Reliable predictions of immunogenic peptides are essential in rational vaccine design and can minimize the experimental effort needed to identify epitopes. In this work, we describe a pan-specific major histocompatibility complex (MHC) class I epitope predictor, NetCTLpan. The method integrates predictions of proteasomal cleavage, transporter associated with antigen processing (TAP) transport efficiency, and MHC class I binding affinity into a MHC class I pathway likelihood score and is an improved and extended version of NetCTL. The NetCTLpan method performs predictions for all MHC class I molecules with known protein sequence and allows predictions for 8-, 9-, 10-, and 11-mer peptides. In order to meet the need for a low false positive rate, the method is optimized to achieve high specificity. The method was trained and validated on large datasets of experimentally identified MHC class I ligands and cytotoxic T lymphocyte (CTL) epitopes. It has been reported that MHC molecules are differentially dependent on TAP transport and proteasomal cleavage. Here, we did not find any consistent signs of such MHC dependencies, and the NetCTLpan method is implemented with fixed weights for proteasomal cleavage and TAP transport for all MHC molecules. The predictive performance of the NetCTLpan method was shown to outperform other state-of-the-art CTL epitope prediction methods. Our results further confirm the importance of using full-type human leukocyte antigen restriction information when identifying MHC class I epitopes. Using the NetCTLpan method, the experimental effort to identify 90% of new epitopes can be reduced by 15% and 40%, respectively, when compared to the NetMHCpan and NetCTL methods. The method and benchmark datasets are available at http://www.cbs.dtu.dk/services/NetCTLpan/.
The online version of this article (doi: 10.1007/s00251-010-0441-4) contains supplementary material, which is available to authorized users.
Cytotoxic T lymphocytes (CTLs) are a subgroup of T cells able to induce cell death of other cells. CTLs kill only infected or otherwise damaged cells. In order to discriminate between infected and healthy cells, all nucleated cells present host cell peptide fragments on the cell surface in complex with major histocompatibility complex class I molecules (MHC class I). Not all possible peptides originating from cell proteins will be presented by MHC class I. In fact, it is estimated that only one out of 2,000 potential peptides will be immunodominant (Yewdell and Bennink 1999). One of the first steps involved in MHC class I antigen presentation is the degradation of intracellular proteins, including proteins from the cytoplasm and nucleus, by the proteasome (Larsen et al. 2007; Paz et al. 1999; Craiu et al. 1997; Altuvia and Margalit 2000; Mo et al. 1999; Stoltze et al. 1998; Juncker et al. 2009). These peptides may be trimmed at the N-terminal end by cytosolic exopeptidases (Lévy et al. 2002). A subset of the peptides is transported by transporter associated with antigen processing (TAP) complex into the endoplasmatic reticulum (ER), where further N-terminal trimming occurs (Ritz and Seliger 2001; Koch et al. 2004; van Endert et al. 1994; Schatz et al. 2008). Inside the ER, a peptide may bind to an MHC class I molecule and the peptide–MHC complex will be transported to the cell surface, where it subsequently may be recognized by CTLs. These successive steps from protein to ligand presented on the cell surface are limiting the number of possible epitopes. The most restricting step in antigen presentation is peptide binding to MHC class I molecule (Yewdell and Bennink 1999).
Reliable predictions of immunogenic peptides can minimize the experimental effort needed to identify epitopes. We have previously described a method, NetCTL (Larsen et al. 2007, 2005), integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions to an overall prediction of CTL epitopes. The NetCTL method has proven successful in identification of CTL epitopes from, for instance influenza (Wang et al. 2007), HIV (Pérez et al. 2008), and Orthopoxvirus (Tang et al. 2008). Several other groups have developed methods for CTL epitope identification by integrating steps of the MHC class I pathway (MAPPP, Hakenberg et al. 2003; WAPP, Dönnes and Kohlbacher 2005); EpiJen, Doytchinova et al. 2006; MHC-pathway, Tenzer et al. 2005). All these methods are limited by the fact that they only allow for prediction of peptide binding to a highly limited set of different MHC molecules. In a large-scale benchmark evaluation of publicly available server of MHC class I pathway presentation prediction, Larsen et al. (2007) showed that the NetCTL method significantly outperformed all these methods, closely followed by MHC-pathway. The MHC-pathway method has recently been updated to include more accurate predictions of MHC binding and a broader allelic coverage (close to 60 human leukocyte antigen (HLA)-A and HLA-B alleles are covered by the default MHC-pathway method in the 2009-09-01 release). In contrast to this, the NetCTL method has not been updated since 2007, and the MHC binding prediction remains limited to the 12 common HLA supertypes (Lund et al. 2004). In the following, we describe an improved and extended version of NetCTL, called NetCTLpan, which is able to make predictions for all MHC class I molecules with known protein sequence. In addition, NetCTLpan can identify 8-, 9-, 10-, and 11-mer epitopes, as opposed to NetCTL, which only allowed for prediction of 9-mer epitopes. The method has been trained on a large data set of experimentally identified MHC ligands from the SYPFEITHI database (Rammensee et al. 1999).
Choosing a performance measure for evaluating a prediction method is a nontrivial task, and the definition of performance measure will often influence the benchmark outcome and subsequent choice of best method. A commonly used measure for predictive performance is the area under the receiver operating characteristic (ROC) curve, the AUC value. This measure integrates the sensitivity curve as a function of specificity for the range of sensitivity from one to zero. This measure might not be optimal if a prediction method is required to have a very high specificity in order to lower the false positive rate for subsequent experimental validation. In such situations, it could be beneficial to use only the high specificity part of the ROC curve to calculate the predictive performance. To match such requirements for a low false positive rate, we have therefore in this work focused on optimizing the method to achieve high specificity at a potential loss in sensitivity.
The predictive performance of the NetCTLpan method is validated on large and MHC diverse data sets derived from the SYFPEITHI (Rammensee et al. 1999) and Los Alamos HIV databases (http://www.hiv.lanl.gov/), and its performance has been compared to other state-of-the-art CTL epitope prediction methods.
It has been suggested that supertype-specific differences exist in how dependent MHC class I presentation of peptides is on transport via TAP molecules (Brusic et al. 1999; Anderson et al. 1993; Henderson et al. 1992; Smith and Lutz 1996) and proteasomal cleavage (Wherry et al. 2006). Likewise, it has been suggested that the rescaling procedure commonly used to correct for possible discrepancies between the allelic predictors (Sturniolo et al. 1999; Larsen et al. 2005, 2007) could mask genuine biological difference between MHC molecules and potentially lower the epitope predictive performance (MacNamara et al. 2009). In the context of the NetCTLpan method, we investigate to what extend such differences are observed in large data sets that are diverse with regard to both MHC restriction and CTL epitopes.
The SYFPEITHI database (Rammensee et al. 1999) was used as the source of MHC class I ligands. MHC class I binding peptides classified as ligands were downloaded in August 2009. Altogether, the database contained 2,966 HLA class I ligand pairs. Considering only ligands with length of 8 to 11 amino acids (the lengths for which the MHC class I binding predictor NetMHCpan can perform predictions), the data set consists of 2,752 unique HLA class I ligand pairs. Data used for training the individual MHC class I pathway predictors—MHC binding (Nielsen et al. 2007; Hoof et al. 2009), proteasomal cleavage (Nielsen et al. 2005), and TAP transport efficiency (Peters et al. 2003)—was removed from the data set, downsizing it to 2,309 unique HLA class I ligand pairs.
Peptides in the data set with only serotypic HLA assignment were assigned to the most common HLA allele in the European population for this serotype (e.g., the serotype HLA-A*01 was assigned to the specific allele HLA-A*0101). The HLA allele frequencies were obtained from the dbMHC database (http://www.ncbi.nlm.nih.gv/mhc/). Subsequently, for every peptide, the source protein was found in the UniProtKB/Swiss-Prot database (Uniprot Consortium 2009). If more than one matching protein was a possible source for a peptide, the protein was selected with preference for human and long protein sequences. Peptides without corresponding source protein in UniProtKB/Swiss-Prot were searched against NCBI NR protein database (http://www.ncbi.nlm.nih.gov). These steps consequently resulted in the SYF data set consisting of 2,267 HLA class I ligand pairs with corresponding source proteins, where 226 ligands are 8-mers, 1,443 are 9-mers, 430 are 10-mers, and 168 ligands belong to the group of 11-mers. Note, that HLA-C ligands are included in these numbers. In the evaluation, HLA-C ligands are merged to a separate test set.
The same HIV data set has been used as for the paper describing the original NetCTL method (Larsen et al. 2007). For comparison reasons, the data set has not been updated. The data set is derived from the Los Alamos HIV database (http://www.hiv.lanl.gov/). It consists of 216 HLA class I ligand pairs with corresponding source proteins covering the 12 supertypes (Lund et al. 2004).
Each of the HLA alleles in the SYF data set was assigned a supertype association using the distance measure described by Nielsen et al. (2007). In short, an HLA allele was associated to the most similar supertype defined in terms of the correlation coefficient between NetMHCpan prediction scores for 1,000,000 random natural 9-mer peptides for the HLA allele in question and any of the 12 supertype representatives (Larsen et al. 2005). In a few cases (less than ten), the supertype association was ambiguous. In these cases, the association was assigned by applying the classification from the work by Sidney et al. (2008). The associated supertypes for each HLA class I allele are shown in Supplementary Table S1. Some supertypes in the 9-mer SYF data set contain more HLA class I ligand pairs than others. Only four out of the 12 supertypes had more than 100 HLA class I ligand pairs assigned. In order to minimize bias toward only a few supertypes, a training data set with maximum 50 randomly selected ligands per supertype was generated. For seven supertypes, it was possible to select 50 ligands for the training set, while the selection for the five remaining supertypes consisted of between 19 and 47 ligands. This results in a training set of 504 HLA class I ligand pairs. Remaining HLA-A and HLA-B ligands not included in the training data were assigned to a separate set used for evaluation. This evaluation set covers seven supertypes and consists of 889 9-mers. All HLA-A and HLA-B 8-, 10-, and 11-mer ligands were merged into another evaluation set, resulting in a total of 806 ligands. The HIV data set was used as a third independent evaluation set. The numbers of ligands per supertypes for the training and test sets are listed in Table 1. Finally, a set of 65 HLA-C ligands from the SYFPEITHI database of length 8–11 amino acids was used as a fourth evaluation set.
The current version of the pan-specific MHC class I binding prediction method, NetMHCpan-2.2 (Hoof et al. 2009), is an updated version of the original NetMHCpan method (Nielsen et al. 2007). It has been evaluated as the best pan-specific method in large benchmark study (Zhang et al. 2009 and is now including the extension to perform predictions for 8-, 10-, and 11-mer peptides (Lundegaard et al. 2008). NetMHCpan-2.2 was trained on a data set of 102,146 quantitative peptide–MHC affinity data points covering more than 100 distinct MHC molecules. The prediction server is available at http://www.cbs.dtu.dk/services/NetMHCpan-2.2/.
The prediction of TAP transport efficiency is based on the matrix method described in Peters et al. (2003). The method predicts TAP transport efficiency of peptides by a scoring method using only the C terminus and the tree N-terminal residues of a peptide. The contribution to the prediction score of the N-terminal residues is down-weighted by a factor of 0.2 in comparison with the score of the C terminus. In the original publication, the TAP transport efficiency score was computed as the average of the values for the 9-mer and its 10-meric precursor. Here, we extend this approach and predict the TAP transport efficiency score for peptides of length from 8 to 11 amino acids, as the average of the values for the original peptide and its precursor extended by one amino acid N-terminally. The matrix published in Peters et al. (2003) was modified as all values in the TAP scoring matrix were multiplied by a factor of −1, in order to have a high predicted value corresponding to high transport efficiency. This way the interpretation is consistent with the prediction of proteasomal cleavage and MHC class I binding affinity.
NetChop C-term 3.0 (Nielsen et al. 2005) was used for predicting cleavage sites. As in the original NetCTL publication, only the C-terminal cleavage score of a peptide was included.
The NetCTLpan prediction value is defined as a weighted sum of the three individual prediction values for MHC class I affinity, TAP transport efficiency, and C-terminal proteasomal cleavage. Optimal relative weights on TAP transport efficiency and proteasomal cleavage were estimated using the training data set and based on the average AUC value per HLA class I ligand pair.
The AUC measure is a commonly used measure for quantitative tests and model comparison. AUC is the area under the ROC curve, summarizing the sensitivity as a function of 1—the specificity. The specificity is given as 1—the false positive ratio defined as the fraction of the number of correctly predicted nonligands relative to the total number of nonligands in the dataset (Lund et al. 2005). A specificity of 100% is interpreted as all nonligands are actually classified as nonligands. The sensitivity is the true positive rate (TPR) and is defined as the number of correctly predicted ligands relative to the total number of ligands in the dataset. The higher the TPR, the more actual positives are recognized. The AUC measure might not be optimal if a prediction method is required to have very high specificity in order to lower the false positive rate in subsequent experimental validations. In such situations, it is beneficial to use only the high specificity part of the ROC curve to calculate the predictive performance. Therefore, a search optimizing the AUC value integrated for specificities from 1 to x (AUCx), where x [0:1] was performed to optimize the method to achieve high specificity. High values of x will focus the method toward high specificity at a potential loss in sensitivity, whereas low values of x will result in equal focus on sensitivity and specificity.
When calculating the AUC value, the source protein was divided into overlapping peptides of the size of the given ligand. All peptides, except those annotated as ligands in either the complete SYFPEITHI or Los Alamos HIV databases, were taken as negative peptides (nonligands) and the given ligand was taken as positive. A perfect AUC value of 1.0 corresponds to the ligand having the highest combined score (NetCTLpan score) compared to all other possible peptides originating from the source protein.
Another important issue to resolve is how to calculate AUC values. Should it have been done per protein, where an AUC value is calculated for each ligand–HLA–protein triplet and the performance reported as the average AUC value over all triplets or should it have been made in a pooled way, where all peptide data for the different source proteins and HLA alleles are merged together before calculating the AUC value? Here, we suggest using the per-protein measure, since pooling data from different proteins and HLA alleles will place ligands in a nonbiological competition for presentation. The source proteins in the SYF ligand data sets have a length distribution varying from 36 to more than 8,000 amino acids. Applying the NetCTLpan method to our training set (most homogenous data set) shows a tendency for shorter proteins having a lower AUC0.1 than longer proteins. Proteins from our training set with length of 0–200 have a mean AUC0.1 of 0.817, whereas proteins longer than 200 AA have a mean AUC0.1 of 0.876. The Spearman's rank correlation between the protein length and AUC0.1 values for the training data set is 0.15. This value is significantly different from random (p < 0.001, exact permutation test). In a pooled evaluation, where source protein data are merged, the predictive performance would predominantly reflect the performance for the longer protein. Further, not all proteins are expressed in equal amounts within the cell and the presentation of peptides in complex with HLA molecules happens in competition with the four most different HLA-A and HLA-B molecules within a given host and not 46, as it would be the case, when all the HLA alleles from the SYF training data set are pooled. Finally, it is becoming apparent that not all MHC molecules present peptides at the same binding threshold (Rao et al. 2009). This observation would make an evaluation, where data for different HLA alleles is pooled, highly problematic, as illustrated in Fig. 1. Here, a ROC curve is shown for a pooled set of 29 HLA-A*0101, 50 HLA-B*4402, and 31 HLA-B*5101 ligands using the NetCTLpan method. In addition, the allele-specific sensitivity (fraction of ligands identified) for each allele is shown as a function of the pooled specificity. The figure clearly demonstrates that different alleles dominate the ROC curve in different specificity ranges. At a specificity of 0.0025, for instance, 60% (66) of the 110 ligands are identified. Of these are 25 (86% of 29) HLA-A*0101, 32 (62% of 50) are HLA-B*4401, and only nine (29% of 31) are HLA-B*5101 restricted. At very high specificities, the ROC curve is thus predominantly shaped by the HLA-A*0101 data, at intermediate specificities values the curve is shaped by the HLA-B*4402 data, and finally at low specificity values, the HLA-B*5101 data defines the curve. This is clearly not an optimal way of evaluating an overall predictive performance of a prediction method that is aimed at achieving uniform prediction accuracy across a broad range of HLA alleles. To conclude, we find that the proposed triplet evaluation per ligand–HLA–protein evaluation constitutes the least biased approach to evaluate a prediction method with broad allelic coverage.
