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2015 was a challenging year. There were a lot of happy moments that I wouldn't trade for the world, and there were also very sad and dark moments that had me questioning every choice that I had made in my life. But looking back, I can say that there is not one choice I made in 2015 that I would change. Each and every decision I made and experience I had taught me a lesson that has made me stronger and wiser today. So for 2016, I don't want to make resolutions. Instead I want to take the lessons I've learned and use them to shape my goals and intentions for 2016. So without further ado, here are the lessons I've learned in 2015.
Trust your instincts – In 2015 I accepted a job that I knew wasn't right for me. I wanted to learn a new field and was excited for the new venture. But from the first day, there were red flags all around and I knew that my experience would never be appreciated. At that point, I learned a valuable lesson. Always, always, always listen to your instincts. While it's great to venture into unknown territory (how else re you gping to learn new things), trust your instincts and follow your heart. When you follow your passion, you'll never work a day in your life.
Listen to your body – Despite working long hours and pushing myself beyond my limits, I refused to listen to my body and suffered the consequences when I hit a wall. We know that it's important to take time for ourselves and reconnect with ourselves. But sometimes that's just not enough.
Forgiveness is for you (not the other person) – For years I didn't want to forgive those who had hurt me because I thought that by doing that, I would be providing them some kind of benefit. Today I know that that's not the case. Holding on to anger is like walking around with a clenched fist all day, every day, which isn't good for you. Forgiveness is all about you letting go of these feelings of hurt and resentment. And the one who benefits from forgiveness the most is YOU.
Life is all about the journey, not the destination – Most of us live our lives with an end goal in mind. While there's nothing wrong with having goals, we are often so focused on the destination that we forget to enjoy the journey (which I am definitely guilty of). Knowing this, I've been trying to be more mindful in everything I do instead of thinking of the next thing. It's not easy, but the times that I have been able to do this, I've truly enjoyed what I was doing, whether it was enjoying a home-cooked meal or doing the dishes. I have discovered that there can be joy in beauty in everything.
Believing in yourself starts with you – If you don't believe in yourself and if you don't believe that you are worthy and capable, don't expect anyone else to. When you don't believe in yourself, you end up acting on that belief and people will treat you based on that belief. Treat yourself knowing that you are a beautiful person capable of amazing things and the world will treat you the same way.
Gratitude is the attitude – I believe in the law of attraction. Science has proven that as humans, it is much easier to focus on the negative than the positive. Instead of looking at all the ways in which the world sucks, look at all the wonderful things around you, all the great things you have in your life and everything you are grateful for. You'll see that the more you do that, the more reasons you'll have to be grateful. A great tool that I've adopted to help me build gratitude is the Five Minute Journal, which has helped me start and end each day with a grateful heart.
All things happen for a reason – Steve Jobs said it best when he said "You can't connect the dots going forward, you can only connect them looking backwards". There are a number of events in my life that I can look back on when I was disappointed when things didn't quite turn out the way I expected, but that ended up being for the best because a bigger and better opportunity was waiting for me just around the corner.
Don't sweat the small stuff – This is a tough one, but one I try to practice. It doesn't really matter who put the dishes away, that a stranger cut me off while walking on the sidewalk, or that it's raining again. These are all little things that just don't matter in the greater scheme of things. Learning to let things go and choose your battles is key to happiness.
You are the company you keep – I know you've heard this one before, but it's true so it's worth repeating – you are a combination of the five people you spend the most time with. If you want to change your life, take a look at the people around you. Do they support you? Do they believe in you? Do they mirror your values?
Every death is different – This year I said goodbye to a dear friend who chose to end his life. Before this, the most significant death in my life was my dad. I never thought that I could feel such strong sadness, but I was wrong. But the pain and sadness I felt this time was different than what I felt when my father passed. Death will aways be difficult to understand and different to deal with in each case. But grieving with friends and family remains the best way to cope and keep their spirit alive.
Don't forget to follow me on Bloglovin, Instagram and Twitter!


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The Philomath Area Chamber of Commerce invites car-lovers of all ages to attend and participate in their annual Classic Car Show. The event is open to participants and spectators alike. Come enjoy the sun, food, vendors, music, and of course beautiful cars, trucks and motorcycles. All vehicles dating from 1900 to 1981 are welcome to enter.
The car show will take place at the Philomath City Park at Applegate Street & 23rd Street, on July 7th. If you wish to participate, registration starts at 7 a.m. Spectators may attend for free between 9 a.m and 3 p.m.


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Bo Senesomxay
Bo Senesomxay (sin-sum-sigh) is a highly sought-after hair and makeup artist in Northwest Arkansas. He has collaborated in widely publicized events, including New York Fashion Week, Essence Music Festival and Walmart Shareholder's Convention. Amongst the various people with whom Bo has worked with are Aerosmith lead singer Steven Tyler, Olympic gold medalist Gabby Douglas, American Idol winner Candace Glover, and David Feherty, host of the Golf Channel's Feherty.
Bo started in the trade while still a student at the University of Arkansas as a makeup artist for MAC Cosmetics in 2010. He graduated from Regency Beauty Institute in 2011 and is currently a stylist/makeup artist at Dead Swanky in Fayetteville, Arkansas. Bo received his B.A. in Communication from the University of Arkansas in 2012.
For booking info, please call Dead Swanky salon (479)251-1050.
Find Bo on Facebook:
Photo Credit:
Keely Yount Photography
JadeFOX Pictures
Raining Light Photography
Amber Hardcastle Herrera
Dean Reeves
- Make-up Artist/Hairstylist


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Veterans Day
There will be plenty of time to talk about the politics and news goings on in the nation and the world. Today, however, is Veterans Day.
By now, you should know that Veterans Day celebrates all of America's Veterans, and began as "Armistice Day," recalling the 11th Hour of the 11th Day of the 11th Month when the Guns of August at last fell silent over the fields of Europe and World War I ground to a halt. It wasn't the end of the war in a technical and legal sense, but it was the end of the shooting. But not all of the dying.
As I have said in recent days, World War I has become something of an interest to me. I am stunned, more than 100 years after the fact, not just at the level of destruction and death, but at the acceptance of those levels by the peoples of the day – at least at first. By 1916, a hundred years ago, there was very little "glory" in going to war, a lot of conscription of men for the armies, much hunger and great despair as more and more men were sacrificed to – in the immortal words of Captain Blackadder – move the Field Marshal Haigs' drinks cabinet six inches closer to Berlin.
That war, particularly on the Western Front, is a study in human psychology and fragility. Men who knew they had no hope of survival whatsoever and had no real reason for doing so, went over the top and attacked in useless assault after useless assault because if they didn't go, they would face being shot by their own leaders. The miracle of the First World War is that troops didn't mutiny long before 1918.
Germany was essentially starved to defeat. Oddly enough, a new scientific breakthrough, achieved in the early 1900's, allowed Germany to produce weapons through the war which would not have been possible otherwise. Ostensibly the Haber-Bosch process was to designed to produce fertilizer to grow food. And it did (and has continued to do so). But a major side effect was that the same material needed for the fertilizer, nitrate, is also the main ingredient in 20th Century munitions. In the most "Guns or Butter" decision ever made, Imperial Germany chose to use the nitrate it produced via the process to continue to make weapons. Her people starved and mourned the dead until they could take it no longer.
Some twenty or so years later, the same Haber-Bosch process would allow Nazi Germany and to a lesser extent Imperial Japan, to continue to make weapons and fight on long after they should have burned through all of the resources they had in the first place. In both wars it is estimated that at least two years were added on to each war by enabling the production of weapons after the natural resources and ability to import nitrates, primarily from Chile, had been exhausted. Think about that for a moment. How many millions of people died in 1916-1918 and later again from 1943-1945?
The same science that allows us to feed nearly 8 Billion people today, also helped to continue the destruction that killed millions upon millions.
When the First World War Armistice went into effect on November 11, 1918, at 11am, the entire world seemed to stop. One of the most fascinating TV shows I have ever watched was a documentary (The Last Day of WW1) about the final hours of the fighting. Imagine being that man who was on a bridge in Belgium, at 10:59am, holding your breath and waiting, and then being hit in the final minute of the shooting. It is incredible, and you can watch it here:
The US involvement in the war is somewhat problematic for historians, especially given that this week was also the anniversary of the re-election of Woodrow Wilson in 1916, on the platform that "He Kept Us Out of the War!" Within months, he would stand in the Capitol and ask Congress to declare war on Germany.
Most of Europe saw the US involvement in the War not as much as an "Arsenal of Democracy" moment as they did a move to protect our investments. America had loaned France and England Billions upon Billions of dollars, and it wasn't clear that they would win. There were a great number of people in European leadership who believed that America was fighting more for money than for any high minded ideals of liberty or democracy.
In the conflict, US Commanding General Black Jack Pershing made no bones about things and would not allow US troops to be amalgamated into French and British units. Americans would fight as Americans, period. Well, except for African-American troops. Those he gladly sent in great numbers to the French, where they fought with incredible ferocity and bravery and to this day are revered by the French.
When the war ended, the celebrations of the Armistice were an annual reminder of the sacrifice and the destruction that the first global "ToTaL War" had wrought, and the belief that it would never happen again. Which, of course, it did. For many of the same reasons and in the same places with the same results, the destruction and death on an unparalleled scale that today we still cannot comprehend.
Today, we don't think about the dead in Flanders Field or Belleau Wood. We don't wear poppies, although if you happen to pass a VFW post or stand they will offer you one to wear. We do celebrate the men and women who have served this nation from its inception to today. We recall their deeds, their sacrifice and their devotion to duty. The citizen soldier who served and serves us still.
I fear for the future of Veterans Day, as it has slowly become a day of "freebies" and "benies," more than a day of recalling the service and no longer a day "dedicated to the cause of world peace," as it was originally.
Maybe today, in the midst of meals and sales or parades or coffee cups, we could step back and ask ourselves what all of that service was for? And remember that millions upon millions were never able to commemorate the day because the human race really hasn't learned the lesson that the day was supposed to teach us.
Posted on November 11, 2016, in American, History, WWI and tagged Haber-Bosch, Veterans Day, World War I, World War II. Bookmark the permalink. 1 Comment.
Very well written Dave. Thank you for the forgotten insite. I have shared with many.
A beloved, heartfelt, thank you to you and all our Veterans.


