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- Methodology article
- Open access
- Published:
The comparative analysis of statistics, based on the likelihood ratio criterion, in the automated annotation problem
BMC Bioinformatics volume 9, Article number: 31 (2008)
3283 Accesses
4 Citations
This paper discusses the problem of automated annotation. It is a continuation of the previous work on the A4-algorithm (Adaptive algorithm of automated annotation) developed by Leontovich and others.
A number of new statistics for the automated annotation of biological sequences is introduced. All these statistics are based on the likelihood ratio criterion.
Some of the statistics yield a prediction quality that is significantly higher (up to 1.5 times higher) in comparison with the results obtained with the A4-procedure.
Many biological databanks, both dealing with protein sequences (e.g., SWISS-PROT) and nucleotide sequences (e.g., GeneBank), contain not only primary structures of sequences (i.e., sequences of letters – amino acids or nucleotides), but also information about functions and properties of these sequences. This information is stored in so called description fields of the sequences. There exist different types of description fields – KW (KeyWords), DE (Descriptions), ..., FT (Feature Table), ...; elements of description fields are referred to as words. Words from KW, DE, ... fields describe a sequence as a whole, while words from FT fields correspond to certain positions (letters) of a sequence.
The automated annotation problem can be described as follows. Consider a biological sequence (referred to as a query sequence) with known primary structure (i.e. letter sequence) but unknown properties and functions (i.e., description fields). The task is to determine functions and properties of this sequence (in other words, to restore its description fields) on the basis of the primary structure. The annotation should be fully automated. This is the subject of the current paper.
There are two main approaches to the solution of this problem. In the first approach (it can be called a static one) a certain fixed protein classification (grouping proteins according to similarity in structure and/or functions), specified beforehand, is used: for a query protein the search of a relative group (super family) is performed on the basis of primary structures, and properties/functions of this group are extended to the query protein. An example of this approach is described in the paper by W. Fleischmann et al. [1], it uses the protein classification (more than 1000 families), stored in the Prosite databank.
The second approach (it can be called a dynamic or an adaptive one) does not use protein classification. Instead, a "dynamic" collection of bank sequences that are similar to a query sequence is generated, and then common properties/functions of these bank sequences are extended to the query sequence. One of the first examples of this approach was described by M.A. Andrade et al. [2]. In this paper the prediction was based on so called word reliability function – a function depending on the degree of similarity between a query sequence and corresponding bank sequences. In other examples of annotation procedures based on the dynamic approach prediction was performed in a "naive" way – all properties/functions of similar proteins were extended to a query protein, or using stochastic methods – only properties/functions that are most frequent for the collection of similar proteins were extended (see [3, 4]).
The current paper uses the dynamic approach. This paper is a sequel to paper [5] that describes the A4 algorithm (the Adaptive Algorithm of Automated Annotation), so results of the paper [5] are constantly used here. The A4 algorithm is based on a stochastic approach. More precisely, it is based on the notion of transfer probabilities. Transfer probabilities are the probabilities of word transfer (extension) from description fields of one sequence to description fields of another sequence; they depend on the measure of similarity between sequences. Transfer probabilities are evaluated on the basis of word transfer frequencies in the found collection of sequences similar to a query sequence. For each word from description fields of sequences included in the collection the prediction of the fact that this word belongs (or does not belong) to the description field of a query sequence is performed; this prediction is based on transfer probabilities.
In the current paper we introduce and analyze a number of new statistics for the prediction. All of them are based on the likelihood ratio criterion [6] (which is the most powerful criterion). As in [5], all these statistics are evaluated using transfer probabilities. Two approaches to statistics definition are introduced: a "discrete" approach and a "continuous" approach. A detailed analysis and comparison of introduced statistics are performed and the best statistics are selected.
The emphasis is on a precise description of the way these statistics can be constructed using well-known concepts from statistical decision theory.
The current A4 algorithm uses SPKW as a language of annotation. Of course, it is possible to use GO terms in A4 as well. That would facilitate comparison with other approaches. However, at the current stage of our research we test and choose "the best decision making algorithm", not "the best annotation terminology". Obviously, which annotation is used hardly matters for the problem of finding the optimal "decision making algorithm"
Algorithm description
Generation of a collection of similar sequences
First, we introduce some notation. For the sake of brevity we write "a word ω belongs to a sequence π" instead of "a word ω belongs to description fields of a sequence π". If ω is a KW-type word (to be definite, further in the paper we consider only amino-acid sequences and KW-type words), we write ω ∈ KW[π].
The application of an annotation procedure to an unannotated amino-acid query sequence (i.e., the prediction of description fields) starts with generating a collection of sequences similar to this query sequence with known description fields. These similar sequences are selected from a certain databank that contains annotated amino-acid sequences, e.g., SWISS-PROT.
There exist different approaches to the generation of a collection of similar sequences (see [7]). This collections can be generated on the basis of global alignments between a query sequence and bank sequences (global alignments can be constructed, e.g., by CLUSTAL procedure) using an identity percentage or, more generally, a similarity percentage as a similarity measure. Another variant is to use local alignments (i.e., alignments of most similar fragments of compared sequences, see [7]) instead of global alignments. Local alignments can be constructed, for example, by a well-known BLAST procedure [7, 8], in which sum of weights or a corresponding e-value serves as a measure of similarity between fragments (and thus between compared sequences). Other alignment procedures are also acceptable; alignment procedure selection does not play a critical role.
Since we build on the A4 procedure, we briefly summarize that approach. In the A4 procedure a collection of similar sequences is generated on the basis of local alignments of a special type. These alignments are constructed by the DotHelix procedure ([9]), in which the "power" (sum of weights divided by the root of the length of the local alignment, see [9, 10] for details) serves as a similarity measure. Each local alignment constructed by DotHelix procedure is a chain of closely located gapless local alignments. Local alignments that are generated during the construction of a collection of similar sequences are referred to as primary local alignments.
Each sequence from a collection of similar sequences π1,...,π n can have several corresponding primary local alignments, but for the sake of simplicity we assume that each similar sequence π i has exactly one corresponding primary local alignment, the one with the maximum similarity measure. Let μ i denote the similarity measure (power) of a primary alignment that corresponds to π i . The value of μ i characterizes the measure of similarity between fragments that constitute this alignment; at the same time μ i can be treated as a measure of similarity between the whole query sequence π0 and the whole similar sequence π i . We assume that similar sequences are ordered in such a way that μ 1 ≥ μ2 ≥...≥ μ n .
The exact stochastic formulation of the problem
Let π0 be an unannotated amino-acid query sequence, π1,...,π n be a collection of sequences similar to π0, and ω be a word that belongs to some similar sequences (i.e., ω ∈ KW[π i ]. for at least one similar sequence π i ). The task is to predict whether this word ω belongs to the query sequence π0 or not.
Let us putξ i = 1, if ω ∈ KW[π i ], and ξ i = 0, if ω ∉ KW[π i ] (i = 1,...,n).
we also putξ0 = 1, if ω ∈ KW[π0], and ξ0 = 0, if ω ∉ KW[π0].
Variables ξ i can be treated as random variates Actually, they depend on ω. For the sake of brevity we write ξ i instead of ξ i (ω).
In this notation the problem can be stated as follows. Measures of similarity μ i between the query sequence π0 and similar sequences π i and values of random variates ξ i , i = 1,...,n, are given. The task is to determine whether the word ω belongs to the query sequence π0 or not. In other words, two hypotheses are considered, H1 : ξ0 = 1 (i.e., ω ∈ KW[π0]), and H0 : ξ0 = 0 (i.e., ω ∉ KW[π0]), and the task is to construct a procedure that allows to decide which hypothesis is true on the basis of ξ = (ξ1,...,ξ n ). As announced in the introduction, we base our procedures on the likelihood ratio. Let us recall the famous Bayes' Theorem that can be written as
Here the left part is the posterior odds, that is the ratio of a posteriori probabilities of hypotheses H1 and H0 (a posteriori means that values ξ1,...,ξ n are known). It is equal to the product of the likelihood ratio (i.e., the ratio of probabilities that the set of values ξ = ξ(ω) is realized for the word ω given the conditions ξ0 = 1 and ξ0 = 0 respectively) and the prior odds, that is the ratio of a priori probabilities of hypotheses H1 and H0.
Statistical decision theory tells us that the optimal prediction procedure should be based on the statistic or equivalently on the likelihood ratio. For any statistic a threshold value should be specified for the procedure: if the value of the statistic is greater than the threshold, hypothesis H1 is accepted, otherwise hypothesis H0 is accepted. The threshold value should be selected in such a way that the total number of incorrect predictions (i.e., the sum of the number of type 1 errors and the number of type 2 errors) would be minimal. It is clear that if the prior odds are equal to 1, then a threshold value of one should be selected for the likelihood ratio; total number of errors would (theoretically) be minimal for this threshold. Surely, the assumption that the ratio of a priori hypothesis probabilities equals 1 does not seem to be natural. Indeed, the number of considered words that do not belong to a query sequence is much greater (approximately 8 times greater) than the number of considered words that belong to a query sequence. But statistics that are obtained from the assumption that this a priori ratio equals 1, and the assumption that this a priori ratio does not equal 1, but is constant (i.e., it does not depend on a word ω) are equivalent. Essentially these are the same statistics (only the threshold value should be changed: a value should be taken instead of 1; as it was noted, this value approximately equals 8 in our data). Therefore, we assume from now on that the ratio of a priori hypothesis probabilities equals 1. Thus all considered statistics are based on the likelihood ratio
Assumption of independence of variables ξ i . Transfer probabilities
By virtue of equation (1) we need to estimate conditional probabilities P{ξ|ξ0 = ε}, where ε = 1 or 0 in order to calculate the likelihood ratio. Our derivation of these estimates uses the assumption that variables ξ i , i = 1,...,n, are independent in the aggregate. Surely, this assumption is false. In reality variables ξ i are dependent, and the dependence is considerably strong. Nevertheless, in our definition of the likelihood ratio statistic (and the statistic that is the logarithm of the likelihood ratio) we use the independence assumption. Since variables ξ i are not independent, one can not assert that the obtained statistics are the most powerful, but these statistics can be still quite good. In decision theory, this approach is known as the naive Bayes procedure.
The independence of variables ξ i implies the equality
Each variable ξ i has exactly two possible values: 1 and 0. Thus, everything is reduced to the following four conditional probabilities:P{ξ i = 1|ξ0 = 1}, P{ξ i = 0|ξ0= 1}, P{ξ i = 1|ξ0 = 0}, P{ξ i = 0|ξ0 = 0}.
In addition, it is clear that
so actually everything is reduced to two conditional probabilitiesP{ξ i = 1|ξ0 = 1}, P{ξ i = 1|ξ0= 0}.
Conditional probabilities (3) are a special case of conditional probabilities of the typeP{ξ i = ε1|ξ j = ε2}, where ε1, ε2 = 1 or 0, i, j = 0,1,...,n
(above special case corresponds to j = 0). We call all this conditional probabilities transfer probabilities and denote them by
Transfer probabilities depend on i, j (and certainly on the word ω):
Conditional probabilities play a central role in our procedure. According to equation (4), it suffices to explain how transfer probabilities p1|1, p1|0 are evaluated.
We suppose that transfer probabilities satisfy the following assumptions ("axioms") (the sense of these assumptions is obvious).
Assumption 1) For a fixed word ω and for sequences π0, π1,...,π n (i.e., for the sequence π0 and sequences similar to π0) transfer probabilities depend only on the measure of similarity between sequences. Thus, we have:p1|1 = p1|1 (i, j;ω) = p1|1(μ ij ;ω) = p1|1(μ ij ) = 1 - p0|1(μ ij ),p1|0 = p1|0 (i, j;ω) = p1|0(μ ij ;ω) = p1|0(μ ij ) = 1 - p0|0(μ ij ),
where μ ij is the measure of similarity between sequences π i , π j (i, j = 0,1,...,n).
Particularly, if one of these sequences is the query sequence π0 and μ j is the measure of similarity between π j , π0, we haveP{ξ j = 1|ξ0 = 1} = p1|1(μ j ), P{ξ j = 1|ξ0 = 0} = p1|0(μ j ).
Assumption 2) Transfer probabilities (for an arbitrary fixed word ω) depend on similarity measure μ monotonically: the probability p1|1(μ) increases (does not decrease) and the probability p1|0(μ)decreases (does not increase) as μ increases.
Assumption 3) The inequalityp1|1(μ) > p1|0(μ)
always holds (if μ > 0).
Transfer probabilities are evaluated on the basis of the measure of similarity between similar sequences for which it is known whether ω ∈ KW[π i ] using the so called isotonic regression procedure (see [11]). (In [5] this procedure was referred to as monotonization procedure). Results of this procedure are piecewise-constant monotonous functions of the similarity measure μ that increase (do not decrease) for probabilities p1|1(μ) and decrease (do not increase) for probabilities p1|0(μ).
We briefly recall the isotonic regression problem. Let one have two number sets (i.e., a set of points in the plain) x i , y i , i = 1,...,n, for which x1 ≤ x2 ≤...≤ x n . The task is to find values z1, z2,...,z n , z1 ≤ z2 ≤...≤ z n , that minimize the deviation .
This is the monotone-increasing isotonic regression problem. The monotone-decreasing isotonic regression problem is similar; the only difference is that here z1 ≥ z2 ≥...≥ z n .
The isotonic regression procedure constructs a monotonic number sequence z1,...,z n (while in linear regression values of z i are linearly expressed in terms of x i : z i = αx i + β).
An algorithm for the solution of isotonic regression problem can be easily constructed. We do not describe it here. We only note that each z i is the mean value of {y j } over a window of variable length: . For different indices these windows either coincide or do not overlap, and the i-th window contains i.
We also note that the values of x i are not essential in the isotonic regression problem, only the order of values of y i is essential.
To obtain the transfer probabilities using isotonic regression we proceed as follows.
For the evaluation of p1|1 we consider pairs of similar sequences π i , π j that satisfy the condition ω ∈ KW[π i ]. Let μ ij denote the measure of similarity between sequences π i , π j . We put ξ ij = 1 if the word ω belongs to the sequence π j , and put ξ ij = 0 if ω does not belong to π j (recall that the word ω belongs to π i ). Then we apply the monotone-increasing isotonic regression procedure to the collection of points (μ ij , ξ ij ), where μ ij are in ascending order. The resulting values are the estimates of the transfer probabilities p1|1(μ ij ).
Similarly, applying the monotone-decreasing isotonic regression procedure to the collection of points (μ ij , ξ ij ) that correspond to pairs of similar sequences π i , π j for which ω does not belong to π i , we obtain values of the transfer probabilities p1|0(μ ij ). As we noted, the probabilities p1|1, p1|0 are supposed to satisfy condition (6). Therefore, if it turns out that functions p1|1(μ), p1|0(μ)obtained after the application of isotonic regression procedure do not satisfy this condition for some values of μ, then we consider that probabilities p1|1, p1|0 are not defined for these values of μ. Hence, it is possible that transfer probabilities are defined only for sufficiently large values of μ, but not for all μ. In particular, it is possible that for some words there are no values of μ such that inequality (6) holds. These words are referred to as degenerate words.
Statistics description
All statistics considered in this paper are based on the likelihood ratio criterion, that is on formula (1) under the assumption of independence of variables ξ i (formula (2)).
Two approaches are used in the definitions of these statistics. One of them can be called a "discrete" approach; the other can be called a "continuous" approach.
The discrete approach is based directly on formulae (1), (2) and the definition of transfer probabilities. It follows from (5) that the following formulae for conditional probabilities hold:P{ξ i |ξ0 = 1} = p1|1(μ i ), if ξ i = 1,P{ξ i |ξ0 = 1} = p0|1(μ i ) = 1 - p1|1(μ i ), if ξ i = 0,
and hence
Relations (7), (8) together with (1), (2) imply the following formula for the logarithm of the likelihood ratio:
One can see that the statistic T(1) can be expressed as a linear combination of the ξ i .
As we noted, theoretically the best threshold value for the statistic T(1) is equal to 1n1 = 0. However, that is only theoretical. As the assumption of independence of variables ξ i is incorrect and the statistic T(1) is only a rough estimate of logarithm of the likelihood ratio, the best threshold value is not necessarily equal to zero (and this threshold does not really equal zero in practice).
Let us put . From the definition of η and relations (9)–(11) it follows that
We note that
The variable η(ω) can be treated as a second statistic. Its values lie between 0 and 1. Statistics T(1) and η are linearly dependent, and hence for a fixed word ω these statistics are equivalent. However, the coefficients α0 and α i (i = 1,...,n) are different for different words ω, so the relation between thresholds, used in the prediction, is different for different words; consequently, the statistics T(1) and η are not equivalent for the whole totality of words (We will see later that if thresholds are well-chosen, then η leads to better results than T(1)).
The other approach to the definition of statistics, used in the annotation procedure, starts from a linear combination of the ξ i as in formula (12). Here coefficients α i are not necessarily given by (13), but should be positive and satisfy the relation (14). This approach uses the assumption that η has the normal distribution. Certainly this assumption is not correct (at least because the inequality 0 ≤ η ≤ 1 always holds). Nevertheless, we use this assumption (and, similarly to the discrete approach, the assumption of independence of variables ξ i (ω)). (As the normal distribution is continuous, this approach can be called "continuous").
Thus, we consider a random variable
and assume that it has a normal distribution. Denote by
the conditional expectations and dispersions of η given that ξ0 = 1 or 0 respectively. We haveM1η = ∑a i M(ξ i |ξ0 = 1) = ∑a i p1|1(μ i ), M0η = ∑a i p1|0(μ i ).
Further, the assumption of independence of ξ i (ω) implies that
(We note that it is the only place where the independence of ξ i is used; therefore, the assumption of independence of ξ i is not as essential in this approach as it was in the definition of the statistic T(1)). It follows from the assumption of a normal distribution that in cases ξ0 = 1 and 0 the variables
As above, we use the logarithm of the likelihood ratio as a statistic and in addition assume that the ratio of a priori hypothesis probabilities is equal to 1. Let us denote this statistic by T(2). Relation (16) implies that
Another variant of this statistic – statistic – was used in the paper [5]. The statistic is the ratio of type 1 error to type 2 error :
where Φ(x) is the cumulative normal distribution function: .
The statistics η and are equivalent for an arbitrary fixed word ω (although not equivalent for the whole totality of words). At the same time, the statistics η and T(2) are not necessarily equivalent, as the dependence of T(2) on η may turn out to be not monotonic. Moreover, it is never monotonic for all values of η. However, we are interested only in values 0 ≤ η ≤ 1, and usually (though not always) the dependence of T(2) on η is monotonous for these values of η, and in this case the statistics η and T(2) are equivalent (for a fixed word ω). Furthermore, even in the case where the dependence is not monotonic, the monotonicity is violated only for the values of η close either to 0 or to 1, and the lack of monotonicity can be disregarded.
We still have to discuss the question of the choice of coefficients a i in (15). One of the variants was described above: formula (13) can be applied. Another variant was introduced in [5]. This variant can be described as follows.
As above, we assume that the variate η = ∑a i ξ i has a normal distribution. Each set of coefficients a i and each threshold value λ have corresponding theoretical frequencies of type 1 and type 2 errors P(1)(λ), P(2)(λ). The idea is to take the set of coefficients {a i } that gives the minimum sum P(1)(λ) + P(2)(λ), where λ is the best threshold value for this coefficient set. However, analytically it is very cumbersome, so the following simplification was implemented. For a fixed set of coefficients {a i } we select the threshold value λ such that the frequency of type 1 errors is equal to the frequency of type 2 errors: P(1)(λ) = P(2)(λ). Then we find the set of coefficients that minimizes these frequencies P(1)(λ) = P(2)(λ). Here the search of the coefficient set can be reduced to a conditional extremum problem. The A4 algorithm uses an iterated procedure for the solution of this problem (see [5]).