The optimal weights on proteasomal cleavage and TAP transport efficiency were calculated for AUC fractions (AUCx) varying x from 0.05 to 1, with a step size of 0.05. With x equal to 1, this corresponds to the conventional AUC value calculation and the way of selecting optimal weights for the original NetCTL method. The result of this analysis is shown in Fig. 2. For an AUC fraction of 1, the optimal weights were zero on both proteasomal cleavage and TAP transport. This implies that NetMHCpan 2.2, the method used for predicting MHC class I binding affinity, has a very high performance and that adding predictions for proteasomal cleavage or TAP transport decreased the overall performance. Figure 2 illustrates that the more the method is focused on high specificity (low values of x), the higher the weights and thus importance of proteasomal cleavage and TAP transport predictions become. This is, however, achieved at a loss in sensitivity at low specificity values. Based on this observation, the best performing weights on proteasomal cleavage and TAP transport were selected using an AUC fraction of 0.1 as benchmark measure and were found to be 0.225 for cleavage and 0.025 for TAP. This selection of weights defines the NetCTLpan method. When interpreting the weights for cleavage and TAP, keep in mind that the contribution of the different prediction methods is not directly reflecting their relative biological contribution in the pathway.
A comparison of the ROC curves for NetMHCpan and our described method NetCTLpan is shown in Fig. 3. The overall AUC value for the NetMHCpan method is 0.980 and the corresponding AUC0.1 value is 0.852. For the NetCTLpan method, the overall AUC value is 0.976 and the corresponding AUC0.1 value is 0.869. These numbers and the graphs in Fig. 3 illustrate the improved specificity of the NetCTLpan method compared to NetMHCpan. Up to a specificity of 0.85, the ROC curve for NetCTLpan has a higher sensitivity than NetMHCpan, indicating that this method will identify more true ligands at a given specificity threshold. On the other hand, below a specificity of 0.85, the two ROC curves cross and the NetMHCpan method achieves the highest sensitivity. This crossover, however, happens at a very low specificity corresponding to a false positive rate of 0.15 (15% of the negative peptides are falsely classified as positive) and is of limited use when doing actual epitope discovery work, underlining the importance of optimizing the methods on high specificity.
Table 2 displays the comparison between NetCTLpan and NetMHCpan for the different data sets using both the overall AUC and AUC0.1 benchmark measures. Using the AUC0.1 measure, the NetCTLpan method has a significantly higher performance compared to NetMHCpan for all data sets. On the other hand, when comparing the overall AUC value, the two methods show comparable performance. Here, for the SYF data set, the NetMHCpan method has the highest performance, while for the HIV data set and the HLA-C test set, NetCTLpan performs best. So, if high sensitivity is essential (even at a cost in specificity), the NetMHCpan method should be preferred. In more common situations, where specificity is the more important issue, NetCTLpan should be the choice.
Results displayed in Table 2 are mean AUC and AUC0.1 values over all ligand–HLA–protein triplets in each data set. Paired tests were used for comparing performance between different prediction methods. In Supplementary Table S2 are given the AUC and AUC0.1 values for each ligand–HLA–protein triple in the SYFPEITHI data sets. From this table, it is clear that the predictive performance does not only vary between supertypes, but also within supertypes. For the training data set, the difference between HLA-B*5101 and HLA-B*0702 (both B7 supertype alleles) for the NetCTLpan method is thus 0.374 in terms of the AUC0.1 measure. These performance variations demonstrate the need for large-scale HLA diverse benchmark data set to evaluate differences in performance between prediction methods, as the performance difference between similar (supertype-wise) alleles often is as high as the difference for individual alleles between two prediction methods within a given data set.
Several ligands appear in the SYFPEITHI ligand data sets as duplicates restricted to multiple HLA class I alleles. One might be worried that the potential peptide similarity/redundancy could influence the performance estimates of the NetCTLpan method. The training data set, for instance, consists of 504 HLA ligand pairs, but only 492 of these are unique peptides. The 9-mer test set consists of 889 9mer HLA ligand pairs, of which 802 are unique peptides. The training and 9-mer test sets share 42 identical ligands and three ligands with one mismatch, all coupled to different alleles. The training set contains four ligands identical with one mismatch. To investigate the impact on this data redundancy within the training data set and between the training and test data sets, we calculated the performance on redundancy-reduced data sets. The performance on the training set was calculated by removing duplicates and ligands with one mismatch and for the test set by excluding duplicates and ligands with one mismatch to ligands in the training data. Predictive performance was shown to be close to identical for both training and test set, suggesting that peptide redundancy plays a negligible role in our performance evaluation (see data in Supplementary Table S3).
In contrast to the NetCTL method, the NetCTLpan method does not use rescaling of predicted MHC class I affinities. Previously, rescaling has been used to make prediction values comparable between MHC class I molecules. It has been suggested that such a rescaling might remove genuine biological differences between MHC molecules and potentially lowers the epitopes predictive performance (MacNamara et al. 2009). To investigate, if the predictive performance of the NetCTLpan method is influenced when including rescaling, we defined a rescaling factor for each MHC allele and used that factor to rescale all MHC binding affinity values before integrating with proteasomal cleavage and TAP scores. For each allele, the rescaling factor was determined as the 1 percentile score of the NetMHCpan method for a set of 1,000,000 random natural 9-mer peptides. An overall performance gain using rescaling as compared to not applying rescaling was observed if focusing on the overall AUC value (no rescaling AUC 0.976 versus rescaling AUC 0.978, p value 0.006, paired t test). For high specificity predictions (AUC0.1), however, the method without rescaling performed similar (AUC0.1 0.869) to the method using rescaling (AUC0.1 0.868) with a p value of 0.835. From these results, and to maintain potential biological differences in specificity between MHC molecules, we chose not to include rescaling in the NetCTLpan method. One might argue that rescaling versus nonrescaling cannot influence the performance of the NetCTLpan method, when the performance is calculated per ligand–HLA allele, as it is the case in this study. When focusing on MHC binding predictions alone, this is true and both methods give identical results. However, when integrated with proteasomal cleavage and TAP transport efficiency, this situation changes. Rescaling places all MHC binding predictions on a similar scale and hence also places the relative weights on TAP and proteasomal cleavage on a similar scale across the set of MHC alleles. This is no longer the case if rescaling is left out. Here, alleles with low (predicted) binding affinity preference will have higher relative weights on TAP and proteasomal cleavage as compared to alleles with high binding affinity preference.
As mentioned earlier, previous work has suggested that different MHC molecules have different dependencies on TAP transport efficiency and proteasomal cleavage. Based on these observations, it seems natural to find allele-specific weights for TAP transport and proteasomal cleavage. Due to the small size of the training data set, we limited ourselves to a search for supertype-specific weights. For each supertype, we estimated the weights on proteasomal cleavage and TAP transport that give optimal average AUC0.1 values. Optimal weights per supertype and performance values for the different data sets can be seen in Table 3. It shows that relative large differences exist between the optimal weights across the different supertypes. Naturally, the average AUC0.1 for the training set is higher with supertype-specific weights as compared to the fixed weights (estimate for the complete training data set). Applying these weights resulted in an inconsistent pattern in performance gain across the different supertypes for the different test sets when compared to fixed weights. Only three supertypes (A24, B8, and B58) showed a consistent performance gain for the SYFPHITHI and HIV test sets using supertype-specific weights. This result strongly indicates that optimal weights per supertype are not reflecting biological differences but occur most likely due to overfitting. Note that we are not stating that proteasomal cleavage and TAP transport dependency could not vary between MHC molecules; we only state that based on our data, we cannot consistently reproduce such a differentiated dependency.
The comparison of the performance between NetCTLpan and NetCTL is based on the 9-mer data sets, since NetCTL is only capable of predicting 9-meric epitopes. Table 4 shows the performance for NetCTLpan and NetCTL on the different data sets. For both SYF data sets, the NetCTLpan method significantly outperforms NetCTL. The HIV test set does not show NetCTLpan being significantly better than NetCTL. The HIV test set is supertype based, and the HLA restriction for each HIV epitope is assigned to the corresponding HLA supertype. This is in contrast to the SYF ligand data sets, where full typing HLA restriction is available for most ligands. One hundred nineteen out of 216 HIV peptide supertype pairs are, however, annotated in the Los Alamos HIV database with full typing for the HLA restriction. Using this additional information about the HLA restriction improves the mean AUC0.1 from 0.612 to 0.745 and the overall AUC from 0.933 to 0.959. Both measurements thus testify NetCTLpan as having a significantly better performance (both p values <0.001, paired t test) compared to NetCTL. These results clearly confirm earlier findings (Pérez et al. 2008; Hoof et al. 2009) of the importance of going beyond HLA supertypes and the use of full-type HLA restriction information when identifying MHC class I epitopes.
To determine the source of the strong gain in predictive performance between the NetCTL and NetCTLpan methods, we compared the predictive performance of the NetCTLpan method to that of NetCTL using the supertype representative for each HLA allele also for the NetCTLpan method. This analysis clearly shows (see Table 5) that the shift from supertype to allele-specific predictions is the main driving force behind the gain in predictive performance between NetCTL and NetCTLpan. In all benchmarks has the NetCTLpan_ST (supertype-specific NetCTLpan method) a similar predictive performance to that of NetCTL.
Next, we compared the performance of the NetCTLpan method to the MHC-pathway method (Tenzer et al. 2005). This method has earlier been shown to be a state-of-the-art MHC class I pathway predictor (Larsen et al. 2005). Like the NetCTLpan method, this method integrates predictions of MHC binding, C-terminal proteasomal cleavage, and TAP transport into a combined pathway presentation score. Here, we use the method with default parameters via the link http://tools.immuneepitope.org/analyze/html/mhc_processing.html. The MHC-pathway method is not pan-specific and hence does not allow predictions for all HLA class I alleles used in our benchmark data. Further, it does not allow for predictions of 8- and 11-mer epitopes and only allows 10-mer epitope predictions for a subset of the included alleles. To allow for a fair comparison, we therefore only included ligands from the SYF data set restricted to HLA alleles covered by the MHC-pathway method. The results of the benchmark calculation are shown in Table 6 and clearly show that NetCTLpan outperforms the MHC-pathway method for all three data sets. The improved performance is maintained for both the AUC and AUC0.1 measure. Further, the table shows that the MHC binding predictors for the two methods have close to identical performance (NetMHCpan versus MHC). The cleavage method employed by the NetCTLpan method is performing consistently better than the immunoproteasome prediction method used by MHC-pathway (NetChop versus Immu). The TAP prediction method is identical between the two methods. These results suggest that the integration method employed by MHC-pathway is not optimal either due to the relative low performance of the immunoproteasome predictor or as a consequence of how the three prediction scores have been integrated in the MHC-pathway method.
Earlier work has demonstrated the benefit of integrating proteasomal cleavage, TAP transport efficiency, and MHC binding predictions when using reverse immunology to identify potential CTL epitopes. However, to the best of our knowledge, none of the publicly available methods providing this integration are pan-specific and hence do not allow for prediction of CTL epitopes restricted to any MHC allele.
Here, we have developed a pan-specific MHC class I epitope predictor, NetCTLpan. The method integrates prediction of proteasomal cleavage, TAP transport efficiency, and MHC binding into a MHC class I pathway presentation likelihood score. In large-scale benchmarks comprising more than 1,000 MHC class I ligands and CTL epitopes restricted by close to 60 different HLA alleles, the method was shown to outperform both the original NetCTL method, as well as MHC-pathway, another state-of-the-art class I presentation pathway prediction method.
NetCTLpan was optimized to achieve high specificity in order to meet the need for a low false positive rate when using the method for large-scale epitope discovery. If focusing on optimal sensitivity, it was shown that the optimal prediction method should exclude both cleavage and TAP predictions reducing the method to MHC binding prediction alone. This is in contrast to earlier work, where proteasomal cleavage and TAP transport efficiency consistently have been reported to improve the predictive performance. Whether this observation reflects true biological aspects of the specificity overlap between the three pathway players (see for instance Nielsen et al. 2005) or it simply occurs because the prediction of MHC class I affinity has gained accuracy during the recent years, whereas predictors for TAP transport efficiency and proteasomal cleavage have not changed or been updated, remains to be seen.
Recent publications have suggested that some MHC molecules are, compared to others, more or less dependent on TAP transport and proteasomal cleavage. Using the NetCTLpan method in large-scale benchmarks, we however find no consistent signal of such an HLA allele differentiated dependency of proteasomal cleavage and TAP transport efficiency. A performance gain using supertype-specific weights could only be observed for the training set. Applying these weights to the test sets resulted in an inconsistent pattern in performance gain for the different supertypes when compared to fixed weights, indicating that optimal weights per supertype are not reflecting biological differences but most likely are a result of overfitting.
NetCTL, the ancestor of NetCTLpan, uses a rescaling of MHC binding affinity values to make prediction values comparable between MHC class I molecules. It has been suggested that such a rescaling might remove genuine biological differences between MHC molecules and potentially lower the method's predictive performance. Here, we show that rescaling has no significant impact on the overall predictive performance of the NetCTLpan method. Further, we observed a tendency of different MHC molecules presenting ligands at different (predicted) binding thresholds. Based on these observations, the NetCTLpan method is implemented without use of rescaling, thus maintaining potential genuine biological differences between MHC molecules. To allow comparison between presentation likelihood scores for different MHC molecules, we include a rank-score for each prediction. The rank-score is calculated as the percent rank of a given NetCTLpan likelihood score to a set of 200,000 random natural 9-mer peptides.
Our results on the HIV benchmark data set confirm the importance of going beyond HLA supertypes and use full-type HLA restriction information when identifying MHC class I epitopes. In this benchmark, we found a significantly improved predictive performance, if full HLA restriction were used, in comparison to the HLA supertype information proposed in the original NetCTL publication.
In contrast to earlier published methods for MHC class I pathway prediction, NetCTLpan allows for predictions of 8- to 11-mer CTL epitopes being presented by any MHC class I molecule of known protein sequence.
NetCTLpan, the method described in this work, has shown to perform best when focusing on high specificity predictions for CTL epitope identification. In order to easily grasp the predictive performance gain, we applied the rank measure as defined by Larsen et al. (2005). The rank measure reports the average fraction of epitopes identified as a function of the percentage rank (percentage of tested peptides) for a set of proteins. This measure indicates how large a fraction of the peptides for a given protein needs to be tested in order to identify the epitope with a given likelihood. To identify new epitopes with 90% likelihood by use of NetCTLpan, the rank measure reports that 3.7% of the peptides need to be experimentally verified. For a hypothetical protein of 300 peptides, this means that on average, 11 peptides need to be tested in order to identify the epitope. The corresponding numbers for NetMHCpan and NetCTL are 13 and 17 peptides. Hence, by applying the NetCTLpan method instead of NetMHCpan, the experimental effort can be reduced by 17%, and compared to NetCTL, approximately 40% fewer epitopes need to be tested. Based on this, it is clear that utilizing the NetCTLpan method can minimize experimental effort needed to identify new CTL epitopes. We believe that this improved performance, combined with the methods ability to provide predictions of potential CTL epitopes of length from 8 to 11 amino acids to any MHC class I molecules of known sequence, will be useful in both rational reverse immunogenetic epitope discovery and interpretation of observed immune responses in HLA diverse patient cohorts. The NetCTLpan method and benchmark data set are available at: http://www.cbs.dtu.dk/services/NetCTLpan.
This work is supported by a grant from the Danish Research Council for Technology and Production Sciences (project title "Disease Gene Finding, Somatic Mutations, and Vaccine Design," principal funding recipient is Søren Brunak) and by NIH (National Institute of Health) grants (contract no. HHSN266200400083C, principal funding recipient is Ole Lund; contract no. HHSN266200400025C, principal funding recipient is Søren Buus; contract no. HHSN266200400006C, principal funding recipient is Alessandro Sette).