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- Research Article
- Open Access
A fast method for calculating reliable event supports in tree reconciliations via Pareto optimality
© To et al. 2015
- Received: 3 May 2015
- Accepted: 26 October 2015
- Published: 14 November 2015
Given a gene and a species tree, reconciliation methods attempt to retrieve the macro-evolutionary events that best explain the discrepancies between the two tree topologies. The DTL parsimonious approach searches for a most parsimonious reconciliation between a gene tree and a (dated) species tree, considering four possible macro-evolutionary events (speciation, duplication, transfer, and loss) with specific costs. Unfortunately, many events are erroneously predicted due to errors in the input trees, inappropriate input cost values or because of the existence of several equally parsimonious scenarios. It is thus crucial to provide a measure of the reliability for predicted events. It has been recently proposed that the reliability of an event can be estimated via its frequency in the set of most parsimonious reconciliations obtained using a variety of reasonable input cost vectors. To compute such a support, a straightforward but time-consuming approach is to generate the costs slightly departing from the original ones, independently compute the set of all most parsimonious reconciliations for each vector, and combine these sets a posteriori. Another proposed approach uses Pareto-optimality to partition cost values into regions which induce reconciliations with the same number of DTL events. The support of an event is then defined as its frequency in the set of regions. However, often, the number of regions is not large enough to provide reliable supports.
We present here a method to compute efficiently event supports via a polynomial-sized graph, which can represent all reconciliations for several different costs. Moreover, two methods are proposed to take into account alternative input costs: either explicitly providing an input cost range or allowing a tolerance for the over cost of a reconciliation. Our methods are faster than the region based method, substantially faster than the sampling-costs approach, and have a higher event-prediction accuracy on simulated data.
We propose a new approach to improve the accuracy of event supports for parsimonious reconciliation methods to account for uncertainty in the input costs. Furthermore, because of their speed, our methods can be used on large gene families. Our algorithms are implemented in the ecceTERA program, freely available from http://mbb.univ-montp2.fr/MBB/.
- Tree reconciliation
- Gene evolution
- Phylogenetics
- Parsimony
- Supports
The evolutionary history of a gene family often differs from the history of the species containing those genes due to macro-evolutionary events other than speciation. Reconciliation methods compare gene trees with a species tree in order to recover these events. The DTL reconciliation model [1–4] accounts for three types of events: gene duplications (\(\mathbb {D}\)), losses (\(\mathbb {L}\)), and transfers (\(\mathbb {T}\)). This model is typically used in a parsimony framework, which searches for the reconciliations that minimize the overall cost, given a cost vector specifying the costs for \(\mathbb {D}, \mathbb {T}\) and \(\mathbb {L}\) events. Unfortunately, ensuring the time-consistency of gene transfers, i.e., satisfying the chronological constraints among nodes of the species tree that are involved in transfer events, is NP-hard [4, 5]. However, if the internal nodes of the species tree are ordered, i.e. using a dated species tree, the problem can be efficiently solved [1, 6]. Although there can be an exponential number of optimal reconciliations for a given cost vector, the DTL-graph can be used to represent them compactly [7].
Some of the events predicted by reconciliations methods may not be reliable due to potential inaccuracies in the input trees and the inherent imprecision of the input costs. Thus, it is necessary to estimate confidence values, or supports, for each predicted event. Supports can be defined, for example, as the frequency of an event over a space of alternative solutions.
In [7–9], the support of an event is defined as the frequency of the reconciliations containing it over all reconciliations in the solution space. In [7, 8], the DTL-graph was used to compute event supports in this sense over the set of all parsimonious solutions (for one cost vector in [7] and for several ones in [8]). Moreover, in [8], the authors used these event supports to identify the median reconciliation, that is the reconciliation that minimizes the sum of event-based distances between itself and all other parsimonious alternatives (here, the event-based distance between two reconciliations is defined as the number of events contained in one but not in the other). Bansal et al. [9] presented another way to address the problem, suitable for non-dated species trees as well as dated ones: they designed a method which samples the space of parsimonious reconciliations uniformly at random and then counts the frequency of each event within all sampled solutions.
However, the approaches presented in [7, 9] only focus on one cost vector; though, it is often difficult to know the appropriate cost vector, and the solutions can be very sensitive to the input costs. In [8], the authors used a time-consuming approach that samples the space of cost vectors around the input one and then apply the algorithm presented in [7] to compute a DTL-graph for each sample. Libeskind-Hadas et al. [10] presented a method to estimate the sensibility of a parsimonious reconciliation with respect to the input costs. They use Pareto-optimal event count vectors to partition the cost space into regions such that the costs in the same region induce reconciliations with the same number of DTL events. These regions are then used to compute events supports, defined as the fraction of regions having an event in all (or one of, depending on the option used) their reconciliations. However, for the support measure to be reliable, the events should be recovered using biologically realistic costs, which can vary with respect to the phylum we are interested in (e.g., in Mammalian, duplications are much more frequent than transfers, whilst transfers are predominant in the evolution of Bacteria).
But, when analyzing a realistic cost space with the method in [10], we observe that, often, a small number of regions, e.g. in many cases only one, is returned; thus, the corresponding supports can only be either 0 or 1 and are almost useless to filter out events. On the other hand, a larger cost space can account for more regions, but the event supports may be unreliable since they consider events retrieved using unrealistic costs (we will see in the Results section that this is indeed the case). Moreover, the method in [10] does not generate median reconciliations. It was shown in [8] that the median reconciliation has better event predictions than random ones.
Here, we use the definition of an event support given in [7, 8] and extend the work in [7] by taking into account several cost vectors. Rather than using the time-consuming sampling method in [8], we construct the space of all solutions corresponding to the input cost range and then compute the event supports over this space. As we shall see, our algorithm also makes use of Pareto-optimal reconciliations, which are efficiently computed via an extended version of the DTL-graph. This has the advantage of efficiently taking into account all relevant parsimonious reconciliations – even those that are parsimonious for very few event cost combinations and that are thus unlikely to be discovered by an ad hoc choice or a random sampling of event costs [10].
The approaches presented in [7–9] only consider optimal parsimonious reconciliations, even though the real gene evolution might not be optimal. In this paper, we also present a method for computing near optimal reconciliations that allows a tolerance ε for the overall reconciliation cost. Our methods are faster than the one presented in [10], substantially faster than the sampling-costs approach presented in [8], and have a higher event prediction accuracy on simulated data than both methods.
Basic notations
Let T be a binary rooted tree where only leaf nodes are labeled. We denote by V(T),E(T), r(T),L(T), and \(\mathcal {L}(T)\) respectively the sets of nodes and edges, the root node, the set of leaf nodes, and the set of species labeling the leaves of T. If u is a leaf node, we denote by s(u) the species that labels u, and if u is not a root we denote by u p its parent and by (u p ,u) the edge connecting u p and u. Note that, in a rooted tree, all edges are directed away from the root. For any node u of T, T u is the subtree of T rooted at u.
A species tree S is a rooted binary tree such that each leaf represents an extant species and there is a bijection between L(S) and \(\mathcal {L}(S)\). A gene tree G is a rooted binary tree such that each leaf corresponds to a contemporary gene and is labeled by the species that contains it.
In this paper, we will use G to denote a gene tree, and S ′ to denote the subdivision of a dated species tree S such that \(\mathcal {L}(G)\subseteq \mathcal {L}(S)\).
Finally, given an order list L, we denote by L i the i t h element of L.
A reconciliation α is defined as a function that maps each vertex u of G onto an order sequence of nodes (x 1,⋯,x k ) of S ′ and such that the mappings of u and its children satisfy some biologically-dictated constraints [1]. In Definition 2, we express these constraints via the function postlist defined below, which, for every u∈V(G), x∈V(S ′) and \(e \in \{\mathbb {S},\mathbb {D},\mathbb {T},\mathbb {SL},\mathbb {TL},\mathbb {C},\varnothing \}\), defines all possible mappings of the children of u, denoted u 1,u 2, if e is a birth-event, or all possible next mappings of u otherwise (see Fig. 2). Hence, p o s t l i s t e (u,x) is a set of sets of pairs if \(e\in \{\mathbb {S},\mathbb {D},\mathbb {T}\}\), and a set of pairs if \(e\in \{\mathbb {SL}, \mathbb {TL},\varnothing \}\).
Definition 1.
- 1.
\(postlist_{\mathbb {S}}(u,x)= \{\{(u_{1},x_{1}),(u_{2},x_{2})\}\), {(u 1,x 2),(u 2,x 1)}} if x is a speciation node and u is not a leaf, otherwise ∅.
- 2.
\(postlist_{\mathbb {D}}(u,x)= \{\{(u_{1},x),(u_{2},x)\}\}\) if u is not a leaf, otherwise ∅.
- 3.
\(postlist_{\mathbb {T}}(u,x)=\bigcup \limits _{z\neq x, \theta (z)=\theta (x)} \{\{(u_{1},x), (u_{2},z)\}, \{(u_{2},x), (u_{1},z)\}\}\), if u is not a leaf, otherwise ∅.
- 4.
\(postlist_{\varnothing }(u,x)=\{{(u,x_{1})\} }\) if x is an artificial node, otherwise ∅.
- 5.
\(postlist_{\mathbb {SL}}(u,x)=\{(u,x_{1}),(u,x_{2})\} \) if x is a speciation node, otherwise ∅.
- 6.
\(postlist_{\mathbb {TL}}(u,x)={\bigcup \limits _{z\neq x, \theta (z)=\theta (x)}\{(u,z)\}}\).
The definition of a reconciliation can be adapted from [1] and [7] by using postlist as follows.
Definition 2 (Reconciliation).
- 1.
i=ℓ, u∈L(G), and x∈L(S ′), s(x)=s(u); \((\mathbb {C}\) event);
- 2.
i=ℓ, u∉L(G), and there exists exactly one event type \(e\in \{\mathbb {S},\mathbb {D},\mathbb {T}\}\) such that p o s t α (u,x)∈p o s t l i s t e (u,x);
- 3.
i≠ℓ, and there exists exactly one event type \(e\in \{\mathbb {SL},\mathbb {TL},\varnothing \}\) such that p o s t α (u,x)∈p o s t l i s t e (u,x).
For example, Figures 1 c, d present two different reconciliations, between the gene tree in Fig. 1 a and the subdivision of a dated species tree in Fig. 1 b. Denote by α the reconciliation in the Fig. 1 d, then α(w)={y}, α(u)={x ′′,A}, α(v)={x ′,B},α(a)={A},α(b)={B},α(c)={C},α(d)={D}.
Note that, due to the subdivision, two events belonging to two different reconciliations located in different branches of S ′ may correspond to the same event in S. This implies that two reconciliations that are distinct according to Definition 2 in S ′ may be equivalent in S. We can thus define a canonical reconciliation [7] as the member of this equivalent set for which each event is located as low as possible within a branch.
For every pair (u,x) of a reconciliation α, where x=α i (u) for some i, with 1≤i≤|α(u)|, we denote by α(u,x) the reconciliation corresponding to the restriction of α on G u such that the first element associated to u by this reconciliation is x; for the sake of simplicity, we call α(u,x) a reconciliation between u and x.
The fact that the solution space is finite when we disallow sequences of consecutive \(\mathbb {TL}\) (and also \(\mathbb {DL}\) events) is implied by the following remark, which is easily deduced from Definition 1:
Remark 1.
If an event that is not a \(\mathbb {TL}\) happens at (u,α i (u)), and if (v,y) is a pair of nodes in p o s t α (u,α i (u)), then either v is a child of u or y is a child of α i (u).
Pareto-optimal reconciliations
Given two vectors v=(d 1,t 1,l 1) and v'=(d 2,t 2,l 2), we say that v≤v' if and only if d 1≤d 2, t 1≤t 2, and l 1≤l 2. Moreover, v<v' if and only if v≤v' and at least one entry of v is strictly smaller than its corresponding entry in v'. Denote by v⊕v' the vector (d 1+d 2,t 1+t 2,l 1+l 2) and by v⊗v' the value d 1·d 2+t 1·t 2+l 1·l 2. Note that ⊕ and ⊗ are used to denote respectively the vector addition and the dot product.
The event count vector (notion proposed in [10]) of an event type e, denoted by v(e), defines the number of duplications, transfers, and losses associated with e. For example, \(\textbf {v}({\mathbb {TL}})= (0,1,1)\), \(\textbf {v}({\mathbb {SL}})=(0,0,1)\), \(\textbf {v}({\mathbb {S}})=(0,0,0)\). Let α be a reconciliation that contains exactly d duplications, t transfers, and l losses, then we denote the event count vector (d,t,l) of α as v(α). Thus, for every pair (u,x) of a reconciliation α, we denote by v(α(u,x)) the event count vector for α(u,x). Since p o s t α defines a traversal for all mappings of α, v(α(u,x)) can be computed recursively as follows: let e be the event type associated with (u,x) in α; if \(e=\mathbb {C}\) then v(α(u,x))=(0,0,0), otherwise \(\textbf {v}(\alpha (\textit {u,x}))=\textbf {v}(e)\oplus (\bigoplus _{(\textit {v,y})\in post_{\alpha }(\textit {u,x})}\textbf {v}(\alpha (\textit {v,y}))\)).
Parsimonious reconciliation with respect to a cost range
Let δ,τ,λ be three positive real numbers that correspond respectively to the costs for a \(\mathbb {D}\), a \(\mathbb {T}\), and a \(\mathbb {L}\) event. For every pair (u,x) of a reconciliation α, the cost of α(u,x) with respect to the cost vector c=(δ,τ,λ), denoted by c o s t(α(u,x),c), is defined by v(α(u,x))⊗c. Hence, the cost of α with respect to c is c o s t(α,c)=v(α)⊗c. A reconciliation α is said to be parsimonious with respect to a cost vector c if and only if there is no reconciliation α ′ such that c o s t(α ′,c)<c o s t(α,c).
Remark 2.
If a reconciliation α is parsimonious with respect to a certain cost vector (δ,τ,λ), then it must be Pareto-optimal. However, the converse is not ensured if only strictly positive costs are considered. For example, there exist five Pareto-optimal reconciliations for the gene tree in Fig. 3 a and the species in Fig. 3 b, associated with the following event count vectors: v 1=(0,4,0),v 2=(0,3,2),v 3=(1,1,5),v 4=(1,2,4) and v 5=(2,0,7). The reconciliation associated with v 4 is Pareto-optimal but never parsimonious for any set of positive costs. Indeed, if τ≤λ, then the solution (0,4,0) is more parsimonious than (1,2,4), because 4τ<δ+6τ≤δ+2τ+4λ. If τ>λ, then the solution (1,1,5) is more parsimonious than (1,2,4) because δ+τ+5λ<δ+2τ+4λ.
Therefore, the space of all Pareto-optimal reconciliations is not necessarily equivalent to the one of all parsimonious reconciliations.
Since the cost is unit-less, and the solutions depend on the ratios between the event costs rather than on the absolute costs, we define, for each cost vector c=(δ,τ,λ), the ratio cost vector associated with c, denoted by r(c), as (λ/τ,δ/τ,λ/δ). Given two vectors r m and r M , a reconciliation α is said to be parsimonious with respect to the ratio cost range [r m ;r M ] if and only if there exists at least one cost vector c such that r m ≤r(c)≤r M , and α is parsimonious with respect to c. A first straightforward formulation of the suboptimal reconciliation problem can be stated as follow. Problem 1: All Parsimonious Reconciliations With Respect to a Ratio Cost Range. Input: A dated species tree S, a gene tree G such that \(\mathcal {L}(G) \subseteq \mathcal {L}(S)\), two ratio cost vectors r m ,r M . Output: The set \(\mathcal {R}_{o}\) of all parsimonious reconciliations between G and S ′ with respect to the cost range [r m ;r M ].
Note that the set \(\mathcal {R}_{o}\) can have an exponential size but can be represented in a polynomial-size graph, as we shall see soon.
ε-Pareto-optimal reconciliations
A given cost vector may fail to yield to the true gene evolution; this happens when the latter is not parsimonious with respect to the given cost vector. This is why we extend here the concepts presented previously in this section to suboptimal reconciliations. Given a dated species tree S, a gene tree G and a cost vector c, denote by c o s t m (G,S ′,c) the cost of a parsimonious reconciliation between the subdivision S ′ of S and G with respect to c (this value can be computed in O(|V(S)|2·|V(G)|) time [1]). Then, given an over-cost ε, we will consider all reconciliations that have cost at most c o s t m (G,S ′,c)+ε. However, allowing a tolerance in the cost can lead to reconciliation sets containing pairs of reconciliations α,α ′ such that α≠α ′ and v(α ′)<v(α). To avoid this, we consider reconciliations with cost at most c o s t m (G,S ′,c)+ε only if they are Pareto-optimal, and we call them ε-Pareto-optimal reconciliations. Moreover, note that by allowing an over-cost ε, we not only consider the reconciliations that are suboptimal with respect to the input cost vector, but also consider some reconciliations that are parsimonious with respect to some other cost vectors. Hence, this approach allows us to implicitly vary the input costs.
The second problem that we are interested in here is the following: Problem 2: All ε-Pareto-optimal Reconciliations Input: A dated species tree S, a gene tree G such that \(\mathcal {L}(G) \subseteq \mathcal {L}(S)\), a cost vector c=(δ,τ,λ) for \(\mathbb {D}\), \(\mathbb {T}\) and \(\mathbb {L}\) events, and an over-cost ε. Output: The set \(\mathcal {R}_{\textit {so}}\) of all reconciliations α between G and S ′ such that c o s t(α,c)≤c o s t m (G,S ′,c)+ε and α is Pareto-optimal.
In this section, we will show how to compute all event count vectors of Pareto-optimal reconciliations (see Algorithm 1). This list of vectors is cleaned to keep either reconciliations that are parsimonious with respect to the given cost range (for solving Problem 1), or those having cost at most c o s t m (G,S ′,c)+ε (for solving Problem 2). Each of the returned lists is then used to construct the corresponding graph (Algorithm S1 in the Additional file 1) that represents all the solutions for the associated problem. Using this graph, we can compute supports for events, as well as median reconciliations.
Computing all event count vectors of parsimonious reconciliations for a given cost range (Algorithm 1)
Here, we adapt the algorithm from [10] to the case of dated species tree. We start by describing the two operations ⊕ p and concatPareto used in Algorithm 1.
A list L of vectors is said to be Pareto-optimal if and only if, for every vector v in L, there is no vector v' in L such that v'≤v. Given two lists of vectors L 1, and L 2, denote by L 1⊕ p L 2 the list consisting of all Pareto-optimal vectors v such that there exists at least a vector v i in L i for every i∈{1,2} such that v=v 1⊕v 2. The method c o n c a t P a r e t o(L 1,L 2) concatenates the lists L 1 and L 2 and ensures that the resulting list is a Pareto-optimal one by removing its non Pareto elements. The implementation of these two operations are detailed in the Additional file 1 (see proof of Theorem 1).
For all u∈V(G) and x∈V(S ′), denote by \(\mathcal {C}(\textit {u,x})\) the list consisting of all triplets (d,t,l) such that there exists at least a Pareto-optimal reconciliation between u and x that has (d,t,l) as its event count vector. Algorithm 1 starts by computing \(\mathcal {C}(\textit {u,x})\) for all u∈V(G) and x∈V(S ′) (lines 3–19). Note that, from Definition 2 and Remark 1, the list \(\mathcal {C}(\textit {u,x})\) can be computed recursively from those of the mappings in p o s t l i s t e (u,x) for all event types except for \(e=\mathbb {TL}\) in a bottom-up order of V(G) and increasing time order of V(S ′). This is done on lines 6–16 of Algorithm 1. However, since consecutive \(\mathbb {TL}\) events are not allowed, the event count vectors due to \(\mathbb {TL}\) can be computed once those of all other events have been computed for all nodes in the same time slice as x (line 19), via the function bestTriplets (more on this below). Indeed, \(\mathbb {TL}\) events are defined only for nodes of S ′ in the same time slice. The event count vectors of all Pareto-optimal reconciliations between G and S ′, denoted by P O(G,S ′), are thus those in \(concatPareto_{x\in V(S')}(\mathcal {C}(r(G),x))\) (line 20). The operation computePars (line 21) filters from the latter list the reconciliations that are not parsimonious with respect to the input cost range. This filtering is done in the same way as in [10] by normalizing the cost vector (e.g. fixing τ=1). Then a vector v∈P O(G,S ′) is retained if and only if the linear system of inequalities where v⊗(δ,1,λ)≤v'⊗(δ,1,λ) for every v'∈P O(G,S ′) and v'≠v has at least one solution (λ,δ) such that r m ≤(λ,δ,λ/δ)≤r M . Following [10], this can be done in time O(|V(G)|2·l o g(|V(G)|)) for each vector v, and O(|V(G)|4·l o g(|V(G)|)) for all the list P O(G,S ′).
Note that, in Algorithm 1, to speed up the computation of transfer events, we use the fact that the event count vectors of the receivers must also form a Pareto-optimal list (this observation can be trivially proved). Hence, we compute for each mapping (u,x) the list b e s t T r i p l e t s(u,x), which is the Pareto-optimal vector list containing all vectors v such that there exists at least a node z≠x (since the receiver must be different from the donor) at the same time slice as x and \(\textbf {v} \in \mathcal {C}(\textit {u,z})\). Again to speed up the algorithm, we can compute, for each time slice ts, the Pareto-optimal list b e s t T r i p l e t s(u,ts) which consists of all vectors v such that there exists at least a node z∈V ts (S ′) and \(\textbf {v}\in \mathcal {C}(\textit {u,z})\). Then, b e s t T r i p l e t s(u,x) for all x∈V ts (S ′) can be deduced from b e s t T r i p l e t s(u,ts) by removing from it all triplets that are only contained in \(\mathcal {C}(\textit {u,x})\).
Theorem 1.
Algorithm 1 returns a matrix \(\mathcal {C}\) such that a reconciliation α between G and S ′ is parsimonious with respect to the range [r m ,r M ] if and only if \(\mathcal {C}(r(G),\alpha _{1}(r(G)))\) contains v(α). The complexity of Algorithm 1 is O(|V(S)|2×|V(G)|5), and the algorithm can be implemented in a space complexity of O(|V(S)|2×|V(G)|3).
The algorithm is very similar to the algorithm presented in [10], except that: 1) we process the operation ⊕ p for two lists of size k in times O(k 2) instead of O(k 2 l o g(k)) [10] by using sorted lists; 2) we consider the dated version of the reconciliation problem while in [10] they consider the undated one; 3) we take into account \(\mathbb {TL}\) events, which are not considered in the model of [10].
The proof of Theorem 1 is deferred to the Additional file 1.
Computing all event count vectors of ε-Pareto-optimal reconciliations
Problem 2 can be solved similarly to Problem 1 as shown in the following lemma, whose proof is deferred to the Additional file 1.
Lemma 1.
Let α be a reconciliation between G and S ′. If α(u,x) is an ε-Pareto-optimal reconciliation between u and x, then α(v,y) is also an ε-Pareto-optimal between v and y for every (v,y)∈p o s t α (u,x).
For each mapping (u,x), denote by c o s t m (u,x, c) the minimum cost over all reconciliations between u and x with respect to the cost vector c. By Lemma 1, the set of all event count vectors for all ε-Pareto-optimal reconciliations can be computed by Algorithm 1 with a small modification: for each pair (u,x) only event count vectors with an associated cost that is at most c o s t m (u,x, c) + ε are retained. Hence, the time complexity of this problem is at most the one of Algorithm 1.
Representing a space of reconciliations in a compact way
There exist cases for which the number of equally optimal reconciliations is exponential with respect to the input trees size. However, by factorizing their common mappings, it is possible to store in polynomial space all optimum reconciliations within a single graph, via the reconciliation graph (or DTL-graph) [7]. This graph is a bipartite graph made of event nodes and mapping nodes. Rather than having only one node for each mapping (u,x), we extend this representation by associating each mapping node with an event count vector. This allows us to keep track of the different combinations of event counts a mapping (u,x) may be associated to in different reconciliations of our solution space. In more detail:
Definition 3 (Reconciliation graph (adapted from [ 7 ])).
- 1.
The root set of \(\mathcal {G}\) consists of mapping nodes m where m G =r(G);
- 2.
For every mapping node m=(u,x, v), if u∈L(G), x∈L(S ′) and s(u)=s(x), then v=(0,0,0), and m has one unique child of type \(\mathbb {C}\). Otherwise, m has a non-empty set of children and each one has a type different than \(\mathbb {C}\);
- 3.
For every event node n e of \(\mathcal {G}\) that is associated with type e, n e has one unique parent, which is a mapping node (u ′,x ′,v'). If \(e=\mathbb {C}\), then n e is a leaf of \(\mathcal {G}\). Otherwise, the children set of n e is not empty and: a) either \(e\in \{\mathbb {S},\mathbb {D},\mathbb {T}\}\), and n e has two children m 1,m 2 such that {m 1,m 2}∈p o s t l i s t e (u ′,x ′); or \(e\in \{\mathbb {SL},\mathbb {TL},\varnothing \}\) and n e has only one child m 1 such that m 1∈p o s t l i s t e (u ′,x ′), b) \(\textbf {v'} =\textbf {v}(e)\oplus (\bigoplus _{m\in children(n_{e})} m_{\textbf {v}})\).
Definition 4 (Reconciliation tree (adapted from [ 7 ])).
A reconciliation tree of G and S ′ is a reconciliation graph of G and S ′ that has one root, and each mapping node has precisely one child. A reconciliation tree T of G and S ′ depicts a reconciliation α of G and S ′ if and only if the root of T has form (r(G),α 1(r G ),v(α)), and for each mapping (u,x) of α, there exists one and only one mapping node (u,x, v) of T where v=v(α(u,x)).
Note that the definition of reconciliation tree given in [7] is actually the same as the one given in Definition 4, but here we reformulated it to be in agreement with the new definition of a reconciliation graph. Following [7], each reconciliation tree depicts a unique reconciliation and conversely each reconciliation is depicted by one reconciliation tree.
Definition 5 (Full subtree).
the root of T is a mapping node m where m G =r(G);
each mapping node of T has precisely one child;
each event node of T has the same children set in T as in \(\mathcal {G}\);
all leaves of T are leaves in \(\mathcal {G}\).
For example, in Fig. 4 the graph has two roots. Starting from one root, and going down to the leaves, we can obtain a full subtree of this graph. Here, each mapping node has only one child, so there are only two full subtrees. In general, each root of the graph can correspond to several full subtrees.
