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Projection to latent pathways (PLP): a constrained projection to latent variables (PLS) method for elementary flux modes discrimination
Methodology article
Ana R Ferreira1,2,
João ML Dias1,
Ana P Teixeira2,3,
Nuno Carinhas2,3,
Rui MC Portela1,
Inês A Isidro1,
Moritz von Stosch4 &
Rui Oliveira1,2
Elementary flux modes (EFM) are unique and non-decomposable sets of metabolic reactions able to operate coherently in steady-state. A metabolic network has in general a very high number of EFM reflecting the typical functional redundancy of biological systems. However, most of these EFM are either thermodynamically unfeasible or inactive at pre-set environmental conditions.
Here we present a new algorithm that discriminates the "active" set of EFM on the basis of dynamic envirome data. The algorithm merges together two well-known methods: projection to latent structures (PLS) and EFM analysis, and is therefore termed projection to latent pathways (PLP). PLP has two concomitant goals: (1) maximisation of correlation between EFM weighting factors and measured envirome data and (2) minimisation of redundancy by eliminating EFM with low correlation with the envirome.
Overall, our results demonstrate that PLP slightly outperforms PLS in terms of predictive power. But more importantly, PLP is able to discriminate the subset of EFM with highest correlation with the envirome, thus providing in-depth knowledge of how the environment controls core cellular functions. This offers a significant advantage over PLS since its abstract structure cannot be associated with the underlying biological structure.
An elementary flux mode (EFM) can be defined as a minimal set of enzymes able to operate at steady state, with the enzymes weighted by the relative flux they need to carry for the mode to function [1]. The universe of EFM of a given metabolic network define the full set of non-decomposable steady-state flux distributions that the network can support. Any particular steady-state flux distribution can be expressed as a non-negative linear combination of EFM. Motivated by these unique properties, EFM analysis has become a widespread technique for systems level metabolic pathways analysis [1–8].
The number of EFM of a metabolic network is in general very high, denoting the innate adaptability and robustness of biological systems. As illustrative example, the central carbon metabolism of a genome-scale reconstructed Escherichia coli metabolic network has approximately 26 million EFM [9]. However, not all of these pathways are thermodynamically feasible or even physiologically reachable [10]. Over the last decade several methods were proposed to reduce the number of EFM founded on different principles (Table 1).
Table 1 Classification of methods for EFM reduction
Some of the proposed methods reduce EFM based solely on structural information of the metabolic network. de Figueiredo et al. [11] presented a method to enumerate the EFM in increasing order of number of reactions. This approach enabled to identify the K-shortest EFM in Escherichia coli and Corynebacterium glutamicum metabolic networks, which are in principle energetically more efficient. Song and Ramkrishna [12] proposed a reduction algorithm based on the effect of EFM on the convex hull volume. This allowed the a priori reduction, without any experimental data, from the initial 369 to 35 EFM for a yeast metabolic network fermenting both glucose and xylose.
EFM can also be discriminated on the basis of reaction thermodynamics. Wlaschin et al. [13] demonstrated with experimentally determined intracellular fluxes that EFM weights are inversely correlated with the entropy generated by the involved metabolic reactions. This suggests that evolution induced cellular regulatory patterns to favour efficient pathways with low entropy generation. Zhao et al. [14] proposed a method for correlating enzyme activity and flux distribution which uses the Shannon's maximum entropy principle, a measure of system complexity, as an objective function to estimate the enzyme control flux.
Several methods have been proposed that merge linear programming and experimental data. Palsson and co-authors [15, 16] suggested linear optimization to determine how extreme pathways (the systemically independent subset of EFM) contribute to a given (measured) steady-state flux distribution. There is a range of possible nonnegative weighting values associated to extreme pathways that produce a given steady-state flux distribution. This range was calculated by maximizing and minimizing the extreme pathway weighting factors, resulting in the so called α-spectrum. Wang et al. [17] presented a method to calculate the EFM coefficients for a large set of EFM by devising a quadratic program to explore the possibility and performance of using a subset of the EFM to reconstruct flux distributions. Alternatively, a framework based on EFM analysis and the convex properties of EFM was developed to calculate EFM flux regulation coefficients (FRC) corresponding to an appropriate fractional operation of this mode within the complete set of EFM [18].
Schwartz and Kanehisa [19] showed that a combination of structural and kinetic modelling in yeast glycolysis significantly constraints the range of possible behaviours of a metabolic system. All EFM are not equal contributors to physiological cellular states, and this approach may open a direction towards a broader identification of physiologically relevant EFM among the very large number of stoichiometrically possible modes.
In a previous paper [20], we have delineated a conceptual approach to map envirome factors to cellular functions based on the correlation of EFM weighting factors and measured envirome variables. Here we study in detail the computational algorithm to reduce EFM based on the degree of correlation of EFM weighting factors with measured envirome factors, which we call projection to latent pathways (PLP). The underlying principles are: (i) only a moderate number of EFM are active at given environmental conditions, (ii) the envirome plays a critical role in their regulation, and (iii) active EFM deliver a characteristic environmental footprint that can be used for their identification. In what follows we present all mathematical details underlying PLP and compare it with PLS in relation to a case study.
Projection to Latent Pathways (PLP) Algorithm
By applying steady-state material balance equations to a metabolic network with m metabolites and q metabolic reactions, the following system of linear algebraic equations is obtained:
N ⋅ r = 0
(1a)
r k > 0
(1b)
with r a vector of q metabolic fluxes, r k the subset of fluxes associated to irreversible reactions and N a m×q stoichiometric matrix. It is a well-known property of system (1) that its null space solution takes the form of a polyhedral cone [21]. Furthermore, the convex basis of system (1) is formed by a large number of base vectors, which are the EFM studied in this paper:
r = ∑ i = 1 n e m λ i ⋅ e m i
with em i a q × 1 vector of reaction weighting factors that defines EFM i and λ i a scalar variable defining the partial contribution of em i to the overall flux phenotype, r, and n em the number of EFM.
In this paper we study the reduction of EFM on the basis of dynamical envirome data sets. The basic premise is that measured fluxome vectors can be systematically deconvoluted into genetic dependent factors (the structure of EFM, em i ) and envirome dependent factors (the partial contribution of each EFM to flux phenotype, λ i ). To implement this method, we developed a discrimination algorithm that works according to the following criteria:
Maximisation of explained variance of flux data sets, R = {r(t)}
Maximisation of correlation of λ i against envirome data, X = {x(t)}
Minimisation of the number of active EFM
with X = {x(t)} a np × nx matrix of np independent observations of envirome vectors x(t) (dim(x) = nx), R = {r(t)} a np × nr matrix of np independent observations of reaction rates, r(t) (dim(r) = q). These criteria are equivalent to a covariance maximisation problem (covariance maximisation implies maximisation of correlation and minimisation of redundancy) between envirome data, X, and observed flux data, R, under the constraint of a plausible set of EFM:
M a x i m i z e cov X , R s .t . R = Λ × E M T Λ = X × C T
with EM = {em i } a nr × nem matrix of nem EFM, em i (dim(emi) = q), Λ = {λ(t)} a np × nem matrix of weight vectors λ(t) of EFM (dim(λ) = nem) and C a nem × nx matrix of regression coefficients.
Unconstrained maximisation of covariance can be performed by the widely used method projection to latent structures (PLS), also known as partial least squares. Figure 1 shows the structural differences between PLS and PLP. Since PLP is derived from PLS, in the lines below we first review PLS decomposition and then show how it can be extended to PLP.
Schematic representation of decomposition operations performed by PLS and PLP algorithms. The main differences between PLS and PLP are related to the computation of Y-loadings. In PLS Q are abstract variables calculated to maximise correlation between X and Y, while in PLP Q comprises a subset of active EFM.
