    names = names.upper()
    namesList.append(names)
    if names == 'X':
        print("It appears that you hve made a mistake!\n You are going to have to start over.")
        print("List Provided:",namesList)
slist = sorted(namesList, key=lambda x:x[-1], reverse=True)
print("List Sorted",slist)
A: I'm not sure if your question has any bearing on the problem you're trying to solve -- but to pair the first and last element, second and second last, etc, you can slice the list in two and zip the first half with the reversed second half:
>>> slist = list(range(10))
>>> list(zip(slist[:len(slist)//2], reversed(slist[len(slist)//2:])))
A: Alternatively, you can try to use the little trick of index - i and ~i to get the desired pairs:  (and it can save some computations...)
Explanations:  each index i - if we negate it, it becomes the backwards index.
    print(i, ~i)
    pairs.append((L[i], L[~i]))
L = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
# running Output:
[(0, 9), (1, 8), (2, 7), (3, 6), (4, 5), (5, 4), (6, 3), (7, 2), (8, 1), (9, 0)]
# running it with get pairs:
[(1, 7), (2, 6), (3, 5), (4, 4), (5, 3), (6, 2), (7, 1)]
 </s> Q: How to plot clustered bar chart from raw Qualtrics excel data? My raw Qualtrics data looks something like this. Basically just 2 questions. Q1-where did you learn your tech skills from? and Q2-Do you agree with the following statement?
I want to plot clustered bar chart in Power BI that looks something like the link below. Basically, for Q1-where did you learn your tech skills from? Each cluster is a channel of learning, and within each cluster there is a standard response from not at all, to a small extent, to a moderate extent, to a great extent, entirely
I figured out I cannot plot straight from the raw Qualtrics data. However, if I unpivot just the columns for Q1, I can get the above clustered bar chart.
But here comes the problem. I have other questions with the same raw Qualtrics format. So I tried to unpivot columns for Q1 FIRST, and THEN unpivot the columns for Q2, and got the following, which does not make sense because Q1 has 4 sub-questions while Q2 has 5 sub-questions. This is like a m:m joins (if I make sense?)
So I thought maybe I could unpivot all the columns except for the Response ID column and I got this
Doing the above has several issues;
*when I want to plot the clustered bar chart, I have no way to restrict the rows just to plot for Q1, or rows just to plot for Q2 etc.
*Your first step should be splitting the data from the different questions into separate tables, then "Unpivot Other Columns" but Response ID. You can later relate the tables in the report via this Response ID.
*I doubt that with Qualtrics Excel imports you'll come anywhere close to Power BI's data limits. However, I am surprised that they are providing such an awkward interface.
 </s> Q: Simplest way to share a variable between angular components I have a button which suppose to change a boolean variable from true to false and vice versa, like a switch. the variable and the button are in different components, how is that possible to share the variable if they components are not parent-child?
A: So basically, there are three kinds of sharing data between components
*@Injectable (services, tokens) - use this when components are located in different trees
*Using template reference, see
*Kind of Mediator approach when special directives can have access
to shared data, see NgSwitch
    this.yourValue$ = this._yourValue.asObservable();
    this._yourValue.next(!this._yourValue.getValue());
  template: `Your value is: {{ yourValue }}`
  yourValue: boolean;
    this.subscription =
      this.yourValue = yourValue;
 </s> Q: scroll event doesn't work on some mobile browsers i made a a navbar that disappears when scroll down and shows again when scroll up or if scrollY == 0.
it works very well on desktop and some mobile browsers, but still not working on some other mobile browsers.
const [showNav, setShowNav] = useState(true);
      changeNavbarColor();
        scrollBefore = scrolled;
    // window.addEventListener("touchmove", scrollHandler);


her mother and requests, “Please, tell me about your wedding.” “We just don’t know how to get started choosing a venue. It’s so overwhelming.” sdon’y’s attire.“We” when he thinks of you.
Ditch the pitch!
In preparation, focus not on what you will say to your customer, but what kind of conversation you will have with your customer. Once the interaction starts, focus not on delivering your message, but on creating a genuine conversation. In your mind refer to “The Conversationometer,” a mental tool I use to monitor how well a conversation is going:
Couldn’’Thanks Andy – You’re so right about the interrogation vs. conversation point. Sales conversations should sound as normal as a non-business discussion. If you go through a list of interrogatory questions, it’s really just a stiff script and won’t engage the drug smugglers …
After shopping last week for new living room furniture, I can definitely be a staunch advocate for everyone to “Ditch the pitch.” My husband and I found ourselves sneaking into furniture stores, trying to remain undetected so we could sit on couches without harassment. Once the well-meaning salespeople found us, it was a barrage of feature dumps and advice that we mostly didn’t care about. That led to conversations like the following:
“Oh’s a very popular model, eh? It’ ‘spice it up’ with ‘exciting’ pillows?
“Could you just leave me alone so I can lay on this couch and pretend I’m watching TV? Neutrals are for the faint-of-heart. Also, I hate couch pillows. They’re always in the way.”n’taul – I couldn’t agree more. When people say “I can’t sell” it’ “Ditch the Pitch” todon’t ’great post Steve,
the scenario really hit home your point.
Andy … a similar story … years ago, my boss and I left a meeting with a sales guy with an airline, and my boss said, “He’s the kind of salesman who thinks his job is done once he’s finished talking.”
I was discussing this with our team the other day as part of the need to put price on the table much quicker in the conversation. Even though the organization has variable pricing based on job traits the desire to know ballpark pricing is interfering with the conversation prior to a quote. The fear, as a high added value supplier, is that this may pull price only shoppers out of our sales funnel. Funny thing is, we suddenly realized that might not be a bad thing…
’t tell you this straight away. Es


Sanatana Dharama College, Hoshiarpuunjab University, Chandigarpurely on contractual basi to be held on 28.08.2020 at 09.00 am at the college premises.
Are you searching for a faculty job in Jalandhar? Sant Baba Bhag singh University, Jalandhar is the first Rural Universite/ Asst. Professo0/8/2020.
Jyoti B.Ed. College, Rampura, Fazilka is affiliated to Panjab University and SCERT Chandigarh & Recognised by NCTEdays from 20/8/2020.


Wine culture in Indiana liveis state. Although Indiana’s climate is challenging to the process of winemaking due to its extreme weather patterns, skilled winemakers are successful in harvesting extravagant and unique varieties. This makes the state a hub for some spectacular wine festivals to indulge all wine-lovers.
From their ever-popular wine slushies to the arcane varieties of reds, everything comes together to create the end product that people enjoy. Whether you prefer dry, sweet, semi-sweet, red, white, or sparkling wine, Indiana is the spot for true connoisseurs to amuse their palate.
Let us dive into Indiana’s wine scene and relish those tempting wines by being part of these amazing festivals listed down below.
Where to get the best wine in Indiana? Don’t worry we got you covered with a list of the best wine festivals in Indiana.
This is the biggest annual wine event to celebrate delicious wines produced in Indiana. This event is nothing less than a day filled with bustling energy, the finest wines, and crazy memories with fun-loving friends and significant others.
Vintage Indiana wine fest propels you to enjoy and appreciate the spirits of the Indiana winemakers and explore their Hoosier hospitality in each wine sip.
 with their best wine creations and mind-boggling vintner-based pairings. The expansive entertainment this fest offers is worth your money.
From live music to dancing on your toes, character drawing to the shopping strolls featured by this fest will make you fall head over heels with Indiana’s vibe.
Moreover, the attendees will have this amazing opportunity to sample award-winning wines, “The Vintage” Indiana exclusive wines that lend the name to the fest, along with the Hoosier blends of wine cocktails.
The food is a treat itself; the state’s best culinary experts will be providing you with local food, readily available for purchase. So, grab your lawn chairs and blankets and do not miss this opportunity to sample, purchase over 300 Indiana wines, and have a crazy time with your 21 years old and above friends, family, and better halves.
The tickets are divided into three categories; a very sweet ticket, an adult ticket, and a designated driver ticket at the cost of 100, 50, and 15 dollarsJune 2023, at Military Park in downtown, Indiana. The time and date will be announced in early 2023.
Wine lovers near and far- the Michiana wine festival is back. If you want to do something extra after the winter thaw, this festival is the right spot to kick off the spring. Michiana wine fest guarantees you an exciting weekend.
Michiana wine fest is a treat to local wines, foods, and entertainment. Multiple Indiana wineries waiting to pour their best samples into your glass will immerse you into Indiana’s wine scene. The burgers, pizzas, BBQ, and all the local tangy, yummy food items will provide a treat for your tastebuds.
Adding more to the excitement is the live music performance to cheer your mood and set the groovy atmosphere as you sip on the bottles of wine. Need more fun? Stop by the vendors and you will find a variety of items to take home.
Get outside and join Fort Wayne’s first wine festival of the season in April 2023, (the exact dates will be announced soon) at Headwaters Park in Downtown Fort Wayne.
Book your tickets at $25 for general admission, very VIP at $75. Hurry up! The first 2000 get a FREE souvenir wine glass!
Are you ready for Indiana’s oldest and largest wine event? An afternoon filled with wine, food, entertainment, and a lot more! Certainly, you will not go home without finding a new favorite wine at this fest.
Indiana wine fair features over 40 of Indiana’s vintage and famed wineries with its extraordinary varieties of wines. From refined Rieslings and roses to mouthwatering malbecs and merlots, you are sure to be amused by the concentrated and enriched flavors.
There will also be local breweries, distilleries, local artist games, and Food trucks to facilitate your food moods. Indiana wine fair is the best location to have a scenic picnic with your friends at Story Inn, 6404 State Road 135 S, Nashville.
Fill up your petrol tanks and get ready for a ride to the Story Inn. You can take your friendly pets too and have a great time partying. The date for the 2023 Indiana Wine Fair is yet to be announced.
It’s the 50th anniversary!! Are you ready for the sparkling fireworks and winy atmosphere to celebrate the golden jubilee of the Swiss wine fest this year? There is a lot to add to this occasion to be held at riverfront park, Vevay, Switzerland county.
It is not just any festival, it’s “The Swiss Festival” that has grown to be one of the top 10 festivals in Indiana. There will be a lot of action from Thursday to Saturday with expansive music lineups including “The Classic Rock Experience”, “Thunderstruck”, and “Huff buck”.
In addition to the entertainment, festival goers will see top-notch ferry rides, cars show, cheerleading competitions, and much more. The fest also showcases the famed grape stomp and both Beer and Wine gardens so CHEERS to all the attendees!
 This year’s kid-friendly theme featuring Dinosaur Museum, magic shows, and amusement rides is the perfect entertainment for your kids.
Moreover, the wine pavilion will again be the popular place at the festival featuring all wines from Indiana. Saturday morning will showcase the Grand and special parade to mark the 50th anniversary that you would not want to miss, ending with the famous fireworks show.
Book your tickets for each event separately. Thursday, August 25 to Sunday, August 28, 2022, 12:30 to 5:30 pm. $5 will be the entry fee to the festival.
As cool as it sounds, WAMM is the annual summer Wine, Arts, Music, and Microbrew fest, organized by the Sertoma Club of Greenwood. It’s a plethora of art, culture, entertainment, and fun.
This event not only serves the purpose to engage the audience and visitors into the liveliness of Indiana but also encounters a bigger purpose by giving all the net ticket proceedings back into the community to support scholarships and community nonprofit organizations.
WAMM will take place on August 19, 2023, at Craig Park, Greenwood, IN, from noon to 9 pm.
Seize the day with savor fragrant varieties of wines from all the world-class wineries of Indiana,Next to the lineup is for the art-loving fans, who are opportunistic to explore and appreciate the talents of over 40 artists and also can shop the most antique and distinct pieces that speak to them.
As you venture along the art booths with a wine glass, I am sure music will add essence to the vibe. Enjoy the musical day with live performances of 5 bands performing various genres throughout the fest. What’s next? Cheers beers, get your hands on the award-winning beers from central Indian breweries.
It is a festival that celebrates and empowers local artists, musicians, bands, and wineries. WAMM is unique and distinct in the global festival market, as the largest of its kind in supporting and celebrating local artists. Get your tickets for $25 for general admission. Early bird discounts are available so hurry up and book your tickets for just $15.
Uncork your perfect wine tasting experience here in Indiana that has something special to compliment your food and treat your taste buds. Do not miss these great events that will make your trip worthwhile in Indiana. Start booking and plan your trip. Happy drinking! Drink responsibly.