"Jason Lilley of Jason Bakery hands-down has the best breads and pastries. He is well known for his sourdough and croissants, they're some of the best in the business! He also makes a doughnut-croissant hybrid aptly named the Doughssant, which has a different filling and topping each week. These are the bomb! In my humble opinion, it surpasses London and New York City's most acclaimed bakeries. Nothing is frozen, everything is fresh, and once it's out — that's it.


The summer season of Drum Corps International's SoundSport® division took off during the DCI Central Florida event on Saturday, hosted at Southeastern University in Lakeland.
Two unique shows performed by Florida-based IMPACT Drum and Bugle Corps (Orlando) and Florida Xpress (Hawthorne), were inspired by SoundSport members' personal input.
As IMPACT's first-ever competitive SoundSport event, members aimed to please the crowd with popular, feel-good music. The performance earned the group a silver rating and "Best in Show" honors.
The team's performance featured several small groups and soloists, including a tuba solo which was originally written for a baritone saxophone.
A Florida Xpress percussionist lays down a bucket drum beat.
Florida Xpress earned a bronze rating with its performance that featured a bucket drum solo. It's an idea that came from a member after a trip to a Firehouse Subs restaurant.
The team is continuing its creative theme by building a vibraphone out of pots and pans. Morin said that members go to hardware and Goodwill stores in search of cookware with the perfect pitch.
Upcoming SoundSport events will take place in Whitewater, Wisconsin (July 16), San Antonio (July 23), Atlanta (July 30) and at the DCI World Championship in Indianapolis on August 13 during the all-new SoundSport International Music and Food Festival.