Lemma 2.
Given a reconciliation graph \(\mathcal {G}\) of G and S ′, every full subtree T of \(\mathcal {G}\) is a reconciliation tree of G and S ′, i.e. T depicts a reconciliation between G and S ′.
Since T is a connected subtree of \(\mathcal {G}\) and T has the four properties in Definition 5, it is easy to check that T respects the three conditions required to be a reconciliation graph according to Definition 3. Moreover, T has a unique root and each of its mapping node has a unique child, thus it respects the definition of a reconciliation tree (Definition 4).
Given a matrix of event count vectors, its reconciliation graph can be constructed by a backtracking process, that searches, for each event count vector, all possible events associated to it that can occur in a reconciliation. Moreover, each mapping node appears just once in the graph. This process is described by Algorithm S1 in the Additional file 1.
Theorem 2.
Algorithm S1 runs in O(|V(S)|3×|V(G)|5) space and time complexity. The returned graph \(\mathcal {G}\) is a reconciliation graph of G and S ′ such that, every full subtree of \(\mathcal {G}\) depicts a reconciliation between G and S ′ whose event count vector is contained in \(\mathcal {C}\); and conversely, every reconciliation between G and S ′ whose event count vector is contained in \(\mathcal {C}\) is depicted by a full subtree of \(\mathcal {G}\). Moreover, \(\mathcal {G}\) has the minimum number of vertices among the graphs having this property.
The proof of Theorem 2 is deferred to the Additional file 1.
Support and median reconciliation
Recall that in this paper we are interested only in canonical reconciliations [7]. Thus, for the sake of simplicity, hereafter reconciliations will always be assumed to be canonical.
The support of an event e, denoted by f(e), is defined as the percentage of reconciliations containing this event, i.e. its frequency in the space of considered reconciliations \(\mathcal {R}\). Once the graph \(\mathcal {G}\) has been computed (Algorithm S1 in the Additional file 1), we can compute the frequency f(e) associated with any event e within the set of reconciliations represented by \(\mathcal {G}\) via two traversals of this graph. The outline of the algorithm is similar to the one given in [7]. First, via a post-order traversal of \(\mathcal {G}\), we compute, for each node z of the graph, the value s c o r e(z) as described in Algorithm 5 of [7]. The value s c o r e(z) corresponds to the number of possible subtrees of the graph \(\mathcal {G}\) rooted at z that are contained in a full subtree of \(\mathcal {G}\). Second, using a pre-order traversal, we can compute, for each node z, the value r e c N u m(z), which corresponds to the number of reconciliations between G and S ′ that contain z, using the following recursive property. If m G (z)=r(G), then r e c N u m(z)=s c o r e(z). Otherwise, if z is a mapping node that has p 1,…,p k as parents (all are event nodes), then \(recNum(z)=\sum _{i=1}^{k} recNum(p_{i})\). If z is an event node that has p as its parent, then r e c N u m(z)=r e c N u m(p)·s c o r e(z)/s c o r e(p). Since in our generalization, each mapping can be associated with different event count vectors, then each event may be present in more than one event node. The frequency of each event e is thus obtained by summing the frequencies f(n e ) of all event nodes n e that represent this specific event.
Dealing with a set of reconciliations can be cumbersome, especially for further analysis and biological interpretations where a single reconciliation will suffice. Rather than randomly picking a reconciliation among the parsimonious ones, in [8] the authors proposed to return one minimizing the sum of event-based distances between itself and all other parsimonious alternatives. The authors also proved that this so called median reconciliation can be computed by two traversals of \(\mathcal {G}\). Their algorithm can easily be adapted to our generalization of the reconciliation graph. Hence, we consider the median reconciliation of \(\mathcal {G}\) as the output reconciliation of our methods, using as event-based (symmetric) distance between two reconciliations the number of events present in only one of the two reconciliations (as done in [8]).
Simulated data and compared methods
Experiments were conducted on the simulated data set in [8, 12], available at http://www.atgc-montpellier.fr/Mowgli/, which we briefly describe below. To construct the data set, first 1000 evolutionary histories, composed of \(\mathbb {D}, \mathbb {T}, \mathbb {L}\) and \(\mathbb {S}\) events, were simulated according to a birth and death process along a phylogeny of 37 proteobacteria [13]. Rates for macro-evolutionary events are as follows: (a) the loss rate was randomly chosen in the [ 0.001,0.0018] interval, where the units are events per gene per million years. Moreover, the ratio between the birth rate (sum of the duplication and transfer rates) and the loss rate was randomly chosen in the [ 0.5,1.1] interval, while the proportion of the duplication rate to the birth rate was randomly chosen in the [ 0.7,1] interval. This led to 1000 simulated gene trees (G True ) on which the numbers of speciations, duplications, transfers, and losses in average are respectively 29.75, 3.87, 0.88, and 8.26. The mean number of genes per family in this dataset is 28.9, the largest family has 67 genes and the smallest 11 genes. Each family is found in 20.88 species in average, with a minimum of 5 and a maximum of 33 species. These gene trees were used to generate DNA sequences with the Seq-Gen program [14]. From these sequences, RAxML [15] was used to infer 1000 maximum likelihood gene trees (G ML ). For more details, we refer to [8], [12] Results section.
We used the so-obtained 1000 pairs of dated species tree/gene trees to compare the performances of the two approaches presented in this paper (strategies s3–s5) with the ones presented in [7, 8] and [10] (strategies s1, s2, s6 and s7 respectively, more details below). We did not test the method presented in [9] as the software is not yet available.
For testing the method in [10], we used the tool evenscape of the software Xscape (version used in [10]). Note that Xscape considers the species tree as undated. Several options to compute event supports are proposed by this program; we tested them all, and present here the ones that give the best accuracy: supports computed using option 'I', based on the number of regions, without taking into account the area of the regions. A list of the options and their description is given in the Additional file 1.
MPR one cost: This strategy computes the median reconciliation, as well as the event supports, via the optimal reconciliation graph, that is the graph containing all parsimonious reconciliations for the given cost vector [7].
MPR sampled costs: This strategy is the one presented in [8]. For each gene tree \(G^{i}_{\textit {ML}}\), we generated a set of 1000 cost vectors around the initial cost of \(G^{i}_{\textit {ML}}\) using a noise level of 20 % for cost vector 1, and 5 % for cost vector 2 – these are the noise levels that gave the best results among the ones we tested on our simulated data. The 1000 optimal reconciliation graphs constructed for each sampled cost vector are then combined in a unique graph that is used to compute the event supports and to construct the median reconciliation.
MPR cost range: This strategy corresponds to Problem 1. We computed the graph containing all the parsimonious reconciliations with respect to a cost range, which are used to compute the median reconciliation as well as the event supports. The input ratio cost range [r m ,r M ] was chosen around the ratio r(c)=(λ/τ,δ/τ,λ/δ) of the input cost c by varying the two last ratios (δ/τ and λ/δ) of ±80 % for cost vector 1, and ±40 % for cost vector 2. We did not constrain the first ratio cost (λ/τ) and let this one be defined by the two others (we did so because the method in [10] does not restrict this ratio, and we want to compare the two methods on the same ranges. Nevertheless, our method can restrict this ratio and, when we do so, we observe a slight improvement – results not shown). For example, for cost vector 2, the initial ratio is (1/3,2/3,1/2), which gives us the ranges [ 60 %·2/3;140 %·2/3] and [ 60 %·1/2;140 %·1/2] for the two last ratios, implying that the range for the first ratio is [ 60 %·60 %·1/3;140 %·140 %·1/3]. Hence, we have r m =(0.12,0.4,0.3) and r M =(0.653,0.933,0.7).
ε -Pareto: This strategy corresponds to Problem 2. It computes the median reconciliation – as well as the event supports – via the ε-Pareto optimal reconciliation graph, which is the graph containing all ε-Pareto optimal reconciliations. This strategy differs from the first one by the set of reconciliations it takes into account to construct the reconciliation graph. The choice of ε is described below.
ε -Pareto ∗ : Similar to strategy s4, but we added more constraints on the retained event count vectors such that, for every retained event count (d,t,l), it does not exist any (d ′,t ′,l ′) with l ′≤l, t ′≤t, and d ′+t ′≤d+t (see the Discussion Section for an explanation on why this latter constraint was added). Note that other types of constraints can be easily integrated into this model.
Region-based: This strategy uses the tool eventscape of Xscape, which is an implementation of the method described in [10]. Since Xscape does not generate reconciliations, this software is used to assign supports to our best median reconciliations – namely the ones of strategy s3 with cost vector (2,3,1). We do this instead of using the full list of events returned by Xscape because the full list provides too many events leading to a large number of false positives; moreover, many events in the list cannot happen in a same reconciliation. This choice gives indeed substantially better results than using the Xscape list (data not shown). The input cost range was chosen as the one used in [10]: λ/δ in [ 0.1;5], and τ/δ in [ 0.1;5]. Hence,we have r m =(0.02,0.2,0.1) and r M =(50,10,5).
Region-based (small range): Similar to the previous strategy, but we used the same input cost ranges as strategy s3. Note that these ranges are smaller than the one used in the strategy 6. As for strategy s6, the event lists are obtained from the median reconciliations of strategy s3.
For the strategies that use the ε-Pareto optimality, the over-cost ε was chosen as the difference between the input transfer cost and duplication cost. This choice is based on the fact that the topological differences between a species tree and a gene tree can be explained by either duplications or transfer events. This over-cost permits some duplications to change into transfer events (and vice versa) if the resulting reconciliations are not too far (that is, their over-cost is smaller than ε) from the parsimonious ones. Obviously, there can be other adequate choices for the over-cost, and depending on the data, one should choose an appropriate over-cost.
The first five strategies all use dated species trees, reconciliation-based supports, and generate median reconciliations. The two last strategies constitute a second group that uses undated species trees, region-based supports, and does not generate median reconciliations.
For each strategy s j with 1≤j≤5, denote by \(\hat {\mathbb {E}}^{i}_{s_{j}}(t)\) the set of all events obtained from the median reconciliation given by strategy s j on the gene tree \(G^{i}_{\textit {ML}}\) that have supports above a given threshold t. For s6 and s7, \(\hat {\mathbb {E}}^{i}_{s_{j}}(t)\) has the same signification, but, as already mentioned, the supports are calculated by the strategy while the sets of events are those of the median reconciliations computed by strategy s3 with cost vector (2,3,1).
Results and discussion
As was done in previous papers [1, 8, 12], the reconciliation error for each strategy s j , gene tree \(G^{i}_{\textit {ML}}\), and threshold t was measured on \(\mathbb {D}\), \(\mathbb {T}\) and \(\mathbb {L}\) events using the symmetric distance between the set of predicted events (\(\hat {\mathbb {E}}^{i}_{s_{j}}(t)\)) and the true event set (\(\mathbb {E}^{i}_{\textit {True}}\)):
\(d\left (\hat {\mathbb {E}}^{i}_{s_{j}}(t),\mathbb {E}_{\textit {True}}\right) = \left | \hat {\mathbb {E}}^{i}_{s_{j}}(t) \backslash \mathbb {E}_{\textit {True}}\right |_{\mathbb {D}\mathbb {T}\mathbb {L}} + \left | \mathbb {E}_{\textit {True}}\backslash \hat {\mathbb {E}}^{i}_{s_{j}}(t)\right |_{\mathbb {D}\mathbb {T}\mathbb {L}}\), where the first and second term respectively correspond to false positive (\(FP^{i}_{s_{j}}(t)\)) and false negative (\(FN^{i}_{s_{j}}(t)\)). Then, \(FP_{s_{j}}(t)\) and \(FN_{s_{j}}(t)\) are respectively the mean of \(FP^{i}_{s_{j}}(t)\) and \(FN^{i}_{s_{j}}(t)\) on all 1000 gene trees of G ML .
Surprisingly, the first cost vector (vector 1) – although computed from the real event frequencies – is less effective for recovering the events than the default cost vector (vector 2). This is probably due to the fact that transfer events are convenient to handle gene tree reconstruction errors. Indeed, in vector 1, transfers have a very high cost since transfers are rare in the simulated gene histories. Thus, using this cost vector, the erroneous misplacement of a leaf in a gene tree, e.g. due to methodological artifacts such as long branch attraction, is preferentially explained using multiple duplication and loss events – rather than a single transfer event, as done when using vector 2 – hence inducing multiple false positive ev ents instead of a single one.
The impact of using cost vector 1 or 2 varies from one strategy to another. The most impacted strategies is s1 – that considers only reconciliations that are parsimonious for the input vector – whereas s6 – at the other extreme, does not make use of input cost vectors. The other strategies adopt a more nuanced approach, accounting for the input cost vector without blindly trusting it. This allows them to perform much better for cost vector 1 than the extreme s1 and s6 approaches. Indeed, this cost vector is inappropriate for reconciling imperfect trees (hence fully trusting its input costs is penalizing s1) but not as much as some more extreme possible costs (hence accounting for these extreme costs is penalizing s6).
Strategy s1 performs so badly with cost vector 1 while not being the best one with cost vector 2 that it seems unreasonable to rely on it while better options (e.g. strategies s2–s5) are available.
As previously shown in [8], the sampling method (strategy s2) provides a big improvement compared to s1 with cost vector 1 (Fig. 5 a). However, it has almost no impact with cost vector 2, for which s1 gives good results (Fig. 5 b).
Strategy s6 and s7 perform poorly compared to methods s2–s5 for both cost vector 1 and 2. No matter the chosen threshold, they lead to an average F P+F N above 8.5 whereas other methods reach a significantly smaller average F P+F N for high thresholds. This can be due to several reasons. First, as already mentioned, s6 does not make use of input cost vectors and can consider non biologically-relevant cost vectors. Second, both strategies have a crude "binary" approach to compute region support: a region either supports or rejects an event and we cannot differentiate the cases, for example, where 10 % of the reconciliations in a region contain this event from cases where 95 % do. Moreover, those two methods are also penalized by being the only ones dealing with undated trees. Indeed, despite using similar cost regions, strategy s3 has significantly better results than s7: s3 reaches F P+F N<8.5 for cost vector 1 when using thresholds above 70 % (while F P+F N is always greater than 9 with s7) and reaches F P+F N<7 for cost vector 2 when using thresholds above 70 % (while F P+F N is always greater than 10 with s7). Note that at threshold 100 %, only events of the median reconciliation that are present in all reconciliations are retained. Hence, for this extreme threshold, the only remaining difference between s3 and the less accurate strategy s7 is that s7 uses undated species trees, while s3 uses dated ones. This confirms that using dated (or at least partially dated) species trees has a strong impact on reconciliation accuracy and should be favored whenever possible.
Strategy s2 uses cost vectors directly sampled from the input one. Its results are thus more influenced by the quality of the input costs than those of the new strategies proposed in this paper, namely s3, s4 and s5. The three new strategies are thus more robust to the choice of the input cost vector than s1 and s2 while avoiding the pitfall of ignoring it as done by s6 and, to a lesser extend by s7. Strategy s4 performs better than s3 and is only slightly less accurate than s5 for cost vector 1; but the performance of s4 (relative to s3 and s5) drops for cost vector 2 – where the transfer cost is much lower. This happens because ε-Pareto optimal reconciliations, considered by s4, may include improbable evolutionary scenarios chaining several transfer events (for example a gene that is transferred and comes back to the donor via a \(\mathbb {TL}\) events) that cannot be parsimonious under any cost vector. The constraint added to s4 to obtain strategy s5 removes all these aberrant event count vectors/reconciliations. This additional constraint indeed improves the accuracy for both input cost vectors, and more clearly for cost vector 2, where s5 significantly outperforms s4 for all thresholds.
when using a single cost vector, the predicted events strongly depend on its quality, and there is currently no way to identify the best cost vector (see strategy s1 and, to a lesser extend, strategy s2);
the extreme Pareto-only approaches may consider some scenarios that are optimal under unrealistic assumptions such as transfers being 10000 times more likely than duplications;
the in-between solutions considering Pareto solutions that are optimal for a reasonable cost range give better results;
event support measures based on the frequency of a event in the reconciliation space seem to work better that those based on the frequency in the cost region space.
Figure 5 c, d show the ratio between FP and FN of each method. Thresholds are not reported in the figures, but we know implicitly that the right extremity of each curve corresponds to threshold 0 while the left one corresponds to threshold 100. This is because the higher the threshold, the fewer events are retained, thus leading to fewer FP and more FN. An efficient method should not increase the number of FN when decreasing the FP. Figure 5 c, d confirm that our new filtering strategies s3, s4, and s5 do not remove too many true events when increasing the threshold. For example, s3 on cost vector 2 (Fig. 5 d) decreases FP from 6.5–1.2 while FN increase only from 3.1–5.1. Besides, while all other curves have at least 1.5 FP, the curves of s6 and s7 using cost 1 extend till 0. This means that, when increasing the threshold until 100 %, these two methods retain almost no events, while other methods always retain some. In other words, the median reconciliation of s3 (the one that strategies s6 and s7 use) does not contain many events whose support computed by strategies s6 and s7 is nearly 1.
Further analyses were conducted for each event type. As already noted in [10], our experiments show that predicting duplications is quite easy, while predicting transfers is harder. The reader is referred to the Additional file 1: Figures S1, S2 for more details.
Average, minimum and maximum running times – given in seconds and for a computer equipped of a 3.2 GHz Intel Core i3 processor with 8 Gb of RAM – for the 7 strategies described in the Experiments Section
Strategy 1
Strategy 2
Strategy 3
Strategy 4
Strategy 5
Strategy 6
Strategy 7
In a parsimony framework, the choice of the costs for basic events may have a strong impact on the set of predicted events. In this paper, we provide a new tool dealing with this problem, which both improves the accuracy of \(\mathbb {D}, \mathbb {T}, \mathbb {L}\) events predicted by parsimonious reconciliation methods and scales up to handle the larger set of gene trees used in phylogenomic studies nowadays. This work combines the complementary ideas of the methods presented in [8] and [10] into new strategies that combine their strengths: speed and reliability. To deal with the inherent uncertainty of the input cost vectors, two approaches have been proposed and tested: explicitly providing an input cost range (strategy s3), or considering non-optimal reconciliations up to a fixed over-cost (strategies s4 and s5). Our approaches and models are flexible so that one can choose the strategy that fits the data better: considering either only parsimonious reconciliations or nearly optimal ones, choosing acceptable cost ranges, the over-cost, and even providing some-user defined additional constraints to filter the event count vectors list (see strategy s5).
Our tests on simulated data demonstrate that models using dated (or partially dated) species trees seem to provide more accurate event predictions than those using undated species trees. Moreover, in our experiments, using median reconciliations is more effective than picking a random one, and better than taking the list of all events, confirming the findings of [8]. Furthermore, our approaches are faster and more accurate than both methods in [8] and [10]. Finally, this work emphasizes the benefits of using the reconciliation graph to manage the space of reconciliations efficiently. Many other tools could be developed by using this graph, for example calculating the reconciliation whose average of the supports is maximized.
This work confirms that erroneously inferred events can be, at least in part, filtered out. This filtering could probably be further improved by also taking into account reconciliations of close alternative gene tree/species tree topologies. Yet, taking such alternative reconciliations into account is challenging, as the reconciliation graph solution [7], proposed to efficiently handle alternative reconciliations, was not designed to handle reconciliations between different gene/species trees.
α(u,x): the restriction reconciliation of α on G u that maps u to x; α ℓ (u): the last element of α(u); α i (u): the i t h element of α(u);\(\mathbb {C}\): Contemporary event;\(\mathbb {DL}\): Duplication Loss;\(\mathbb {D}\): Duplication event;\(\mathbb {L}\): Loss event;\(\mathbb {SL}\): Speciation Loss event;\(\mathbb {S}\): Speciation event;\(\mathbb {TL}\): Transfer Loss event;\(\mathbb {T}\): Transfer event;\(\mathcal {C}(u,x)\): the set of event count vectors of all reconciliations between u and x of the considered problem; ⊕: vector addition; ⊕ p : addition of two lists of vectors, the resulting list must be Pareto-optimal; ⊗: vector dot product;\(\varnothing \): No event; c o s t(α,c): the cost of α with respect to the cost vector c; c o s t m (u,x, c): the minimum cost over all reconciliations between u and x with respect to the cost vector c; f(e): the support of the event e; L i : the i t h element of the list L; m G : the first element of the mapping node m, which is a node of the gene tree G; m v : the third element of the mapping node m, which is an event count vector;\({{m}_{S'}\phantom {\dot {i}\!}}\): the second element of the mapping node m, which is a node of S ′; p o s t l i s t e (u,x): the set of possible next mappings of (u,x) associated with the event e; u 1: the first child of u; u 2: the second child of u; u p : the parent of u; V t (T): the set of nodes of the tree T having time t; c: the cost vector (δ,τ,λ); r(c): the ratio cost vector (λ/τ,δ/τ,λ/δ) of the cost vector c=(δ,τ,λ); r M : the upper bound of the ratio cost vector; r m : the lower bound of the ratio cost vector; v(e): the event count vector of the event e; v(α): the event count vector of the reconciliation α; c o s t m (G,S ′,c): the cost of the most parsimonious reconciliation between S ′ and G with respect to the cost vector c;
This work was partially funded by the French Agence Nationale de la Recherche Investissements d'Avenir/ Bioinformatique (ANR-10-BINF-01-02, Ancestrome).
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Authors' Affiliations
- Doyon JP, Scornavacca C, Gorbunov KY, Szöllősi GJ, Ranwez V, Berry V. An efficient algorithm for gene/species trees parsimonious reconciliation with losses, duplications and transfers. In: Research in Computational Molecular Biology: Proceedings of the 14th International Conference on Research in Computational Molecular Biology (RECOMB). LNCS. vol. 6398. Berlin/Heidelberg, Germany: Springer: 2010. p. 93–108. Software downloadable at http://www.atgc-montpellier.fr/Mowgli/.Google Scholar
- Gorbunov KY, Lyubetsky VA. Reconstructing genes evolution along a species tree. Mol Biol (Mosk). 2009; 43:946–58.View ArticleGoogle Scholar
- Tofigh A. Using trees to capture reticulate evolution, lateral gene transfers and cancer progression. PhD thesis, KTH Royal Institute of Technology, Sweden. 2009.Google Scholar
- Tofigh A, Hallett MT, Lagergren J. Simultaneous identification of duplications and lateral gene transfers. IEEE/ACM Trans Comput Biology Bioinform. 2011:517–535.Google Scholar
- Ovadia Y, Fielder D, Conow C, Libeskind-Hadas R. The cophylogeny reconstruction problem is NP-complete. J Comput Bio. 2011; 18(1):59–65.View ArticleGoogle Scholar
- Libeskind-Hadas R, Charleston MA. On the computational complexity of the reticulate cophylogeny reconstruction problem. JCB. 2009; 16(1):105–17.Google Scholar
- Scornavacca C, Paprotny W, Berry V, Ranwez V. Representing a set of reconciliations in a compact way. J Bioinforma Comput Biol. 2013; 11(2).Google Scholar
- Nguyen TH, Ranwez V, Berry V, Scornavacca C. Support measures to estimate the reliability of evolutionary events predicted by reconciliation methods. PLoS ONE. 2013; 8(10):73667.View ArticleGoogle Scholar
- Bansal MS, Alm EJ, Kellis M. Reconciliation revisited: Handling multiple optima when reconciling with duplication, transfer, and loss. In: Proceedings of the 17th International Conference on Research in Computational Molecular Biology. RECOMB'13. Berlin, Heidelberg: Springer: 2013. p. 1–13.Google Scholar
- Libeskind-Hadas R, Wu YC, Bansal MS, Kellis M. Pareto-optimal phylogenetic tree reconciliation. Bioinformatics. 2014; 30(12):87–95.View ArticleGoogle Scholar
- Bansal MS, Alm EJ, Kellis M. Efficient algorithms for the reconciliation problem with gene duplication, horizontal transfer and loss. Bioinformatics. 2012; 28(12):283–91.View ArticleGoogle Scholar
- Nguyen TH, Ranwez V, Pointet S, Chifolleau AM, Doyon JP, Berry V. Reconciliation and local gene tree rearrangement can be of mutual profit. Algorithms Mol Biol. 2013; 8(1):12.View ArticlePubMedPubMed CentralGoogle Scholar
- David LA, Alm EJ. Rapid evolutionary innovation during an archaean genetic expansion. Nature. 2011; 469:93–6.View ArticlePubMedGoogle Scholar
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- Stamatakis A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. 2006; 22(21):2688–690.View ArticlePubMedGoogle Scholar
- Conow C, Fielder D, Ovadia Y, Libeskind-Hadas R. Jane: a new tool for the cophylogeny reconstruction problem. Algorithms Mol Biol. 2010; 5:16. Software downloadable at http://www.cs.hmc.edu/~hadas/jane/.View ArticlePubMedPubMed CentralGoogle Scholar