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What Olivia Wilde's 5-Year-Old Son Said to Her That Made Her Cry: 'I Got Full Body-Chills'
"The other day You never know if your kids really understand. He said, 'Yeah, Mom, you're a director.' I got full-body chills and started crying," Wilde tells PEOPLE. "Hearing it from him made it real."
"He's one of the greatest improvisers on earth," she says. "I was so excited I was like, 'oooh I get to have that magic in my movie.'"


Property from the Perry Collection
signed and dated 'Hartigan '62' (lower right); signed again and titled 'THE PHOENIX HARTIGAN' (on the stretcher)
80 ½ x 88 ½ in. (204.5 x 224.8 cm.)
Beatrice Perry65
Kunstmuseum Bern, Kunst aus Amerika und Spanien, February-March 1964, no. 55 (illustrated).
Orlando, Mennello Museum of American Art, Grace Hartigan 1960-1965, The Perry Collection, January-March 2018, no. 9 (illustrated).
In 1958, Grace Hartigan was the only woman to b ‘59. This show effectively ended Paris’s reign as art world capital, as New York emerged as the center of all that was exciting and innovative. At the time, Hartigan’s paintings were already found in nearly every major American museum. The Museum of Modern Art acquired Persian Jacket (1952) and River Bathers (1953), and the collector Nelson Rockefeller purchased City Life (1956) and Salome (1963) for his Kykuit estate. Thus, Hartigan’s reputation as one of the leading painters of her generation was already set in motion, culminating in a decade of successful exhibitions in the U.S. and abroad.
During the early 60s, Hartigan worked with what she called “maximum color intensities,” in creating high-keyed abstractions where interlocking planes of color create a kind of push and pull on the canvas surface. It is perhaps not surprising that Matisse was a key influence in her use of color, especially as seen in Mimosa, 1949/1951, and that she learned the shallow depth of surface from William de Kooning, who was a close friend from her Cedar Tavern days in New York in the early 1950s. “A big issue for her in these works was how to avoid leaving any ‘negative space,’” her biographer Cathy Curtis has explained, “so that every square inch of the canvas would be activated - if not with a recognizable object, then with areas of paint that create their own drama” (C. Curtis, Restless Ambition: Grace Hartigan, Painter, Oxford University Press, 2015, p. 151).
In the present work, Hartigan has moved away from the raw, gestural immediacy of her 1950s paintings to a more composed and lyrical abstraction, using transparent, thinned-down pigments that she rubbed with rags and fine-grade steel wool. In her earlier work, Hartigan had attempted to render the world seen in “fragments…like being on a f,” she said (G. Hartigan, quoted in A. Barber, “Marking Some Marks,” Arts Magazine, Vol. 48, No. 9, June 1974, p. 51). She had found inspiration from the fruit vendors and street carts outside her loft on the Lower East Side. By the time she painted The Phoenix, in 1962, however, her forms have softened, becoming more lyrical and Matissean, rendered in thinned-down, jewel tones that have the transparency of stained glass.
I have left expressionism, and although the new paintings have feeling, the emotion is that which each painting demands…Left behind is the ‘anguish.’ I now hope to triumph, to bring to each painting as much clarity, beauty and even ecstasy as I am able. The scream has become a song.”
The change in Hartigan’s paintings of the ‘60s is related to her move away from New York to Baltimore, following her marriage to the Johns Hopkins epidemiologist Winston Price. The couple spent a prolonged honeymoon in rural New England, renting a cottage with a lily pond in Clark’s Cove, on Maine’s Bristol Peninsula. It was there that her work began to evolve, taking on more lyrical forms and a brighter palette. “Grace had worried about the softening influence of nature on her work,” Curtis explained, “yet the combination of a fulfilling love life and the pleasures of a summer retreat inspired a strong group of newly lyrical abstract paintings -- including Lily Pond, The Phoenix, Marilyn [and] William of Orange...suffused with bright color and laced with the curving shapes of fruit, leaves and sexual organs” (C. Curtis, op. cit., 2015, p. 206).
Hartigan would go on to explain this newfound change in her work later that Fall, when the paintings were exhibited at Martha Jackson Gallery in New York. “The ‘look’ [of the paintings] is the result of a completely new (to me) attitude toward emotion. ” (G. Hartigan, quoted in Hartigan ‘62, exh. cat., Martha Jackson Gallery, New York, 1962, np.).
Hartigan was a major figure of the downtown New York arts scene in the 1940s and ‘50s. Her the poet Frank O’Hara, and countless other artists and writers. She was arguably the most celebrated woman artist of her day, with profiles in Life magazine, Newsweek and Time, and a string of successful shows with Tibor de Nagy throughout the ‘50s. Through de Kooning, she developed the use of free, gestural movements, using a thickly-loaded brush, and a kind of shallow perspectival space, where “there’s no recessional space, there’s no perspective,” she explained. “That comes out of the Abstract Expressionist idea of projecting surface. ...it’s a very shallow, loose, moving, mobile surface space” (G. Hartigan, quoted in “Oral History Interview with Grace Hartigan,”May 10, 1979; accessed viarace-hartigan-12326)
Shortly after it was painted, The Phoenix was acquired by Beatrice “Beati” Perry, who became Hartigan’s dealer after her break with Tibor de Nagy. (It was also through Perry that Hartigan had first met her future husband). Beati was an elegant and stylish gallerist who was the director of Gres Gallery in Washington D.C. She developed an interest in Hartigan’s paintings, and convinced her to join the gallery around 1960. A few years later, Beati would go on to partner with Martha Jackson in showing Hartigan’s latest series of paintings in the Fall of 1962. The show was favorably reviewed, with the New York Times art critic Stuart Preston declaring them “beautiful” paintings.” Preston wrote that Hartigan “manages to suggest something sensuous and even sensual in the way softly rounded forms snuggle and embrace….Form and glowing color are one and indivisible” (S. Preston, “Drawings and Paintings,” The New York Times, October 28, 1962, Section 2, p. 15).
Lot Essay Header Image: Grace Hartigan in her Lower East Side studio, New York, 1957. Photo:mages. Artwork: (C) Grace Hartigan Estate.


Now what about the new Tropical Depression Seven?
But how are these two systems related?TD Seven is following right behind Florence. Looking at the GOES-12 water vapor shot shows the two systems in close proximity with a cloud streamer seemingly connecting the two storms. I suspect that this could be something called a "vorticity streamer" which is a ribbon of air containing constant "potential vorticity."