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Our family-owned and operated business has been serving the greater Dallas - Fort Worth area for over 25 years.
Brick Repair in Baytown, TX 77520
An Overview of Chimney Brick Mortar Repair in Baytown, TX
When Harris County homeowners are in need of reliable, efficient, and affordable brick mortar repair services for their chimneys, Masters Services is the first company they call. With more than 18 years of experience and a proven track record of success, our fully licensed and insured, locally owned and operated Baytown, TX brick repair company specializes in all aspects of masonry repair. Whether the mortar joints in your chimney have just started cracking or they're crumbling and falling out, if you're looking for chimney brick mortar repair services, for the best results possible, contact Masters Services.
What You Need to Know About Chimney Brick Mortar Repair in Baytown, TX
When the weather outside is frightful, gathering around the fireplace is so delightful. A fireplace is a source of reliable warmth, creates a welcoming ambiance, and adds to the charm and aesthetic appeal of your Harris County home; however, if it isn't properly maintained, instead of being an asset, a fireplace can become a serious liability.
A chimney is the most important component of a fireplace, as it exhausts the smoke, excess heat, and other byproducts of fire from your home. Needless to say, in order to ensure that it performs these vital functions, it has to be in tip-top condition, and that means it needs to be structurally sound. The mortar plays a vital role in the structural integrity of your chimney; however, at one point or another, it may need to be repaired.
What is mortar? Why is it important? Does mortar sustain damage? What are the signs of mortar damage? Why are professional brick mortar repair services important? For the answers to these questions and to find out when you should contact a Baytown, TX brick repair specialist, keep on reading.
What is Mortar?
Mortar is a bonding agent that binds masonry materials, such as brick, concrete, and stone together. It's kind of like glue. Typically, it's composed of cement, fine sand, limestone, and water, though it may also include other materials, such as polymer additives, and latex. The materials are mixed together, creating a malleable paste, which is applied between masonry materials, and when it dries, it becomes rigid cement. In the case of a brick chimney, mortar secures the blocks together so that the structure can stand and maintain its shape. It fills the gaps between the bricks, too.
Why is Mortar Important?
Mortar is a vital component of a brick chimney, as it binds the bricks together; without mortar, a chimney wouldn't be able to stand. Since the material is so rigid and because it fills the gaps between the brick blocks, mortar adds a degree of fireproofing and waterproofing to the chimney. If the spaces between the bricks were left open, the excessive heat and byproducts of combustion would pour through the chimney, and moisture from the elements would pour into the structure. In other words, mortar is an essential part of a brick chimney, as it maintains its structural integrity and secures the safety of your Harris County home.
Does Mortar Sustain Damage?
While mortar is very durable, it isn't infallible. While the rigidity is important, as it allows a chimney to maintain its form, there is a downside to its inflexibility, as it doesn't absorb the movement of your Harris County house. And while it may not absorb movement, it isn't impermeable, meaning that it does absorb moisture. Plus, it's exposed to the high temperatures and the caustic exhaust that fire produces. All of these factors combined mean that chimney mortar is vulnerable to deterioration. Ground settling can crack the material. It can absorb moisture from humidity and rainwater, which can crack and crumble mortar. High winds, along with fire's intense heat and corrosive exhaust, can weaken and erode the material.
In other words, over time, mortar joints do sustain damage. At first, the damage may be minimal, but as time goes on, it can worsen. If the damage is severe enough, you can be looking at serious problems. Leaks can develop in the chimney, the byproducts of combustion can escape through the cracks and make contact with the building materials that your Harris County home is constructed of, increasing the risk of a fire. The bricks can loosen, shift, and completely fall out, and eventually, the entire structure could collapse.
What Are the Signs of Mortar Damage?
Because it plays such a crucial role in the structural integrity of your brick fireplace, and since it adds a degree of fire- and waterproofing, mortar needs to be in tip-top condition. As such, it's important to pay attention to the condition of the mortar that holds your Harris County chimney together. Here's a look at some of the signs of mortar damage that you should be on the lookout for:
Cracks. Cracks of any size are a concern, as water can seep through them and into your chimney and fireplace, causing further, more severe, and costly damage.
Flaking. If the mortar appears to be flaking and falling off, that's a sign of spalling, which means the material is weakening.
Color changes. If the mortar appears to be a different color than it used to be, that's another cause for concern, as it indicates that the material is weathered and is starting to deteriorate.
Rust. When the mortar is cracked, moisture can seep through the masonry work, and since both are made of metal, when they're exposed to moisture, they can rust. If you see spots of rust inside the firebox or if the damper is hard to open or close, the mortar in your brick fireplace may be failing.
Damaged drywall. If the walls surrounding your chimney are peeling, curling, warped, molded, or are showing any other sign of water damage, it could be the result of mortar damage. If the brick mortar in your chimney is cracked, crumbling, or missing, moisture could be making its way into the chimney and traveling into the drywall.
Why is Brick Mortar Repair Important?
If you notice any of the above-mentioned issues with your Harris County chimney or fireplace – or you're experiencing any other problem that you suspect is related to the mortar – you're definitely going to want to call in a Baytown, TX brick repair contractor that specializes in mortar restoration.
Brick mortar repair – referred to as either tuckpointing or repointing – is a restorative process that involves removing the old, damaged mortar in a chimney and replacing it with new mortar. It not only strengthens and restores the structural integrity of the chimney, but it also helps to prevent further or future water damage. It enhances the aesthetic appeal of this beloved feature of your Harris County home, too. Brick mortar repair services can also minimize the risk of chimney fires. In other words, brick mortar repair services help to prevent more extensive and costly damage and improve the safety of your Harris County home.
To ensure the highest quality results, you're going to want to hire a professional Baytown, TX brick repair specialist. Repointing is a complex job that requires a great deal of knowledge and hands-on experience. A licensed, trained, and experienced professional will perform a thorough assessment of your chimney, will develop the most effective strategy, and will use the most advanced equipment and state-of-the-art materials to deliver the safest, most effective, and longest-lasting results. When done by a credible expert, professional brick mortar repair can last about 30 years.
For All of Harris County Brick Mortar Repair Needs, Contact Masters Services!
A fireplace is one of the biggest assets of your Harris County home. Make sure it stays that way with brick mortar repair services from Masters Services. As a premier Baytown, TX brick repair specialist, we're experts in chimney repointing. Our team of fully licensed and insured experts bring more than 18 years of experience and a proven track record of success to the table, and are committed to delivering nothing less than exceptional results. For more information or to schedule a free consultation, browse through our website and submit a contact form, or give us a call directly at 972-877-4650. At Masters Services, we're looking forward to restoring the safety, function, and beauty of your chimney.
Some information about Baytown, TX
Baytown is a city in the U.S. state of Texas, within Harris and Chambers counties. Located in the Houston–The Woodlands–Sugar Land metropolitan statistical area, it lies on the northern side of the Galveston Bay complex near the outlets of the San Jacinto River and Buffalo Bayou. It is the sixth-largest city within this metropolitan area and seventh largest community. Major highways serving the city include State Highway 146 and Interstate 10. At the 2010 U.S. census, Baytown had a population of 71,802, and it had an estimated population of 77,192 in 2019.
White American settlers first arrived in the now-Baytown area in 1822. One of its earliest settlers was Nathaniel Lynch, who set up a ferry crossing at the junction of the San Jacinto River and Buffalo Bayou. The still-operating ferry service is known as the Lynchburg Ferry. Other early settlers of Baytown included William Scott, one of Stephen F. Austin's Old Three Hundred, and Ashbel Smith, who owned a plantation in the area.
The city now known as Baytown was originally three separate towns. The first of these was Goose Creek, named for the bayou of the same name where Canada geese wintered and whose name is still reflected in the area's Goose Creek school district, whose establishment dates back to before 1850. With the discovery of the Goose Creek Oil Field, the rival communities of Pelly in the late 1910s, and East Baytown in the early 1920s, developed as early boomtowns. The 'East' in East Baytown was later dropped because it was west of Goose Creek.
Serious talk of merging the three cities began shortly after World War I, but the community of Baytown was opposed to this idea. However, in 1947, the three cities finally agreed to consolidate. The citizens settled on the name Baytown for the new combined city. Baytown as it is known today was officially founded January 24, 1948.
Learn more about Baytown.
Useful links for Baytown, TX
Open a Baytown, TX map
Find the Baytown, TX United States Post Office
Locate nearby Baytown, TX pharmacies
View the current Baytown, TX weather report
Browse a list of Baytown, TX public and private schools
Baytown, TX is located in Harris county in Texas State
Directions from Baytown, TX to Masters Services
Here are some masonry-related links:
The International Masonry Institute
Mason Contractors Association of America
The Masonry Institute
The Masonry Society
National Concrete Masonry Association