Projection to Latent structures (PLS)
PLS is a multivariate linear regression technique between an input (predictor) matrix, X, and an output response matrix, Y. It differs from traditional multivariate linear regression in that it decomposes both the predictor and the response matrices into reduced sets of uncorrelated latent variables, which are then linearly regressed against each other.
The most widely used PLS algorithm is the NIPALS (non-iterative partial least squares) algorithm [22], which provides the basis for PLP derivation. NIPALS proceeds according to the following steps:
Set the initial ny × 1 Y-loading vector, q, equal to an arbitrarily chosen nonzero row of Y, y t
q = y t T y t
in case of univariate PLS, ny = 1 and q = 1
Compute the np × 1 Y-score vector, u
u = Y ⋅ q
Compute the nx × 1 weight vector, w
w = X T ⋅ u X T ⋅ u ,
Compute the np × 1 X-score vector, t
t = X ⋅ w
Recalculate the Y-loading vector, q
q = Y T ⋅ t Y T ⋅ t
Repeat steps 1-5 until the convergence criterion ||t-t old || < eps is obeyed with, for instance, eps = 1 × 10-8. In case of univariate PLS, Eq. 8 yields q = 1 hence no iterations are performed.
Compute the X data block loadings, p, and rescale accordingly:
p = X T ⋅ t t T ⋅ t
p n e w = p p
t = t ⋅ p
w = w ⋅ p
Compute the regression coefficient of the inner linear model
b = u T ⋅ t t T ⋅ t
Compute the X and Y residuals
E X = X - t ⋅ p T
E Y = Y - b ⋅ t ⋅ p T
Then go back to step 1 and repeat the procedure for the next latent variable after making
X = E X
Y = E Y
Steps 1-10 are repeated for k = 1, ..., Fac latent variables resulting into the following overall decomposition:
X = T ⋅ W T + E X
Y = U ⋅ Q T + E Y
U = T ⋅ B T + E U
with E i residuals matrices. Finally, the prediction of Y from X is given by
Y ^ = X ⋅ R C T
with RC the ny × nx regression coefficients matrix given by
R C = Q ⋅ B ⋅ W T
For more details about PLS and NIPALS see Geladi and Kowalski [23].
Projection to latent pathways (PLP)
PLP can be viewed as a constrained version of PLS that maximises the covariance between X and R under the constraint of known EFM. PLP performs essentially the same decomposition described by Eq. 18-22. The main difference lies in the computation of the output loadings,Q. Since EFM are unique and non-decomposable flux solutions, any observed flux distribution can be expressed as a non-negative weighted sum of EFM (Eq. 2). Thus, EFM em i can be interpreted as latent variables (or principle components of a metabolic network) while the weights λ i can be interpreted as score values of such latent variables. According to this analogy, PLS was modified as follows:
For each EFM k, set the loadings equal to em k and compute the respective score vector, λ k :
q k = e m k
λ k = R ⋅ q k ≡ u k
Perform a univariate PLS (with q = 1) with input X and target Y = λ k for Fac latent variables as described in the previous section and compute the predicted λ k
λ ^ k : p r e d i c t e d λ k f r o m u n i v a r i a t e P L S
Compute the predicted R by the k EFM and the respective explained variance
R ^ k = λ ^ k ⋅ q k T
v a r k % = 100 ⋅ 1 - ∑ i R - R ^ k T ⋅ R - R ^ k ∑ i R T ⋅ R
Repeat steps 1-3 for every EFM k = 1,..., nem and choose the best, kopt, as the one that exhibits the highest variance value given by Eq. 27.
k o p t : EFM with highest v a r k value
Remove kopt from the list of EFM and make
R = R - R ^ k o p t
Go back to step 1 and repeat the procedure for a maximum number of EFM or until the explained variance of R does not increase any further.
With this procedure the output loadings, Q, hold a subset of EFM from matrix EM while the output scores, U, are equivalent to the EFM weights matrix, Λ:
R = Λ × E M T + E R
As such, while PLS and PLP are structurally equivalent, the loadings and scores in PLS are abstract variables while in PLP they have a physical interpretation:
The number of latent variables in PLS is analogous to the number of active EFM in PLP. Thus the subset of EFM that explain most of the variance of R are interpreted as the set of metabolic pathways activated by environmental factors.
The regression coefficients vector, RC kept , of the inner univariate PLS, being directly associated with EFM, show the contribution of each environmental factor to the up- or down-regulation of EFM.
The PLS and PLP algorithms were coded in Matlab™(Mathworks, Inc). The code is freely available for academic use under a free academic license and can be downloaded at http://www.dq.fct.unl.pt/sbegroup.
In what follows we compare both algorithms in relation to a case study.
Case study: recombinant BHK cell line
Data of a recombinant baby hamster kidney (BHK) cell line expressing a fusion glycoprotein IgG1-IL2 was used to compare PLS and PLP. The data set comprises 134 observations acquired from 7 independent bioreactor experiments operated in batch and fed-batch modes. The predictor matrix, X (dim(X) = 134 × 26), includes measured data of 26 environmental factors (pH, osmolarity and concentrations of viable cells, glucose, lactate, ammonia, IgG1-IL2 and 19 amino acids) while the target matrix, R (dim(R) = 134 × 24), comprises 24 production or consumption fluxes of extracellular compounds. Further details about the data can be found elsewhere ([20]).
A relatively small BHK metabolic network comprising 35 metabolites and 57 metabolic reactions was constructed. Its EFM were computed using Metatool 5.0 [24] resulting in 251 EFM. Details can be found as Additional Files 1 and 2. These 251 EFM were used as constraints to PLP decomposition.
Comparing PLP and PLS decomposition results
The full data set was divided into two partitions of randomly selected points with equal size for calibration and validation (with 67 points each). The results of a single run of PLS and PLP decomposition for the calibration data set are shown in Tables 2 and 3 respectively. PLS decomposition stops at latent variable 18, when the X variance reaches 100%. The final explained R variance is 90.1%. As for PLP, decomposition progresses up to the 17th EFM, explaining 82.5% of R variance, thus 7.5% less than PLS. PLP decomposition stops when the threshold degree of correlation between λ i and Xcan no longer be satisfied (r2 > 0.75 and p-value < 0.05, see Table 3). This procedure ensures that the identified EFM are the ones with highest correlation with environmental state. Figure 2 depicts predicted against "measured" λ i illustrating the high degree of correlation with envirome variables for the discriminated set of EFM.
Table 2 PLS decomposition results in terms of % of explained variance (Var) over number of latent variables (LV).
Table 3 PLP decomposition results showing the subset of EFM with highest correlation with the envirome (as denoted by the r2 and p-value).
Correlation between EFM weighting factors and envirome variables. Observed weighting factors are plotted against a linear function of 26 envirome variables for the BHK data set. Blue circles and red triangles represent the calibration and validation data points, respectively.
Assessment of EFM reduction consistency
PLS belongs to a class of multivariate regression techniques that can be used to model high dimensional data sets with low number of sampling points [25]. However, when the number of samples is too low, the partitioning into calibration and validation sets may have a high impact on the final model structure. Since stemming from PLS, the same problem does in principle apply to PLP. In order to assess EFM discrimination variability due to data partitioning, a bootstrapping technique was implemented, in which PLP and PLS were repeated 200 times with randomly selected calibration and validation partitions with 67 points each. Figure 3 shows the frequency of selection of EFM resulting from the bootstrapping analysis. The complete set of results is provided as Additional File 3. These results evidence a subset of frequently selected EFM, which include EFM1, EFM2, EFM4, EFM6, EFM11, EFM179 and EFM210 with frequency of selection higher than 75% and EFM69, EFM72, EFM173 and EFM206 with frequency of selection higher than 50%. Less frequently selected EFMs are very sensitive to the data partitioning and to experimental noise and thus less reliable to interpret.