In New York City, on the days that followed the calamity, almost everybody gathered themselves and went back to work. They refused to run. The tunnels were not filled with refugees heading for North Dakota. New Yorkers stayed. They volunteered to help the process of recovery. They lined up by the thousands to give blood (which was not needed, because almost everybody was dead). They stood on street corners and cheered the passing fire engines and other rescue vehicles. Thousands of them assembled in Union Square, which faces south to where the Twin Towers once stood, and they created improvised altars, ablaze with candles, and held posters full of sorrow and anger. Under the trees, a saxophone player gave them the perfect sound track: a blues. [...]
Working at the 9/11 Memorial: Through the Eyes of a Communications Intern


Genome-wide biochemical profiling experiments have revealed millions of putative regulatory elements in diverse cell states. These massive datasets have spurred the development of deep neural network (DNN) models to predict cell-type specific or context-specific molecular phenotypes such as TF binding, chromatin accessibility and gene expression from DNA sequence1–3. Beyond high prediction accuracy, the primary appeal of DNNs is that they are capable of learning predictive sequence features and modeling non-linear feature interactions directly from raw DNA sequence without any prior assumptions. Hence, interpreting these purported black box models could reveal novel insights into the combinatorial regulatory code.
Advances in feature attribution methods for DNNs have enabled the identification of predictive cis-regulatory patterns in DNA sequences used as input to the models. Feature attribution methods estimate the contribution (or importance) of features, such as individual nucleotides or contiguous subsequences (e.g. motifs), in an input DNA sequence to a model’s output prediction. A perturbation-based, forward-propagation approach known as in-silico mutagenesis (ISM) quantifies the importance of a nucleotide in an input DNA sequence as the maximal change in the output prediction from the DNN model when the observed nucleotide (e.g. a G) at that position is mutated to any of the alternative bases (e.g. A, C or T). ISM has been used to score the effects of genetic variants in regulatory DNA sequences1–3. However, ISM is computationally inefficient since each perturbation at every position in an input sequence requires a separate forward propagation to the output through the network. ISM also fails to highlight important features masked by saturation due to buffering interactions with other features (e.g. multiple motif instances in a sequence that buffer each other) 4. SHAP is a perturbation-based feature attribution method that borrows from game theory5. Max-Ent is a feature attribution method that uses a Markov chain Monte Carlo algorithm to find the maximum-entropy distribution of inputs that produced a similar hidden representation to the chosen input6. While SHAP and Max-Ent show improved sensitivity and specificity relative to ISM, they do not scale efficiently to comprehensively characterize feature importance across millions of regulatory sequences. An alternative family of computationally efficient backpropagation approaches decompose the output prediction corresponding to an input sequence by recursively propagating contribution scores through the layers of the DNN from the output to the input. One single backpropagation pass provides the contribution of all nucleotides in an input DNA sequence to the output prediction. The gradient of the output with respect to each nucleotide in the input DNA sequence – known as a saliency map 7 – is one such estimate of importance and has been used to identify predictive nucleotides in regulatory DNA sequences. Other related approaches such as DeepLIFT 4 and integrated gradients 8 differ in the definition of the importance score that is backpropagated and provide improved sensitivity in the presence of saturation effects. DeepLIFT 4 has also been shown to be an efficient approximation of SHAP scores 5.
Current feature attribution methods only provide the importance of individual features. They do not highlight predictive, higher-order feature interactions that are encoded in the DNN model. Perturbation-based approaches such as ISM cannot scale to comprehensively score all pairwise and higher-order interactions between nucleotides or subsequence features. Recently, an efficient algorithm was proposed to calculate SHAP-based pairwise feature interaction scores9 specifically from tree-based ensemble models. However, computing SHAP interactions from neural network models between all pairs of features in regulatory DNA sequences is computationally inefficient (Section 2.6) and cannot scale to reveal comprehensive interaction maps across millions of regulatory sequences.
Here, we present an efficient approach called Deep Feature Interaction Maps (DFIM) to estimate pairwise interactions between features (nucleotides or subsequences) in an input DNA sequence mapped to an associated regulatory phenotype by a neural network. We define a novel Feature Interaction Score (FIS) between any pair of features (source feature and target feature) in an input DNA sequence as the change in the importance score of the target feature when the source feature is perturbed, while keeping all the other features in the sequence intact. By leveraging efficient backpropagation-based feature attribution methods, we can efficiently compute FIS between all pairs of nucleotides or predictive motifs across large sets of input DNA sequence. Aggregate summary statistics of the pairwise Feature Interaction Score across multiple sequences provide insights into common, shared patterns of feature interactions. We benchmark DFIM in controlled simulations that explicitly encode motif interactions. We use DFIM to reveal synergistic interactions between GATA1 and TAL1 motifs from in vivo TF binding models. We apply DFIM to reveal epistatic interactions involving nucleotides flanking the core motif of the Cbfl TF in yeast from in vitro TF binding models. We also apply DFIM to regulatory sequence models of in vivo chromatin accessibility to reveal interactions between regulatory genetic variants and proximal motifs of target TFs as validated by TF binding quantitative trait loci.
We assume that we have trained a deep neural network to accurately map one-hot encoded DNA sequences X of length L to a categorical (binary or multiclass classification) or continuous (regression) output 0. Let Y refer to the scalar predicted output 0 from the neural network for regression tasks. For classification tasks, let Y refer to the scalar input to the final output sigmoid of the neural network.
2.1 Nucleotide-resolution Feature Interaction Score
We are given a one-hot encoded input DNA sequence X0 ∈ {0,1}{4 x L} i.e. a matrix of size [4, L] such that X0 [b, p] = 1 for the observed nucleotide b ∈ {A, C, G, T} at position p ∈ [1, L] (Fig. 1).
Figure 1. Deep Feature Interaction Maps
The DFIM, illustrated in 6 steps, quantifies the maximal Feature Interaction Score (FIS) of every position in a sequence with all other positions.
First, we compute CX0 a matrix of size [4, L] that contains the importance (or contribution) of every nucleotide (rows) at each position in the sequence (Fig. 1(Step 1)). While our approach extends to any other efficient feature attribution method, for the analyses in this paper, we show results using both DeepLIFT 4 and gradient saliency maps as importance scores7. In gradient-based saliency maps, for a specific input sequence X0, the output Y0 can be approximated by a first-order Taylor expansion Y0 ≈ Σp, b w0 [b, p]. X0 [b, p] where w0 is the partial derivative (gradient) of Y with respect to the input sequence variable X evaluated at X0 i.e. w0 =
| X0. It is worth noting that the entire gradient matrix w0 can be computed efficiently in one backpropagation pass. We then perform a point-wise multiplication of the gradient matrix w0 with the one-hot encoded observed input sequence X0 to obtain the importance scores for the observed nucleotides b at each position p i.e. CX0[b,p] = w0[b,p].X0[b,p]. Only the observed nucleotides at each position can have non-zero values. DeepLIFT contribution scores quantify the sensitivity of the output to finite changes in the input4. This is in contrast to gradients, which measure the sensitivity of the output to infinitesimal changes in the input. Specifically, the DeepLIFT algorithm backpropagates a score (analogous to gradients) which is based on comparing the activations of all the neurons in the network for the actual input sequence X0 to those obtained when using neutral ‘reference’ sequences4. We use dinucleotide-shuffled versions of X0 as reference sequences unless otherwise specified.
Our goal is to query the neural network to estimate the interaction between the observed nucleotide at one position in the sequence (source feature) and the observed nucleotide at some other position (target feature) in the sequence. Let (α, s) represent the source feature i.e. the observed source nucleotide α ∈ {A, C, G, T} at a source position s such that X0(α, s) = 1. Let (β, t) represent the target feature i.e. observed target nucleotide β ∈ {A, C, G, T} at some target position t such that X0(β, t) = 1.
Intuitively, we define the Feature Interaction Score FISX0 ((β, t)| (α, γ, s)) of the target feature on the source feature as the change in the importance score of the target feature (β, t) when the source feature (α, s) is mutated to a different nucleotide (γ, s). To compute FIS, we create a new mutated sequence
from X0 where we switch the observed nucleotide α at source position s to a different mutant nucleotide γ ∈ {A, C, G, T} – {α}, while keeping the nucleotides at all other positions as they were in X0 (Fig. 1(Step 2)). We then compute the importance matrix CX0′ for
as we did for X0 (Fig. 1(Step 3)]. The FIS of the target feature with the source feature is defined as
Since, only two backpropagation passes are required to compute CX0 [, t] and
[, t] for all t ∈ [1, L], we can efficiently compute the FIS of all target features FISX0(∗ | (α,γ, s)) in a sequence with a specific source feature mutation (Fig. 1(Step 4)). Note that the FIS is a directional interaction score of the target with the source. In some cases, we may only be interested in the magnitude of the score rather than its sign. In such cases, we use the absolute value of the FIS.
We define the maximal Feature Interaction Score (maxFIS) of the target feature with the source feature as the maximal FIS marginalized over all possible values of the mutant nucleotide γ at the source feature (α, s) i.e maxFISX0((β,t) | (α,s)) = maxγ(FISX0((β,t) | (α,γ,s)) (Fig. 1(Step 5)).
A nucleotide-resolution Deep Feature Interaction Map (DFIM) summarizes the maxFIS scores for all pairs of source and target features in an input DNA sequence (Fig. 1(Step 6)).
2.2 Aggregate statistics of nucleotide-resolution FIS over multiple input sequences
In order to analyze the prevalence of the FIS between a source position s and target position t across a collection of input sequences Xt, we first identify the subset of sequences S = {Xi} that have identical source nucleotides at the source position and identical target nucleotides at the target position i.e ∀ Xi Xj ∈ S, Xi [α, s] = Xj[α, s] = 1 AND Xi[β, t] = Xj[β, t] = 1. We then compute aggregate statistics such as the mean of the FIS or absolute FIS corresponding to each ((β, t) | (α, γ, s)) over all sequences in the subset S. (See Fig. 8 as an example).
2.3 Motif-resolution Feature Interaction Score
We are often interested in the FIS of a specific target motif {(βP, tp),…,(βq, tq)} i.e. a specific subsequence of nucleotides {βp … βq} at a specific subset of contiguous target positions {tp … tq} with a source nucleotide-resolution feature (α, s) (i.e. specific source nucleotide at specific source position) such as a regulatory single nucleotide variant (SNV). In such a case, we compute the FIS of a target motif with a source nucleotide feature as the difference of the sum of importance scores across all target nucleotides ((βp,tp),…, (βq, tq)) in the target motif in the original sequence X0 and the mutated sequence
