt available. Eventually, however, I believe the mcrypt dependency will be migrated to another tool (such as OpenSSL).

 

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Kotlin is only seven years old but it is already playing in the big leagues. Its massive success can be traced back to the moment when Google announced Kotlin support in Android last year but that was just the tip of the iceberg. According to Pusher’s State of Kotlin 2018 research report, great things are coming Kotlin’s way.

Say yes to Kotlin

The latest edition of the biannual Technology Radar from ThoughtWorks was released a couple of months ago and highlighted what most of us already know: you absolutely need to adopt Kotlin and Kubernetes but generic cloud usage should be avoided at all costs. Let’s focus on Kotlin, shall we?

This young programming language was among the few superstars on the “adopt” list, along with AssertJ, a Java library which promises to simplify the writing of assert statements in tests and Enzyme, which  has become “the defacto standard for unit testing React UI components.”  Why adopt Kotlin, you ask? For starters,  it keeps on rising, according to RedMonk’s latest report and Pusher’s State of Kotlin 2018 research report.

Kotlin was also one of TIOBE’s finalists for “the programming language of 2017” but it lost to C. We still think it should have won; here’s why.

Speaking of Kotlin, our survey results show that this young padawan has stepped up its game and has traded places with Scala. Unlike last year, when Scala occupied the fifth position and Kotlin the sixth, these languages have gone through a “Freaky Friday” type of situation; shooting star Kotlin is officially in the top 5 most interesting programming languages.

Pusher’s State of Kotlin 2018 report: Key findings

Last week, Pusher, the leading communications and collaboration API provider, released the findings of its State of Kotlin 2018 research report. Over 2.700 developers participated in the survey which was carried out earlier this year with the aim to take the pulse of the new Kotlin adoption trend in the developer community. Just a quick fyi, their next survey (slated for 2019) will focus on the tooling ecosystem. 

First came the experienced developers, then came Google now students are interested in Kotlin, report results show. Adoption among students has skyrocketed; before last year’s Google I/O, less than 25 percent of students were interested in this programming language but after Google announced Kotlin support in Android, the adoption rate went through the roof.

Kotlin and Java go well together

The Kotlin team participated in a Reddit AMA in December 2017; one of the most discussed topics was the “battle” between Kotlin and Java and if the former could be rendered irrelevant since Java is now stepping up its feature game (like auto type inference, or reified generics with project Valhalla). Kotlin lead language designer Andrey Breslav answered the following:

  • The JVM features we can and will pick up as they appear (this applies to Valhalla), so we are only happy when they appear.
  •  If the Java programming language becomes better than Kotlin in every respect, it will be really cool, because the world will benefit so much from it. Before it did, I think it’s still good that it’s making progress now. And competition tends to make you fit. So, I see it as a positive thing.

Also, if any language gets ahead of Kotlin, it means that it tries some features first, and it’s only to our benefit because we can learn from their experience and hopefully make the feature even better (this is what happened to coroutines, for example).

Bottomline: I’m not concerned with any positive progress any language in the world makes. I’m only happy with it 🙂

Another Redditor added that “there’ve been calls from Java community for backward-incompatible ‘Java 2.0’ many years ago. We finally have it. Kotlin is Java 2.0. The future of Java is Kotlin.”

Since we don’t have a magic 8 ball right now, let’s not jump to conclusions. However, one thing is certain: most Kotlin developers come from a Java background, or also work with Java.

What’s more, report results show that null safety is the most important feature “for everyone who ever had a NullPointerException in Java – i.e. every Android developer”. Meanwhile, coroutines and multi-platform support are considered the least important but this may have something to do with the fact that both projects are still experimental.

Type inference, which made a splash in Java 10, received love from 41.6 percent of respondents while Java interoperability managed to impress 61.4 percent of developers.

Speaking of Java and Kotlin, almost 90 percent of respondents have done migrations using a wizard or rewriting code manually. Only 10 percent of those who migrated entire projects (let’s take a moment to salute their courage!), 22 percent were students or had less than one year experience working as software developers. Coincidence? I think not.

As it turns out, more than a quarter of respondents who migrated Java to Kotlin needed to revert; the reasons are both technical and organizational but, according to the results, “tools that use reflection or generate code have been most often mentioned as technical reasons to revert to Java.