In order to optimize the automated annotation procedure, that is to increase the prediction quality, certain modifications of the procedure were introduced. For each statistic (η, T(1), T(2), ) many variants (up to 36), including the variant that was described above, were considered – each variant corresponds to a certain combination of these modifications. Some variants really turned out to be better than the variants described above.
A simplified scheme of modifications (and thereby of statistic variants) can be described as follows.
What primary local alignments are considered? In the described variant of statistics only one primary local alignment (the one with the maximum power) was considered. Meanwhile, all constructed local alignments with sufficiently high power can be considered, as it was done in [5]. In this case indices i in (9), (12), (15) correspond not to individual similar sequences, but to primary local alignments of this sequences.
Are the lengths of primary local alignments taken into consideration? In the described variant lengths of primary local alignments were not taken into consideration, but these lengths can be considered as well. In this case indices i in (9), (12), (15) correspond not to similar sequences or primary local alignments, but to individual positions of these primary local alignments (as in case of FT-type words). In this case the total number of variates ξ i is equal to the overall length of all primary local alignments. For these variants of statistics long local alignments turn out to be more significant than short local alignments with the same power.
How are the coefficients a i calculated? The coefficients a i in (15) can be calculated using different methods – either formula (13) can be used, or an iterative method (described in [5]) can be applied. (For the statistic T(1) the coefficients a i are always calculated using formula (13)).
As there are 3 modifications, the total number of basic variants (in the described simplified scheme) equals 23 = 8 (and for the statistic T(1) it equals 4).
So, variants of the statistics η, T(1), T(2), are considered in this paper. Moreover, for the purpose of comparison (as in the paper [5]) the simple statistic
(the frequency of occurrence of a word ω in the collection of similar sequences) is also considered.
Finally we quote a scheme of the A4 algorithm in Figure 1 (this scheme is essentially taken from [5]). A short description of each stage can be found in [5]. Here we only note that the most time-consuming stage is the first one – the generation of a collection of similar sequences. We also note that in the current investigation regions were not determined, and the prediction was performed for the whole query sequence.
Testing results
Testing scheme
A collection of 518 sequences, randomly selected from SWISS-PROT databank, was generated. Note that only initially annotated sequences (i.e., sequences whose description fields were not obtained by the extension from similar sequences) were selected. The prediction was performed for KW-type words. All selected sequences were divided into two groups. The first group contained 210 sequences. This group was used during the "learning stage": the procedure was applied to all sequences from the group, and values of procedure parameters (including optimal threshold values) that minimize the total number of errors for the first group were selected. The remaining 308 sequences were used for the "main testing" that was performed using parameter values obtained on the basis of "learning" results. A list of all these 308 sequences is given in Additional file 1.
The total number of KW-type words in the description fields of 308 selected sequences, used as query sequences, was equal to 1176 (the positive prediction was preferable for these words). As it was noted, a collection of similar sequences was generated for each query sequence; the number of sequences in these collections equaled 100. Then the list of all KW-type words from description fields of similar sequences was formed. The majority of these words belonged only to one similar sequence. All such words are degenerate, transfer probabilities are not defined for them. At the same time nearly all these words did not belong to the query sequence. Hence it was sensible to perform prediction only for words that belonged to at least two similar sequences. For all these words transfer probabilities were evaluated, non-degenerate words (i.e., words with defined transfer probabilities) were determined, and the prediction was performed (for both degenerate and non-degenerate words).
Table 1 shows certain characteristics of sequences used for the testing.
The average number of words considered per query sequence equals 30 and the entire range was from 3 to 67.
Final results
Testing results are presented in Table 2.
The table contains testing results for the basic statistics η, T(1), T(2), as well as for the statistics q and S And , included into the table for the purpose of comparison (See the Discussion section). Each line of the table corresponds to a certain variant of studied statistics. The first column shows which statistic and which variant of this statistic corresponds to a line. Variants are given in square brackets: the first number equals 0 if lengths of primary local alignments are not considered, and 1 otherwise; the second number equals 0 if the coefficients a i are calculated using formula (13), and 1 if the coefficients a i are calculated using the iterative procedure (for the statistic T(1) the second number is always 0). The second column contains the number N1 of type 1 errors (i.e., number of cases when the prediction for a word that belongs to description fields of a query sequence is negative). The third column contains the number N2 of type 2 errors (i.e., number of cases when the prediction for a word that does not belong to description fields of a query sequence is positive). The forth column contains the total number of errors Nall= N1 + N2. The next columns contain sums P(1) + P(2) and P(1) + P(+), where P(1) is the proportion of type 1 errors, P(2) is the proportion of type 2 errors, and P(+) is the ratio of false positive predictions to the total number of predictions: , , (n all is the total number of words for which the prediction is performed, i.e., total number of KW-type words in description fields of all sequences, similar to at least one query sequence, n q is the number of words (from the list of these n all words) that belong to query sequences, n+ is the total number of words for which the prediction is positive; here n all = 9236, n q = 1176, n all - n q = 8060; the value of n+ of depends on the threshold evaluated for the given version of statistic at the "learning stage"). Lines are ordered according to the prediction quality: higher lines correspond to statistic variants with lower total number of errors. For a fixed statistic, lines that correspond to the best variant of this statistic (i.e., for the variant that leads to the lowest total number of errors) are marked. Recall that the statistic [1,0] is exactly the statistic that was considered in [5] (note that it is the best variant for the statistic ).
The results could also have been presented as a confusion table as laid out in Table 3, but doing so for all variants would take a lot of space.
The testing results showed that the first modification (consideration of all primary local alignments or only one primary local alignment with the maximum power) did not significantly affect the prediction quality, so results are only presented for one case (all local alignments are considered, similarly to [5]).
Note that the results presented in the Table correspond to the whole totality of words including degenerate words (though statistics η, T(1), T(2), , T(nik)were evaluated only for non-degenerate words, for degenerate words the prediction was performed on the basis of the statistic q, i.e., on the basis of word frequency). In particular, the total number of errors includes errors for degenerate words. It is worth noticing that the total number of errors for degenerate words in case of the best choice of threshold q0 for the frequency q(ω) turned out to be surprisingly small: only 13 (whereas the number of errors for the whole totality of words was 220); these errors included twelve type 1 errors and one type 2 error (recall that the prediction was performed for 2029 degenerate words, and the prediction was incorrect only in 13 cases). Such a small number of errors can seemingly be explained by the fact that for degenerate words the frequency q(ω) is nearly always close either to 1 or to 0, otherwise in almost all cases transfer probabilities p1|1, p1|0 can be defined for at least some values of similarity measure μ and hence a word ω turns out to be non-degenerate.
We also note that along with type 1 and type 2 errors other errors can occur, as it is possible that some words from description fields of a query sequence do not belong to description fields of similar sequences and hence the prediction is not performed for these words at all. However, we used a large number of similar sequences (100) for the prediction, so such errors were extremely rare (only two words for the whole test set of 518 sequences, whereas the number of words for which the prediction was performed equaled 9236). Hence, in our case these errors can be disregarded.
Statistical analysis showed that the precision of the evaluation of N1, N2, Nallis reasonable: it can be checked that the relative precision of Nall(the standard error of ln(Nall)) is in the order of 7–10% which is in line with the relative precision of a Poisson random variable that is given by . However, since the "methods" (the statistics and their variants) are compared on the same sequences, the standard error for the comparison between methods is much smaller due to the high correlation of results for the same sequence and is in the order of 2–4%. This implies that "methods" for which Nalldiffer by more than 10% can considered to be significantly different.
This shows that size of the experiment with 210 randomly selected sequences in the learning stage and 308 sequences in the testing stage is large enough to obtain valid statements about the accuracy of the proposed methodology and allows statistical comparisons of different statistics and variants.
When the set of tested sequences is fixed, the total number of errors Nallis an objective characteristic of prediction quality, and the optimal threshold values (selected during the "learning stage") provide exactly the minimum of the total number of errors. However, if different testing results (based on different sets of query sequences) are compared, then absolute numbers of errors can not be treated as a procedure quality measure, and relative quantities (proportions) should be considered instead of absolute quantities. Usually a sum of the proportion of type 1 errors and the proportion of type 2 errors P(1) + P(2) is used as a quality measure; values of this sum are presented in the fifth column of Table 2. However, in our situation the number of words n all - n q that do not belong to query sequences is much larger than n q , and for the majority of these words it is obvious that they do not belong to a query sequence. Consequently, in case of optimal parameter values the proportion of type 2 errors P(2) is small, it is considerably smaller than P(1). Thus the quantity P(1) + P(2) is not representative in our case (see [5]). The ratio P(+) of the number of wrong positive predictions to the total number of positive predictions is more representative than P(2). Hence, it is natural to measure procedure quality by the sum P(1) + P(+). Exactly this procedure quality measure was used in [5].
In medical decision making (diagnostic testing) the terms sensitivity (sens = 1 - P(1)) and specificity (spec = 1 - P(2)) are frequently used to quantify the accuracy of a procedure, while the quantity 1 - P(+) is known as the positive predictive value. Alternative terminology is discussed in [12]. The ROC curve plotting sens against 1 - spec is a popular way of showing the overall performance of a diagnostic test without specification of a cut off value. It is also used and discussed in [12], but with different labels for the axes. Figure 2 contains ROC curves for the best variants of the statistics (i.e., for variants that were marked in Table 2) applied to the non-degenerate words.
Overall comparison of the statistics T(1), η, T(2),
Mean values of Nall(where averaging is performed over all variants presented in Table 2) for different statistics are the following: 241 for η, 310 for T(1), 320 for T(2), and 356 for (recall that differences in the order of 10% can be considered to be significant). These numbers show that the statistics can clearly be ordered with respect to the prediction quality: the best statistic is η, then comes T(1), then T(2), and finally . For the comparison of the statistics η, T(1), as well as for the comparison of statistics the T(2), this conclusion is obvious. For the comparison of the statistics T(1), T(2) it seems to be less obvious, but the consideration of variants in which the calculation of coefficients a i is performed using formula (13) (i.e., variants [0,0] and [1,0]; recall that the statistic T(1) has only such variants) clearly shows that the statistic T(1) is considerably better than T(2). The same conclusion can be drawn from the comparison of ROC curves presented in Fig. 2.
Testing results show that the prediction quality for the best variant of the statistic η (see the first line of Table 2) is much higher (higher by 50%) in comparison with results of [5] (see the line of Table 2 that corresponds to [0,1]). That is the most striking finding of the current paper.
It is interesting to see that the statistic η turned out to be better than T(1): the latter would be expected to be better as it equals the logarithm of the likelihood ratio (in reality, it does not equal this logarithm because the ξ i are not independent). We suppose that this fact can be explained as follows. Recall that T(1) and η are equivalent for an arbitrary fixed word ω. These statistics turn out to be not equivalent only if they are compared on the whole totality of words. It is worth noting that the variation of the values of the statistic T(1) (i.e., the difference between values of T(1) in cases ξ1 =....= ξ n = 0 and ξ1 =....= ξ n = 1) significantly depends on a word ω: for some words these values vary from -700 to 700, for some other from -0.01 to 0.01. In principle, the optimal threshold value is different for different words. As the ranges of T(1) values essentially vary, then it is probable that optimal thresholds also essentially vary. The optimal threshold for the whole totality of words is a certain mean value of thresholds over individual words ω. Since the optimal thresholds are significantly different for different words, the quality of the mean is low: for certain words (e.g., words with small variation of T(1)) this mean is completely unrepresentative. In the same time, the range of values of η is the same for all words (these values lie between 0 and 1). Consequently, the difference between optimal thresholds for individual words is probably not so significant, and the mean gives better quality for individual words in comparison with T(1).
From the point of view of prediction quality the statistic T(2) turned out to be worse than T(1). It is not surprising, because during the derivation of the formula for T(2) along with the incorrect assumption of independence of ξ i we also made the incorrect assumption of the normal distribution of η. This consideration is applicable to as well. Since the cut-off points for these statistics are determined empirically in the test set and validated in the training set, the violation of the normality assumption does not invalidate the procedures as such, but might affect their performance.
Effect of procedure modifications
The next issue is the dependence of prediction quality on procedure modifications. One can see that for a fixed statistic the prediction quality significantly depends on the choice of the variant. Thus, the introduction of modifications turned out to be effective.
It is interesting that all statistics for which coefficients a j can be calculated in different ways (these are the statistics η, T(2) ) prediction quality was better in case when these coefficients were calculated using the iterative procedure.
At the same time the dependence of prediction quality on the modification related to the consideration of lengths of primary local alignments is more intricate: for the statistic η, and to a lesser extent for the statistics T(1), T(2), the results are better if lengths of primary local alignments are considered, but for the statistic results are better when lengths of primary local alignments are not considered. Currently reasons of this fact are not clear for us.
As it was noted, the dependence of results on modifications dealing with the number of considered primary local alignments for similar sequences is not essential. (However, we note that for nearly all variants that were described above as well as variants that were not described, prediction quality is better in case when all primary local alignments are considered).
Comparison with other procedures
For the purpose of comparison we compared our findings with the simple statistic q(ω) (the frequency of word occurrence in the list of similar sequences, see (18)) was also considered, using a threshold of q = 0.422. As expected, q(ω) gave essentially worse results in comparison with T(1), η, T(2), (see Table 2).
Furthermore, we compared our results with the results, which predicts all of the words for which there is among similar sequences at least one sequence with a power above a certain threshold. Such an approach was used by Andrade M. et al. [2]. This is the statistics S And as already mentioned in Table 2. It is defined as S And (ω) = max μ j , where μ j is the measure of similarity between sequences π0, π j , and the maximum is taken on the j for which ξ j (ω) = 1 (i.e. the word belongs to these sequences). In our application similarity is measured by the power value (see [9, 10]) as used in the other statistics in this paper and mentioned before, while [2] used the E-value. That makse the cut-offs hard to compare. The results for these statistics are given in the same Table 2. The results were better than in the statistics q(ω), but worse than in any of our statistics.
Surprisingly, it turned out that the prediction quality is better when only some similar sequences (e.g., 10 from 100) are used for the evaluation of a statistic (but not for the evaluation of transfer probabilities!). It means that a large part of similar sequences only leads to an increase of "noise". (See [13] for details.)
We would also like to mention the work [14] on FunCat categories containing a set of about 7,500 well annotated proteins and providing a benchmarking for different methods of automated annotation. It would be interesting to apply our procedure to this database, but that has not been realized yet.
We did not attempt at this stage to apply our approach to the GO annotation. It would be interesting further research to switch to GO data and to compare our approach with other approaches in the literature.
There is a similarity with the approach of Kajan et al. [15]. They also base their procedures on the likelihood ratio, but use approximations based on maximal similarity (or minimal distance), while we attempt to estimate the likelihood ratio from pairwise comparisons within the set of similar sequences.
There is also an interesting relation with the GOPET tool presented in [16] based on earlier work by the same authors [17]. These authors use a number of characteristics of the set of found similar sequences for term (word), such as maximal e-value, frequency of the term etc., as a coordinates of decision making space. They use more features while we concentrate on the similarity. That might be an advantage for their method. On the other hand we try to use the information from all similar sequences in an optimal way relying on statistical decision theory by means of the use of transfer probabilities and the concept of likelihood ratio. It is an interesting topic of further research to compare the two methods.
So far we only predicted the presence of a key word. It is quite a challenge to obtain a true prediction of function.
The main conclusion of the paper is that the introduction of the concept of likelihood ratio coming from statistical decision theory is very helpful in the development of automated annotation procedures. We obtained a substantial improvement when compared with our previous results.
We are sure that there is room for further improvement. Issues for further research are the size of the set of similar sequences and the combination of different statistics into a "super-predictor".
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The authors thank V.K. Nikolaev, A.E. Gorbalenya, V.V. Galatenko and I.V. Antonov for valuable comments and discussions. Work of A.M. Leontovich and K.Y. Tokmachev was partially supported through the Joint Program in Bioinformatics between Leiden University Medical Center and Moscow State University administrated through the CRDF GAP1473 grant to A.E. Gorbalenya. Also A.M. Leontovich acknowledges support of the EU grant FP6 IP Vizier LSHG-CT-2004-511960. and grants RFBR 06-07-89143a and RFBR 06-01-00454.
Additional information
Authors' contributions
A.M. Leontovich and H.C. van Houwelingen developed the approach. A.M. Leontovich developed the methodology and the algorithms in cooperation with K.Y. Tokmachev and H.C. van Houwelingen. K.Y. Tokmachev carried out all computations.
Electronic supplementary material
Additional file 1: Testing sequences. The file contains the list of the 308 sequences used in the testing phase. (XLS 30 KB)
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Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Leontovich, A.M., Tokmachev, K.Y. & van Houwelingen, H.C. The comparative analysis of statistics, based on the likelihood ratio criterion, in the automated annotation problem. BMC Bioinformatics 9, 31 (2008). https://doi.org/10.1186/1471-2105-9-31
DOI: https://doi.org/10.1186/1471-2105-9-31