redpajama_set_name: RedPajamaCommonCrawl


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theendoc
Research News for Engineers, Doctors and other professionals
Dinosaurs' tooth wear sheds light on their predatory lives
Predatory, bird-like theropod dinosaurs from the Upper Cretaceous (100.5-66 million years ago) of Spain and Canada all relied on a puncture-and-pull bite strategy to kill and consume their prey. But close examination of patterns of wear and modeling of their serrated, also suggest that these dinosaurs weren't necessarily in direct competition for their next meal. Some of them apparently preyed on larger, struggling prey, while others stuck to softer or smaller fare.
"All these dinosaurs were living at the same time and place, so it is important to know if they were competing for food resources or if they were aiming for different prey." "Through this work we [can] begin to understand the interactions between these predatory dinosaurs in the ecosystem a bit better.
"We find that, in general, predatory coelurosaurian dinosaurs bite in the same way through a puncture-and-pull system, but troodontids and dromaeosaurids may have preferred different prey," she adds, noting that troodontids apparently favored requiring lower bite forces in comparison to dromeosaurs. Coelurosaurians include a group of theropod dinosaurs more closely related to birds than to other dinosaurs, including the allosaurs.
Torices has always had an interest in the teeth of carnivorous dinosaurs. At first, her goal was to match tooth remains to the dinosaur species they had come from. Over time, she grew curious about how various dinosaur species used their teeth, how that related to specific tooth shapes and sizes, and what she might learn about dinosaurs' lives based on that.
Torices first examined the microwear, or patterns of small scratches on the teeth, to see whether she could establish any pattern in the way various dinosaurs were eating. She, along with colleagues including Ryan Wilkinson from the University of Alberta, Canada, also used a modeling approach called finite elements analysis, commonly used to solve problems in engineering and mathematical physics, to explore how the dinosaurs' teeth most likely behaved at different cutting angles.
Both approaches led to the same general conclusion, she says. All of the dinosaurs studied employed a puncture-and-pull feeding movement, in which parallel scratches form while they bite down into prey, followed by oblique scratches as the head is pulled backwards with the jaws closed, the researchers report. However, they found, the different tooth shapes performed differently under a variety of simulated biting angles.
The evidence suggests that Dromaeosaurus and Saurornitholestes were well adapted for handling struggling prey or for processing bone as part of their diet. By comparison, Troodon teeth were more likely to fail at awkward bite angles. The findings suggest that troodontids may have preferred softer prey such as invertebrates, smaller prey that required a less powerful bite or could be swallowed whole, or immobile prey such as carrion.
Torices says they are now working to develop more complex models to include teeth along with their roots and jaws to better understand the biting process.
This entry was posted in Doctor, Editorial, Engineer, Technical, Uncategorized and tagged Doctor, Engineer, General, Professional, technology, The Endoc.
Navigating with the sixth sense: Desert ants sense Earth's magnetic field
Forty-four genomic variants linked to major depression
Hotel Shagun Bhopal
Best Luxury Hotel in Bhopal
Click Here Hotel Shagun is located just away from Bhopal City Centre Market, And only 2 Km away from railway station, and bus stand; thus making it an apt location for enthusiastic explorers who are looking for convenience plus luxury. Each of the 28 well-appointed


