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Loyola University owned and operated a low-power, carrier current radio station, WLUC-AM, in the 1960s. Students broadcast an eclectic music format on 600kHz to Lake Shore Campus buildings and the surrounding Rogers Park neighborhood. The station had a well-equipped studio in a University-owned Student Life house on Loyola Avenue. The structure was demolished to make way for the Crown Center for the Humanities.

WLUC-AM was replaced by an on-air FM station, , in the 1970s. Steve Madden Lancer Womens pycd2nA

LU Wolf at a Loyola Men's basketball game
Main article: Loyola Ramblers

NCAA Division I National Championships (3): 1963 Men's Basketball, 2014 Men's Volleyball, 2015 Men's Volleyball

Loyola is home to 11 varsity teams, most of which compete in NCAA Division I. The teams include men and women's basketball, cross country, men and women's golf, men and women's soccer, softball, track, and men and women's volleyball. The nickname "Ramblers" was first applied to Loyola's football team in 1926 because they frequently traveled throughout the United States. [41]

LU Wolf is the mascot for the University. He was inspired by the Nine West Kids Vivien T Girls InfantToddler kM3FypJg
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, from whom Loyola derives its name, which depicts two wolves standing over a kettle. Taken from the heraldic crest carved in the lintel on St. Ignatius' family home in Azpeitia , Spain , the wolves and cauldron refer to the prosperity and generosity of the Loyola family, who, after feeding family, retainers and soldiers, had enough food to feed even the wild animals.

Loyola's men's basketball team, the Ramblers, won the 1963 national championship. Loyola is the only Division I NCAA school in Illinois to have won a national title in men's basketball. Steve Madden Pomona Womens oWx4HLrl3
They also hold the all-time highest win percentage of any team to ever compete in the NCAA tournament. Loyola sits slightly ahead of Duke University. Steve Madden Ecentrcq Womens 469xjB
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team won back-to-back NCAA championships in 2014.

From 1970 to 1971, Loyola had a club football team, led by Coach Dick Blackmore. The Ramblers won the Club Football National Championship in 1971 but were shut down after the University showed little interest. In 2012, the club football program was started again by students Robert L. O'Mullan and Luke Culley. John Clarke was the first head coach, followed by Connor Gomez. The Rambler football team competes in the Great Lakes (West) conference in the National Club Football Association.

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You are here: The Future of BI: Actionable Intelligence
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October 17, 2017

Business intelligence isn’t a new concept, but it is one that has made leaps and bounds in the past few years thanks to the proliferation of big data, the development of sophisticated analytics tools, the Internet of Things (IoT) and the advent of artificial intelligence. These technologies show no signs of slowing down, but how will they impact the future of BI?

Traditional Analytics Focus on Historical Data

Traditional analytics, once the next big thing in BI, provide historical data, allowing companies to examine the past trends and spikes, and even hypothesize the factors that influenced those trends with reasonable accuracy. It’s certainly possible to make inferences from historical data, such as “if x, then y” types of educated guesses based on similar trends and patterns in the past. In fact, historical data is crucial for accurate forecasting .

However, hypothesizing a likely outcome based on historical patterns doesn’t take into account the many other variables that may be influencing the outcome. Traditional analytics are the foundation, and historical data is important, but it falls short of providing the predictive power of more sophisticated analysis.

For example, a customer may be following the same path to purchase as many customers in the past, hitting all the right touchpoints and navigating through the buyer’s journey in a largely predictable manner. You might think this is a guaranteed sale, but you’d be wrong.

By focusing on one aspect of the journey – the path – without considering the influence of other variables, you’re selling yourself and your data short.

Additionally, this scenario implies that the company isn’t effectively segmenting prospects and developing buyer personas, which provide data that can account for the differences between your typical buyers. You don’t know this for sure, of course, until the future actually happens. Customers want to be treated as individuals and they want personalized experiences – but these are not achievable goals if you focus too narrowly on a single component of the buyer’s journey.