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*Myrtle Fillmore's Healing Letters (Unity Classic Library)
By Frances W. Foulks
In 1890 Myrtle Fillmore, cofounder of Unity, formed what was to become Silent Unity. She received many letters requesting advice on how to deal with life's challenges. This collection of writings, drawn from the "healing letters" Myrtle lovingly wrote to so many, contains wisdom, insight, and practical advice to help you deal with any situation you may be facing today.


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The Early Head Start (EHS) program recently hosted its annual "Dad's" Resource Fair. The event was designed to provide information about social services and job opportunities for members of the EHS community. Despite its title, the Fair welcomed all dads and other male figures that play an important role in the lives of children in the program.
Providers from across the region attended including the Laborers' District Council, Philabundance, Catholic Community Services and PA Career Link.
"The representatives were all incredibly engaging and very energetic in the way they motivated the participants to take advantage of the opportunities available to them. The providers shared their own personal stories and words of encouragement to help build the confidence of those in attendance" said Lisbel Madera, the EHS family community involvement specialist.
Attendees were able to spend one-on-one time with each provider to receive available resources, materials and have their questions answered. Participants were encouraged to submit resumes for jobs at the fair.
Madera added "One of the dads in attendance was able to submit a resume to a provider and hopes to hear back from them soon; so best of luck to him."
For more information about the Health Federation's Early Head Start Program, visit the program page.
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It was the year 2038, The old purple haired broad walked slowly down the street. On a big monitor above her she saw that old Flipside Logo. Now part of an advertising campaign for public office. Those people running for office with pig hearts. And she thought it was weirder when she heard the Ramones' song as part of a bike exercise commercial back in 2018.
After about 14 years of over reflecting, relieving, and watching all the Flipside stuff I help produce become dissected, rearranged bought and sold and resold I can now let it all go. T-shirt logos and documentaries too. I came out with my own D.I.Y. publications to tell my story. It has been out for over a year or two.
Yes, I have been asked to tell my story in other people's books. Some I did because I felt the need to save the memory of Flipside. Not anymore. I let it go. This is a reality that I had to face, and it was not easy letting go of the need to protect Los Angeles Flipside Fanzine.
"It may be bad, but it feels good."
I hope others that were part of Flipside tell their own stories in their own way. Who knows if we could put together a blog or book that includes our individual stories as writers, shit workers, photographers, people who caused considerable trouble etc.? That would be fun.
One thing that shines through as a truth that Al and I believed in…. "You don't have to ask permission." If you want to do it just do it. "
So, letting it go means just that. You do not have to ask me if you want to use this or that from Flipside's vast products in anything you are doing. Just do it. I will not be part of that anymore. It is not my responsibility.
Yet, I do have my projects if you need to see what it was like for me as a Flipside punk person. A simple story that I did in my little bedroom converted into an office. Where I do my art, poetry and writing of all kinds, so be it. It took me 2004 to 2017 to finish the Punk@lullaby project. Just like condensed soup but a condensed punk rock story.
To all my old & new friends of the Big Punk Rock @ beyond. ~ Hudley