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September is here and it's also back to school for the kids. They all started good this morning and can't wait to their stories. Happy it's also back weekend 🙂
First of all, I want to introduce you to my new Creative Team. I'm so happy with them and I'm so excited to work with them, they are one by one talented ladies.
Let's meet them…
It's also the first Friday of the month and it's release date for the BYOC products. For this month, I don't have a collection but a new set of templates for the Clean and Simple Collection. You can save 20% off + the additional Buy More, Save More discount.
Have a look at these amazing pages, thanks so much to my creative team!
That's it for today, have a great weekend!


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Cities' Adviser: Nix Comcast Deal
The law firm of Creighton, Bradley and Guzzetta LLC — consultant to Nashville, Tenn., and a slew of Midwestern hamlets — has reviewed new information on the AT&T Broadband-Comcast Corp. merger but stands by its original, if singularly held conclusion: it must be rejected.
Communities advised by the Minneapolis firm have delayed their merger-related franchise-transfer votes, rather than voting to deny the transfers. This was done so Comcast could negotiate with the consultant in hopes of reversing the recommendation.
But when the consultant sent clients a supplemental report late in June, the firm reiterated its conclusions.
The supplemental report compares the transaction that would create AT&T Comcast Corp. to the "unbridled corporate growth" — and subsequent meltdowns — of Tyco International Ltd., Global Crossing Inc. and Enron Corp. Those companies gobbled up others to become giants by assuming debt, then used illicit accounting practices to disguise it.
While there is no evidence of non-standard accounting in the cable deal, the law firm said it has still not been supplied with projected revenues and the assumptions supporting them; projected expenses and their assumptions; or cash-flow projections for the merged company.
Instead, the "new information" included positive reports by other consultants and Wall Street sources including Bear Stearns & Co., Merrill Lynch Inc. and Sanford Bernstein & Co., added to the approximately 5,000 pages of documents already submitted by the company.
"The fatal flaw remains Comcast and AT&T's steadfast refusal to provide a business plan from which [local franchising authorities] can extrapolate a picture of the potential for harm to subscribers and LFAs as a result of this transaction," according to the supplemental report obtained by Multichannel News.
"We believe the that the CBG report does our community partners an unfortunate disservice by relying on a factually inaccurate and misleading financial analysis," said Comcast spokeswoman Jenni Moyer.
The firm displays a "trust me, I'm right" attitude ignoring all other views, she noted, adding the company has a significant investment in the communities it serves. Comcast hopes to continue those relationships, she said.
But the consultant contends that his peers and Wall Street sources did not delve deeply enough into the financial issues.
He still faults some of the merged company's basic assumptions.
AT&T Comcast will reduce debt by selling AT&T Corp.'s share in Time Warner Entertainment L.P., but the consultant believes the company will end up actually swapping systems to get out of the partnership with AOL Time Warner Inc.
The company also relies on selling more products at higher prices, a "reckless and dangerous assumption," according to the supplemental report.
The firm's Tom Creighton was out of town and did not return calls for comment by deadline.
Nashville, the consultant's largest client, has delayed a vote on the transfer until August.