(obtained by mutating (α, s) in X0 to {γ, s)).
To compute the FIS of a target motif {(βp,tp), …, (βq, tq)} with a source motif {(αk, sk), …, (αℓ sℓ)} (See Fig. 3 as an example), we use a different source mutation method. One option would be use the maximal FIS of the target motif over all possible single nucleotide mutations of each position in the source motif. However, this procedure is computationally infeasible for long motifs. We instead, generate one mutant sequence, where we mutate the one-hot encoding (where rows 1-4 correspond to A,C,G,T) of all positions {sk … sℓ} in the source motif to the expected background GC nucleotide frequency fGC i.e. the mutant sequence
[(2,3),s] =
The FIS of the target motif with the source motif is once again the difference of the sum of importance scores across all target nucleotides {(βp,tp), …, (βq, tq)} in the target motif feature between the original sequence X0 and the mutated sequence
2.4 Statistical significance of FIS
Given a continuous distribution of FIS, across a collection of input sequences, we define statistically significant interactions based on an empirical null distribution of scores from dinucleotide shuffled versions of the input sequences. For each dinucleotide shuffled input sequence, we compute FIS for all nucleotide pairs. We fit a Gaussian distribution to this null empirical distribution of FIS scores. FIS values passing a p-value of 0.05 with respect to this null distribution are considered statistically significant. We use the Benjamini-Hochberg procedure for multiple hypothesis correction. SFig. 1 (bottom row) demonstrates how the null model can be used to identify responding motifs in the context of a longer sequence.
2.5 Comparison of DFIM to SHAP Interaction Scores and pairwise ISM interaction scores
For an input sequence with F features (nucleotides/motifs), SHAP interaction scores scale quadratically to compute all pairwise interactions giving a complexity of O(F2)9. A pairwise ISM-based interaction score (Supp. Methods), defined as the difference between the ISM score obtained by jointly mutating two features and the sum of the ISM scores of individual features, also has a complexity of O(F2). For DFIM, we require one backpropagation pass to obtain importance scores for the original sequence. Then for each of the F source features, we need one more backpropagation pass to obtain FIS of that source with ALL target features. Thus, DFIM exhibits a complexity of O(F) scaling linearly in the number of features. Our proposed FIS is essentially an efficient approximation of SHAP interaction scores. Further, in contrast to SHAP interaction scores and pairwise ISM interaction scores which are necessarily symmetric over the source and target, FIS is directional and can produce asymmetric interaction scores.
3.1 Benchmarking FIS on ground-truth motif interactions embedded in simulated data
To benchmark FIS, we simulated 60K random DNA sequences (0.46 G/C frequency) of length 200 bp. We divided these into 3 sets of 20K sequences. We randomly embedded 1 or 2 instances of the ELF1 motif (using the highest affinity sequence from Position Weight Matrix 10) in the sequences in Set 1, 1 or 2 instances of the SIX5 motif 10 in Set 2 and 1 or 2 instances of both ELF1 and SIX5 motifs in Set 3. We further independently embedded 0 or 1 instances of the AP1 and TAL1 motifs in a random subset of sequences across all 3 sets10 (Supp. Methods). We then set up a binary classification task where all sequences in Set 3 (ELF1 and SIX5) were labeled as positive and all other sequences from Sets 1 and 2 were labeled as negatives (Fig. 2A). We trained a Convolutional Neural Network (CNN) with one convolutional and one dense layer (Supp. Methods.). We achieved 100% classification accuracy on held out validation set of sequences indicating the model had learned the necessary interaction between ELF1 and SIX5. We computed motif-resolution FIS (Section 2.3) for all pairs of embedded motif instances (SIX5, ELF1, AP1 and TAL1) for all sequences in the positive class (i.e. Set 3). We used DeepLIFT with a fixed GC reference for computing importance scores since the underlying sequences were generated using a fixed GC background. Only pairs of SIX5 and ELF1 motifs (positive control) showed strong FIS (Fig. 2B, green distribution), compared to all other pairs of motifs (negative controls) demonstrating that FIS can effectively discriminate ground truth interactions learned by a neural network. We further assessed the significance of these interactions using a empirical null distribution from dinucleotide shuffled sequences (Section 2.4) and found that the vast majority of true ELF1-SIX5 interactions have significant (p < 0.05) p-values, even after multiple hypothesis correction. None of the other motif pairs show statistically significant interactions (SFig. 2A,B). The results are replicated using gradient saliency maps as importance scores (SFig. 2C,D).
The simulation set up where positives must detect an interaction and find both ELF1 and SIX5 present in the sequence.
ELF1 and SIX5 motifs, required together for a positive prediction, are the only pair of motifs with significant Feature Interaction Score as expected.
3.2 Uncovering epistatic motif interactions of co-binding TFs from CNN models of in vivo TF binding
We analyzed CNN models of in vivo TF binding to investigate epistatic interactions between motifs of cobinding TFs. We trained a multi-task CNN model to classify 1 kbp sequences centered at GATA1, GATA2 and TAL1 ENCODE ChIP-seq peaks (positive class) in erythroid K562 cells from all other chromatin accessible DNase-seq peaks in K562 (negative class) 11. The CNN model with 5 convolutional layers (25 convolutions, size 10), a max pooling layer (size 25) and a sigmoid activation (Supp. Methods), achieved mean auROC of 0.953 and mean auPRC of 0.459 across all three tasks on held-out test set. Next, we identified all matches to the known motifs of GATA1 and TAL1 in all ChIP-seq peak sequences (Supp. Methods). We then computed motif-resolution FIS (using DeepLIFT with shuffled reference as importance scores) for all pairs of GATA1, TAL1 motif instances across all sequences using GATA1 as the source motif. We observed several instances with strong FIS between proximal GATA1 and TAL1 motifs which corroborates their experimentally validated cobinding interactions12 (Fig. 3A). To understand the relationship between the distance between motif instances and their interaction scores, we binned GATA1 and TAL1 motif pairs into 4 distance bins - within 20bp (n=13,004), 20-50bp (n=18,898), 50-100bp (n=28,684), and 100-200bp (n=21,1154). We compared the distribution of FIS for the motif pairs across the bins. As expected, TAL1 and GATA1 motifs in close proximity (<20 bp) showed statistically significant higher interaction scores than all three other bins (p<1e-16, Mann Whitney test for all 3 comparisons) (Fig. 3B). However, interestingly, we observed some strong long-range interactions between motifs as far as 70 bp apart (Fig. 3B), an observation corroborated by a recent analysis of SNP effects on TAL1 ChIP-seq signal in erythroid cells that found that GATA1 motif mutations impact TAL1 binding at distances as great as 75 bp13. The interactions were also symmetric, such that mutating TAL1 demonstrated a distribution of FIS on GATA1 (SFig. 4).
The source GATA motif is mutated and the target TAL1 motif responds with a strong reduction in importance (high FIS). TAL1 is not predictive without GATA present, indicating an interaction between the two features.
GATA1 mutations primarily affect TAL1 summits within 20bp compared to all greater distances, which is corroborated by a known interaction between the two factors in K562 cells.
3.3 Discovering interactions between regulatory variants and their target TF motifs from CNN models of in vivo chromatin accessibility
DNNs mapping regulatory DNA sequences to TF binding and chromatin accessibility have been previously used to score the predicted in-silico allelic effects of putative regulatory genetic variants based on ISM1–3. Here, we instead use FIS to investigate an orthogonal question – What proximal sequence features are affected by (interact with) regulatory genetic variants? Tehranchi et al. developed a pooling-based approach to identify thousands of SNVs that have allelic effects on TF binding (as measured by ChIP-seq) across a large collection of genotyped lymphoblastoid human cell-lines14. They provide coordinates, effect sizes, reference/alternative alleles and the allele with stronger binding for statistically significant binding QTLs (bQTLs) and non significant background SNVs in ChIP-seq peaks for JUND, NFKB, SPI1, STAT1 and POU2F1. This dataset provides an excellent resource to investigate the feature interactions of bQTLs. Further, we wondered if we could discover bQTL feature interactions for different TFs from a single DNN model trained to predict chromatin accessibility (instead of TF binding) from sequence.
Hence, we trained a multi-task (18 tasks) CNN model to map 1kbp length DNA sequences to binary chromatin accessibility profiles across 16 primary hematopoietic cell types (with ATAC-seq data) and 2 ENCODE cell-lines (with DNase-seq data) including the GM12878 lymphoblastoid cell-line (LCL) (Supp. Methods). The model achieved high performance on the test set (average auPRC = 0.69, auROC=0.91). We used the LCL task to investigate bQTL feature interactions using FIS (DeepLIFT with shuffled reference as importance score). We restricted our analysis to the statistically significant (allelic binding p<5e-05 as recommended by Tehranchi et al.) bQTLs that overlapped the DNase-seq peaks in GM12878.
To understand proximal interactions, for each bQTL, we used FIS to estimate the effect of mutating the reference allele to all alternate alleles at the source QTL on every target nucleotide +/− 15 bp around the QTL. First, we observed strong positive (Fig. 4-left) and negative (Fig. 4-right) interactions of bQTLs with nucleotides of overlapping target TF motifs. The direction of the allelic effect (stronger or weaker ChIP-seq signal) of the reference and alternate bQTL alleles on TF binding also matched the predicted direction of change (stronger or weaker motif score) E.g. A significant JUND bQTL at chr22:<DIGITS> falls in a high affinity JUND binding motif (Fig. 5A). The reference A allele has higher binding than the alternative G allele with p-value 1.71e-140 in the Tehranchi et al. study. FIS predicts that the G allele (weaker allelic binding) but not the A allele (stronger allelic binding) will destroy the importance of the entire JUND motif.
Many bQTLs fall directly in important features, visibly disrupting known binding domains and demonstrating strong Feature Interaction Score with surrounding sequence. We observe bQTLs both create and disrupt predictive motifs. For both bQTLs shown, the G allele is found in the stronger feature and also the measured stronger binding allele in Tehranchi et al.
Many binding QTLs fall directly in the canonical binding site of the transcription factor as determined through importance scores in our model. This JUND bQTL (p=1.71e-140) on chromosome 22 has a visible effect on the importance of the surrounding motif. Top: Sequence importance scores before mutation, after mutation, FIS and significant FIS only. Bottom: DFIM showing dependency structure of sequence with bQTL at position 15.