Frequency of selection of EFM. A bootstrapping technique was implemented in which 200 PLP runs are performed for randomly selected calibration and validation data sets with 67 points each. Frequency is calculated as the EFM selection count divided by the total number of runs.
Metabolic interpretability
As mentioned previously, while in PLS the output latent variables have no physical meaning, in PLP they are EFM. To illustrate this difference we plot in Figure 4 the output loadings of the first two PLS latent variable (Table 2) against the reaction weighting factors of the first two selected EFM (179 and 1) (Table 3).
Normalized PLS output loadings versus reaction weighting factors of selected EFM. Blue circles and red triangles represent the loadings of the first and second PLS latent variable plotted against the corresponding metabolic reaction weighting factor of the first and second selected EFM (EFM179 and EFM1 respectively; see Tables 2 and 3).
It can be seen that the first PLS loadings vector q 1 calculated by Eq. 8 does show a residual correlation with the first selected EFM 179 structure (r2 = 0,26). However, the second loadings vector q2 shows no correlation at all with second select EFM 1. Despite the fact that both the calculation of the output loadings q and the selection of EFM obey to the same criterion of maximization of the correlation between X and Y, it is clear that the data structure identified by PLS cannot be easily associated with the underlying biological structure.
It is beyond the scope of this paper to present a detailed metabolic interpretation of the discriminated EFM by PLP (for a detailed analysis see [20]). Here we just highlight a few illustrative examples, the most frequently selected EFM for biomass synthesis is EFM 179 followed by EFM173. The product formation EFM (EFM 1) is also frequently selected. The anaerobic conversion of glucose into lactate was also frequently selected (EFM 11). Serine transamination into glycine (EFM 6) was also among the most frequently selected EFM. EFM 4 corresponds to the glutaminolysis pathway, well known as a major carbon source for energy production in mammalian cells. In general, these are important pathways known to be active in mammalian cells.
Regression coefficients
While PLS regression coefficients are associated with latent variables lacking physical meaning, PLP regression coefficients are directly associated to the discriminated EFM (see Figure 5). Thus they provide information of how the envirome up- or down-regulates each EFM. This interpretation should however be done with care as regression coefficients cannot disclose between a cause and an effect. An EFM is per definition a non-decomposable sub-network. Most of them start and end in extracellular compounds. Each EFM produces a characteristic dynamic footprint in the environment in terms of consumed or produced metabolites, which is more an effect rather than a cause. Moreover, it is an important feature of PLS and per inheritance of PLP that the X-loadings are computed in a way to maximise predictive power of Y in detriment of interpretability of the individual contribution of X variables. Although many papers have attempted to develop interpretation of PLS regression coefficients (e.g. [26, 27]), other techniques are in principle better suited for this purpose. Even so, main causal-effects can be extracted from the analysis of regression coefficients. For this analysis it is however important to calculate the confidence intervals of the regression coefficients, which can be obtained from the previously described bootstrapping technique [28]. From the z = 200 PLP runs with randomly selected calibration and validation data sets, z = 200 vectors of regression coefficients are calculated (see Additional File 3). The respective mean and standard deviation can be estimated as follows:
PLP regression coefficients. Regression coefficients of selected EFM quantify the contribution of each environmental factor in X to the respective EFM weighting factor.
B ̄ = ∑ i = 1 z B i z
S = 1 z − 1 ∑ i = 1 z ( B i − B ¯ i ) 2
The 95% confidence intervals can then be calculated from the t-student distribution with 0.975 half interval and z-Fac degrees of freedom
B = B ¯ ± S × t 0 , 9 7 5 , z - F a c
As illustrative example, Figure 6 plots the confidence interval against the mean of the regression coefficients for the product formation EFM (EFM 1). It can be observed that only a subset of regression coefficients lay below the one half threshold line. These include the regression coefficients associated with pH, osmolality, glutamine, lactate, IgG, valine and lysine. These regression coefficients are the most statistically significant and thus more reliable interpretations can be withdrawn from them. As example, it is a rational result that the weighting factor of the product EFM 1 is highly correlated with the product concentration since the product results from EFM 1. All other identified environmental parameters are potential targets for manipulation in order to improve product synthesis. This analysis can be systematically extended to the full set of envirome components and full set of EFMs to support the concept of cell functional enviromics as defended in [20].
Regression coefficients confidence intervals for EFM 1. Confidence interval as function of regression coefficients obtained for the product formation EFM (EFM 1). Black full circles are envirome factors. The light and dark blue regions correspond to confidence intervals higher than 50% and 100% of the nominal value of the regression coefficient, respectively.
Predictive power
To test the predictive power, PLS and PLP models were calibrated with the calibration data set composed by 50% of data points and then simulated on the validation data set composed by the remaining 50% measured points. The PLS model with 18 latent variables explained 90.1% of R variance in the calibration dataset but only 76.8% of the validation dataset. The quality of the results can be visually inspected in Figure 7. The degradation of accuracy in the validation dataset is rational given that the model is requested to predict data points, which may lay outside of the domain of experience defined by the calibration data set. As for PLP it is a very interesting result to verify that despite explaining a lower variance in the calibration data set (83.2% against 90.1% for PLP and PLS respectively), the accuracy of the validation data set was higher than that of PLS (81.9% against 76.8% for PLP and PLS respectively). Moreover, the variance of the validation data set is almost equal to that of the calibration data set, denoting a more consistent model, with higher predictive power than the PLS one (Figure 8).
Predicted metabolic fluxes by PLS. Predicted against measured fluxes computed by the PLS model for the BHK data set. Blue circles and red triangles represent the calibration and validation data points, respectively.
Predicted metabolic fluxes by PLP. Predicted against measured fluxes computed by the PLP model for the BHK data set. Blue circles and red triangles represent the calibration and validation data points, respectively.
In order to screen out the possibility of a casual better performance of PLP in relation to PLS due to the particular data partitioning employed, the same variance analysis was performed for the z = 200 PLP and PLS runs performed with randomly selected calibration and validation data points according to the bootstrapping technique previously described. The results show that the explained variance of the validation data set varied between 78.8-85.6% for PLP and 50.4-82.7% for PLS (see Additional File 3). In 194 out of 200 runs the PLP outperformed the PLS, thus confirming that while PLS is consistently more accurate in describing the calibration data than PLP, the latter is consistently more accurate at predicting the validation data than PLS.
The key PLS feature is identifying independent X and Y-scores so that the relationship between successive pairs of scores is as strong as possible. PLS may be thus viewed as a robust form of redundancy analysis, seeking directions in the factor space that are associated with high variation in the response Y but biasing them toward directions that are more accurately predicted. Due to its advantages in handling highly redundant data sets, PLS has become a widely used regression analysis technique in systems biology. It has been applied as an inference tool for predicting metabolic fluxes using isotopomer flux data [29], analysing genomic and proteomic data [25], identifying signalling networks by inducing cellular response to different stimuli [30–32] and network structure using metabolomic data [33]. Moreover, PLS has also been applied for the identification of active cellular pathways as a function of the environment using metabolic and gene expression profiles [34], detection of gene-gene interactions from microarrays data [35, 36] and culture media optimization using nutritional profiling data [26, 27].