 

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Although I have a degree in computer science and teach Microsoft programming, coding is not my day job. As a result, I go through relatively frequent periods when I’m not writing a single line of code, and then, when I get some free time, I try to jump back in.

Mostly, my programming now is supporting an open source donation system, and I do it both because I like to code and because I want to keep my chops up. It’s easy to lose touch with a skill like programming, so making sure you do some of it regularly is one way to stay reasonably fresh.

My normal workflow involves jumping between three main computers: My desktop, a machine by my couch, and my laptop. With cloud-based applications, this isn’t a problem. But with coding, it can be a pain.

To explain, let’s jump back in time. Back in the day, email users used to keep their email on their local computers. Outlook used PST files, and Eudora (if I remember correctly) used box files. The POP3 protocol was designed to allow users to connect into a mail server and download their messages.

This mean that your mail lived on one machine. If you were on another machine, you couldn’t see your mail store. IMAP sort of fixed that, as did Exchange. But many users were still POP3 users and did not have a central email repository.

Cloud-based email services like Gmail changed all that. Most of us, now, use a cloud-based email service and login through our browsers. We can look at our email from any machine (or phone) that has access to the cloud. This is the whole SaaS model. Whether its email like Gmail, CRM like Salesforce, accounting like Quickbooks online, to-do list management like Todoist, or big ERP systems like those offered by SAP, cloud-based SaaS services make mobile anywhere access a breeze.

Oddly enough, programming systems are somewhat behind the curve. There are certainly cloud-based programming environments like Amazon’s Cloud9, CodeAnywhere, CodeEnvy, and Visual Studio, but they’re far from perfect — at least for some coding needs.

For me, cloud-based coding is the holy grail. I’ve long wanted to be able to jump from system to system, and even anywhere there’s a browser, to be able to code. My current project lives in the cloud, has to be tested in the cloud, so why shouldn’t I be able to code it in the cloud?

CODING TOOLS

The thing is, I could. There are a ton of exceptional cloud-based code editors. Think of code editors as word processors, but instead of fancy formatting, they manage the unique aspects of the programming language and environment you’re working in.

They provide code completion (finishing out an object or function name), syntax highlighting (automatically different parts of code in different colors), refactoring (renaming elements of code throughout a project), code-folding (so you can see the big picture), and code reformatting (so the editor does the work of making it all pretty and readable).

These are huge helps for programmers. But there’s another key feature that I, at least, find absolutely essential: A symbolic debugger. A symbolic debugger is a tool that lets you set breakpoints in your code as it runs, examine the values of variables and objects, change those values on the fly, and more. It’s, essentially, the test probe of programming work.

Every modern browser has a built-in JavaScript debugger. JavaScript is run in the browser, so when you inspect a web page element, you can often get to the debugger. As a result, cloud-based coding environments have no problem providing JavaScript debugging.

While part of my donations engine is in JavaScript, the bulk is in PHP. This is the core language that WordPress runs, and my donations system runs inside of WordPress.

There are two main debugging engines for PHP: Xdebug and Zend. When they work, they’re both really excellent. But even in the best of times, they’re hard to setup and very fiddly. They have to execute code both locally and remotely, sometimes they have to set up an SSH channel to communicate debugging info, and they often require lots of configuration tweaks that are unique to every coding environment and every host.

CLOUD-BASED IDES (AND THEIR LIMITATIONS)

Every six months or so, I take a run at the various cloud coding environments to see if they finally support PHP debugging. And every six months or so, I come away disappointed.

CodeAnywhere has a number of wonderful features, including letting you store your source code in Google Drive or Dropbox. But it has absolutely no debugging support for PHP. It understands PHP syntax, but if you want to debug something, you have to drop in the equivalent of print statements throughout your code. It’s not optimal at all.

I tried to get CodeEnvy to work. CodeEnvy is based on Eclipse Che, a cloud-based version of the very powerful Eclipse IDE (interactive development environment). CodeEnvy actually has documentation on getting Zend and Xdebug working. That’s a rarity.

But after about four hours trying to get their default stacks to spin up with all the right configurations and work, I gave up. While some developers tune their development environments over the course of months or years, I need to be able to get in and get out. Clearly, while Eclipse and CodeEnvy are powerful, they’re too time-consuming to solve the problem.

Then I spent quite a while with AWS Cloud9. Amazon acquired Cloud9 in 2016. I thought this would be ideal, since AWS is AWS. I liked the idea of quick integration with S3, LightSail, and all the other AWS services. Unfortunately, I couldn’t get debugging PHP to work.

I did find an April 2018 post from an AWS guy that said, and I quote, “That should be possible, though none of us at Cloud9 have experience with doing this.” He pointed users to a six-year-old Stack Overflow note postulating how to do remote debugging, but it said nothing about making it work with the native Cloud9 EC2 image.

While both Microsoft and Google have cloud development offerings, neither seems to have an answer for PHP.

WHAT ABOUT A CLOUD-BASED DESKTOP?

I thought about setting up a VDI account on Azure or AWS. That would have worked in that I could remote connect into one of those accounts, keep the desktop and configuration on the cloud, and remote desktop from almost any browser.

The thing was, I didn’t want to manage another desktop. Using a Windows-based setup is easiest, at least for me. But WordPress is fussy on Windows, so I’d have to set up Windows, an IDE on the cloud Windows install, and then set up remote debugging to a Linux box hosting my software.

Setting up a virtual Linux host in the cloud is cake, but getting a remote desktop configuration is a bit more work. It’s doable, but then I’d be switching to a Linux UI in a browser window to do all my coding. Meh.

It was when I considered setting up a remote Mac desktop in the cloud using a service like MacInCloud and MacStadium that I realized the idea of a cloud-based remote desktop is dumb. First, the Mac services are either underpowered or very expensive.

But more to the point was this: If I were going to rent someone else’s ancient Mac mini colocated in a cloud somewhere, why wouldn’t I just put one of my own Mac mini units online, connect to it via a remote desktop, and go to town? There’s no reason at all, except I’ve done that already before and it was suboptimal.

Because I’ve been down that road before, I decided not to go there again. I really do want a true cloud-based IDE environment without having to sysadmin another computer, virtual or not.

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Logistics is the process of planning, implementing, and controlling procedures for the efficient and effective transportation and storage of goods, including services and related information.

This press release was orginally distributed by SBWire

Pune, India — (SBWIRE) — 07/09/2018 — Global Logistic Software Market Research Report.” global logistic software market can be bifurcated as software type, end-user, and region. Based on software type, the market is segmented into warehouse management, labor management, transportation management and data management. The research provides a detailed assessment of recent cutting-edge technologies in the IT industry and the factors influencing the applications of the global market. It estimates the growth rate of various segments over the forecast period

The report segments the International Logistic Software Market on the basis of product, end users, and on the basis of topography. The product type segment includes the market’s division into basic, advanced, and professional versions. These sectors are primarily comprised of applications in IT, IoT, managed services, and more. The applications covered by the logistic software include automotive, government & defense, healthcare, telecommunication and it, industrial, engineering and manufacturing, oil & gas and others.

logistic software market is expected to reach approximately USD +16 billion by the end of 2023 with +8 % CAGR during forecast period 2017-2023.

The major players profiled in this report include: Advantech Corporation, Digilogistics, UTI Worldwide Inc, Hexaware Technologies, IBM Corporation, JDA Software, Oracle, Samsung Electronics Co, SAP AG, Tech Mahindra

Steady support to the IT & Telecom sector in developed regions is likely to enable their steady dominance in the Global Logistic Software Market in the coming years, but the growing acknowledgment of the high volume of unmet demand in emerging regions has driven the healthcare sector in countries such as North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa, notwithstanding other dynamic economies in the region such as South East Asia, Japan, and China.

The global logistic software market is highly competitive. Various established international brands, domestic brands and as well as new entrants form a competitive landscape. The key players are nonstop increasingly seeking market expansion through various strategic mergers and acquisitions, innovation, increasing investments in research and development and cost-effective software portfolio. The logistic software market is highly competitive due to the presence of several large vendors.

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As human beings, we are faced with dilemmas on a day to day basis. From smartphones like iPhone vs Samsung to soft drinks such as Pepsi vs Coke. It can be difficult to know which one is best for you. It goes without saying that these products are different in one way or another. The same can be said about Java vs Python but with no rivalry. Unless you have been living in a cave, you already know that Java and Python are two of the most popular programming languages we have today. Therefore, you will never miss programmers who prefer one programming language over the other.

However, if you want to choose one programming language over the other, you need to know how they compare in terms of their ideal uses, application, similarities, and differences. This article will take you through a beginner-friendly comparison between Java vs Python so that you can know which programming language to choose.

Java vs Python: Differences between the two languages

Static and dynamic typing

The main difference between Java vs Python is that Python is dynamically typed while Java is statically typed. Python uses dynamic typing which allows users to change the variable type. This makes Python language easy to write, read but more complex to analyze. This explains why novice programmers find it easier to learn Python faster than other programming languages such as Java. With dynamic typing, newbie programmers can use their variables as they wish without bothering too much what their types are.

Java, on the other hand, uses static typing where a user is forced to choose a variable type which cannot be changed later in the programming language. Many developers say that static typing minimizes the chance that errors might go undetected, which can affect your program. If you don’t have to declare a variable before using them, there is a risk that you might misspell a name of a variable without knowing, thus creating a totally different variable.

Portability and speed

When it comes to portability, both Java and Python are very portable. However, Java popularity makes it a clear winner as it is possible to use it for desktop programs and web applications. In addition, the Java Virtual Machine is available almost everywhere, thus you can be sure your program will be used by almost every user. Java is faster than Python because the latter is interpreted, which makes it run slower than its counterpart. Therefore, if speed is not a big deal, you may want to use Python.

Indentation and Curly Braces

Java uses the traditional curly braces to define the start and end of each class definition and function. That means, you have to define blocks using semicolons, otherwise, the code will not work. Python is different from Java and other programming languages in that it uses a new way to separate a code into blocks. This is known as indentation. The good thing about indentation is that programmers are forced to set out their programs so as to enhance readability. Hence, there is no way an error can occur from missing braces.

How are these programming languages useful?

Both Python and Java are very useful in web and app development. Interactive websites can use Java to emphasize power and wide compatibility. While Python can be used thanks to its simple to learn syntax and potent scripting capabilities. Hence, Java and Python can be used in the creation of web apps. Also, Python and Java can be used for stand-alone programs. Java can be used to create video games like Minecraft and independent programs like OpenOffice.

On the other hand, Python is not commonly used for stand-alone programs, though it was used to create the first BitTorrent. However, Python is now dominating the world of AI, Data science and other cutting-edge technologies that make it one of the highly demanding programming languages.

How is one programing language better than the other and for what purpose?

Most programmers swear that they cannot use any other programming language other than Python. On the other hand, you will never miss people who still use Java. So, the million dollar question is which programming language is better than the other? If you go through various polls and online communities, you’ll see that Python is the winner. Most people believe that Python is easier to learn compared to Java. In addition, Python code is easier to understand and read than Java.

However, the popularity of Java means that you have to learn it so that you can create applications, which can run on different platforms such as Android. The potent Java Virtual Machine explains why Java is still used to build large applications used by most businesses today. Java is also better than Python thanks to its database access. Java Database Connectivity is stronger than Python’s data access layers, hence the reason why Java is preferred in companies with a critical database.

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SOFTWARE GOLIATH Oracle has announced a new subscription model that covers all Java SE licensing and support needs, providing support for users across servers, desktops, and cloud deployments.

With the new launch, they’ve really made an effort to shock you with the name. It’s called. Wait for it… Java SE Subscription. Wow.

“In order to further support the millions of worldwide businesses running Java in production […] Java Subscription removes enterprise boardroom concerns around mission critical, timely, software performance, stability and security updates,” the firm said in its release.

Java SE Subscription is all about complimenting the company’s long-standing Java SE releases as well as the OpenJDK ecosystem, where Oracle now produces open source OpenJDK binaries, enabling developers that don’t need commercial support or enterprise management tools.

The new model also gives commercial licensing to organisations, including commercial features and tools such as the Java Advanced Management Console, which can be used to identify, manage and tune Java SE desktop use across the enterprise. There’s Oracle Premier Support for current and previous Java SE versions bundled in as well, Orale said.

“Companies want full flexibility over when and how they update their production applications,” said Georges Saab, Java Platform Group’s vice president.

“Oracle is the world’s leader in providing both open source and commercially supported Java SE innovation, stability, performance and security updates for the Java Platform. Our long-standing investment in Java SE ensures customers get predictable and timely updates.”

He added that while the subscription model for updates and support has been long established in the Linux ecosystem, people are increasingly used to paying for services rather than products, so it’s a natural progression for Oracle to make the move to start offering a monthly Java SE subscription to suit service-based procurement models for enterprise customers.

The subscription announcement comes just one month after Java turned 20 years old.

Java was originally designed by James Gosling of Sun Microsystems in 1995 as a universal language which would overlay a virtual machine on any platform to run its ‘applets’.

The language, mostly based on C# and C++, has gone on to be the basis of set-top boxes, watches, modems, routers, in fact anything that requires an operating system, as well as an estimated 2.1 billion low-level mobile devices running its embedded version, Java ME.

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Is it possible for microscopes to learn a bit about the brain? Even be taught by neuroscientists to reliably recognize parts of brain cells… all on their own? Though it may seem like something straight out of “The Jetsons”, a neuroscientist and software engineer in the lab of MPFI’s Scientific Director, Dr. Ryohei Yasuda, Ph.D., is developing new software with the goal of vastly improving the daily life of a microscope user. Combining a specialized algorithm, aptly named a neural network with a small amount of training, microscopes can now autonomously and efficiently identify small neuronal compartments called dendritic spines with over 90% accuracy. Just like Rosie the robotic maid, microscopes equipped with this Spine Identification software are helping scientists streamline their daily routine, bringing research one step into the future.

In recent years, there has been a rapid expansion and advancement in powerful imaging technologies, capable of taking a look inside the brain with unprecedented resolution and sensitivity. From imaging technology alone, neuroscientists have gained an immense wealth of new information about the brain. But despite many advances in implementation and capabilities, powerful microscopes (and the software running them) are still lacking when it comes to ease of use and overall user experience.

In a new study published in PLOS ONE, Dr. Michael Smirnov, Ph.D., has created an imaging software he hopes will craft a new type of experience for users.

Smirnov explains that “When engineers and scientists design cutting-edge microscopes, they usually focus on the actual physical components and design. They are mostly interested in what these imaging technologies can do, what boundaries they can break and how they perform. Far less attention is placed on how these complex technologies can be made accessible for the average user and really improve their workflow. Each time I write my software, I always think about the user first; how can I make a difference for the person using it, make their research a little easier.”

The Yasuda lab studies the complex process called synaptic plasticity, which is thought to be the cellular basis of learning and memory. When single dendritic spines are stimulated, hundreds of signaling molecules are mobilized to carry new information throughout the neuron. Members of the lab investigate this process in detail utilizing 2-photon microscopy, hoping to glean insights into how the interplay of these molecules translates into memory.

Live 2-photon imaging experiments can be an intensive endeavor. Scientists must patiently sift through a neuron’s dendritic arbor, scanning hundreds of spines for suitable candidates to image. Experiments are frequently repeated to amass sufficient data and those that fail mid-way must be restarted. This often unspoken aspect can quickly turn prolonged imaging into a tedious and time consuming chore, ultimately slowing scientific progress.

Dr. Smirnov’s software aims to rescue neuroscientists from the humdrum of spine imaging. Weaving in an element of machine learning, the algorithm can be taught how to differentiate between dendrite backbone and dendritic spines after being fed a training data set of previously identified spines. Once the training period finishes, the software is capable of automatedly scanning through an image and demarcating spines it comes across with high precision. Unlike previously developed programs which can be computing intensive or optimized only for post imaging analysis, Smirnov’s software is fast, scalable and compatible with most live imaging setups and post analysis applications.

“We incorporated a machine learning approach, because we wanted our software to be flexible, adaptable and as easy to use as possible.” Says Smirnov “The program only requires the user to input an image and specify the scale. After that, the program does the rest.”

Smirnov describes that by automating the process of spine identification, the software has the potential to dramatically increase experiment workflow, shaving off hours of time. In addition, he has also made his program’s code readily available to the broader neuroscience community. A move he hopes will engage the community of coders working in the field and give them the ability to freely improve upon and customize the software, making it useful for wider applications.

Dr. Smirnov notes that Scientists at MPFI are not only committed to conducting high caliber basic neuroscience research, but also look for ways to continuously improve the research process; fast tracking new discoveries and positively impacting scientists’ daily lives.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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