Next, we also found several TF-bQTLs in the flanking nucleotides of weak affinity motif matches of the target TF having significant interaction effects with the entire motif. E.g. a significant SPI1 bQTL at chr1:<DIGITS> has reference allele T (with stronger binding) and alternate allele C. The bQTL is in the flanking nucleotides of a low affinity SPI1 site where only the core “GGAA” matches the canonical motif. FIS predicts that the C allele (weaker binding) destroys the importance scores of the core GGAA element (Fig. 5B, Top). Tehranchi et al. and several other studies have reported that a large fraction (70-90%) of QTLs do not overlap high affinity instances of canonical TF motifs. We hypothesize that several of QTLs may be affecting flanking nucleotides of weak affinity matches of TF motifs. Finally, while most bQTLs with statistically significant FIS exhibit the maximal absolute interaction with other nucleotides within 10 bp of the bQTL, we also observe strong and significant longer-range interactions at distances ranging from 20-200 bp (Fig. 6A). E.g. An SPI1 bQTL has a significant interaction with a proximal SP1 motif but also a strong interaction with a RUNX1 motif 20 bp away (Fig. 6B). SPI1 QTLs were also found to affect motifs 100s of base pairs away (SFig. 5).
Many binding QTLs fall directly in the canonical binding site of the transcription factor as determined through importance scores in our model. This SPI1 bQTL (p=3.13e-12) on chromosome 1 falls in the flank of a weak affinity site with a poor match to the canonical SPI1 motif. Top: Sequence importance scores before mutation, after mutation, FIS and significant FIS only. Bottom: DFIM showing dependency structure of sequence with bQTL at position 15.
Maximum absolute FIS (y-axis) as a function of distance between SPI1 bQTL and maximally interacting nucleotide within 1 kbp of the bQTL.
We observe features at longer distances can also be affected by binding QTLs. We see RUNX1 and SPI1 interact and a mutation disrupting the canonical SPI1 binding site results in a significantly decreased importance score for the RUNX motif 40bp away.
As a negative control, for each TF, we also evaluated the FIS of a matched number of conservative control SNVs from the Tehranchi et al. study that overlap the TF’s ChIP-seq peaks and LCL DNase-peaks with least significant allelic effects on binding (allelic binding p ≈ 1). For each bQTL and control SNV, we recorded its maximal absolute FIS (maxAbsFIS) over all target nucleotides +/-15 bp around the SNV. For all the TFs, we found that the bQTLs exhibit significantly (Mann Whitney test) stronger maxAbsFIS than control SNVs (Fig. 7), indicating that FIS may be an alternative approach to ISM to identify putative regulatory variants.
For all 5 reported transcription factors, we observe a statistically significant increase in Feature Interaction Score in the surrounding sequence for known binding QTLs as compared to non-significant bQTLs.
3.4 Discovering interactions between nucleotides flanking the core sequence motif of the Cbf1 TF in yeast from in vitro binding DNN models
Paralogous TFs have been recently shown to have distinct sequence affinity preferences to nucleotides flanking the core canonical binding motifs. Le and Shimko et al. recently developed a microfluidics based in vitro TF binding assay called BET-seq to investigate this question15. They used the BET-seq assay to measure high-resolution in vitro binding affinity landscapes of the yeast TFs Cbf1 and Pho4 to a high complexity library of > 1 million DNA sequences with a fixed central core E-box sequence (CACGTG) and 5 variable flanking nucleotides on either side. They trained a feed forward neural network to predict relative binding affinity (ΔΔG) for each of the TFs from the 10bp flanking sequences (using a flattened one-hot encoding) in the library15 (Fig. 8A). The model architecture consisted of 3 dense layers of sizes 500, 500 and 250 with ReLU activation followed by batch normalization and dropout (p=0.25) with a final dense classification layer having a linear activation. They used a distillation approach to interpret the NN model by fitting a linear model with all mononucleotide features across all positions and all dinucleotide features across all pairs of positions to the output predictions of the NN. They found that dinucleotide features were critical for the linear model to have a good fit (r2 > 0.95) especially for Cbf115. They then estimated the contributions of all pairwise interaction terms by comparing the mononucleotide+dinucleotide linear model to a mononucleotide-only linear model. Cbf1 was found to exhibit significant interactions between several pairs of flanking nucleotides15.
A) A feed forward neural network mapping 10 bp flanking sequences to binding affinities. B) Pairwise feature interaction scores for all pairs of nucleotides in an example sequence for Cbf1 shows strong interaction between source (−1, T mutated to G) with target (+1, A). C) We observe several strong pairwise interactions between positions in the marginalized aggregate DFIM for Cbf1 across the top 5K highest binding affinity sequences. The rows correspond to (source position, source base, argmax mutant base). The columns correspond to (target position, target base).
We instead used DFIM to directly query the Cbf1 neural network model and estimate pairwise nucleotide-resolution FIS between all pairs of nucleotides at all positions for all sequences in the library (Fig. 8B). We compute aggregate statistics (mean) of the absolute nucleotide-resolution FIS (Section 2.2) for all pairs of nucleotide features across the 5,000 sequences with strongest binding affinity (lowest measured ΔΔG). We obtain four (40 × 40) DFIMs where each map corresponds to one of the 4 bases {A,C,G,T} as the observed source nucleotide. The rows in each 40 × 40 map correspond to 4 mutant bases x 10 source positions, while the columns correspond to 4 target bases x 10 target positions. To ease interpretation, we compute a marginalized 40 × 40 DFIM that records the maximal average FIS score over all mutant bases for each source base, source position, target base and target position (Fig. 8C), marginalized over the 3 potential mutations for a given source base. We observe that the marginalized aggregate DFIM for the high binding affinity sequences exhibit several strong interactions between flanking nucleotides (Fig. 8C). The map corroborates several of the strongest interactions identified by Le and Shimko using the distillation approach such as the strong interaction between a T at the −1 position and an A at the +1 position. Our maps also identify novel interactions such as a strong interaction between T at −1 and T at +2. In contrast, the aggregate DFIMs across 5,000 sequences with weakest binding affinity (highest measured ΔΔG) exhibit uniformly weak interaction scores (SFig. 6).
We present an efficient method called Deep Feature Interaction Maps (DFIM) to identify epistatic interactions between all pairs of nucleotides or motif features in any DNA sequence input to a deep learning model for regulatory genomics. Our method accurately recovers ground truth interacting motifs in simulated regulatory DNA sequences. When applied to deep learning models of in vivo TF binding, we recover known proximal interactions between motifs of interacting co-factors while also discovering long-range interactions between motifs as far as 75 bp apart. We interpret deep learning models trained on in vitro TF binding to discover extensive interactions between pairs of nucleotides in sequences flanking core TF binding motifs. Finally, we interpret deep learning models of in vivo chromatin accessibility to generate nucleotide-resolution interaction maps for non-coding regulatory sequences surrounding SNVs (bQTLs) that affect binding of transcription factors. Our maps link binding QTLs to nearby sequence features including high and low affinity matches to the canonical binding site of the TF whose binding is disrupted. We also find bQTLs interacting with motifs of multiple co-binding TFs. These epistatic interactions seem to capture both cooperation and competition. While our primary focus in this manuscript is on interpreting feature interactions in DNA sequence inputs, DFIM can easily be generalized to other data modalities.
Partial dependence plots are commonly used to understand the sensitivity of a prediction to a one or more features 16. DFIM serves as complementary approach to understand the predictive higher-order, non-linear interactions between features. DFIM is most efficient to estimate all pairwise interactions between predetermined features such as known binding sites or SNVs or a sparse set of de-novo discovered predictive features with significant importance scores. However, DFIM also scales well to estimate interactions between all nucleotides in large sets of sequences because it leverages efficient backpropagation-based feature attribution methods. While DFIM is generally compatible with any efficient feature attribution method, we have not evaluated our approach on all such methods. However, we have found overall strong replication of DFIM results and associated conclusions by using two separate importance scores namely DeepLIFT and gradient saliency maps (SFig. 2, SFig. 3). This suggests that DFIM could generalize to other importance scoring approaches.
There are several potential caveats to using DFIM including some that are independent of the methodology itself. For example, DFIM will only work well with a high performance deep learning model that has been appropriately trained, with a suitable architecture, and if the importance scoring methods appropriately capture salient features. DFIM depends on these two cornerstones and so will only perform as well as the model and the importance scores. Changes in model architecture can also change the interactions encoded by the model and thus the interactions learned with DFIM. In addition, DFIM is limited to detecting interactions within the same input sequence (i.e. for a model with a 1 kbp input, the interactions must be within that 1kbp). Despite these mentioned caveats, the case studies we present here showcase the utility of DFIM to provide a nuanced view into the combinatorial code of regulatory DNA sequences through the lens of predictive neural network models.
We have made code for this project publicly available at: <URL> We include Jupyter Notebooks to demonstrate several use cases.
We would like to thank Johnny Israeli and Nathan Boley for their help training a TAL1, GATA1, GATA2 transcription factor binding model. We would also like to thank Avanti Shrikumar for discussions during early development of the methodology. PG was supported by a BioX Stanford Interdisciplinary Graduate Fellowship (SIGF). AK was supported by NIH grants 1DP2GM123485, 1U01HG009431 and 1R01HG<DIGITS>.
Contact: pgreens{at}stanford.edu, akundaje{at}stanford.edu
Kelley, D. R. & Reshef, Y. A. Sequential regulatory activity prediction across chromosomes with convolutional neural networks. BioRxiv (2017). doi:10.1101/161851
Shrikumar, A., Greenside, P. & Kundaje, A. Learning Important Features Through Propagating Activation Differences. (2017).
Lundberg, S. & Lee, S.-I. A Unified Approach to Interpreting Model Predictions. (2017).
Finnegan, A. & Song, J. S. Maximum entropy methods for extracting the learned features of deep neural networks. PLoS Comput. Biol. 13, e1005836 (2017).
Simonyan, K., Vedaldi, A. & Zisserman, A. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. (2013).
Sundararajan, M., Taly, A. & Yan, Q. Gradients of Counterfactuals. (2016).
Lundberg, S. M., Erion, G. G. & Lee, S. I. Consistent Individualized Feature Attribution for Tree Ensembles. arXiv preprint arXiv:1802.03888 (2018).
Kassouf, M. T. et al. Genome-wide identification of TAL1’s functional targets: insights into its mechanisms of action in primary erythroid cells. Genome Res. 20, 1064–1083 (2010).
Behera, V., Evans, P., Face, C. J. & Hamagami, N. Exploiting genetic variation to uncover rules of transcription factor binding and chromatin accessibility. nature.com
Tehranchi, A. K. et al. Pooled ChIP-Seq Links Variation in Transcription Factor Binding to Complex Disease Risk. Cell 165, 730–741 (2016).
Le, D. D. et al. Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding. BioRxiv (2017). doi:10.1101/193904
Friedman, J. H. Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics 29, 1189–1232 (2001).