The main disadvantage of PLS lies in its empirical data-driven nature with limited added-value in terms of mechanistic knowledge generation. Although carrying some internal structure, this structure is not inspired by any a priori mechanistic knowledge of the system. PLP may be viewed as a constrained version of PLS, attuned to the structure of the biological system under study. While in PLS the loadings and score are abstract variables, in PLP loadings and scores refer to well defined metabolic structures. Specifically, PLP explores EFM as "principle components" of a metabolic network. Indeed, EFM obey to the principle of non-decomposability, meaning that any particular flux distribution can be expressed as a nonnegative weighted sum of EFM. Thus the ranking obtained in PLP refers to active pathways as inferred by their level of correlation with the environmental state. In terms of data requirement, PLS belongs to the class of multivariate regression techniques particularly suitable to handle highly dimensional data sets even if the number of observations is limited [25]. PLS is typically used to model spectral data such as near infrared or 2D-fluorescence maps [37]. A basic requirement is that the number of latent variables must be lower than the number of observations in the calibration data set. This means that reliable linear models can be identified from a moderate number of observations of highly dimensional datasets. The same properties apply to PLP. A basic constraint is that the number of discriminated EFM cannot be higher than the number of observations in the calibration data set. However the method offers no restriction in terms of the dimensionality of the input data set.
Finally it should be commented on the computational power requirements, which scales linearly with the number of EFM. In the present study with 251 EFM, computation requirements are in the order of seconds in a common PC. For a genome scale network with several million of EFM, computation power might easily rise to the scale of days in a common PC.
In this work we have developed an algorithm for the discrimination of active EFM on the basis of dynamical envirome data called projection to latent pathways (PLP). The algorithm is designed to maximise the covariance between envirome data and observed flux data under the constraint of universe of genes translated into a plausible set of EFM. In general lines, the algorithm discriminates a minimal set of envirome correlated EFM that maximise the variance of measured flux data. Thus the algorithm may be viewed as a reverse, envirome-to-function metabolic reconstruction methodology as opposed to the generally accepted gene-to-function reconstruction approach. Although presented here as a method to analyse envirome data sets, PLP has broader scope. It is rather a general methodology for statistical elimination of redundant metabolic structures that, in a broader sense, has the potential to bring together all layers of 'omic' information under a common computational framework.
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Boulesteix AL, Strimmer K: Partial least squares: a versatile tool for the analysis of high-dimensional genomic data. Brief Bioinform. 2007, 8 (1): 32-44.
De Alwis DM, Dutton RL, Scharer J, Moo-Young M: Statistical methods in media optimization for batch and fed-batch animal cell culture. Bioprocess Biosyst Eng. 2007, 30 (2): 107-113. 10.1007/s00449-006-0107-7.
Selvarasu S, Kim DY, Karimi IA, Lee DY: Combined data preprocessing and multivariate statistical analysis characterizes fed-batch culture of mouse hybridoma cells for rational medium design. J Biotechnol. 2010, 150 (1): 94-100. 10.1016/j.jbiotec.2010.07.016.
Faber NM: Uncertainty estimation for multivariate regression coefficients. Chemometrics Intell Lab Syst. 2002, 64 (2): 169-179. 10.1016/S0169-7439(02)00102-8.
Antoniewicz MR, Stephanopoulos G, Kelleher JK: Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway. Metabolomics. 2006, 2 (1): 41-52. 10.1007/s11306-006-0018-2.
Ivakhno S, Armstrong JD: Non-linear dimensionality reduction of signaling networks. BMC Syst Biol. 2007, 1: 17-10.1186/1752-0509-1-17.
Janes KA, Albeck JG, Gaudet S, Sorger PK, Lauffenburger DA, Yaffe MB: Systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis. Science. 2005, 310 (5754): 1646-1653. 10.1126/science.1116598.
Miller-Jensen K, Janes KA, Brugge JS, Lauffenburger DA: Common effector processing mediates cell-specific responses to stimuli. Nature. 2007, 448 (7153): 604-U611. 10.1038/nature06001.
Bundy JG, Papp B, Harmston R, Browne RA, Clayson EM, Burton N, Reece RJ, Oliver SG, Brindle KM: Evaluation of predicted network modules in yeast metabolism using NMR-based metabolite profiling. Genome Res. 2007, 17 (4): 510-519. 10.1101/gr.5662207.
Li Z, Chan C: Integrating gene expression and metabolic profiles. J Biol Chem. 2004, 279 (26): 27124-27137. 10.1074/jbc.M403494200.
Pihur V, Datta S: Reconstruction of genetic association networks from microarray data: a partial least squares approach. Bioinformatics. 2008, 24 (4): 561-568. 10.1093/bioinformatics/btm640.
Tenenhaus A, Guillemot V, Gidrol X, Frouin V: Gene Association Networks from Microarray Data Using a Regularized Estimation of Partial Correlation Based on PLS Regression. IEEE-ACM Trans Comput Biol Bioinform. 2010, 7 (2): 251-262.
Teixeira AP, Oliveira R, Alves PM, Carrondo MJT: Advances in on-line monitoring and control of mammalian cell cultures: Supporting the PAT initiative. Biotechnol Adv. 2009, 27 (6): 726-732. 10.1016/j.biotechadv.2009.05.003.
Llaneras F, Pico J: A procedure for the estimation over time of metabolic fluxes in scenarios where measurements are uncertain and/or insufficient. BMC Bioinformatics. 2007, 8-
Financial support for this work was provided by the Portuguese Fundação para a Ciência e Tecnologia through projects PTDC/EBB-EBI/103761/2008 and MIT/Pt7BS-BB/0082/2008, PhD grant SFRH/BD/36285/2007 (ARF), SFRH/BD/36676/2007 (NC), SFRH/BD/51577/2011 (RP), SFRH/BD/70768/2010 (IAI) and SFRH/BD/36990/2007 (MvS) and Post-Doc grant SFRH/BPD/46277/2008 (JD).
REQUIMTE, Systems Biology & Engineering Group, DQ/FCT, Universidade Nova de Lisboa, Campus Caparica, Portugal
Ana R Ferreira, João ML Dias, Rui MC Portela, Inês A Isidro & Rui Oliveira
Instituto de Biologia Experimental e Tecnológica (IBET), Apartado 12, 2781-901, Oeiras, Portugal
Ana R Ferreira, Ana P Teixeira, Nuno Carinhas & Rui Oliveira
Instituto de Tecnologia Química e Biológica - Universidade Nova de Lisboa (ITQB-UNL), Apartado 127, 2781-901, Oeiras, Portugal
Ana P Teixeira & Nuno Carinhas
LEPAE, Departamento de Engenharia Química, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias s/n, 4200-465, Porto, Portugal
Moritz von Stosch
Ana R Ferreira
João ML Dias
Ana P Teixeira
Nuno Carinhas
Rui MC Portela
Inês A Isidro
Rui Oliveira
Correspondence to Rui Oliveira.
The project was conceived by RO. Experiments were performed by AT and NC. PLP algorithm was developed and implemented by JD and RO. Algorithms (PLP and PLS) benchmarking and data analysis was performed by AR, MvS, IAI and RP. Manuscript writing was performed by JD and RO. All authors read and approved the final manuscript.
Ana R Ferreira, João ML Dias, Ana P Teixeira, Nuno Carinhas, Rui MC Portela, Inês A Isidro, Moritz von Stosch and Rui Oliveira contributed equally to this work.
12918_2011_777_MOESM1_ESM.DOC
Additional File 1: BHK metabolic network. Biochemical reactions/pathways, enzymes and biomass composition considered in the metabolic model of BHK cells. (DOC 66 KB)
Additional File 2: BHK elementary modes. List of elementary modes obtained from the BHK metabolic network (Additional File 1). Elementary modes are represented in reduced form in terms of extracellular metabolites. (DOC 628 KB)
12918_2011_777_MOESM3_ESM.XLS
Additional File 3: Envirome factors regression coefficients. List of PLP regression coefficients and the respective confidence intervals resulting from the bootstrapping analysis. (XLS 96 KB)
Ferreira, A.R., Dias, J.M., Teixeira, A.P. et al. Projection to latent pathways (PLP): a constrained projection to latent variables (PLS) method for elementary flux modes discrimination. BMC Syst Biol 5, 181 (2011). https://doi.org/10.1186/1752-0509-5-181
Latent Variable
Metabolic Network
Flux Distribution
Baby Hamster Kidney
Elementary Flux Mode


1	 Sound Machine Country Radio - We play from 1930's to current country hits. Support Indie Artist. We have option to do request with over 30000 songs in our playlist.