Awful smelling feet isn't something that people like to hear about and also have. Having smelly feet is quite embarrassing, especially when you have to take your shoes off in front of many people.
Foot odour is very annoying. Some people say it smells like rotten cheese and others claim it smells like rubbish. Smelly feet smell bad no matter what way you look at it. A lot of people are related to and also know someone with bad foot odour.
People have various ways of hiding foot odor. Spraying perfume or neutralising spray on their feet is only a source of temporary comfort. Shoes that restrict air flow are no good when it comes to taking away foot odours.
Sweat is the main thing that makes our legs smell bad. Bad smelling feet is mainly due to are wet with perspiration building up in our shoes and in our socks. The moisture is usually trapped in the socks or the insides of the shoes once a long period of time the smells start to come out.
The human bodies feet have over two hundred thousand sweat glands. The feet perspire a lot of sweat every day. Sweat is essentially salt and even water. The smell isn't from the sweat, but in the bacteria on our skin which feeds on the sweat.
Typically the bacteria eating the sweat will excrete waste that features a very strong and bad odour. This bad odour is difficult to get rid of. The bacteria found on the skin of our body is rather normal. Constant showering every few hours could remove a great deal of this bacteria but they will just keep coming back.
Each of our feet smell particular bad compared with our armpits or possibly hands which also contain a lot of sweat glands since our feet are typically covered with socks and shoes or boots. Sweat is trapped in our socks and shoes together with bacteria are having an all-you-can-eat banquet inside your shoes. They might feed off the sweat and then excrete foul-smelling odours.
Everybody has a different odour because our bodies only allow certain microorganisms to survive on the skin. Different people allow different kinds which is why anyone smells different. A person that is sweating more often than yet another will more than likely have smelly feet.
Foot health isn't a issue we hear about every day. Looking after our foot health to remove bad-smelling foot odours is quite easy. Just wear secure foot wear that have great air ventilation. Change shoes once or twice a day and wash your feet at least once a day.
Pancreatitis in Dogs – What is It?


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It is clinically important to suppress or inhibit traumatic memories, which are formed after fearful experiences. In animal models, fear memory is formed by repetitive presentation of a tone paired with an electrical foot-shock1. It is well known that an extinction protocol, in which the tone is repeatedly presented without the foot-shock, gradually decreases the pre-acquired fear response to the tone. However, this fear extinction protocol is not sufficient to erase the fear memory; fear responses may recover spontaneously or relapse under some conditions. If the fear memory is retrieved or reactivated by a single presentation of the tone without the shock 1 h before the extinction session, the fear responses are permanently removed by this retrieval–extinction protocol2. This suggests that a critical brain state is caused by the retrieval procedure, in which the fear memory becomes labile and can be destroyed by the subsequent extinction procedure. Clem and Huganir3 have found a critical receptor for the permanent erasure of fear memories by this retrieval–extinction protocol. They focused on the Ca2+-permeable type of α-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate receptor (Ca2+-permeable-AMPAR) located in the lateral amygdala, an essential region of the brain for learning. They suggested that removal of Ca2+-permeable-AMPARs, the content of which in the synapses is elevated for a few days after fear conditioning, is responsible for the permanent erasure of the fear memory by that protocol.


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Bischweier – gmina w Niemczech, w kraju związkowym Badenia-Wirtembergia, w rejencji Karlsruhe, w regionie Mittlerer Oberrhein, w powiecie Rastatt, wchodzi w skład związku gmin Gemeindeverwaltungsverband Nachbarschaftsverband Bischweier-Kuppenheim. Leży ok. 7 km na wschód od Rastatt, przy drodze krajowej B462.

Powiat Rastatt
Gminy w Badenii-Wirtembergii

redpajama_set_name: RedPajamaWikipedia


Home Yukon Communities Old Crow Didee & Didoo - Poem "Margaret Blackfox"
Didee & Didoo - Poem "Better To Be Poor"
Didee & Didoo - Poem "Victor Cheated Death"
Didee & Didoo - Poem "Margaret Blackfox"
that's how come she's fit.
'cause she was an athlete.
An' she never comes last.
'cause she liked to cruise.