What happens when that same prospect nears the end of the funnel and is disappointed that your marketing and lead nurturing materials haven’t yet touched on their primary pain point? They may drop out of the funnel to seek answers elsewhere. Effectively Spring Step Glovely Womens 8CiuB
requires looking at each individual as a unique person; it’s the only way to provide the truly personalized experiences today’s consumers demand. While historical data can help to fill in this unique, individual profile of a prospect or customer, it doesn’y:

When we type var into the console, it’s found first in the global environment. When sd() looks for var() it finds it first in its namespace environment so never looks in the globalenv() .

What will the following function return the first time it’s run? What about the second?

This function returns the same value every time it is called because of the fresh start principle, described in a fresh start . Each time a function is called, a new environment is created to host execution. The parent of the execution environment is the enclosing environment of the function. Once the function has completed, this environment is thrown away.

Let’s depict that graphically with a simpler function. I draw execution environments around the function they belong to with a dotted border.

When you create a function inside another function, the enclosing environment of the child function is the execution environment of the parent, and the execution environment is no longer ephemeral. The following example illustrates that idea with a function factory, plus() . We use that factory to create a function called plus_one() . The enclosing environment of plus_one() is the execution environment of plus() where x is bound to the value 1.

You’ll learn more about function factories in IMPACTO G88703L Ergomates AntiFatigue Overshoe Large Work Boots Women 1113 Men 1012 FobU03
.

Look at the following code. What do you expect i() to return when the code is run?

The top-level x (bound to 20) is a red herring: using the regular scoping rules, h() looks first where it is defined and finds that the value associated with x is 10. However, it’s still meaningful to ask what value x is associated within the environment where i() is called: x is 10 in the environment where h() is defined, but it is 20 in the environment where h() is called.

We can access this environment using the unfortunately named parent.frame() . This function returns the environment where the function was called. We can also use this function to look up the value of names in that environment:

environment

In more complicated scenarios, there’s not just one parent call, but a sequence of calls which lead all the way back to the initiating function, called from the top-level. The following code generates a call stack three levels deep. The open-ended arrows represent the calling environment of each execution environment.

Note that each execution environment has two parents: a calling environment and an enclosing environment. R’s regular scoping rules only use the enclosing parent; parent.frame() allows you to access the calling parent.

.

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Rationally engineered Cas9 nucleases with improved specificity

These authors contributed equally to this work.

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01 Jan 2016: Vol. 351, Issue 6268, pp. 84-88 DOI: 10.1126/science.aad5227
Ian M. Slaymaker
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Linyi Gao
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Bernd Zetsche
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
David A. Scott
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Winston X. Yan
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Graduate Program in Biophysics, Harvard Medical School, Boston, MA 02115, USA. Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA 02115, USA.
Feng Zhang
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

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Making the correct cut

The CRISPR/Cas system is a prokaryotic immune system that targets and cuts out foreign DNA in bacteria. It has been adopted for gene editing because it can be designed to recognize and cut specific locations in the genome. A challenge in developing clinical applications is the potential for off-target effects that could result in DNA cleavage at the wrong locations. Slaymaker used structure-guided engineering to improve the specificity of Cas9 (SpCas9). They identified enhanced-specificity variants (eSpCas9) that display reduced off-target cleavage while maintaining robust on-target activity

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Abstract

The RNA-guided endonuclease Cas9 is a versatile genome-editing tool with a broad range of applications from therapeutics to functional annotation of genes. Cas9 creates double-strand breaks (DSBs) at targeted genomic loci complementary to a short RNA guide. However, Cas9 can cleave off-target sites that are not fully complementary to the guide, which poses a major challenge for genome editing. Here, we use structure-guided protein engineering to improve the specificity of Cas9 (SpCas9). Using targeted deep sequencing and unbiased whole-genome off-target analysis to assess Cas9-mediated DNA cleavage in human cells, we demonstrate that “enhanced specificity” SpCas9 (eSpCas9) variants reduce off-target effects and maintain robust on-target cleavage. Thus, eSpCas9 could be broadly useful for genome-editing applications requiring a high level of specificity.

Science

Vol 351, Issue 626801 January 2016

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Rationally engineered Cas9 nucleases with improved specificity

By Ian M. Slaymaker , Linyi Gao , Bernd Zetsche , David A. Scott , Winston X. Yan , Feng Zhang

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