2	 Sound Machine Radio - Top 40/Rock/Pop/Soul/Oldies Station. We play it all. Come join us and make your request.


text:
Research & Creative Work
Community & Employer Partners
Jean O'Brien
obrie002@umn.edu
1014 Heller Hall
Collegiate Affiliation
Educational Background & Specialties
Ph.D.: History, University of Chicago, 1990
State and Federal Recogntion
Settler Colonial Studies
US Colonialism
O'Brien, Jean, Daniel Heath Justice. Allotment Stories: Indigenous Land Under Settler Seige. Minnesota, 2022.
Monumental Mobility: The Memory Work of Massasoit. Lisa Blee, University of North Carolina Press, O'Brien, Jean, 2019.
Firsting and Lasting: Writing Indians Out of Existence in New England. University of Minnesota Press, O'Brien, Jean, 2010.
Dispossession by Degrees: Indian Land and Identity in Natick, Massachusetts, 1650-1790. Cambridge University Press, O'Brien, Jean, 1997.
Why You Can't Teach U.S. History without Indians. University of North Carolina Press, O'Brien, Jean, 2015.
Sources and Methods in Indigenous Studies. Chris Andersen, Routledge, O'Brien, Jean, 2017.
Recognition, Sovereignty Struggles and Indigenous Rights in the United States: A Sourcebook. University of North Carolina Press, O'Brien, Jean, April 2013.
Tracing Settler Colonialism's Eliminatory Logic in Patrick Wolfe's Traces of History: Elementary Structures of Race: O'Brien, Jean, American Quarterly, 69, 249-55, 2017.
What is a Monument to Massasoit Doing in Kansas City? The Memory Work of Monuments and Place in Public Displays of History: O'Brien, Jean, Ethnohistory, 61, 635-53, 2014.
Building a Professional Infrastructure for Critical Indigenous Studies: A History and Prospectus for the Native American and Indigenous Studies Association: O'Brien, Jean, Critical Indigenous Studies in the First World, 2015.
Recognition and Rebuilding: Routledge, O'Brien, Jean, The World of the Indigenous Americas, 2014.
"'Our old and valuable liberty': A Natick Indian Petition in Defense of their Fishing Rights, 1748". Kristina Bross and Hilary Wyss, eds., Early Native Literacies in New England: A Documentary and Critical Anthology, O'Brien, Jean, 2008.
Memory and Mobility: Grandma's Mahnomen, White Earth: O'Brien, Jean, Ethnohistory, 64, 345-77, 2017.
"Why Here? Scholarly Locations for American Indian Studies": American Quarterly, O'Brien, Jean, 2003.
"'Vanishing' Indians in Nineteenth-Century New England: Local Historians' Erasure of Still-Present Indian People": Sergei Kan and Pauline Turner Stong, eds., New Perspectives on Native North America, O'Brien, Jean, 2006.
Divorced from the Land: Accommodation Strategies of Indian Women in 18th Century New England: O'Brien, Jean, Gender, Kinship, and Power, Routledge, 1996.
Cobell Education Scholarship Fund Board of Trustees: Appointed by the Secretary of the Interior. 2014
Critical Indigeneities: Book series co-editor. 2014
Native American and Indigenous Studies: Journal, founding co-editor, Minnesota Press. 2013
American Studies Association annual meeting: Program committee member. 2014 - 2016
Native Americans of the Northeast: Book series co-editor, University of Massachusetts Press. 2007
American Historical Association: Program Committee member. 2011 - 2012
National Council: American Studies Association. 2003 - 2006
Advisory Board: Five-part series on American Indian history, "We Shall Remain," PBS American Experience. 2005 - 2008
Editorial Collective: Reviews in American History. 2019
Co-founder and past president, Native American and Indigenous Studies Association
President, American Society for Ethnohistory 2012 - 2013
Northrop Professor 2019
American Studies Association Annual Meeting: Program Co-Chair. 2015 - 2016
Public History Project: • Climates of Inequality: Stories of Environmental Justice, Humanities Action Lab.
Co-edited volume: Allotment Stories: Narrating Indigenous Land Relations Under Settler Siege.
Book project: Memory and Mobility: Grandma's Mahnomen, White Earth.
Member, American Academy of Arts and Sciences, 2022
Elected Member, Society of American Historians, 2016
Richard A. Yarborough Award for Mentoring, American Studies Association, 2022
American Indian History, Lifetime Achievement Award, Western History Association, 2014
Board of Trustees, Newberry Library, 2018
Member, American Antiquarian Society, 2000
McKnight Distinguished University Professor, 2015
Northrop Professor, 2019
College of Liberal Arts Dean's Medal, University of Minnesota, 2018
Member, Academy of Distinguished Teachers, University of Minnesota, 2006
Outstanding Contributions to Postbaccalaureate, Graduate, and Professional Education, 2006
Sara Evans Faculty Woman Scholar/Leader Award in the Humanities, Arts, and Sciences, 2014
Best Subsequent Book in Native American and Indigenous Studies, Native American and Indigenous Studies Association for Firsting and Lasting: Writing Indians Out of Existence in New England (Minneapolis: University of Minnesota Press, 2010). Awarded ...
Winthrop Prize for the Outstanding Book on Seventeenth-Century New England for 2019-2020, Colonial Society of Massachusetts for Monumental Mobility: The Memory Work of Massasoit (with Lisa Blee) (University of North Carolina Press, 2019)., 2019
Best New Journal Award, Council of Editors of Learned Journals, for Native American and Indigenous Studies, 2016
McKnight Land Grant Professorship, 1992
American Association of University Women's Recognition Award for Emerging Scholars, 1996
University of Minnesota Imagine Fund Award, 2014
HIST 3001: Public History
HIST/AMIN 3872: American Indian History, 1830-Present
HIST 1903: Memory, Commemoration, and Monuments in the U.S.
HIST/AMIN 5890: Readings in American Indian and Indigenous Studies
history@umn.edu
Find information on ways to give to the Department of History

redpajama_set_name: RedPajamaCommonCrawl


Elia Exchange » Welcome to our 20th university member!
Welcome to our 20th university member!
We are happy to welcome the University of Ljubljana, the 20th university to join Elia Exchange and the first from Slovenia.
The Department of Translation at the University of Ljubljana offers undergraduate and postgraduate programmes of study. At the undergraduate level, the Department offers a three-year course in Interlinguistic Communication (BA), and on the postgraduate level students can choose between an MA in Interpreting, an MA in Translation and two joint MA programmes maintained with institutions in Austria and France. Postgraduate students can further pursue a PhD degree in Translation Studies.
To learn more about the study programmes offered by the University of Ljubljana, visit http://www.prevajalstvo.net/department-of-translation.


text:
Q: How to remove border of MenuItem of sub MenuItem in WPF I am using MenuItem control in WPF. But I dont't know how to remove border of sub MenuItems.
MainWindow.xaml
<Menu DockPanel.Dock="Top" Grid.Row="0" Background="#383838" Foreground="White" VerticalAlignment="Top">
    <MenuItem Header="TEST" FontSize="20" Margin="20,0,0,0">
        <MenuItem Header="TEST"/>
        <MenuItem Header="TEST"/>
        <MenuItem Header="TEST"/>
        <MenuItem Header="TEST"/>
        <MenuItem Header="TEST"/>
        <MenuItem Header="TEST"/>
    </MenuItem>
</Menu>

I want to remove white border of above image.

A: I think you should add the control template for the MenuItem like so...
<ControlTemplate x:Key="MenuItemControlTemplate1" TargetType="{x:Type MenuItem}">
    <Border x:Name="templateRoot" BorderBrush="#383838" BorderThickness="0" Background="#383838" SnapsToDevicePixels="True">
        <Grid VerticalAlignment="Center">
            <Grid.ColumnDefinitions>
                <ColumnDefinition Width="Auto"/>
                <ColumnDefinition Width="Auto"/>
            </Grid.ColumnDefinitions>
            <ContentPresenter x:Name="Icon" Content="{TemplateBinding Icon}" ContentSource="Icon" HorizontalAlignment="Center" Height="16" Margin="3" SnapsToDevicePixels="{TemplateBinding SnapsToDevicePixels}" VerticalAlignment="Center" Width="16"/>
            <Path x:Name="GlyphPanel" Data="F1M10,1.2L4.7,9.1 4.5,9.1 0,5.2 1.3,3.5 4.3,6.1 8.3,0 10,1.2z" Fill="{TemplateBinding Foreground}" FlowDirection="LeftToRight" Margin="0" Visibility="Collapsed" VerticalAlignment="Center"/>
            <ContentPresenter ContentTemplate="{TemplateBinding HeaderTemplate}" Content="{TemplateBinding Header}" Grid.Column="1" ContentStringFormat="{TemplateBinding HeaderStringFormat}" ContentSource="Header" Margin="{TemplateBinding Padding}" RecognizesAccessKey="True" SnapsToDevicePixels="{TemplateBinding SnapsToDevicePixels}"/>
            <Popup x:Name="PART_Popup" AllowsTransparency="True" Focusable="False" IsOpen="{Binding IsSubmenuOpen, RelativeSource={RelativeSource TemplatedParent}}" PopupAnimation="{DynamicResource {x:Static SystemParameters.MenuPopupAnimationKey}}" Placement="Bottom">
                <Border x:Name="SubMenuBorder" BorderBrush="#383838" BorderThickness="0" Background="#383838" Padding="0">
                    <ScrollViewer x:Name="SubMenuScrollViewer"
                                  Style="{DynamicResource {ComponentResourceKey ResourceId=MenuScrollViewer, TypeInTargetAssembly={x:Type FrameworkElement}}}"
                                  Background="#383838" Margin="0" Padding="0">
                        <Grid RenderOptions.ClearTypeHint="Enabled">
                            <Canvas HorizontalAlignment="Left" Height="0" VerticalAlignment="Top" Width="0">
                                <Rectangle x:Name="OpaqueRect" Fill="{Binding Background, ElementName=SubMenuBorder}" Height="{Binding ActualHeight, ElementName=SubMenuBorder}" Width="{Binding ActualWidth, ElementName=SubMenuBorder}"/>
                            </Canvas>
                            <Rectangle Fill="#383838" HorizontalAlignment="Left" Margin="0" Width="0"/>
                            <ItemsPresenter x:Name="ItemsPresenter" KeyboardNavigation.DirectionalNavigation="Cycle" Grid.IsSharedSizeScope="True" SnapsToDevicePixels="{TemplateBinding SnapsToDevicePixels}" KeyboardNavigation.TabNavigation="Cycle"/>
                        </Grid>
                    </ScrollViewer>
                </Border>
            </Popup>
        </Grid>
    </Border>
    <ControlTemplate.Triggers>
        <Trigger Property="IsSuspendingPopupAnimation" Value="True">
            <Setter Property="PopupAnimation" TargetName="PART_Popup" Value="None"/>
        </Trigger>
        <Trigger Property="Icon" Value="{x:Null}">
            <Setter Property="Visibility" TargetName="Icon" Value="Collapsed"/>
        </Trigger>
        <Trigger Property="IsChecked" Value="True">
            <Setter Property="Visibility" TargetName="GlyphPanel" Value="Visible"/>
            <Setter Property="Visibility" TargetName="Icon" Value="Collapsed"/>
        </Trigger>
        <Trigger Property="IsHighlighted" Value="True">
            <Setter Property="Background" TargetName="templateRoot" Value="#3D26A0DA"/>
            <Setter Property="BorderBrush" TargetName="templateRoot" Value="#FF26A0DA"/>
        </Trigger>
        <Trigger Property="IsEnabled" Value="False">
            <Setter Property="TextElement.Foreground" TargetName="templateRoot" Value="#FF707070"/>
            <Setter Property="Fill" TargetName="GlyphPanel" Value="#FF707070"/>
        </Trigger>
        <Trigger Property="CanContentScroll" SourceName="SubMenuScrollViewer" Value="False">
            <Setter Property="Canvas.Top" TargetName="OpaqueRect" Value="{Binding VerticalOffset, ElementName=SubMenuScrollViewer}"/>
            <Setter Property="Canvas.Left" TargetName="OpaqueRect" Value="{Binding HorizontalOffset, ElementName=SubMenuScrollViewer}"/>
        </Trigger>
        <Trigger Property="IsKeyboardFocusWithin" Value="True">
            <Setter TargetName="templateRoot" Property="Background" Value="{StaticResource ClickedColor}" />
        </Trigger>
    </ControlTemplate.Triggers>
</ControlTemplate>

Then you would add this template to you menu item like so...
<Menu DockPanel.Dock="Top" Grid.Row="0" Background="#383838" Foreground="White" VerticalAlignment="Top">
    <MenuItem Header="TEST" FontSize="20" Padding="0" Template="{DynamicResource MenuItemControlTemplate1}">
        <MenuItem Header="TEST" Foreground="White"/>
        <MenuItem Header="TEST" Foreground="White"/>
        <MenuItem Header="TEST" Foreground="White"/>
        <MenuItem Header="TEST" Foreground="White"/>
    </MenuItem>
</Menu>

Obviously, I set all the colors to #383838 just to get the flat look and no borders. I guess it's up to you to customize this template the way you see fit.

redpajama_set_name: RedPajamaStackExchange


text:
Chinese Spacecraft Plants China's Flag on Moon and Takes off With Surface's Samples.
Chinese spacecraft has made its way to the Moon, planting the country's red flag there. Chang'e-5 lunar mission took off from the Moon on Thursday with lunar surface substance. "It will be a milestone for China's aerospace development," Song Zhongping, an aerospace expert and TV commentator, said.
By Maha Sabir
After successfully controlling the spread of coronavirus, China has achieved yet another milestone-in the sky this time. Chinese spacecraft has made its way to the Moon, planting the country's red flag there. After half a century, we are witnessing the inspiration from the Apollo missions. China is now the second country-after U.S.-to plant its flag on the earth's natural satellite.
Per CBS News, China Aerospace Science and Industry Corporation's Chang'e-5 lunar mission took off from the Moon on Thursday. Loaded with soil and rock samples, the spacecraft took off within 19 hours after landing on Moon's surface.
Speaking to the 'The Times', Wang Ya'nan, Chief Editor of Aerospace Knowledge magazine, said, "This is the first attempt in China's aerospace history to lift off from a celestial body other than Earth. The launch is a major test, in that the vehicle had to rely entirely on automatic maneuvers without any ground command," He further said, "If the probe receives a command from the Earth to help it control the separation, altitude, and speed, there will be at least one-second delay, which will put the process in great danger."
Chang said that his team worked hard to select the right materials for the flag to tolerate the extremity of weather on the moon for a longer time. The finalized flag was planted on December 3.