The arrangement by Maurice Ravel (below), produced in 1922, represents a virtuoso effort by a master composer. His instrumental colors — a trumpet solo for the opening Promenade, dark woodwind tones, the piccolo and high strings for the children’s “chicks in shells” — are widely admired. The influence of Ravel’s version may often be discerned in subsequent versions of the suite.
ehnesMajor funding for the February concerts is provided by:MO Harris Bank, Boardman and Clark LLP, Capitol Lakes, Dr. Robert and Linda Graebner, Marvin J. Levy, and Cyrena and Lee Pondrom.
Additional funding is provided by Martha and Charles Casey, and byOn Saturday, April 7, there is a 6:45 p.m.  lecture by MBM founder and artistic director Trevor Stephenson followed by a 7:30 p.m. performance.
Purcell’s vivid and eloquent operatic masterpiece, Dido and Aeneas is based on the tragic love story told in Book IV of Virgil’s epic Latin poem “Aeneid”and is depicted in the 1815 painting (below) by Pierre-Narcisse Guérin.
J. S. Bach’s witty Coffee Cantata will add some mischievous fun to the program.


Home » High Above The Subterranea Club 2000
CatNo: SMACD1031
Featuring the band's unique internet broadcast performance at the Subterranea Club London on April 6th 2000, 'High Above ..' brilliantly captures the exciting and unpredictable nature of Gong.
'Zero The Hero and his old friends return to earth...the satisfying, the surreal and the downright silly' - Q
3. Radio Gnome Invisible [8:06]
4. Yoni On Mars (Live) [6:52]
6. Bodilingus (Live) [5:57]
7. Zero The Hero And The Witch's Spell (Live) [9:24]
11. Infinitea (Live) [3:56]
14. Oily Way (Live) [3:38]
At The Pongmasters Ball


At Texas Primary Care Clinic in Frisco, we provide safe way to lose weight.


Knives have been made in Mora since the 1600'sStill today every Morakniv is made in our own factory in Mora. The fact that the entire process - from steel to complete knife - s.


Senior Care Authority offers a Comprehensive Senior Care Placement Program in Petaluma, California
Besides placement assistance, the organization offers eldercare consultation services, advocacy, and family coaching. Eldercare consulting services include helping families with challenging transitions, navigating a complex healthcare system, and decision making. Their advisors offer custom services depending on the client’s need. Their advocacy program ensuresquestions are answered thoroughly and appropriately. Their family coaching program, on the other hand, empowers families to move forward by navigating tough decision-making and reaching consensusince they are part of the senior living referral service. They also have a corporate caregiver support program which provides a local single point of contact for guidance and other resources that employees may need.
Senior Care Authority has a franchise program that presents an additional revenue opportunity. The organization began franchising in 2014 and so far has franchise owners throughout the nation. They receive unparalleled training, superior technology tools, and ongoing support to secure a competitive edge in the market Clients can call the organization at (888) 854-3910 or visit its website to learn more about Senior Care Authority and its services.
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While it's a small start, I'm so excited to have officially launched my "indie" game career and I look forward to future collaborations. Check out John's website for details on upcoming title releases.
Photographer Martin Wilson, upon receiving his first camera at age 8, was given sage and ultimately prophetic advice by his father, "make every picture count." Wilson has been following his advice ever since. His work is created frame by frame on 35mm film, a painstaking process whereby every frame from the roll is on display and every image has been shot in sequence. The film is developed, scanned, and then pieced together digitally to make a large contact sheet. Ultimately the contact sheet becomes the final piece of artwork. He does not post process the film or digitally manipulate the images beyond arranging them side by side. He calls the works "records of real journeys, the visual remnants of hours walking or cycling round town, bringing to life unheard of voices of the city." Martin Wilson is currently on exhibit at the London Tap Gallery in Altrincham, Cheshire.
For more information about purchasing the work, visit the artist's website.
Nick Cann began his career as a set illustrator at MGM Studios during the Golden Age of cinema, later branching out to set and credit designs for television and film. Using "" "A parallel aspect of these 'geodes't." Visit her website for information on the specific location of the geodes.
A Common Name is also creating art in custom spaces, "less unexpected but equally beautiful." Contact her here for more informationl's website.
Visit the store's website for more information on purchasing."by" Hi. "I" The works "re." Stewart's work is available online at his Etsy shop.
or his Etsy store.
GF Smith partnered with n, to test their new printing technology. FIELD was tasked with creating 10,000 unique digital paintings to test the technological potential of GF Smith's latest printing presses. Each painting features a different view of a hyper complex sculpture created through generative coding and creative intuition. The resulting paintings captured "the energy of a dynamic process-caught in a timeless medium." GF Smith took the paintings and printed them onto 10,000 unique promotional sleeves to showcase their premium papers and printing capabilities. A hybrid of human and machine creation, the paintings challenge the idea of what art is and where it is going.
For more on the project, visit GF Smith or FIELD.


“Excuse me if I launch into some toddler chat every now and again,” says Adele Vuko when we connect over the phone. With one eye on her two kids and the other on our interview, Vuko manages to remain in control of both during the 10-minute chat. Multitasking has become her forte, especially since recently turning director on short film, The Hitchhiker, though she at least avoided acting on that one.
“Did I say that?” she asks rhetorically. “That’s a sub-conscious thing. Well… do!
“I wish we had Groundlings and UCB,” Adele says. “I think that’s still in its very, very early form here, the live sketch community, but we found that going through online sketch, going through YouTube, was incremental to our visibility.
“My husband Christiaan [Van Vuuren] is part of the Bondi Hipsters, that’s how he got into comedy and filmmaking, via YouTube. Almost every other sketch comedian or collaborator that’s started out on YouTube, like Aunty Donna, and Superwog, and SketchShe and all these really wonderful acts, they all started on YouTube and they’ve all had a really wonderful career trajectory.
S Ma’am and they went viral with Activewear.
“This short’s always been a part of a larger picture feature film idea,” she continues, acknowledging more of that future planning. “That’s what the feature film will also explore but in more depth.


Augmented reality (AR) is already part of your life even if you don't realize it. After all, this technological advance changes the way the virtual world combines with the real one. Therefore, its interferences are not always clear. Still, knowing what augmented reality is is more than a trend. This is because AR offers a new dimension to our lives, changing the way tasks are performed and automating activities with the help of machines. Does everything seem too confusing? So, read this post, understand what this concept means and learn how technology is inserted in your life. summary What is Practical examples of augmented reality Clothing and footwear store Google: see in 3D Google Maps Froggipedia GeoGebra Augmented Reality SketchAR BBC Civilizations AR Measure Pokémon GO Ink Hunter Augmented reality surgeries Common questions What is augmented reality? The term augmented reality Phone Number refers to the interaction between virtual and physical environments through a camera. For this, a marker is used, such as a flat space, an image or some more complex elements. This ensures that the virtual object is inserted into the image. To better understand this concept, just know that it comes from a need related to labels.
Barcodes did not carry all the information that was needed. Therefore, the QR code, a two-dimensional code, was created. It was precisely programmed to project virtual objects into the real worl The first adds virtual items to an existing scenario. Therefore, it is accessible in several apps available on the cell phone. The second creates an entirely new environment. Therefore, it requires the use of specific glasses to view the context. That is, AR is much more present in our lives. How does augmented reality work? As we explained, AR adds virtual objects to a real scenario.