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Whole Foods Market in Capitol Hill to close Nov. 12
The Cap Hill store is expected to close 5 p.m. Nov. 12 ahead of the new location near 17th and Wewatta streets opening Nov. 15.
The Whole Foods Market in Denver's Capitol Hill neighborhood is set to close Sunday, Nov. 12, a spokeswoman for the organic and natural grocer confirmed Tuesday.
Whole Foods announced back in February that the company would be closing the store at 900 E. 11th Ave. and transferring about 75 employees to the new, larger location near Denver Union Station.
The Cap Hill store is expected to close 5 p.m. Nov. 12 ahead of the new location opening Nov. 15 near 17th and Wewatta streets, said Heather Larrabee, Rocky Mountain region spokeswoman for Whole Foods.
Whole Foods plans to hold a 50 percent off sale at the Cap Hill store during its closing weekend. After that, Capitol Hill residents will have to decide whether to go two-ish miles to the new store or head in the other direction to the Whole Foods in Cherry Creek or Washington Park.
The Austin, Texas-based grocer said it plans to hold on to the Cap Hill property but still needs to decide how to use the space.
The Whole Foods in Cap Hill is roughly 20,500 square feet, per city records. Whole Foods is considering any of its locations smaller than 35,000 square feet for its new millennial-focused, less-pricey 365 concept.
Subscribe to Denverite's newsletter here.
Business & data reporter Adrian D. Garcia can be reached via email at email@example.com or twitter.com/adriandgarcia.