The Chinese National Space Administration (CNSA) announced that the Chang'e-5 lander also planted the National Flag of the People's Republic of China 🇨🇳 on the lunar surface pic.twitter.com/R3GFGH42pi
— StarBoy 🥭 (@StarboyHK) December 4, 2020
He stated: "An ordinary national flag on Earth would not survive the severe lunar environment."
CASIC
An exciting part of the mission was the use of a lunar probe to plant the country's flag into the surface. The flag was planted in a volcanic region called Mons Rümke, northwest of the nearside of the Moon's surface. While talking to the Global Times, Wang told that the launch from the moon could not afford any time delay. "If the probe receives a command from the Earth to help it control the separation, altitude, and speed, there will be at least one-second delay, which will put the process in great danger."
A challenging factor for the lunar takeoff is that the lander-ascender combination might sit on a slope, which would bring uncertainty to the probe's altitude and position. It means that the probe must ensure its own altitude is precise enough to enter the designated orbit through automatic decision-making, according to a statement sent to the Global Times from the China Aerospace Science and Technology Corporation (CASC.).
To verify the takeoff, the researchers that were a part of the mission conducted ground simulations. Still, the verifications are difficult due to the particularities of the lunar environment, the CASC said.
"It will be a milestone for China's aerospace development," Song Zhongping, an aerospace expert and TV commentator, said, calling it "a foresighted practice that lays a technological foundation for future deep-space explorations."
"It will also verify the viability of manned lunar landing missions, and even the construction of a lunar research base, or Mars missions," he told the Global Times.
Also Read: Russia's Head Of Space Agency Declares Venus A "Russian Planet".


Hi, how do i not become a bad rdruid in dungeon ?
By being a good rduid in dungeon.
Read questionablyepic.com and then...practice a lot.
Don't know what your trying to heal, but for most pve related activities, rdruids are good.
The higher the requirements, i.e. m+, you will be required to pre-hot, e.g. know when the DMG waves are coming in, and when to use cooldown.
Make mouseover macros or use an add on for all your healing abilities, it will help you alot.
Other than that it's just like everything else in life: practice.
There is no such thing as a class or role where you can just boost a character and be the almighty god that gets praised and invited by everyone just by existing.
Since I tracked the hots and took it a bit more seriously I was more "in control" if you get me - does that help?
On these forums are some great people - if you post logs someone may analyse it all for you. I've had that and it's really amazing what you can pick up.


text:
Establishing partnerships, building hope
Last summer, the Ethno-Cultural Brotherhood Association at Dorchester Penitentiary established a community partnership with the African Methodist Episcopal Church in Amherst, Nova Scotia. The goal of the partnership was to help restore the church manse (Minister's residence), with the guidance and supervision of several community volunteers who are experts in construction.
Five members of the Ethno-Cultural Brotherhood Association, known for their excellent work and ethics, were selected to participate and volunteer their time each Sunday.
In protective gear and equipped with pry bar tools, their first task was to tear down the entire ceiling in the basement, disinfect the brick walls of its mildew, and dispose of the large industrial garbage bins.
Theresa Halfkenny, a member of the Regional Ethno-cultural Advisory Committee of CSC explained, "the last couple of winters there was some water damage inside the manse, the basement and walls were really bad."
For the remaining weekends, they worked on dry-walling, scraping, plastering, and painting. They also caulked windows, fixed door jams, and installed a new ceiling in the washroom. Finally, they painted the entire outdoor wooden siding and completed half the roofing.
Ally Masoud, vice-representative of the Ethno-Cultural Brotherhood Association and an active volunteer on the project, expressed, "this particular venture opened doors for future engagement with the community and their special activities. I felt very welcomed and accepted by everyone who stopped by to compliment us on a job well done. "
Halfkenny experienced the same openness, "the congregation accepted [the offenders]. It was so nice to see them engaged in conversation as they would with anyone else."
Some of the church members went so far as to prepare meals for the offender volunteers, showing that there are people in our communities that are accepting and will give them a chance.
The main idea of the Ethno-Cultural Brotherhood Association has evolved to improve self-leadership skills, take accountability of organizing its activities from beginning to end, and to be all-inclusive in the recruitment of new members.
"I just feel that they are human beings firstly, and I sometimes think that we are all just a step away from maybe being where they are. But underneath, there are some really caring people," said Halfkenny.
Mr. Masoud was quick to point out that, "All of this was made possible because of the kind support by Theresa Halfkenny, REAC Member, Elizabeth Cooke-Sumbu, Director of CANSA (Cumberland African Nova Scotian Association, Amherst, NS), and Frank Landry, Social Programs Officer."
In the end, the inmate association benefits a lot from participating in diverse community service outings, which can help guide individual members to function as productive citizens in society, to develop healthy community relations, and to increase awareness of the resources available to them.
Myles Smith, Chairman of the Ethno-Cultural Brotherhood Association states, "the community support brings hope for the inmates who wish to focus on their correctional plan."
Former warden of Dorchester Penitentiary Jennifer Fillmore, couldn't agree more with this sentiment, "Dorchester Penitentiary continues to foster partnerships with multiple community organizations that provide opportunities and support for ethno-cultural offenders. Through our Community Service Escorted Temporary Absence program, projects such as the restoration of the church Minister's residence have offered ethno-cultural offenders with an opportunity not only to give back to the community, but to make real connections with individuals in local communities that share similar cultural backgrounds. This has fostered a sense of belonging for these offenders and provided them with a network of support in the community in preparation for release. We are very fortunate to have community partners with a genuine interest in working with our offenders and these benefits are crucial to their successful reintegration."


text:
Apple AirPods (2nd generation) vs Samsung Galaxy Buds Pro
The AirPods is a staple among iPhone owners, but how does it compare to the noise cancelling Samsung Galaxy Buds Pro?
Lily Katz
Apple and Samsung's rivalry grows stronger by the day and both companies strongly influence the wearable tech market. No, we're not talking about smartwatches. Instead, we want to see which pair of true wireless earbuds is better, the Apple AirPods (2nd generation) or Samsung Galaxy Buds Pro. Both headsets are convenient to use so long as you pair them with the right hardware. Let's dig in and see which is the better choice for you.
Editor's note: this article was updated on October 16, 2022, to answer frequently asked questions about the Apple AirPods Pro (2nd generation) and Samsung Galaxy Buds 2 Pro.
Apple AirPods (2nd generation) vs Samsung Galaxy Buds Pro: Fit and hardware
The Apple's earpieces don't seal to the ear, making it difficult to move around with them in—let alone exercise with them.
The Apple AirPods (2nd generation) (2019) looks identical to the original AirPods with their slender stems and hard, open-fit ear tips. The clean design looks great, but it isn't the most secure: many listeners, our staff included, report a poor fit with the AirPods. The earbuds either jostle around too much or fall out completely. If you're one of the lucky ones whose ears cradle the AirPods just right, you still hear everything going on around you (which can be a feature or drawback, depending on how you spin it).
The untextured stems are touch-sensitive, and you can control playback and field incoming calls through a combination of taps. Each earbud houses a sensor for automatic ear detection, which automatically pauses or resumes playback when you remove or insert an earbud (iOS only). Apple's famed earbuds don't have an IP rating, so the warranty doesn't cover water damage.
Apple's charging case matches the AirPods perfectly, with a glossy finish that adds a bit of interest to an otherwise unremarkable white plastic. A Lightning input rests at the bottom of the case and if you want to pay an extra $40, you can get the AirPods bundle with a wireless charging case. The AirPods case is unmarked save for the LED indicator below the seam, and the lid and body are held together via internal magnets.
The Samsung Galaxy Buds Pro is a great all-around headset with few drawbacks for the price.