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It seems that Singapore is just one ordinary place to me right now, maybe because I visited this country countless times that I can't remember the exact number how many times that I went to this country. I thought the shortest trip that I ever made to Singapore was the time when I picked-up my clearance from the previous company I worked in since I'm going back to my homeland. This happened last November 29, 2010, stayed for almost a day and left November 30. By assessing it, I spent at least 24 hours in Singapore including arrival and departure time
But my recent visit in Singapore just lasted at least 13 hours. Wondering what had been happened? Did I tour around within those hours? The answer was no. My sister and I just visited Singapore to spend New Year eve together with my brother and his family. We arrived at Johor Bahru immigration around 7:45. PM December 31,2012 after we visited Legoland in Malaysia, then left Singapore around 9:15 AM January 1, 2013 going to Malacca Malaysia. What we did in this Merlion country? We had new year dinner at my brother's home, slept for at least 6 hours, had breakfast and then left. Is it not cool, to have new year dinner abroad?
What were the things we experienced during the time were traveling to Singapore? The significant thing was at the time we were on our way to my brother's place, since its New Year eve, there are lots of people outside, taking trains going to the places where there will be fireworks. In Singapore, there are few designated places for this event like Marina Bay and Harbourfront. And most people were on their way to Marina Bay. If you're not in either of these places, New Year will most likely a quiet time for the family. Kind of unique experience right?


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Empathetic Literary Analysis of Jack London's the Call of the Wild: Understanding Life From an Animal's Point of View
Beierl, Barbara
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Numerous tomes have been written about Jack London's The Call of the Wild. This is the first one to talk about the dog Buck's perspective in the novel. Beierl takes an empathetic approach to discussing the domestication of Buck in the story to use this novel as a platform for building empathetic relationships with animals. Very few scholarly works discuss literature from the perspective of an animal, and this one attempts to bring a fresh perspective at an old novel by theorizing empathetically with the characters, which plays a critical role in narrative-based responses to the novel. If characters can gain empathy from their audience there is a higher likelihood that the readers will have a positive response to the story. This book discusses how Jack London creates animal characters that form an empathetic bond with his readers. When readers can understand the inner, mental states of characters, they become motivated to form emotional attachments with them.
"Emotions are the gifts of our ancestors. We have them and so do other animals. We must never forget this… Dr. Beierl's technique of empathetic literary analysis encompasses the replication of mirror neuron dynamics and validates a relationship between author-as-text and reader."
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University of Colorado
"A significant text which underlines the importance of empathy for social and psychological well-being… Dr. Beierl demonstrates empathetic sharing between Buck and several men, in which the author creates meaningful empathetic characters.
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Boston College
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Foreword by Marc Beckoff
Chapter 1: Philosophical Foundations and Psychological Underpinnings of Empathy
Chapter 2: Reading as an Empathetic Activity
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Chapter 4: The Empathic Reader: Analyzing Jack London's
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Developmental Stage 1: Buck's Odyssey from Innocence to Experience
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There's a Story Here
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Maurice K. Goddard State ParkBy Rich Reed Photography
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While many destinations have lakes, waterways, trails and other natural amenities, few can offer such a diverse array of these in one location like Crawford County. Here, you can enjoy activities against the beautiful backdrop of our four seasons.
The story of Crawford County's local history cannot be told without telling of its impact on the nation and even the world. Learn about the people, innovations, and industries that changed the country's political, social, and economic legacies forever.
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Crawford County's culture is cultivated from the deep-rooted, down-to-earth values that yield authenticity. It's a destination that proudly preserves and celebrates the simplicity of life through artistic creativity, home-grown products, and community events to tell a story that's inviting to others.
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Crawford County is a place with stories. Stories of natural beauty, cultural legacy, and far-reaching history. But that wouldn't be the entire story. What completes the story of Crawford County? It's our people.Read all Stories
This local, family-run farm provides the community with fresh, pesticide-free produce all-year-round.Read Story
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Passionate about conservation, this organization is educating others about biodiversity and preserving a key part of the environment.Read Story
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Crawford County is a rural oasis of fun activities and beautiful sights. With peaceful countryside views and bustling towns with shops and restaurants to explore, there's no end of ways to enjoy some R&R. Catch a local play production, paint your own decor, grab some coffee, hunt for hidden treasures in our antique stores… truly, there are so many options for a fun-filled weekend. And for outdoor lovers, there's a variety of hiking, biking and water trails around every corner!


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History of Hockey and FIH
The roots of hockey are buried deep in antiquity. Historical records show that a crude form of hockey was played in Egypt 4,000 years ago, and in Ethiopia around 1,000 BC. Various museums offer evidence that a form of the game was played by Romans and Greeks, and by the Aztec Indians in South America several centuries before Columbus landed in the New World. The modern game of hockey evolved in England in the mid-18th century, primarily around schools.
The first Olympic Hockey Competition for men was held in London in 1908 with England, Ireland and Scotland competing separately. After having made its first appearance in the 1908 Games, hockey was subsequently dropped from the 1912 Stockholm Games, and reappeared in 1920 in Antwerp before being omitted again in Paris in 1924. The Paris organisers refused to include hockey on the basis that the sport had no International Federation.
Hockey had made its first steps toward an international federation when in 1909 the Hockey Association in England and the Belgium Hockey Association agreed to mutually recognise each other to regulate international hockey relations. The French Association followed soon after, but this was not considered sufficient.
Hockey took its most important step forward in 1924 when the International Hockey Federation, the world governing body for the sport, was founded in Paris under the initiative of Frenchman, Paul Léautey. Mr. Léautey, who would become the first President of the FIH, was motivated to action following hockey's omission from the program of the 1924 Paris Games.
Mr. Léautey called together representatives from seven national federations to form the sport's international governing body, the Fédération Internationale de Hockey sur Gazon. The six founding members, which represented both men's and women's hockey in their countries, were Austria, Belgium, Czechoslovakia, France, Hungary, Spain and Switzerland.
The women's game developed quickly in many countries and in 1927, the International Federation of Women's Hockey Associations (IFWHA) was formed. The founding members were Australia, Denmark, England, Ireland, Scotland, South Africa, the United States and Wales. After celebrating their respective Golden Jubilees - the FIH in 1974 and the IFWHA in 1980 - the two organisations came together in 1982 to form the FIH.
The growth of the International Hockey Federation from its early beginnings has been most impressive. Denmark joined in 1925, the Dutch men in 1926, Turkey in 1927, and in 1928 - the year of the Amsterdam Olympics - Germany, Poland, Portugal and India joined. India's addition marked the membership of the first non-European country.
By 1964, there were already 50 countries affiliated with the FIH, as well as three continental associations - Africa, Pan America and Asia - and in 1974, there were 71 members. Today, the International Hockey Federation consists of five Continental associations - Europe and Oceania have since joined - and 127 member associations.
Today, the work of the International Hockey Federation is accomplished through the efforts of the FIH President, Honorary Secretary General and Treasurer, working together with an Executive Board, 13 Committees, and the professional staff in its Lausanne headquarters.
In many ways, the FIH serves as the 'guardian' of the sport. It works in co-operation with both the national and continental organisations to ensure consistency and unity in hockey around the world. The FIH not only regulates the sport, but is also responsible for its development and promotion so as to guarantee a secure future for hockey.


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In the Book of Acts, chapter 10, we find the account of the Centurion Cornelius and his household. Since he was a gentile, Acts 10, is most commonly used as one of the proofs showing that salvation was not limited to the Nation of Israel; but that it was also extended to the Gentile Nations. However, the account contained in this chapter of a book of the Bible is also a good proof of the process of salvation. It clearly lays out the events that take place in the salvation process.
(Note: This page is in support of the Bible studies contained on Baptism, what the Bible teaches and Salvation - the order of events pages.)
We read in Acts 10:2, that Cornelius has come to a knowledge of the One, True God.
- - " one devout and fearing God, with all his household, both doing many merciful deeds to the people, and praying continually to God."
Bible study also shows us from these verses:
- - " Jehovah looked down from Heaven on the sons of men, to see if there were any who understood and sought God.
All have gone aside, together they are filthy; there is none who does good, no, not one."
(Psalm 14:2-3)
- - " as it is written: 'There is none righteous, no not one;
there is none that understands, there is none that seeks after God."
(Romans 3:10-11)
- - " But the natural man does not receive the things of the Spirit of God, for they are foolishness to him; neither can he know them, because they are spiritually discerned."
(1 Co 2:14)
that natural man does not seek or understand God. Therefore it is evident that something extraordinary has happened to Cornelius, and his household. They, being born as natural man, now fear, acknowledge, and pray to the One, True God. Jesus gives us the answer to how this has occurred:
- - " Truly, truly, I say to you, Unless a man is born again, he cannot see the kingdom of God."
(John 3:3)
- - " It is the Spirit that makes alive, the flesh profits nothing." (John 3:8)
The Bible clearly teaches that natural man must be born again (quickened) as spiritual man by the action of the Holy Spirit, which is the first step in the order of events of salvation.
Although Cornelius, and his household acknowledged and prayed to the One, True, God it is evident that this was not enough to save them, for we find in Acts 10:22 & 30-32:
- - " And they said, Cornelius the centurion, a just man and one who fears God, and one of good report among all the nation of the Jews, was warned from God by a holy angel to send for you (Peter) to come to his house and to hear words from you. "
- - " And Cornelius said, Four days ago I was fasting until this hour. And at the ninth hour I prayed in my house, and behold, a man stood before me in bright clothing. And he said, Cornelius, your prayer is heard, and your merciful deeds have been remembered before God. Therefore send to Joppa and call here Simon whose last name is Peter, he is staying in the house of Simon, a tanner by the seaside; who, when he comes, shall speak to you. "
We know from Acts 10:34-43, that Peter spoke the Gospel message to all those who were at Cornelius' house. This brings us to the next step of salvation, which Jesus teaches us, in John 5:24, is two-fold,
- - " Truly, truly, I say to you, He who hears My Word and believes on Him who sent Me has everlasting life and shall not come into condemnation, but has passed from death to life."
Jesus, through Holy Scripture, says that a person must hear and believe the Gospel message to be saved.
That those present believed the saving message of the Gospel is evidenced by Acts 10:44,
- - " While Peter was still speaking these words, the Holy Spirit fell on all those hearing the Word."
This bible verse shows us that the final element in the salvation process occurs at the moment of belief in the salvation message of the Gospel of Jesus Christ. Scripture tells us in Eph 1:13-14,
- - " in whom also you, hearing the Word of Truth, the gospel of our salvation, in whom also believing, you were sealed with the Holy Spirit of promise, who is the earnest of our inheritance, to the redemption of the purchased possession, to the praise of His glory."
The sealing, and in-dwelling (Romans 8:11), by the Holy Spirit is the final step in the actual process of salvation. All those present, who believed, received a guarantee of their salvation by the in-dwelling of the Holy Spirit. Additionally, Scripture tells us that they have received, what is called by some, the Baptism of the Holy Spirit. For we find confirmation of this in Acts 10:45-46,
- - " And those of the circumcision, who believed (as many as came with Peter), were astonished because the gift of the Holy Spirit was poured out on the nations also. For they heard them speak with tongues and magnify God."
It is evident, from Bible study, that Cornelius, and his household are saved by the grace of God.
What makes this biblical account noteworthy in the studies of Baptism and
Salvation. is that neither Cornelius, nor his household have been baptized; yet, they are guaranteed their heavenly inheritance by, and have received gifts of, the Holy Spirit. We know they have not been baptized from Acts 10:47-48,
- - " Can anyone forbid water that these, who have received the Holy Ghost as well as we, should not be baptized? And he commanded them to be baptized in the name of the Lord."
Acts 10, Cornelius & his household, and Luke 23, the crucified thief, disprove the assertions, and teachings, of some Churches & Denominations, that to be saved you must be baptized. (NOTE: read the next paragraph before you discount being baptized.)
Although salvation is not obtained through baptism, the Bible clearly teaches, that in the normal course of events, if you are saved, you are required to be baptized as a matter of obedience to Our Lord & Savior Jesus, Christ. (For more in-depth info see Baptism, what the Bible Teaches.)


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I have discovered something about myself. I am very competitive. Okay, those who know me are saying, "duh!"
Don't get me wrong, this is not a new thing. I have been competitive for as long as I can remember. The subject came up recently however when I was discussing scripture reading with a friend. I have always had a hard time remembering to or making time for scripture reading.
What I proposed to my friend (I'll leave his name out for now since I haven't been given his permission to mention it) that we make it a contest. We each vow to read a chapter in our scriptures each day and the first one that remembers after 2pm to text the other one what scripture he read and what it was about gets a point. At the end of each month, whoever has the most points is treated to a meal by the other.
The act of making it a contest could be just the motivation I need to set aside time for reading. We will see how it goes between now and the end of April. Hopefully, it will work and we both will do much more reading. Hopefully, it will inspire us to not only read more but prompt some discussion about what we've read.
Regardless, when a new contest is at hand, I get pumped like Conan the Barbarian when he was asked, "What is best in life?"
"To crush your enemies, see them driven before you, and to hear the lamentations of their women!"
Yes, I am a bit competitive!
by Chris Doelle


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In December 2011 MPs permitted Universities to raise undergraduate fees from £3,350 to £6,000, and up to £9,000 in "exceptional circumstances" such as Oxford and Cambridge. Financing a child's higher education pathway has therefore potentially become significantly more difficult for a large percentage of those entering University this year and beyond.
This report investigates ways in which the burden of financing a child's University costs can be alleviated via sound financial planning and a solid investment strategy.
Junior ISAs
Junior Individual Savings Accounts (JISAs) were introduced in November 2011 as a means to establish an investment portfolio for young children in order to give them as secure a financial start in life as possible.
Junior ISAs can hold cash like normal savings accounts, or they can take the form of Stocks & Shares investments, but all benefit from having a yearly tax-free allowance.
Depending on the type of ISA chosen, they can either provide instant access funds or a longer term investment fund helping to offer and secure a child's financial future, whilst promoting the concept of structured saving to the next generation of investors.
Investment Trusts
It is calculated that three years of University schooling plus living expenses will run up to about £50,000 in the current environment. This cost is projected to increase steadily throughout the coming decade.
As a result, investment trusts can be one of the most effective ways of accumulating funds in time for a child's higher education.
Investment trusts consist of diversified portfolios that are professionally managed. Because the portfolio contents are pre-selected by experts, opening an investment trust can be one of the simplest methods of establishing a successful long-term investment strategy.
Trusts exist with a variety of risk levels which allow investors to select on the basis of financial risk exposure versus return potential.
The level of risk accepted by an investor should depend upon factors such as the value of the initial investment, the length of time available before the child begins University and the investment growth requirement.
As independent journalist Malcolm Anderson states for example;
A trust might hold 30% of assets as cash, 30% as reliable bonds and 40% in diverse equities guaranteeing a stable return that is not overly subject to the volatility of the stock market.
However, a portfolio of up to 80% diverse equities may make better financial sense as the opportunity to earn greater dividends is increased but if the stock market takes a downturn the investor will still have cash on hand to recoup losses.
If the investment trust can be structured for the long-term – 10 years+ – even greater financial risks can potentially be taken which will yield the greatest returns in stable or positive conditions. Successful investments can be exploited for longer and there is also time to regain losses should stock markets suffer a fall. The fact that the natural short-term volatility of stock markets is balanced out over time means that investment trusts can be considered to be solid options for long-term financial growth.


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The home of Retronauts, the greatest classic gaming podcast of all time! Or at least the oldest. New episodes every Monday (with bonus episodes every other Friday). Hosts Bob Mackey and Jeremy Parish explore the history of video games through casual conversations, developer interviews, and deep-dives into both influential games and esoteric topics alike.
May 19, 2017
Live from a very noisy Milwaukee stage, Jeremy and Bob are joined
by Splatterhouse experts and world record holders Caitlin Oliver
and Kevin Bunch to contemplate the complete history of Namco's
gross-out brawler franchise.
The original classic gaming podcast continues its endless quest to explore the history of video games, one game at a time. Join hosts Jeremy Parish and Bob Mackey each week as they team up with a host of expert guests to chronicle the creations that have paved the way for today's hits. From forgotten black-and-white arcade machines to modern-day remakes, Retronauts spans more than four decades of vintage gaming greatness!
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Do you ever look at your parents or think about your parents and realize you're so much like one of them? Or, maybe you have some good and some bad traits from each? I'm so much like my dad. He walks to through the kitchen, opens half the cabinet doors and leaves them open; gets the milk out of the fridge, pours a glass and leaves the milk sitting on the counter. Though I try really hard to close cabinet doors, and put the milk away, it just doesn't come natural for me. Mom, on the other hand, would never walk away with a cabinet door open; nor would she take the first sip of her milk without putting the carton back in the fridge.
Mom loves to decorate. I believe she would paint her walls a different color once a week, and she would love to buy new furniture to match! I keep my furniture forever . . don't matter to me if it matches. When I move into a house, the color of the walls are what they're going to stay. In the horrible event I do have to paint . . it's torture if I have to choose a new color.
And, when she wants to paint, she does it all by herself! Not me . . I hire painters. I am not a painter . . don't like it, make a mess, ruin everything!
She loves little vases and flower arrangements. Not me . . I have junk everywhere! A shelf in my house means a place to collect clutter.
And then, you accidentally wander into my dad's room!
You might think he has sunflower curtains. Nope, there are curtains in there but that's just some fabric that was leftover from years ago when I had made sunflower curtains in one of my houses and I think the extra fabric was in a garage sale box and dad has it pinned up over the curtains to make it darker in his room. Yep, that's my kind of decorating!
And, nowhere in his room is a quilt that I've made for him . . and there have been several! What am I going to do with him?


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Monday, March 05, 2012
Photoshop LineArt
Finally put together the tutorial I mentioned a few posts ago
Separating Line Art in Photoshop
- it's pretty old skool now, but it still works really well, and is still the best method I've seen ( other than obviously drawing straight in with yer wacom or cintique! ) But once u do this a couple of times it's easy and quick, so thanks to Pete Clark for teaching me this in College all those years ago!!
3 comments:
- embedded design said...
good pic...
embedded design
- 11:53 am
- Curious Junk said...
i don't know, that seems like a lot of work. isn't it just easier to put your line art on a layer and set it to 'multiply?' that works well for me, and it only involves 2 steps.
- 8:38 am
- Photoshop tutorials said...
Is that technique used in movies? Will you share its video
photoshop tutorials ?
- 11:37 am
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For nearly the past decade, I have been speaking, writing, and advising businesses about the lack of transparency, excessive and unnecessary fees, and conflicts of interest in the retirement plan industry. For a litany of reasons I have explained, the industry does not operate anywhere close to a free and competitive market, and as a result, retirement plan service providers have continually had participants and employers at their mercy. Finally, however, a new provider has emerged that empowers participants and employers and exposes the industry's inefficiencies.This new provider is called Guideline, and it has an interesting story. The founder Kevin Busque had previously co-founded TaskRabbit, an online and mobile marketplace that matches freelance labor with local demand, allowing consumers to find immediate help with everyday tasks, including cleaning, moving, delivery and handyman work. Having trouble finding a high quality affordable retirement plan for his employees, he did what any successful entrepreneur would do to solve a problem. He started his own company.Guideline provides seamless payroll integration, record keeping, 3(16) administration fiduciary, and 3(38) investment fiduciary services where they streamline the compliance and fund selection process to the point where employers don't even have to sign the 5500 form, send out participant notices, manage vesting, eligibility, or loan policies, or approve distributions and hardship withdrawals, and participants can simply choose a customized portfolio consisting of low cost index funds based on their answers to a few questions.Guideline also allows for companies to choose eligibility requirements, performs profit sharing calculations, provides vesting options, and includes robo-advising and participation and savings rate boosting communications. In addition, Guideline offers full phone, email, and chat support as well as educational drip campaigns to employees who have opted out to encourage participation and provides a full library of articles that cover any 401(k) based question an employee would have that can be accessed at success.guideline.com.Guideline uses Benefit Trust Company as a custodian who charges 0.03% of plan assets, which they do not pass on to participants. Their only ongoing fees are $39 per month plus $8 per month per employee or $99 per month plus $8 per month per employee for their Prime service offering which also includes new comparability profit sharing allowing employers to optimize contributions to certain groups, support for any payroll provider including those outside Guideline's integrated partners, a dedicated account manager, and priority support allowing employers to receive faster responses.The following additional non-recurring fees may be charged directly to the plan sponsor:
|Service Wind-Down Fee
|ACH Chargeback/Reversal Fee
|Extraordinary Services Fee
Participant Fees
Guideline does not charge participants a plan administration fee unless the participant ends employment. When participants utilize individual services, a transaction fee will apply, as detailed below.Individual Participant FeesThe following are services or transactions outside Guideline's general 401(k) plan administration which will be charged to the participant's account if the service is requested:
|Type of Service
|Distribution or Refund (including Hardship)
|Loan Application
|Loan Maintenance (annual)
|Qualified Domestic Relations Order (QDRO)
|Check/Stop Payment Fee
|Overnight Mail Fee (within U.S.)
Terminated Participant Maintenance FeeIf a participant ends employment, Guideline will charge the participant a monthly maintenance fee of $4, after a 90-day grace period.What makes Guideline especially interesting is that they currently require employers to pay both their administration and record keeping fees and the advisory fees if they choose to use an advisor. Since I have always advocated that employers pay the service provider provider fees, I have no issue with this requirement. In fact, I have often suggested that service provider fees would decrease drastically if there was ever a rule that required them to write a check instead of passing the fees on to participants. The reason is simple: Employers are more sensitive to fees if they have to write a check.Guideline's incredibly low fee structure and streamlined process not only enable small employers to afford a retirement plan both in terms of time and money, but they also help give participants a chance to avoid the exorbitant fees (often close to 2%) that they otherwise would have incurred. The Department of Labor provides an example to quantify the effect of just 1% in additional fees:"Assume that you are an employee with 35 years until retirement and a current 401(k) account balance of $25,000. If returns on investments in your account over the next 35 years average 7 percent and fees and expenses reduce your average returns by 0.5 percent, your account balance will grow to $227,000 at retirement, even if there are no further contributions to your account. If fees and expenses are 1.5 percent, however, your account balance will grow to only $163,000. The 1 percent difference in fees and expenses would reduce your account balance at retirement by 28 percent."To elaborate on just how low Guideline's fee structure is for small businesses, let's compare them to Employee Fiduciary, another low cost provider who charges $1,500 for annual record keeping and administration services (up to 30 participants and $30 for each additional participant) and 0.08% of plan assets for custodial services provided by MG Trust Company, not including a $1,000 set-up fee ($500 for start-up plans). According to Guideline co-founder Kevin Busque in this Fireside Chat on May 31, 2018, Guideline handles 4,000 plans (the website states they currently work with 7,750+), $500 million in assets, and $30 million in monthly payroll contributions and has an average plan size of about 12 participants (and an average plan asset size of $125,000) with plans ranging from 2 to 700 participants, so their average annual fee is about $1,152 ($96 * 12) not plus their monthly fee which ranges from $39 to $99.Employee Fiduciary, unlike Guideline, does not provide 3(16) administration or 3(38) investment fiduciary services, so the total service fees would be significantly more if employers were to add those services. Granted, unlike Employee Fiduciary, Guideline does not allow for self-directed brokerage accounts, but the segment that Guideline focuses on typically doesn't require this feature. Neither company offers defined benefit plans, which are also not typically relevant for small employers.Guideline's services also shine light on the futility of just about every retirement plan financial advisor by demonstrating how little value there is to selecting and monitoring a fund line-up, which is the primary reason why advisors get paid – and they almost always base their fee on the size of the account to ensure that it keeps increasing! Here is what Ted Benna, who is commonly referred to as the "father of the 401(k)" because he created and gained IRS approval of the first 401(k) savings plan, had to say about 401(k) plan financial advisors:"The advisors are getting paid each time they go through the process with an employer to help pick funds as if they're doing an original piece of work. There are more than half a million 401(k) plans, so that's happened over half a million times. The fund menus aren't that much different. But advisors are getting paid as if they're doing an original piece of work. That's just bizarre, extremely inefficient and much too expensive.First of all, they need to get away from asset-driven compensation and be paid a fee for service, the same as accountants or attorneys, who don't get paid a percentage of corporate [client] assets. Their role should shift to helping people focus on how to succeed at retiring successfully, not on investment return. Building a smarter investment mix is pretty much of a commodity now. The focus should be on goals: "I want to retire successful. Help me do that."So in effect, Guideline is forcing advisors to actually provide value. First, advisors have to come up with the kind of value proposition that Benna suggests because Guideline has taken over the process of selecting and monitoring plan assets, which, along with providing misleading benchmarking services, is the foundation for most advisors' value proposition. To illustrate, I recently had a conversation with an unusually proactive business owner who decided he no longer wanted to pay for the advisor's 0.35% fee because he didn't believe the advisor was providing any value. I say "unusually" because most employers don't even think about eliminating the advisory fee, but rather just accept it as part of the cost. The advisor responded by claiming that his fee was standard throughout the industry – and while he was right, even he couldn't come up with a single example of value to justify the fee. Unfortunately, few employers have the time, energy, knowledge, and motivation to not only question the advisory fees, but to actually fire the advisor. Now thanks to Guideline's approach, employers have no choice but to carefully evaluate the advisory fees in proportion to the value before engaging their services.Furthermore, even if employers investigate Guideline's services and decide that Guideline is not the right fit because of services not offered or because they don't want to cede control of the investment line-up to a 3(38) fiduciary, they will at least begin to understand the ease with which a service provider can develop and monitor a fund line-up at a tiny fraction of the cost of a typical financial advisor. For that reason, they might begin to question why they are continuing to unnecessarily use their participants' retirement savings to drastically overpay their advisor for a commoditized service, especially when the advisor is often taking a more limited level of fiduciary responsibility than a 3(38) fiduciary or none at all.In summary, Guideline is a great provider for any small business that has basic needs not including self-directed brokerage accounts or defined benefit plans, appreciates the value of technology, and truly cares about providing the best possible retirement plan benefits to its employees. It acts a disruptor because it shifts the paradigm through which we view retirement plans from an opaque model where retirement plan service providers claim to help participants save for retirement and somehow make money in ways they don't understand to a more honest, transparent, efficient, and cost-effective model that helps participants use technology to their advantage. It acts as an enabler because it frees participants from the shackles of an outdated retirement system that aims primarily to enrich providers at their expense.


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The ornament presents a Navy blue and red cross. The product was created by polish artists. Made with great precision and attention to the smallest details, e.g. small decorations. Each element has been hand-painted by artists. The item is packed in an original box that will protect against damage and also serves as a gift. The collection belongs to a limited, exclusive edition. Perfect for a gift.
Size ~ 17 cm
– bauble: glass
– box: decorative cardboard printed in the colors of Bombkarnia
Each of the elements was produced in Poland.


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Reach: Are you getting people's attention?
Table of Contents
- Average cost per impression
- Average organic search position
- Average number of URL errors (server errors, 404 errors, Not Found)
Online Ads
- % change in impressions
- Open rate
- Unsubscribe rate
- List growth rate
Word of Mouth (Social)
- Growth rate of inbound links
- % change in sentiment
- Growth rate of social mentions
- Social media fan/follower growth rate
- % engaged fans and followers
Acquisition: Are people interested in finding out more?
- Average cost per visit
- Landing page bounce rate
- % change in referrals
- Ratio of search queries to clicks
- Ratio of search impressions to clicks
Online Ads
- Click-through rate
- Click-through rate
Word of Mouth (Social)
- Ratio of inbound links to referrals
- Ratio of social mentions to referrals
Conversion: Do your interactions with people end in value for your organization? (transactions)
- Average cost per visitor
- Average cost per visit
- Average cost per conversion
- Average order value
- Average revenue per visitor
- % revenue from new and returning visitors
- Shopping cart start rate
- Shopping cart completion rate
- Download completion rate
- Form completion rate
Retention: Have you created ongoing loyalty?
- % of conversions from new and returning visitors
- Recently of visit
- Frequently of visit
- Recency of purchase
- Frequency of purchase
- Lifetime value of customer
Source from nonlinear digital


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When it comes to choosing favorites, I can't pick just one. So, I'm very excited to share fifteen things I'm loving this spring. Here's a recap of the things I've used up and frequently bought to keep myself looking and feeling beautiful. Check out the video to see more and take a peek at the list below for product details and links.
The contents of a woman's purse can reveal everything you need to know about her. If you take a peak inside my bag, you'll see that I like my things to be organized in cute, feminine packaging, that I carry enough pens and lipstick for myself and five other people, and that I pack like a Girl Scout on her way to a weekend-long trip. In other words, I like to be prepared for anything and look cute while I'm out and about. My love for handbags started quite early. Ever since I was a little girl, I've been obsessed handbags, especially satchels and totes, the bigger, the better. This past summer, I was on the lookout for a pastel purse that was medium in size yet spacious enough for all the junk I carry every day. I found my perfect match in a pale pink Adrienne Vittadini satchel (find it here). To learn more about my purse essentials, watch this video from my YouTube channel.


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How Game On Thrones Season 8 Can Grow Your Social Media Audience FAST
Game Of Thrones Season 8 Can GROW Your YouTube Channel FAST // Game Of Thrones returns and this viral trend could be a great way to use game of thrones final episodes to grow your channel. Wrap your niche around the hype, make related content and see how much of the viral traffic could splash your way. https://youtu.be/q2GlIDHPN3M Game Of…Read More


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The Dorea Institute is known of the Duphlessis orphans case. Some years ago, the church and the Canadian government set up a scheme, where children out of wedlock were taken from their mothers, declared mentally ill, and were sent to these orphanages which of many were named facilities for mentally ill. This way institutions like the Dorea Institute got higher federal subsidies for the orphans they had there. Reportedly the children were beaten, battered, tortured, experimented and murdered under false claims of helping their mentally ill state. The scheme was revealed and the orphans got some compensations for their suffering, which hardly made matters better.
Dimensions: 5184 x 3456 px
File size: 19,6 MB


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Inkbit's 3D printer uses a contactless material jetting process driven by vision-based feedback control and artificial intelligence.
Inkbit, a 3D printing start-up spun out of Massachusetts Institute of Technology's (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL), has announced the closing of a 12 million USD equity financing round led by Stratasys and DSM Venturing.
With additional participation form Ocado, 3M and Saint Gobain, the capital will be used to industrialise Inkbit's multi-material additive manufacturing system, expand its materials offering for medical, life sciences and robotics applications, and install first units. The company aims to release initial systems, which use a contactless material jetting process driven by vision-based feedback control and artificial intelligence, to select customers in 2021 and is already working with early access customers such as Johnson & Johnson to develop applications.
"We are excited to partner with such an extraordinary team of industry-leading players and impressed by their entrepreneurial spirit and commitment to innovation," said Davide Marini, Inkbit cofounder and CEO. "The composition of this syndicate was chosen to maximise the speed of development and commercialisation of our platform, with each investor bringing to us their unique expertise in equipment manufacturing, high-performance materials and applications in robotics, medical devices and life sciences tools. Our value proposition to customers is simple: we are adding a layer of machine vision and machine learning to material jetting, increasing its accuracy, reliability and enabling its use with production-grade materials."
The funding builds on Inkbit's first round of 2.8 million USD back in 2017 led by IMA with support from the Defense Advanced Research Projects Agency (DARPA), the National Science Foundation and MassVentures. This latest round will see Guy Menchik, VP of R&D at Stratasys, Luda Kopeikina, Director of DSM Venturing, Paul Clarke, Ocado's CTO and Magnus René, CEO at Ovzon and formerly CEO at Arcam, join Inkbit's Board of Directors.
Ronen Lebi, Vice President of Corporate Development at Stratasys, said: "As pioneers of jetting-based additive manufacturing solutions, we are excited to help Inkbit bring their technology to the factory floor. Vision-based feedback control and artificial intelligence will take additive manufacturing to a whole new level and will help to enable its widespread use for production."
The news marks the third investment in the same number of weeks for DSM Venturing, which also recently led funding rounds in Additive Manufacturing Technologies and Voxel8.
Pieter Wolters, Managing Director of DSM Venturing, commented: "Materials always play a major role in industrialising breakthrough technologies and in additive manufacturing they become absolutely critical. We are delighted to have Inkbit in our investment portfolio and look forward to helping them develop the best materials for customers world-wide."