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FORT ST. JOHN, B.C. — Officials with the Fort St. John Ministerial Association in Fort St. John, with the help of Prince George-Peace River-Northern Rockies MP Bob Zimmer, will be hosting a Mayor's Prayer Breakfast in the Energetic City next month.
Andrew McGregor said the Prayer Breakfast used to held in Fort St. John up until 2013, when the event went on hiatus. He said that he wanted to host the event again this year to honour local elected officials, firefighters, police, and others that make such contributions to the community.
McGregor said that the event's committee, of which he is a member, asked MP Zimmer to help host the event, since he's chaired the National Prayer Breakfast Committee for the past four years. This year, the breakfast will feature a performance from Manitoba-based band The Colour, whose music is described by McGregor as "a Christian version of One Direction."
In addition to Fort St. John mayor Lori Ackerman, the breakfast invitees also include Zimmer, Peace River North MLA Dan Davies, and officials from the City, the Fire Department, and the local RCMP detachment. McGregor explained that this year, the committee wanted to focus the breakfast on honouring those from the Fort St. John area before possibly expanding to a regional event in the future.
The Mayors Prayer Breakfast is taking place on Saturday, April 21st at the Northern Grand Hotel. Tickets can be purchased on Eventbrite: https://www.eventbrite.ca/e/fort-st-john-mayors-prayer-breakfast-tickets-43218515744