Louisiana's constitution allows for less than unanimous jury verdicts in most felony cases. Louisiana is one of only two states – Oregon is the other – that allows people charged with felonies to be convicted when only 10 of 12 jurors agree on guilt.
Attorney Ben Cohen with the Promise of Justice Initiative poses for a photo in a legal library in New Orleans, La., Wednesday, March 28, 2018. Cohen works to reform Louisiana's criminal justice system.
Allen stood accused in the killing of Dicarie James on a back road south of Houma. The two men had quarreled over a drug deal inside James' car. As he lay dying, James told deputies it was Allen who shot him three times with a .22-caliber revolver, twice in the chest.
Allen initially professed innocence, but he later admitted killing James, saying he fired in self-defense. The jury deliberated for 2½ hours until it reached a decision: guilty as charged of second-degree murder.
But as with many verdicts in felony trials across Louisiana, this one came with serious misgivings — and a pronounced racial divide.
The two black jurors disagreed with the rest. It didn't matter.
In Louisiana, unlike anywhere else in America, that was good enough to send Allen off to a mandatory life prison term, with no chance of parole.
That was too much for juror Willie Newton, 71, who builds cemetery vaults.
Newton and Bobby Howard, a soft-spoken man who runs an accounting firm in Houma, both said they felt manslaughter, with its maximum 40-year sentence and no minimum, was the fairer verdict.
"Maybe my life experience is a little different than some of the white people," Howard, 62, said in explaining his dissent.
Both men said they were bothered their voices were silenced by the vote — and both believe Louisiana should require unanimity.
The unique law that allowed a Louisiana jury to send Allen to prison for life without the consent of all 12 jurors did not happen by accident.
The newspaper's analysis found that 40 percent of trial convictions, like that of Matthew Allen, came over the objections of one or two holdouts. When the defendant was black, the proportion went up to 43 percent, versus 33 percent for white defendants. In three-quarters of the 993 cases in the newspaper's database, the defendant was black.
Had Matthew Allen killed Dicarie James in any state but Louisiana, his murder trial could not have ended the way it did — with a conviction opposed by two jurors.
Tilting the scales: In Louisiana, is it truly a 'jury of one's peers' when race matters?
The right of a defendant to be judged by "a jury of one's peers" is a bedrock concept in American justice, dating to ancient English common law.
"I wasn't there yet in my mind, that he should be found guilty," said Robin Edgens, who was a "no" vote in the East Baton Rouge Parish cold-case murder trial of Symond Taylor in 2016, before deliberations wrapped up with a conviction after less than 90 minutes.
The Advocate's database of 993 cases shows that — contrary to what logic might dictate — unanimous verdicts tend to arrive more quickly. When a jury is sure of guilt, unanimity often comes easily. In many split verdicts — unlike the case Edgens described — it's a struggle to get to 10 votes.
The median deliberation time for unanimous verdicts in these 993 cases was 77 minutes, compared with 121 minutes for split decisions.
Edgens said the other holdout was a young woman who, like Taylor, is black. She seemed ready to dig in.
Lorenzo Neff was more certain of his position as one of two holdouts in the 2013 manslaughter trial of Leroy Woods, who stood accused in East Baton Rouge Parish of clubbing a man to death with a gin bottle.
While split verdicts in any trial are illegal in 48 states, they are routine in Louisiana. On average, the state sends one person to prison every five days on the word of a divided jury. About half of them face sentences so stiff they likely will die in prison.
But trials tell only part of the story about the effect of Louisiana's 10-2 verdict scheme in a justice system dominated by plea deals, with only a small share of all felony prosecutions ever making it to a jury.
Thousands more Louisiana defendants take deals minimizing their prison time rather than risk the often massive consequences of a guilty verdict.
In shifting away from unanimous juries 120 years ago, Louisiana loosened a cornerstone of Anglo-American legal tradition: the notion of guilt "beyond a reasonable doubt." If one-sixth of a jury thinks a defendant is innocent, some critics wonder, how can the threshold for a conviction possibly have been met?
Supporters of split verdicts say Louisiana needs its unique justice system because it's more efficient.
The notion of unanimous juries is so woven into the fabric of American courts that even jurors who show up for service in Louisiana often do not know that the state allows split verdicts.
Melissa Dottley said she had braced for rising tensions in a jury room inside the East Baton Rouge Parish courthouse as the holdouts moved one by one toward convicting Marvin Varnado in a 2013 kidnapping trial.
Dottley and another juror remained "no" votes when the deliberations ended.
Before a black defendant even gets their day in court, the odds already have started to pile up against them.
How dramatically depends on the jurisdiction. But The Advocate's analysis of jury selection shows that for most trials, a much smaller share of black people shows up in the pools of people who respond to jury summonses than in the populace as a whole.
In East Baton Rouge Parish, the difference between the proportion of voting-eligible black people and their percentage in the "venires" of prospective jurors called to the court exceeded 10 percentage points. That's a gap the U.S. Supreme Court defined as unacceptable in a 1979 decision.
In that case, involving Missouri, the court said it didn't matter whether the falloff was purposeful or not.
Would-be black jurors face a second round of cuts inside the courtroom. In a typical Louisiana trial, lawyers sift through 25 to 50 people before settling on a panel of 12.
Prospective jurors field basic questions meant to weigh their fitness. Lawyers for either side may ask the judge to remove any juror for "cause" based on biases or relationships they reveal, a request the judge can grant or reject.
Next, both sides may use "peremptory" challenges to eliminate up to 12 jurors apiece without giving any reason.
The court minutes from the February 2013 trial of Jarrell Arline on a charge of dealing cocaine are so cursory they border on cryptic.
So even when a black defendant draws a venire well stocked with black people, the panel that's selected often fails to reflect that diversity.
Take Shawn Myron Stevenson, who was tried in Terrebonne Parish in December 2013. He had been booked on cocaine-dealing charges after he bolted from a car pulled over by State Police, who said they saw him ditch a bag as he ran. The bag held about a quarter-pound of cocaine, police said, and Stevenson had about $5,000 in cash in his pocket.
Stevenson maintained his innocence, saying there were multiple people in the car and that the drugs weren't his.
In the courtroom that morning was a remarkably diverse group of 35 prospective jurors — 10 of them black — in a parish where only one in six adults is black.
Prosecutors urged Judge Randall Bethancourt to excuse one black woman because she appeared to be sweating. The judge declined but let her go after she said her relationship to the defendant's girlfriend could affect her ability to be impartial.
Prosecutors used peremptory strikes to get rid of four other black jurors and persuaded Bethancourt to remove two other black people for cause. Two more black prospective jurors were excused by the judge, one because of a felony record and another because she knew some of the defendant's relatives.
In the end, not one of the 10 black people made it onto the panel. One black man was assigned the role of alternate, or backup, juror.
Two days later, a half-hour after beginning deliberations, the jury foreman sent a note to Bethancourt inquiring about an eye-popping fact on a police report presented during the trial.
"We, the jury seen that he was arrested ten times before. What was his prior arrests?" the note read.
Four minutes later, the jury found Stevenson guilty on both counts. Now 41, he is four years into a 15-year stint at the Iberia Parish prison.
The data gathered by The Advocate show that — if not always as flagrantly as in the Stevenson case — prosecutors go hard to remove black jurors. But defense attorneys do the same thing in reverse, trying every bit as hard to remove white jurors.
In Louisiana's most diverse parishes, these racially selective strikes tend to cancel out each other. But in parishes with a smaller share of black residents, prosecutors are often able to make juries much whiter than they otherwise would be.
In Ascension Parish, the 31 juries examined by the newspaper were on average 19 percent less black than the venires from which they were drawn. In Terrebonne Parish, the dilution was even more stark — particularly when the defendant was black.
In the 22 Terrebonne cases with black defendants examined by the newspaper, the proportion of black people fell by 33 percent from venire panel to actual jury. When the defendant was white, the drop was only 8 percent.
Joseph Waitz, Terrebonne Parish's district attorney since 1994, defended his office's practices and said he trains young prosecutors to seek a fair jury, nothing more.
"There's no coaching that says we strike minorities," Waitz said. "Each attorney does it as they see fit, and they try and get a fair and impartial jury.
The U.S. Supreme Court's 1986 ruling in Batson v. Kentucky was meant to solidify the meaning of "a jury of one's peers." That decision didn't guarantee a right to a jury that is representative of the community. Rather, it said there can be no systematic effort to exclude certain people or groups.
Ever since, when lawyers for either side lodge a "Batson challenge," the lawyer seeking to strike a juror must offer race- or gender-neutral reasons for the judge to review. But the reasons don't always have to be sound — leading many scholars to complain that the ruling achieved little.
Critics of Batson included Justice Thurgood Marshall, the Supreme Court's first black member. Though he was part of the 7-2 majority in the case, Marshall fretted that Batson's protections would prove "illusory." Nimble lawyers could easily devise innocuous rationales for their actions, Marshall reasoned.
Many lawyers acknowledge privately that they rely partly on stereotypes — especially race — in picking juries, in part because prospective jurors are often tight-lipped.
In Shawn Stevenson's case in Terrebonne Parish, there was an obvious racial pattern to the strikes. The defense ultimately got rid of 11 white prospective jurors, while the prosecution eliminated seven black prospective jurors, along with a Native American and a Caucasian — the only strikes that fell outside the pattern.
The prosecution lodged three Batson challenges and the defense two, but the judge rejected them all.
"Y'all are pretty hand in hand" in terms of racial striking, said Bethancourt, the judge. "So what's good for the goose is good for the gander."
Many critics of the Batson decision say the ruling wrongly focused on the rights of the jurors rather than those of the defendant. Countless pages of legal journals are filled with their arguments.
The Batson ruling's shortcomings are magnified in Louisiana, where a jury can simply ignore two of its members, nullifying their votes — and that problem has been little-studied.
Though the Louisiana Supreme Court has upheld the 10-2 scheme itself, it grappled in 1989 with one troubling consequence of the law: that it can help prosecutors cloak a racial motivation behind juror strikes.
Calvin Collier had challenged his armed robbery conviction, complaining that East Baton Rouge Parish prosecutors blatantly targeted black prospective jurors. The District Attorney's Office had used eight of its peremptory challenges. All eight targeted black people.
That left two black people on the jury, and Collier was convicted on a 10-2 vote. It was his second trial. The first ended in a mistrial when the jury — which included three black people and nine white people — could not agree on a verdict.
In a 4-3 decision, the state's high court awarded Collier a new trial because prosecutors' supposedly "nonracial" reasons for two strikes withered under scrutiny. The court scoffed at their contention that they struck two black jurors because of their Baptist affiliation, when they had approved four other Baptists for the jury — two black and two white.
Prosecutors opted not to try Collier a third time, instead offering him a deal: He could cop a plea on a charge of simple robbery and accept a six-year sentence.
Collier said he took the deal to get out of jail. He already had served enough time by then that he was released one year later. Had he been convicted of armed robbery, he would have been required to serve at least 10 years, and as many as 99.
The Advocate data show that the calculus the court highlighted in Collier is often in play. In close to half of Louisiana's 64 parishes, a perfectly representative jury would wind up with two or fewer black members — in some cases without interference — and in others that would be achieved with only a slight dilution of black people from the jury pool.
Terrebonne and Ascension parishes neatly illustrate this phenomenon. An "average" jury would have 2.7 black members in Ascension Parish, and 2.1 in Terrebonne. But in reality, the "average" jury in Ascension has 1.7 black members; in Terrebonne, the number is 1.3.
If jurors in these parishes were picked at random from the adult population, about half of all Ascension juries would have at least three black members — a threshold that would ensure their relevance to the verdict. In reality, less than one in five juries meets that mark.
In Terrebonne, meanwhile, the disparity is even more pronounced. One-third of Terrebonne juries filled at random would have at least three black members. In reality, only one in 16 reaches that threshold.
Despite its concerns over abuses of the split-verdict scheme, the state's highest court has consistently refused to tamper with the system itself, even when confronted with its dubious origins at the 1898 constitutional convention.
And most of the attempts to rid Louisiana of the law have focused on those ignominious origins. But so far, that argument hasn't carried the day with any judges, largely because the rationale for the law and its mechanics changed more than four decades ago.
In 1972, a deeply divided U.S. Supreme Court validated nonunanimous verdicts in state courts — but not federal ones — when it ruled on a pair of cases from Louisiana and Oregon. The next year, Louisiana lawmakers opted to change the rule at a new constitutional convention.
In a compromise, the number of jurors required for a verdict was raised from nine to 10. And there was no lingering stench of white supremacy permeating this debate.
Backers insisted split verdicts saved jurors' time and avoided costly retrials caused by hung juries.
The law's sordid history, little known at the time, didn't come up. State voters ratified the new constitution, which contained numerous other important changes, by a 58-42 percent margin.
Jurists and prosecutors, regardless of their sympathies, have since argued that the change effectively cleansed the majority-verdict law of any racist intent. Former 4th Circuit Court of Appeal Judge Paul Bonin, who is white, used those grounds to shoot down an appeal filed by the notorious New Orleans gangster Telly Hankton that challenged the 10-2 law's constitutionality.
The appeal argued that the law's racist genesis was a permanent stain on it. Bonin agreed that the law began with racist intent but denied the appeal. Now an Orleans Parish Criminal District Court judge, Bonin said he thinks defense attorneys must move on to the present.
Another New Orleans judge drew a similar conclusion after entertaining a rare evidentiary hearing on the question last year. Legal scholars and historians testified about the law's racist origins and its effect on defendants and jurors, while offering a litany of cases where people convicted on 10-2 or 11-1 votes were later cleared.
Judge Arthur Hunter, a black former police officer who took the bench in 1996, found those arguments unpersuasive.
"The court needs direct evidence and exacting science," Hunter wrote in denying the challenge. "To show disparate impact, the court needs to see a full-scale study which looks at the numbers to provide conclusive demographic data which necessarily shows disproportionate impact."
Some scholars who have written extensively on the topic say The Advocate's research provides the evidence that Bonin, Hunter and others requested.
Ben Cohen, a New Orleans-based attorney who has filed more than 20 petitions asking the U.S. Supreme Court to reconsider the Louisiana law, said the data support a constitutional argument that the high court thus far has declined to take up.
And although the state's district attorneys association is on record opposing efforts to change the law, the district attorneys are not in complete solidarity.
Warren Montgomery, district attorney for conservative St. Tammany Parish, told The Advocate last summer that he was tired of being put on the defensive over the 1898-born rule.
Presented with the newspaper's analysis in February, Montgomery said he was ready to support a change, assuming the findings are not challenged.
This is one part of an occasional series examining Louisiana's nonunanimous jury law. Click here to see the entire series.
Needing only 10 of 12 jurors to agree, Louisiana is one of only two states in which juries can convict by nonunanimous decisions (Oregon being the only other state).