Samantha Ruth Prabhu is emerging to beSamantha has impressed fans with her acts time and again. Be it on the big screen, or on digital platforms, Samantha has delivered quite a few memorable performances, which has earned her a huge number of fans and admirers over the years. Theupdates on her life, and treats netizens to gorgeous and candid pictures as well. Speaking of which, a few hours back, Samantha pictures in which she can be seen enjoying her day by a waterfall.
After a few moments after Samantha shared the post, it was flooded with a lot of likes and love-filled comments from fans and friends alike.


Rhynwa sighed. Their first day on the road, and she wasn't sure they would make it to the town. Carter's constant depression and moping about his superpowers made her offer to send him to perfection several times; Akron's jibes made her TRY to send him to perfection several times; and Heart's constant questioning about magic made RHYNWA want to seek perfection several times. Oh, and Airhglen, the aurumvorax, kept trying to get into her change purse and eat their only cash. What was she doing wrong?
On the other hand, Akron was no worse than Tila (at least as far as the commentary went). Heart wasn't repeating questions, so once her curiosity was satisfied, she would relax. Airhglen was only a baby after all. And what was an adventure without psychodrama?Carter certainly fell into that niche. So maybe, thought Rhynwa, I am just not used to it ?
She looked over her group and thought: Maybe in the morning I will also remember how to be flexible and yielding. Hah!And maybe I'll wear a pastel pink dress and attend a high tea. Screw it ?
"They got this tavern there, with the finest ale in Nylevia." and sub rosa to Carter "And some of the ripest serving wenches too."
As the group of heroes came to the first farmhouse on the outskirts, Joe yelled "Dang it!Look there!The dang whatever has done come down here too!" He was pointing into a filed with strange animals. They were very low to the ground, green and fuzzy. "That's whut happened to OUR cows. They was tryin to turn into GRASS. That ain't fuzz ?that's GRASS growin' out of them there backs. They's getting shorter and shorter, and soon you won't be able to see 'em. They'll just be done gone!Dang!"
Heart removed her hands, and the poor beast contorted again, regaining the mobile turf look. "I don't know what it is that is causing this morphing ability, but it seems to depend on contact."


As the value of Bitcoin goes up and down, the same question is being asked by people around the world: What exactly is the potential of crypto-assets?The technology behind these assets—the blockchain—is an exciting advancement that could in addition...
This isare reviewing and setting new goals for the year ahead. If you are a business owner looking to embark on a significant growth trajectory in 2018, then passion is...
If you'recan benefit you. As with every year, 2018 will also see a change in how you do and manage the business with the increasing use and adaptation of technology. Here are...
B ...


ProCons
Cons: We have used Retail Edge about 2 years and just added another terminal at a new location. We are learning how to make sure each user can only see their specific data unless given security rights to see both/all locations. I feel there are a few reports that still show all locations data even though based on their user "rights" they should have limited visibility. Maybe I missed a step during setup?
Comments: The software does everything!
"New to R.E. in June. Very easy transition."
"The sales process was fantastic. The training was the worst experience ever"
"right from the demo download to buying and installing sales and customer support was awesome"
"Been with Retail Edge for 18+ years. It runs my bricks & mortar store. Gold service plan is great."
"I have used RE in-store for the last 6 years. Excellent POS and management for small business."
"Retail does all I need and more"
"Retail Edge is an excellent software we use at our boutique. We have used it from the beginning."
"The Team at Retail Edge is great to work with and very helpful."
"Retailedge overview"
"Solid retail POS. Good reporting. Great customer service"
"Easy to use, broad functionality"


t mag.<! /282843435/Tab_300x250_mobile >e’hat’good ‘’t“Drake fired up Raptors’ after 3-pointer.” ’So I pushed down my MacBook, with its chipping silver edges, and closed the cover. I harumphed over my stove and put on the kettle. “Drake,” I said. “I’ll tell you what I think of that, Drake, stupid Drake and his stupid self, all Toronto and OVO and courtside seats and melancholy clubbangers and all.”
Previous: Drake Enjoys Drake at Drake Night at Toronto Raptors Tilt
Drake Dedicates New Toronto Club to Bubbe and Zay. </s>
Interesting ceramic version of the Catrinas that are part of the decorations The festivity emphasis is on honoring the lives of the deceased, ignoring the sad part of the subject. Made by Gerardo Garcia en central Mexico.Dimensions: H 22 inches, W base 6 </s> The Blades will embark on a fifth campaign in League One next season, but Adkins, who became their fourth boss since relegation from the Championship in 2011 when appointed as Nigel Clough’s successor earlier this month, has a proven track record.
“There were many lessons learned. We joined Southampton when they were 23rd in League One at the time and we went 51 places before I eventually left, leaving them 15th in the Premier League, so that was a fantastic journey.
“When we look at ourselves here that is what we need.
“” </s> He said: “We end up hugely frustrated with a point instead of three and that makes life again so difficult for us. It happened at Southampton and now it’s happened here today – that’s four points thrown away.
“But’s the bottom line.
The international break means Allardyce now has two weeks during which to stew on another missed opportunity before West Brom head for the Stadium of Light.” </s> A number of national newspapers are reporting that the organisation’s flagship tournament will be rebranded as the Emirates FA Cup in a deal reported to be worth around £30m over the next three seasons.
The FA has so far resisted such a drastic change, preferring instead to use the phrases “in association with” or “sponsored by” in relation to their previous commercial deals with Littlewoods, Axa, E.ON and Budweiser.
ek. </s> MS OWA
VMware'sn, Frank Rauch, points to a bigger partner opportunity following the


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The head of PepsiCo Inc.'s Frito-Lay North America business is leaving the company, Pepsi said Wednesday.
Shares in the company, up 6.4% over the past 12 months through Wednesday's close, were inactive after hours.


September 8, 2013 By Tim
First year was overwhelming. There was a new city to navigate, with new concepts to master, new people to meet, and new professors to learn from. For me, the hardest thing to adjust to was the amount of time spent in the classroom. The excessive time spent in class brought with it additional challenges, suchgrades, studying, veterinary school
August 30, 2013 By Shannon
First day of school! First day of school! That’s what I was shouting inside my head as I drove myself to class for the first time here at the AVC. Nervousness, excitement, fear, and all the feelings in between plagued me as I entered the doorway on Monday, but five days later I’m a well-acclimated Class, veterinary school


Ninkovic aggravated the injury just before the hour mark against the Mariners when he raced into the box to connect with a through ball from Alex Baumjohann, throwing himself at the chance, skying his shot and tumbling to the ground.
“I started to feel it even after 15 minutes but I thought it is going to be better.
But Sky Blues full back Rhyan Grant put the result down to the resilience of his side more so than luck, as he and his teammates continue to pull win after win out of the bag.
“We are obviously very happy,” Grant told FOX Sports.
“It was just one of those games.
"But, as much as they had a few chances, I thought we still dominated.
After facing the last-placed Mariners the Sky Blues will now prepare for a clash with Wellington Phoenix who will be no easy beats after their five-game undefeated streak continued with a 1-1 draw against Melbourne Victory on Saturday afternoon.


Home NEWS Buhari Sacks AEDC Management 24 Hours After Strike Action
over the strike embarked upon by its employees which caused disruptions in power supply across Abuja, Nasarawa, Kogi and environs.
The .
As part of the development, a statement issued revealed that Buhari has.
The statement reads, "The staff union had in a notice said, "Pay the remaining: batches of 2020 bonus. Pension remittances (19 months). Third batch of bulk rent. Thrift deductions (17 months) and union check off dues (4 months. AEDC management pay us now!".
ALSO READ: Former Prison Boss Sentenced To 5 Years Imprisonment For Stealing From Inmate In Rwanda
Previous articleNext articleNCDC Confirms Three New Cases Of Omicron COVID-19 Variant


The government and corporate America are no match for this shift and even though the Supreme Court lifted the ban othey have only postponed the inevitable. Corporations will spend a ton of money on their propaganda machines, our voices will continue to grow louder through the use of social media and in the end…


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How does The Primordial tie into the flood?
- Did he create the flood or was he simply just a spawn of flood mixed with a precursor or something like that?
and the main thing i'm interested in is how does the flood's logic plague work?
I know the graveminds has to talk to AI but there has to be more to it than just twisting words, right?


Camera Support System - Body Mount Supporter - DSLR Supporter - Laptop on Tripod Mount Holder - - - - - - - - - - - - - - -
B M S - Body Mount Supporter
total hands free carrying around
light & easy to use
This is a body mounted support system for photo and video cameras which gives you a two hands total control on your cameras and a long time steady camera shooting without arms and shoulders pain.
Lightweight and rigid aluminum construction gives you an easy, fast and comfortable use. It fits for almost any body size.
This is the best and the most efficient camera support system on the market. It is an one time investment for many years of use. It is designed for heavy and long time cameras use only.
Handheld + Monopod + Tripod = BMS
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BMS is best for these lenses
Differences between the BMS and the others
For professional sport and wildlife photographers and videographers
Flexible as handheld - Stable as on tripod
All accessories are on one supporter, best for instantly photo and video shooting.
Turn the moving arm to the back side and put it on your left shoulder to have it held on your shoulder and make it feel much lighter on your arms for stable shooting need.