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Capital Southwest said it appointed Michael S. Sarner as SVP. Following an announced spin-off transaction, Sarner will be appointed CFO of the company's business development company.
Capital Southwest Hires SVP/CFO of Post-Spin BDC
June 30, 2015
Goldman Registers Liberty Harbor Capital
April 2, 2013
Goldman Sachs has registered a specialty finance company, Liberty Harbor Capital, which will be regulated as a BDC. The company will focus on middle-market companies that have been underserved in recent years by banks.


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"Chapter Headings For Old Novels"- part two.
Luckily Nils has a plan, doesn't seem to go too well for Jer, but at least the castle won't go to Dougal.
Quote of the issue;
Jer: "And how did you spend your last night on Earth, Jeremy?" "Well, I got in a boat with a mythical sex-vampire creature, put on a pair of stupid boots and went stargazing." "Ah, but did you take a CHAIR with you?"
First printing May, 1994.
Back to The Platinum Grit Home Page.


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Educators visit Kings Bay's Trident Refit Facility
May 2, 2013
Educators visit Kings Bay's Trident Refit
Schools stress STEM classes to prepare future workforce
From Youth Apprenticeship, Camden County High SchoolTrident Refit Facility Commanding Officer Capt. Lawrence Hill and his command hosted nearly 40 educators from 20 southeast Georgia school systems to share the need to increase Science, Technology, Engineering and Math.
The event was co-hosted by The Camden Partnership and President Sheila McNeill, in an effort to develop a series of briefing and tour opportunities for Kings Bay Naval Submarine Base.
The Partnership is working towards its goal to advocate, support and strengthen military missions and improve the quality of life for our military members by enhancing military and community partnerships.
Hill briefed the group on the history, size and scope of the command. He discussed TRF's role in maintaining submarines for the safety of the crews that serve and the defense deterrent these submarines and crew provide.
Most of the educators serve in a similar capacity as Rachel Baldwin and Jacqueline Hart in their work-based learning coordinator roles at Camden County High School.
Baldwin shared with the group how students at Camden High had been able to follow a trade and industrial curriculum and co-op their senior year of high school. Through meeting specific testing and application criteria, they were able to then apply and be accepted into the TRF apprentice program.
Some of the students she and TRF Student Trainee Coordinator Georgianna Anderson have served since 1995 are nearly 35 years old, with younger students graduating from Camden High in 2011 and selected for apprenticeship."It is rewarding to hear from former students when they assist TRF's mission in Diego Garcia and at Kings Bay, as well as the pride they share when they reach milestones like purchasing their first home," Baldwin said, stressing the opportunities the program has provided to Camden students.
The group toured the electronics, optical and mechanical shops.
Darren Harper, Kingsland economic development director, and Leslie Hamrick, president of the Camden Chamber of Commerce, accompanied the group as members of the chamber's Workforce Development Committee.
Harper said it was helpful to see the specialized work that TRF performs to support maintenance and the refurbishing of equipment to other military entities.
The shops shared data that showed the cost savings to the Department of Defense and taxpayers that has resulted from their skill and ability to rebuild, refurbish and refit expensive equipment, hardware and electronic circuitry, as well as network and database support that must meet military ready standards.
That the industrial and technical applications relate to the skill base and expertise needed to diversify and grow a more industrial workforce in Camden County, Harper said.
Carra Goodman, workbased learning coordinator in Clinch County, said she had never thought about life on a submarine and how it was a self-contained unit that required the occupants to reuse, and filter their air, water and waste while at sea.
"The fact that the submarine shoots missiles was really all I considered the submarine had to do," Goodman said, as she watched the many tasks Hill described regarding life on submarines.
Hill discussed the relative, sustained comfort levels that top-working equipment necessitates for the submariner. Several educators remarked that their knowledge of submarines was limited to what they had seen in the movie, Hunt for Red October.
To expand educators exposure to the intricate, industrial applications that are detailed is what is needed to relate STEM opportunities to students planning their future.
The Camden Partnership's chairman of forums and tours, Barrett King, also accompanied the group, anticipating that similar events will occur through the collaboration of the partnership and Baldwin.
King expressed thanks that Hill recognizes the need to host educators and community stakeholders to become more informed about TRF's mission.
"With a local civilian workforce at TRF averaging around 1,000, we want educators to be able to relate opportunities to the rising workforce as well as community leaders to link skill sets to growth opportunities that could diversify Camden's economic base," he said.
A luncheon followed the tour coordinated by Naval Submarine Base Kings Bay School Liaison Officer Clainetta Jefferson. Jefferson encouraged educators serving military students to partner with their respective SLO to form alliances with their local military installation.
This need was emphasized by McNeill, luncheon speaker, who encouraged advocacy.
McNeill related how the community's advocacy for the submarine force and the conversion of the four boats from SSBN to SSGN had expanded capability to the mission of the submarine force. That allowed Camden County to welcome the USS Florida and the USS Georgia to Kings Bay.
She encouraged educators to advocate for the training needed to prepare the emerging workforce to have the science, technology, engineering and math skills needed for the military and to grow southeast Georgia's regional economic development.