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Ludwig zu Sayn-Wittgenstein ist der Name folgender Personen:
* Ludwig I. (der Ältere), Graf von Sayn zu Wittgenstein (1532–1605), Graf von Wittgenstein, siehe Ludwig I. (Wittgenstein)
 Ludwig II. (der Jüngere) Graf zu Sayn-Wittgenstein zu Wittgenstein (1571–1634), deutscher Adliger und Begründer der Linie Sayn-Wittgenstein-Wittgenstein, siehe Ludwig II. (Wittgenstein)
 Ludwig Adolf Peter zu Sayn-Wittgenstein (1769–1843), Generalfeldmarschall der russischen Armee

 Ludwig zu Sayn-Wittgenstein-Hohenstein (1831–1912), deutscher Fürst und Standesherr
 Ludwig Christian zu Sayn-Wittgenstein-Hohenstein (1629–1683), Regent der Grafschaft Sayn-Wittgenstein-Hohenstein
 Ludwig Adolf Friedrich zu Sayn-Wittgenstein-Sayn (1799–1866), Fürst zu Sayn und Wittgenstein, Fürst zu Sayn-Wittgenstein-Sayn und kaiserlich-russischer Feldmarschall


Not once have I actually written "cleaning" on my holiday to-do list and, yet, when it comes down to entertaining, cleaning is one of the things that pushes me over the edge. You know what I'm talking about: you've got your heels on and you realize there's a ring around the toilet.
In the interest of saving you and yours some holiday hair pulling and maybe even some last-minute tears, here are a few of my favorite cleaning tips and products that will not only help beforehand but will make cleaning way less gross if you do find yourself with an up-do and a dirty bathroom.
I don't mean you need to be one of those super humans that dusts every night. There are, however, some sneaky things you can do to keep things from getting super dirty to begin with. For instance, put Epsom salts in your bathwater to keep soap scum from building up in the tub.
And have you heard of the rimless toilet? Under the rim… that's the part that get's extra dirty, so take away the rim and add in extra strong swirling action, and you've got the Cascade toilet. In addition to the self cleaning toilet properties, it's also a high efficiency toilet at 1.28 gpf, saving 20% more water than the 1.6 gpf toilet. Save yourself from dirty work and save dollars on water so you can serve fancy hors d'oeuvres.
I used to be embarrassed when someone inevitably peeked inside my oven during a dinner party. That is, until I started dousing the oven with cleaner on the nights we eat out. Let it sit overnight then give it a good wipe. So much easier than waiting until it takes a chisel to remove the char.
There used to be an extra special feeling in my lungs after I'd bleached the color out of the bathroom tile. Believe it or not, you can get your dirtiest cleaning jobs done without using harsh chemicals. I have one bottle filled with vinegar and water. That cleans pretty much everything, along with some baking soda and borax. Seriously, you can clean an entire house with these three things without singeing your precious lungs.
Other tips: use baking soda and water on shower doors, use a halved lemon on faucet water spots, and grab olive oil to clean stainless steel surfaces like dishwashers and refrigerator doors.
I can't stand vacuuming. So cumbersome. That's until I invested in a heavy duty vacuum cleaner that is seemingly fueled by pet hair (a Dyson, if you're curious). I also own a high-powered steam iron, because ironing is a thankless job but sometimes it has to happen (a Rowenta). I now believe that it's worth it to invest in appliances and cleaning products that will help you get the job done in the most pleasant way possible. It might even change how you feel about your most hated chores, so that you're not waiting until a molehill is a mountain to do the really hard work.
Now, if you'll excuse me, it's time to jingle some bells.


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6 Foods That Trigger Rosacea Symptoms | Well+Good
6 Foods and Drinks That Can Trigger Rosacea Flare Ups
Brenna Holland・July 3, 2021
Photo: Getty Images/Nazar Abbas Photography
Here's a not-so-rosy reality: More than 16 million Americans live with rosacea, a chronic skin condition characterized by redness and swelling of the nose, cheeks, and forehead. Its symptoms tend to come in waves, with intervals of active flare ups, followed by periods of time where the inflammation recedes. As such, managing rosacea is all about limiting these recurrences by expanding the amount in between—and a big part of that effort has to do with diet.
Like other inflammatory conditions, certain foods can make symptoms of rosacea, which include flushing, textural changes to the skin, swelling, broken capillaries, and watery eyes, to become worse. Below, Rachel Maiman, MD, a cosmetic dermatologist who helps her patients tailor their diets to limit skin reactions, breaks down the foods most likely to trigger rosacea flare ups and how best to avoid them.
That's not to say that you, personally, will have an adverse reaction to everything on this list. "As is almost always the case in medicine, no one patient is the same, and so assessment of one's triggers is a case-by-case necessity," says Dr. Maiman. "Just because chocolate triggers a rosacea flare in one patient does not mean that it will trigger flares in another."
Because what sparks rosacea symptoms is so individualized, Dr. Maiman suggests anyone looking to optimize their diet work in tandem with a dermatologist. "Knowledge of potential triggers is a large component of what a dermatologist learns in their training in order to be capable of managing patients with this condition," she says. "Thus, we can be very helpful in guiding patients towards making the right dietary choices."
As a general guideline, an anti-inflammatory diet rich in fresh vegetables and fruits, lean protein, and healthy fats can be helpful for anyone with an inflammatory skin condition, including rosacea.
6 foods that trigger rosacea symptoms
High-acid vegetables
Examples: tomatoes, carrots, hot peppers, beets, eggplants, onions, radishes, and spinach
"The rationale for avoidance of high-acid foods is that basic cellular mechanisms such as detoxification, elimination, and repair fail to function as optimally as they should in the presence of an acidic pH," Dr. Maiman said.
When these essential mechanisms fail, the skin barrier struggles to repair itself, so inflammation and redness caused by rosacea become more difficult to treat and heal at a slower pace. "Rosacea sufferers may want to stick to neutral vegetables with high water content, like asparagus, cucumbers, sweet potatoes, leafy greens, pumpkins, broccoli, cauliflower, celery, okra, lettuce, green beans, and zucchini," suggests Dr. Maiman.
Refined/concentrated sugars
Examples: soda, candy, commercial fruit juices, sugary breakfast cereals, and snack foods like chips, cookies, and crackers
If you've been around the high-fructose corn syrup block, you know what we're talking about here: junk food. "Foods high in refined/concentrated sugars can trigger rosacea because these simple carbohydrates trigger high insulin release, which activates inflammatory hormones, such as prostaglandins."
Examples: chili peppers, jalapenos, hot sauces, curry, cayenne pepper, certain salsas, and tabasco pepper
The reason these foods can cause a rosacea flare up is a common ingredient called capsaicin, which is the very substance that gives these foods their characteristic heat. It's a chemical irritant that messes with the receptors in your skin that recognize and feel warmth, leaving people who have rosacea feeling hot and looking red. "Choice of spices should be done carefully, with more soothing spices like coriander, cardamom, saffron, fennel, and turmeric being favored," says Dr. Maiman.
Histamine-rich foods
Examples: citrus fruits, chocolate, bananas, nuts, broad-leaf beans, and pods (like lima or navy beans and peas), avocados, and shellfish
Histamine is involved in local immune responses, like inflammation, and can cause your red blood vessels to dilate, "thus increasing blood flow and, by extension, redness," Dr. Maiman explains. When blood vessel dilation occurs frequently, it can progress into a state of permanent dilation, producing the visible vessels (known as telangiectasias) that are characteristic of rosacea.
Examples: primarily products that contain trans fatty acids, like yogurt, sour cream, and cheese (except cottage cheese)
Dairy, as you may be expecting at this point, is also an inflammatory food group that promotes redness and swelling. Luckily, we are living in the golden age of dairy substitutes, where there's no shortage of non-dairy options to satisfy that milk, cheese, or yogurt craving.
Examples: especially red wine, beer, bourbon, gin, vodka, or champagne
Another blood vessel-dilating, inflammation-promoting situation here. Red wine, in particular, contains a chemical called tyramines—a histamine-like compound that dilates vessels even more.
If, rosacea-be-damned, you still want to enjoy brunch or happy hour, you can limit the effects of alcohol by alternating your cocktail with sips of water. You can also keep track of what kinds of alcohol are better or worse for your rosacea and steer clear of your unique triggers when imbibing.
Rachel Maiman, MD.
Tags: Healthy Body, Skin-Care Tips


– As seen on this week's WWE SmackDown, AJ Styles defeated Chad Gable to earn a spot in the Independence Day Battle Royal, which AJ went on to win and become new #1 contender to WWE United States Champion Kevin Owens. Gable's "American Alpha" partner Jason Jordan also worked the battle royal.
In the Fallout video above, Gable is asked about having strong singles outings despite last week's loss to Owens and this week's loss to AJ. Gable says it's great to have the strong singles showings and he wants to be competing against the top guys but he did lose. Gable says a lot of people don't like to talk about how losses can sometimes be good. Gable says he took losses in his amateur wrestling days but those losses are what made him the guy that was able to make an Olympic team. Gable says this whole losing thing is not going to become a habit because he doesn't lose to the same person twice, he learns and fixes the problem.
Regarding the status of American Alpha, Gable says they thrive on competition and they want competition. He says they had their run with the titles, other tag teams are in the hunt for the SmackDown Tag Team Titles right now and they were kind of pushed to the back of the line but that's fine because all they want is competition in the meantime. Gable says Jordan supports his singles outings 100% and when Jordan gets his chance, he will show what Gable did – that they can hang with any single guy on the roster, not just the tag teams.