T-Shirt relief fund for Magnitude 10.0 Gymnastics! Magnitude Gymnastics located in Hermitage, TN was competently destroyed by the tornado that ripped through Nashville, TN in the early morning of March 3, 2020. 100% of the proceeds from the shirts will be given to Magnitude to help in the rebuilding of their gymnastics facility. Shirts will be $15 each. You can pick up at the Optional or Compulsory State Meet or choose to have your shirts shipped. Please place your order quantity in each box. You will be invoiced via Quickbooks.


j“, „srednji sloj“ i „izlazni sloj“. Manji prekidači su manje težine, pouzdaniji i genereralno lakši za pravljenje, a samim tim i jeftiniji.
Telefonski sistem jedino treba da napravi jedan-na-jedan konekciju. Intuitivno, na osnovu ovoga se čini da bi broj ulaza i broj izlaza uvek mogao da bude jednak u svakom potprekidaču, ali intuicija ne dokazuje ovo, niti nam govori kako to da postignemo. Pretpostavimo da želimo da sintetizujemo 16 po 16 unakrsnih komutatora. Ovaj dizajn bi mogao da ima 4 potprekidača na ulaznoj strani, svaki sa 4 ulaza, za ukupno 16 ulaza. Dalje, na izlaznoj strani, mogli bismo takođe da imamo 4 izlazna potprekidača, svaki sa 4 izlaza, za ukupno 16 izlaza. Poželjno je da dizajn koristi što manje žica, jer su one prilično skupe. Dakle, najmanji mogući broj žica koje mogu da povežu dva potprekidača u jednu žicu. Ovako, svaki ulazni potprekidač će imati jednu žicu ka svakom srednjem potprekidaču. Takođe, svaki srednji potprekidač će imati jednu žicu ka svakom izlaznom potprekidaču.
Pitanje je koliko srednjih potprekidača je potrebno i otuda, koliko ukupno žica bi trebalo da povezuje ulazni sloj sa izlaznim. Pošto su telefonski prekidači simetrični (pozivaoci i prijemnici su zamenjivi), ista logika se primenjuje i na izlazni sloj i srednji potprekidači će biti „kvadratni“, imaće isti broj ulaza i izlaza.
Broj srednjih potprekidača zavisi od algoritma korišćenog za alociranje veze ka njima. Osnovni algoritam za upravljanje troslojnim prekidačem je traženje srednjeg potprekidača koji ima neiskorišćene žice za potrebne ulazne i izlazne prekidače. Jednom kada se pronađe srednji prekidač sposoban za konekciju, povezivanje sa pravim ulazima i izlazima, u ulaznim i izlaznim prekidačima je trivijalno.
Teoretski, u primeru, potrebana su samo četiri centralna prekidača, svaki sa tačno jednom vezom sa svakim ulaznim prekidačem i sa jednom vezom sa svakim izlaznim prekidačem. Ovo se naziva „minimalni obuhvatni prekidač“ i rukovođenje ovim, bio je sveti gral istrage Belovih laboratorija.
Kako god, uz malo posla sa olovkom i papirom pokazaće se da je lako dobiti ovakav minimalni prekidač, u stanju u kom nijedan srednji prekidač nema vezu sa oba potrebna ulazna i izlazna prekidača. U stvari, ako postoji jedan poziv po ulaznom prekidaču i to rezultuje jednim pozivom po izlaznom prekidaču, onda prvih šesnaest poziva ovog hipotetičkog prekidača, zapravo blokira čak do petnaest dodatnih poziva kojima bi bile potrebne slične veze.
Iz ovog razloga, za „jednostavi povezani neblokirajući prekidač“, 16x16 prekidač, sa četiri ulazna potprekidača i četiri izlazna prekidača, smatralo se da zahteva 7 srednjih prekidača. Zbog toga, nekada se ovaj raspored naziva „2n-1 prekidač“, gde je n broj ulaznih potprekidača.
Primer je namerno mali i u tako malom primeru, reorganizacija ne čuva mnogo prekidača. 16x16 unakrsni ima 256 kontakata, dok 16x16 minimalni obuhvatni prekidač ima 4x4x4x3 = 192 kontakta. Stvarna telefonska centrala ima stotine hiljada ulaza i desetine miliona prekidačkih kontakata.
Upravljanje minimalnim obuhvatnim prekidačem
Presudno otkriće bio je novi način reorganizacije veza u srednjim prekidačima da „razmenjuju žice“, tako da bi nova konekcija mogla biti završena.
Prvi korak u neblokirajućem minimalnom obuhvatnom prekidačkom algoritmu, samo je naivna ranija shema (iznad): traži potprekidač srednjeg sloja koji ima potrebne ulazne i izlazne veze. Ako je pronađen potprekidač srednjeg sloja, koji povezuje oba potrebna ulazna i izlazna potprekidača, onda je alocirana i konekcija prolazi.
Međutim, pošto je broj srednjih potprekidača manji u minimalnom obuhvatnom prekidaču nego u „2n-1“ prekidaču, nekada je potraga neuspešna. Ako potprekidač sa potrebnim parom konekcija ne može biti pronađen, par potprekidača mora biti pronađen. Jedan potprekidač mora imati vezu sa odgovrajućim ulaznim prekidačem; drugi mora imati vezu sa odgovrajućim izlaznim potprekidačem. Ovi potprekidači moraju da postoje, jer za svaki ulaz u minimalni obuhvatni prekidač, postoji žica iz ulaznog potprekidača ka srednjem prekidaču. Pošto je ceo prekidač za telefonski sistem, takođe je i simetričan, tako da postoji i slobodna žica od jednog od prekidača srednjeg sloja ka odgovarajućem izlaznom potprekidaču.
Sada, konceptualno, algoritam mora da reorganizuje konekcije u ova dva srednja potprekidača (nazovimo ih A i B). Ideja je da se zadrže sve postojeće veze koje već prolaze kroz A i B, kako bi se izbeglo prekidanje poziva, a opet okupile ili u A ili u B dve žice koje vode ka odgovarajućim ulaznim i izlaznim potprekidačima.
U standardnom objašnjenju crtanjem, A i B se predstavljaju kao veze koje u stvari leže jedna na drugoj. Ulazi od A moraju da leže na odgovarajućim ulazim od B. Izlazi iz A moraju, takođe, da leže na odgovarajućim izlazima iz B.
Veze koje prolaze kroz A i B su smešten


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0 large largemigration request can proceed.
Parameter Sets: LocalPublicFolder, Onboarding, Offboarding, PublicFolderToUnifiedGroup
The Locale parameter specifies the language for the migration batch.
### -MoveOptions
### -NotificationEmails
The NotificationEmails parameter specifies oneSpecify the value as a string array, and separate multiple email addresses with commas.
If you don't use this parameter, the status report isn't sent.
Parameter Sets: Local, LocalPublicFolder, PreexistingUserIds, Preexisting, WorkflowTemplate
### -PrimaryOnly
The PrimaryOnly switch specifies that only primarythat also have archive mailboxes (archive mailboxes aren't migrate only use this switch for local moves and remote move migrations.
Note : If the users don't have archive mailboxes, don't use this switch.
### -PublicFolderToUnifiedGroup
The PublicFolderToUnifiedGroup switch specifies a migration from public folders to Office 365 gr PublicFolderToUnifiedGroup
### -ReportInterval
The ReportIsent to the email addresses listed within NotificationEmails.
This parameter should only be used in the cloud-based service.
### -SkipMerging
### -SkipSteps
The SkipSteps parameter specifies the steps in the staged Exchange migration that you want to skip. Valid values are:
- None (This is the default value)
- SettingTargetAddress: Don't set the target email address on the source mailbox. This setting prevents mail from being forwarded from the original mailbox to the new migrated mailbox.
This parameter is only enforced for staged Exchange migrations.
Type: SkippableMigrationSteps[]
The SourceEndpoint parameter specifies the migration endpoint to use for the source of the migration batch. You create the migration endpoint by using the New-MigrationEndpoint cmdleigration endpoint. For example:
- Name (the Identity property value)

Parameter Sets: Onboarding, PublicFolderToUnifiedGroup


 - more than 1,000 incidents in all.
---States in the -.
The CDC had asked Americans about whether they kept loaded, unlocked guns in their homes as part of an annual telephone survey that asked 400,000 adults about various health risks. Using that data, researchers estimated that 1.5 million children lived in such homes - and the results varied greatly by state based on gun ownership levels.
Gun control advocates say Child Access Prevention laws - on the books in about two dozen states - a.


By sandyb on Mar 05, 2008
For the 8th consecutive year, Sun employees in the Boston area are recognizing National Women's History Month (WHM) during March. (I have to admit that I will miss Larry R's annual email to me asking when National Men's Month is celebrated!) :-)
We're also looking forward to a panel discussion with some of Sun's women VPs and Directors as well as a roundtable discussion with Donnalee Lozeau, the first woman Mayor in Nashua, NH's 153+ yr history. (I was at our City Hall last year and walked down the hall where all the previous Mayor's pictures are hanging - It was so obvious that a woman's face was missing! It's great to see Donnalee's up there now and we're looking forward to her being at Sun at the end of March.)
We'll post updates about our activities throughout the month, so check this blog to see what's going on. And let us know via the comments option what you are doing to celebrate WHM in March!


Personal Pensions and PRSAs are private pensions that are managed for you by a life insurance or investment company. If you are earning an income but are unable or not entitled to join a Group Pension plan, or if you are self- employed, then you can start either a Personal Pension or a PRSA.
Personal Pensions and PRSAs give you the freedom to decide how much you want to contribute, and where your contributions are invested. However, bear in mind that the older you are when you start, then the more money you will need to contribute to reach your target.
Subject to conditions, a PRSA can also be used alongside a company pension to make Additional Voluntary Contributions.
A Group Pension or Occupational Pension Scheme is a savings account designed to accept contributions from both you and your employer. These contributions then grow into a lump sum (pension fund) that will provide you with pension benefits when you retire.
Attractive tax reliefs for companies and individuals are available under current legislation.
Any contributions made by the company can be fully offset against corporation tax and do not attract personal income tax as a Benefit- in- Kind.
These days, the average person retiring at age 65 can expect to live for at least another 20 years or more. Making the most of these retirement years involves careful financial planning. The first part of this is choosing the right pension plan. The second part is choosing the right post- retirement options for your needs. Post retirement options include: