The changed patent law brings United States patent regulations more in line with the rest of the world, resulting in a need for American companies to reassess which ways the use of an electronic laboratory notebook can be most beneficial in terms of experiment documentation.

While emphasis prior to the law change was on capturing timestamps and signatures within experimentss that resulted in novel invention, the newly enacted “first to file” rule means the pressure has switched from proving initial discovery to preparing laborious documentation as quickly as possible.

In the case of the Ensemble® E-Notebook, the functionality to facilitate expedited patent reporting has already been in place as an underlying capability for the last couple of years. While it will require some basic configuration, simple patent reporting tools can help shave a precious number of days off of patent filing preparation. The tools already present within Ensemble are not new, but the concept of using them to speed up patent reporting is. Early adoption of these semi-automated patent reporting tools within ELN will undoubtedly provide an immediate advantage over more traditional patent reporting methods.

After basic configuration, the ELN can be used to quickly and easily create compiled experiment documentation for use within a patent report. In this short tutorial video, Ensemble E-Notebook for Chemistry is used to demonstrate how existing ELN tools can be used to populate and export reports combining chemical synthesis experiments, resulting in a much faster patent reporting process.

If you would like assistance in performing the simple configurations necessary to create a patent reporting tool within your Ensemble E-Notebook, please reach out to our customer support team here.

 The blue line represents the lifetime cost of sick care vs. the red line cost of healthy care.

Yet increasing life expectancy also means increasing medical expenses. The most medically expensive years of an individual’s life are the last five; by increasing the amount of time a person continues to age also increases the amount of money that person must spend to sustain life.

When it comes to health and longevity, there are many interesting relationships occurring in today’s society related to lifestyle. Countries with the highest energy consumption rates are topping the charts with lowest life expectancy. The average number of calories consumed daily has increased more than 50 percent in the last 40 years.

These figures strongly suggest that food addictions to products such as sugar and corn, which comprise a large proportion of foods found on today’s modern grocery store shelves, are playing a devastating role in the decline of health and life expectancy.

To combat these circumstances, we must revolutionize the way we approach healthcare today. Instead of practicing medicine reactively by treating symptoms and diagnoses, a cultural change needs to be made so that as a society, we embrace preventative, personalized healthcare. By spending more money and energy upfront to understand an individual’s personalized environmental health risks, the long-term effect is longer life and decreased medical expenses through effective disease prevention.

Join experts and peers on May 8 and 9, 2013, in Newton, Massachusetts in attendance of the Revolutionaries for Global Health Summit. The summit, hosted by PerkinElmer, will feature numerous presentation tracks focusing on subjects relating to global health, such as next generation sequencing, in vivo imaging, targeted small molecules, tissue and cellular imaging, proteins and biologics, informatics, epigenetics, cellular and tissue imaging, and biotherapeutics.

Click here to watch a video PerkinElmer Life Sciences and Technology President Kevin Hrusovsky give a presentation on modern health trends at a previous RGH Summit.

Complete your free registration to attend RGHS in Newton here.

Use hashtag #RGH13 to follow related discussions on Twitter.

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The proposed effort to map the brain’s cells and neural connections in entirety, called the BRAIN initiative, will make strides towards understanding cause and treatment of diseases such as Parkinson’s, Alzheimer’s, stroke and brain injury.

Yet some of the biggest effects of the project will be seen in the advancement of computational technology and data analysis. The acronym BRAIN stands for Brain Research through Advancing Innovative Neurotechnologies, which makes clear that the project will push the technological envelope to develop equipment and computational capability to allow scientists to track electrical activity at the micro level of individual cells and connections.

New methods for sensing and recording electrical activity in the brain will need to be developed in order for researchers to track brain processing patterns in greater number and speed, ideally “at the speed of thought”, said the White House.

The field of neuroinformatics will be greatly affected as new IT solutions for analyzing massive sets of brain-related data will need to be developed in order to facilitate the collection of data from trillions of points in the brain. The implications of the increased data-crunching ability will no doubt extend far beyond neuroscience to affect the manner in which big data and computational processing is handled across various industries.

Down the line, just as advancements in whole genome sequencing have opened the door for widespread access to genetic testing as a method to manage personal health, mapping the human brain could introduce individualized brain maps as another accessible tool to understand, prevent and treat disease at the level of an individual patient.

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Drug discovery researchers can work more efficiently to bring new therapies to market using innovative user-defined data capture solutions to document experiments and analyze data.

Within large research and development organizations, the need for standardized data reporting and analysis conflicts with the need for flexibility within experimental data capture. Scientists often resort to self defining data capture parameters on a case-by-case basis, which make it difficult to compare data across experiments, perform consistent data analyses and share data within a research organization.

Scientifically-intelligent software platforms not only allow for creative data capture but also reduce or eliminate the time scientists traditionally have had to spend transcribing and combining data from different sources. Automated merging of data allows for information to be combined through the introduction of metadata to support input from different sources of capture, and removes the possibility for manual transcription errors to occur. This merge of data is achieved without the need for writing code, which reduces the need for substantial IT support traditionally necessary to maintain an organized, searchable collection of experimental information.

Read our white paper titled “User-Defined Data Management Solutions Free the Drug Discovery Researcher” to learn more specifically how pharmaceutical scientists can leverage informatics to improve their research and development processes.

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Laboratories operate under extensive rules and regulations in order to prevent hazardous accidents from occurring through chemical or biological experimental processes. Even with preventative measures and protective guidelines in place, the experimental nature of laboratory research means accidents are inevitable.

The challenge for scientists is how they can organize their data in a manner that allows them to learn incrementally from each accident that occurs. For humans to document the parameters that caused accidents and then distribute that information in an effective way to their peers is a daunting task. A solution developed by Bristol-Myers Squibb chemistry safety officers demonstrates how an electronic laboratory notebook enterprise network can be used to deliver automated warnings based off of historical data.

The key to creating a safety system is realizing the important role the electronic laboratory notebook plays in experimentation: recording the experimental plan and parameters in the ELN is essentially the last step a scientist takes before carrying out an experiment. This allows the ELN to take on a gate-keeping role through the implementation of a safety net, drawing on historic accidents to warn scientists when experiment plans mimic past failures.

Not only does the safety system alert the primary researcher of a potentially hazardous experiment, the system can refer researchers to appropriate procedural guidelines and notify members of a laboratory’s safety committee that a potentially hazardous experiment may be imminent..

The ELN safety customization created at Bristol-Myers Squibb was recognized by the 2012 Bio-IT World Best Practices Awards competition. Other scientific organizations are now turning to Bristol-Myers Squibb asking for advice on how to construct their own ELN-based safety enforcements.

Read a detailed account of the Bristol-Myers Squibb solution here, which was developed on top of our E-Notebook® ELN: http://bit.ly/XMuzpF

 Some genetic sequencing providers are opting to upload client genome information to cloud-based analysis networks

With genetic sequencing rapidly becoming a more and more affordable service for public consumption, genetic sequencing providers are growing more concerned with how to crunch the sequencing data rather than with the sequencing technology itself. As the field of personalized medicine continues to expand and individuals opt to have their genomes sequenced for as little as $1,500, the costly part for sequencing providers will be to maintain enough servers and personnel to analyze and identify information in the completed sequences.

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So much scientific research goes into discovering methods to increase energy creation and efficiency while minimizing environmental impact, but ironically laboratories themselves use up extremely high amounts of energy to maintain operation.

Research facilities hoping to increase energy efficiency and sustainability can decrease electricity usage through simple steps like remembering to power off equipment that is not in use, disconnecting seldom-used equipment and remembering to turn off lights whenever a room is unoccupied.

More aggressive energy conservation efforts can be taken to optimize water usage by selecting low-flow faucets and water retention systems. Natural sunlight can be captured through rooftop and building-side photovoltaic panels so that laboratories can generate self-sustaining electricity. Airflow systems use a lot of energy to provide heating and cooling throughout laboratories; special window treatments and roofing can be used to leverage natural sunlight. Changes can be made to the heating and cooling system to reduce energy waste. PerkinElmer’s Santa Clara data center recently implemented an airflow optimization project that reduced annual energy usage by 69,000 kilowatt hours through the elimination of unnecessary overcooling.           

Laboratory employees should also maintain careful inventory records to avoid over-purchasing which can contribute to waste. Once products are ordered, stock chemicals and reagents should be used in the chronological order in which they were purchased. The purchasing of recycled products should be encouraged whenever possible, and supply managers should select vendors that participate in equipment buy-back programs. Inventory management can be achieved through diligent recordkeeping, but laboratory managers are also wise to invest in inventory management software that will help make automated, real-time decisions about the quantity and frequency of ordering new materials.

Laboratories can also opt to participate in organizations such as the Carbon Disclosure Project and the EPA’s Green Power Partnership to promote and contribute to the awareness of sustainable practices.

You can read more about specific ways to establish more environmentally-friendly, energy-efficient  and sustainable laboratory practices here:

Green chemistry made easy: Simple ELN configurations

Sustainable science: Reducing the environmental stress of research

Informatics for a greener tomorrow: Guidelines and solutions

Traditional data reporting just isn’t up to snuff when it comes to generating meaningful data for business leaders to make decisions, a fact solidified by Gartner Inc.’s decision to rename its annual BI quadrant rankings report from “Business Intelligence Platforms” to “Business Intelligence and Analytics Platforms”.

“The dominant theme of the market in 2012 was that data discovery became a mainstream BI and analytic architecture,” Gartner’s report stated.

So what does that mean for scientific companies looking to harness the most powerful data analysis software in 2013?  It means looking beyond data reporting to find tools that not only are capable of extracting relevant data but also present that data in a scientifically-meaningful way.

And the future of harnessing all the information contained within that data? That’s where data visualization comes in: Visualization provides an intuitive method for researchers to sift through and expose relationships between data sets. By replacing rows and columns of data with pictures and charts to graphically represent information, users can absorb information in real-time and also locate information much faster using visual discovery tools.

Critically, data visualization allows for optimization of personnel resources. Whereas traditional enterprise-level data reports often necessitate the efforts of an IT support group to write code allowing end-users to use query language to find data, data visualization allows end-users to both enter and retrieve data from enterprise systems.

Not only does data visualization allow more personnel to be dedicated to discovering patterns within data, it allows those people to make better and more informed discoveries. A 2013 scholarly article found that when doctors and patients processed and discussed diagnosis findings, visual aids increased comprehension of probabilities and numerical data.

This finding isn’t just meaningful for data relevancy in the medical field – it demonstrates that humans innately are better at understanding data when it is presented graphically. Not surprisingly, this natural proclivity for humans to draw meaningful information from visual representations is what makes data visualization tools so powerful when used in data analysis.

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Between all the procedural methods, standards for documentation and experimental parameters, it can be difficult to remember that research is intended to be an inherently creative, exploratory and innovative process.

Yet with so many regulatory practices in place to validate and document research data, that creative element can become stifled in the experimental process. Traditional data documentation and analysis software limits scientists to working within data sets instead of across data sets.

The challenge that emerges for scientists is to find software solutions that allow them to work creatively within scientifically-intelligent organizational platforms. While using data reports as the only source of analysis findings prohibits comprehensive findings, dynamic and fluid data visualization platforms engage researchers with data in an intuitive and interactive manner.

By removing barriers to creativity through effective data capture and analysis systems, scientists are enabled to do what they do best: create and discover. Instead of spending hours transcribing, organizing and sorting through traditional paper records, electronic laboratory notebook (ELN) technology can give back several hours of weekly productivity to each scientist. That time can be spent in experimentation or engaging in advanced, out-of-the-box analysis using data visualization software that allows for data filtering, manipulation and cross-referencing.

In the end, the goal of facilitating end-to-end comprehensive data solutions is to generate more research and more results. The more efficient it is to record and sift through data, the quicker research can benefit us all as new discoveries give way to new products and therapies entering the marketplace.

Now, read a more in-depth take on this subject, titled “Busting Drug Discovery Bottlenecks”, published in Drug Discovery and Development.

First and foremost, we of course have to list “ChemDraw Magic 2”. This second video was published after the original “ChemDraw Magic” video was posted in November to widely-received rave reviews by ChemBioDraw users. The video was so well circulated that Morieux is now employed by PerkinElmer Informatics as an applications specialist.

In his second video, “ChemDraw Wizard” Morieux spends time showing off tips and tricks for several new features introduced in the latest version of ChemDraw, ChemBioDraw 13.0. Video viewers learn how to draw peptides and nucleic acids using the new biopolymer tool, as well as learning time-saving shorts cuts such as saving “style sheets” to pre-set document settings and engaging the hotkey for the Marquee tool.

ChemBioDraw users can also reference the latest how-to videos posted in our own tutorial series, “ChemBioDraw 13.0: Step by step”:

Thin Layer Chromatography tool in ChemBioDraw

Struct=Name in ChemBioDraw

Improve workflow and file management with ChemBioDraw and ChemBio3D

Predicting proton and carbon NMR shifts with ChemBioDraw13

To stay up to date on the latest ChemBioDraw tutorial videos, follow PerkinElmer Informatics on Twitter and Facebook to receive weekly links to the latest “ChemBioDraw 13: Step By Step” videos.

Are there any other ChemBioDraw how-to videos you would like to see? Contact us via social media and give us your suggestions, we’d love to help you get the most out of your ChemBioDraw use.