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Tips and Walkthrough: Candy Crush Soda Level 2109
Candy Crush Soda Level 2109 Tips
- Find the bears
- You have only 40 moves.
- 5 Bears
Soda Level 2109 guide and cheats:
This level has hard difficulty. For this level try to play near to blockers and make special candy. Combine them with each other or play near to snow blockers to break them all.


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About Us
Swiss Commerce is a web design and development agency located in New York since 1998. We turn your ideas into a viable business. We analyze patterns, measure your competition and finally create a custom solution for your business. Our services are being used by Startups, SMB's as well as Fortune 100 companies. We strive to create a strategic plan for our customer and to achieve the wow-effect upon completion. Swiss Commerce is known for its honest approach where long-term relationships are created with each customer.
Year Founded: 1998
Website: https://scbw.com/


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Bible Reading & Commentary for the Week October 3-8, 2016
Bible Reading & Commentary for the Week
October 3-8, 2016
Monday, October 3, Genesis 19
Vv. 4-5 — God also speaks of the sin of homosexuality and its punishment in Rom. 1:26, 27, 32 and I Cor. 6:9-10, Lev. 20:13 .
v. 14 — The Lord sheds further light on Lot's positive spiritual life in II Peter 2:6-8.
v. 26 — True repentance is turning away from sin, not looking back to it. We need to make a clean break from a sinful way of life. Jesus uses Lot's wife as a warning for all of us (Luke 17:30-33).
Tuesday, October 4 , Genesis 20
Vv. 11-12 — While Abraham is telling the truth about Sarah since she was his half-sister (compare Genesis 11:31), it's a cover-up for the truth that she is also his wife. What he did was sinful. In spite of his sin, the Lord intervened on his behalf. (Compare Ps. 105:13-22) What an astoundingly merciful God we have! Celebrate that fact by reading Ps. 103:11, 12.
Wednesday October 5, January 21, Genesis 21
v. 2 — Sarah was 90 years old when she gave birth to Isaac (Gen. 17:17). It demonstrates the truth expressed in the New Testament, "For nothing is impossible with God." (Luke 1:37) As God did the impossible then, so he can do it for us, too. He actually has, in bringing us from spiritual death to a spiritual life. (Col. 2:12, 13)
Vv. 9-10 — Although Sarah's action seems harsh, she was correct. That can be seen in Gal. 4:29-31. Ishmael's mocking was actually ridiculing the Savior whom God intended to bring through Isaac. If you read the entire section of Gal. 4:21-31, you see that God uses the example of Isaac's birth and Ishmael's birth to describe the difference between the Law and the Gospel. Ishmael represented the Law, Isaac, the Gospel. From Isaac's descendants God brought the Savior into the world.
Thursday, October 6, Genesis 22
v. 1 — Abraham's great test of faith is described in chapter 22. As Abraham did, we are to love God above everything–and that includes our greatest earthly "treasure." That he passed the test with flying colors is evident from this chapter and also from Hebrews 11:17-19.
v. 2 — Mount Moriah has an interesting history in the Old Testament. This was the property King David had purchased from Araunah, the Jebusite. Read II Sam. 24:18-25. Later, Solomon built the temple on Mount Mariah (II Chron. 3:1).
Friday, October 7, Genesis 23
Vv. 1-4 — Abraham described himself as "an alien and stranger among you." What did he mean? Read Hebrews 11:8-10. Abraham mourned for Sarah, as Christians mourn at the death of a loved one. But he still had hope. That's why, although funerals are sad because we feel the separation from someone we love, they are also joyful events because we have the chance to review how Jesus won for us eternal life, and we know our loved ones who believe in Jesus as their Savior, are now in the presence of their Savior in heaven.
Saturday, October 8, Genesis 24
vv. 3-4 — Abraham was a concerned parent. He wanted to be sure that his son married the right woman. As Abraham did, we will guide and encourage our children to marry someone who shares their Christian values and faith in Jesus Christ as their Savior.
v. 12 — Abraham's servant used prayer in helping him find Isaac's mate. That power is also available for Christian parents today! (John 16:23-24)


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When Marnie Was There (and five other indie animations you need to see)
Every time a new animation from the much-loved Studio Ghibli arrives, it's difficult not to get excited. The Japanese animation studio founded by Hayao Miyazaki has produced some of the most unique and beautiful animated films ever committed to celluloid over the three decades since its foundation in 1985, not to mention some of the best-selling; Miyazaki's Princess Mononoke held the Japanese box office record for years up until the release of Titanic, only to break the record again with another of his greatest films, Spirited Away.
But in 2014, the year in which When Marnie Was There was first released in cinemas, the film's arrival was accompanied with a pang of sadness because, following Miyazaki's announcement that he intended to retire, the future looked bleak for the animation studio. Fans of Studio Ghibli's work were hardly encouraged when the director later stated that while he had not intended his retirement to signal the end of the studio's output, he thought that "the era of pencil, paper and film is coming to an end." For a while, it seemed like When Marnie Was There might be the last time we ever got to see a new Studio Ghibli film.
However, all is not lost; while the studio's future is still a little unclear, rumours of its demise appear to have been exaggerated – in fact, a new collaboration between the studio and Dutch animator Michael Dudok de Wit named Red Turtle emerged earlier this year and was included in the Un Certain Regard selection at this year's Cannes Film Festival. It also seems that Miyazaki is something of a restless retiree, still spending most of days in the animation studio, according to reports from his colleagues. Still, whether or not Studio Ghibli is able to continue long term - and we sincerely hope it is - it does mean that When Marnie Was There is a thing to be savoured.
Like many of Studio Ghibli's films, When Marnie Was There features a young female protagonist. Anna is an introverted and emotionally distant young 12-year-old living with her foster parents who is sent to spend the summer with relatives who live in a remote rural area by the sea, following an asthma attack she has at school.
While there she discovers a dilapidated old mansion which, she discovers, used to belong to a foreign family, but has been abandoned for years. However, one night she sees lights on in the mansion and the house looks new. When she investigates she befriends a girl called Marnie, who may or may not be real. It isn't clear at first whether Marnie is a ghost or just a figment of her imagination, but what follows for Anna is a journey of discovery about herself that is, in typical Studio Ghibli fashion, both heartwarming and enlightening.
Like all their films, When Marnie Was There is beautifully animated and brilliantly written, not to mention totally absorbing, but even if it does turn out that the end is near for Studio Ghibli, don't despair; there are still plenty of indie animations in a similar vein to enjoy. You can find a trailer for When Marnie Was There below, beneath that we've listed five others you should check out if you haven't already...
Based on the graphic novel of the same name by Iranian author Marjanne Satrapi, Persepolis is Satrapi's autobiographical story of growing up in Iran and experiencing the changes brought about by the Iranian revolution. If you think that sounds like a dry subject for an animated feature film, think again; Persepolis is charming and surprisingly funny in places, especially given the heavy subject matter, but it's also a unique view into the internal conflict of Iran told from a very human perspective.
Chico & Rita
This Oscar-nominated animation from directors Fernando Trueba, Javier Mariscal and Tono Errando tells the story of a young jazz pianist and a talented singer whose love for each other is tested by a series of challenges including the perils of success and battles against the U.S. authorities determined to return Chico to his native Cuba. The film is a spellbinding tale of love in the face of adversity and features a soundtrac that includes music by the likes of Dizzy Gillespie, Thelonius Monk and Cole Porter. This is a must-see for anyone with a love of jazz and latin music, or anyone looking for a different twist on the themes of love and loss.
Song of the Sea
Irish animator/director Tomm Moore's second feature film is a charming adventure story about a boy named Ben and his younger sister Saorise and their journey to escape the clutches of their mean, city-dwelling grandmother and return to their home, a journey that leads them to discover a world of magic and meet some intriguing characters of Irish folklore. The film received an Oscar nomination and features a voice cast that includes Brendan Gleeson, David Rawle and singer-songwriter Lisa Hannigan. If you've never seen Moore's films before, this is a great place to start and we'd also recommend his debut feature The Secret of Kells.
Mary & Max
This stop-motion animation from Australian director Adam Elliot is hilarious and heartwarming tale of a young, lonely girl living in a remote area where she struggles to make friends. The other kids at school tease her because of her glasses and an unfortunate birthmark on her forehead and her parents – and alcoholic father and kleptomaniac mother – aren't much help either. To remedy this she decides to find a pen-pal and picks a random name from a New York telephone directory, an obese 44-year-old Jewish man named Max Horrowitz (voiced by the late Philip Seymour Hoffman), striking up an unlikely friendship. If you're a fan of Nick Park's films but fancy something a little different, this is brilliant viewing.
The Iron Giant
Our final pick is, technically, a bit of a cheat on the 'indie' front as Warner Bros. did have a hand in the film, but any Studio Ghibli fans will find Brad Bird's excellent animation about an alien robot who crash-lands on earth and befriends a young American boy utterly compelling. Set in the 1950s during the height of McCarthyist paranoia about communist infiltration in the United States, the film is not only a heartwarming tale of friendship but also a biting commentary on arrogance of government, and with the film receiving the remaster treatment in 2015 the new version is visually stunning too.