The Galaxy Buds Pro makes more of a splash, with an interchangeable silicone ear tip design in three colorways to choose from: white, black, and violet. Like the Samsung Galaxy Buds Plus before them, the Galaxy Buds Pro are extremely comfortable to wear for hours at a time. The Galaxy Buds Pro feature noise cancelling, but to avoid inner-ear discomfort, Samsung included a pressure-relief vent on each earbud.
You control the Galaxy Buds Pro from either earbud's reflective touch panel, and the touch sensitivity here is much higher than with the AirPods. This is a bit annoying as it results in many false-positive taps, though you may disable this from the mobile app. Samsung decked the Galaxy Buds Pro out with a host of advanced sensors that includes an accelerometer, gyro, hall, touch, and voice pickup unit (VPU). The buds automatically pause music when you remove them, and this works regardless of your OS.
The oblong ear tips are comfortable and maintain a good seal.
Samsung's earbuds are much more durable than Apple's, as shown by the IPX7 rating. You can submerge the Galaxy Buds Pro for up to 30 minutes at a time without damaging them, though we don't recommend doing so just for the thrill of it. Even still, the IP rating makes these a great pair of workout earbuds. The clamshell charging case features a USB-C input and supports wireless charging to boot.
Does the AirPods or Galaxy Buds Pro support more Bluetooth codecs?
The Apple AirPods (2nd generation) and Samsung Galaxy Buds Pro both use Bluetooth 5.0, but where the headsets diverge is Bluetooth codec support. The AirPods supports just the SBC and AAC Bluetooth codecs, while the Galaxy Buds Pro supports SBC, AAC, and the Samsung Scalable Codec. The proprietary codec works similarly to aptX Adaptive and makes constant adjustments to optimize audio and connection quality; it only works with Samsung hardware.
The AirPods works on both Android and iOS, but don't expect them to work perfectly.
The lack of aptX support means that neither pair of earbuds is particularly great for Android phones across the board. It's no secret that the Android OS struggles to efficiently encode the AAC Bluetooth codec. Some Android phones perform perfectly fine when they stream audio over AAC, while others fall very, very short of high quality. If you want the best sound quality for your non-Samsung Android phone, you're better off considering a pair of earbuds like the Sennheiser MOMENTUM True Wireless 2 or any one of our favorite aptX headphones.
You won't get Bluetooth multipoint support from either pair of earbuds, but both feature automatic device switching (again, limited to their respective hardware). The Galaxy Buds Pro can automatically switch between Samsung Galaxy source devices, so long as they're logged into the same Samsung account. The AirPods are able to perform the same function across Apple devices associated with the same Apple ID.
Samsung and Apple limit software features to their smartphones
The Galaxy Wearable app is easy to use and makes customizing your experience a breeze.
As ever, Apple's contentious walled garden keeps its software goodies for the fans, leaving just the basics for those who don't own Apple products, and Samsung follows suit. The Galaxy Wearable app is limited to Android OS, and a handful of important features are further limited to Samsung hardware. Yes, iOS has the Samsung Galaxy Buds app but it doesn't support the Galaxy Buds Pro. A software update may fix this though.
The Galaxy Wearable app lets you cycle through noise modes (ANC intensity or ambient passthrough), toggle and customize touch controls, select from EQ presets, and more. The Galaxy Buds Pro is the first Samsung Galaxy Buds to support Samsung 360 Audio, which is an audio standard that takes head tracking and verticality into account when processing audio. Its functionality is limited to Samsung Galaxy devices and Dolby Atmos content.
Besides the H1 chip inside, there is basically no difference between this and the previous AirPods.
The Apple AirPods (2nd generation) doesn't have a dedicated app; instead, you must open the iOS Settings app, navigate to the Bluetooth menu, and select the AirPods to access various toggles. From there you can disable or customize the double-tap command for either earbud. Other than that, you can change the name of your AirPods, adjust automatic ear detection, and select the right or left earbud's mic for calls—or let the AirPods choose automatically.
Unfortunately, both sets of earbuds limit firmware update access to certain operating systems. You can only update the Galaxy Buds Pro firmware from an Android device, and for the Apple AirPods (2nd generation), you need an Apple device. This limitation alone may be enough to push you to one pair of earbuds or the other.
Does the AirPods or Samsung Galaxy Buds Pro have better battery life?
The case supports charging via USB-C and wirelessly with Qi-compatible devices.
The Apple AirPods (2nd generation) last 4 hours, 7 minutes on a single charge (when streaming over AAC from an iPhone X), which falls short of the Samsung Galaxy Buds Pro's battery life of 4 hours, 48 minutes with ANC on. The AirPods last a shorter duration when you connect them to an Android device; we recorded just 3 hours, 29 minutes of playtime under these conditions. To get these battery measurements, we subjected both headsets to a constant 75dB output until the batteries depleted. It's pretty clear that the Galaxy Buds Pro is the winner for battery life.
Apple's charging case comes in two flavors, wired and wireless. The former charges via Lightning cable only, while the latter supports both Lightning and wireless charging—though you have to cough up a bit more for the extra functionality. Regardless of which variant you choose, the case provides up to an extra 19 hours of playtime. It's also capable of fast charging the AirPods: 15 minutes in the case provides 180 minutes of playback.
The Galaxy Buds Pro case supports wireless charging by default, but you have to pay extra for that feature with the AirPods.
The Samsung Galaxy Buds Pro case supports USB-C and wireless charging by default. You can even place the case on top of a Samsung Galaxy device and charge it that way, thanks to Wireless PowerShare. The squared off case supplies an additional 13 hours of playtime and fast charges the Galaxy Buds Pro: 10 minutes in the case yields 85 minutes of playtime.
AirPods vs Galaxy Buds Pro: Would you rather hear it all, or hear nothing?
The Apple AirPods (2nd generation) use an open-fit design and doesn't seal to the ear, meaning you hear virtually everything going on around you. This isn't great for sound quality, but it's important if you live in a city and want to remain aware of your surroundings at all times. This design means the AirPods passive isolation is nonexistent, so if you want a pair of earbuds that quiet the world around you, get the Samsung Galaxy Buds Pro.
With no meaningful isolation (due to a poor seal over the ear canal) and zero noise cancellation, you will hear basically everything around you on the AirPods (2nd generation).
With ANC on the highest setting, the Galaxy Buds Pro can render low, droning sounds three times quieter than they'd sound otherwise.
When you turn the Galaxy Buds Pro ANC to its highest level, you benefit from low-frequency attenuation which is ideal for daily commutes and flights. Noise cancelling technology shines brightest when it combats low, predictable sounds, but it can't effectively hush incidental noises (sounds higher than 1kHz)—that's where passive isolation comes into play. Since the Galaxy Buds Pro seal to the ear canals, they effectively quiet syncopated sounds like nearby conversations and the clang of dishware.
Does the Samsung Galaxy Buds Pro sound better than the AirPods?
If you're looking for something solely for exercising, there are probably better options, but this is still a good switch-hitter.
The Samsung Galaxy Buds Pro boasts a more accurate frequency response than the AirPods, which makes nearly all genres of music sound close to how the audio engineers intended.
The Galaxy Buds Pro amplifies sub-bass notes just enough to add some oomph to your favorite tracks without letting auditory masking run amok. Meanwhile, the earbuds accurately reproduce frequencies from about 100-1000Hz, which is the most important range for music. Don't worry too much about the 10kHz emphasis: that just makes it easier for you to perceive really high-pitched harmonics.
The Apple AirPods (2nd generation) (cyan) has a more amplified bass response than the Samsung Galaxy Buds Pro (yellow dash), the latter of which closely follows our house curve (pink).
The Apple AirPods (2nd generation) amplify a wider range of bass and midrange notes which yields a less accurate sound profile. While this isn't objectively appealing, it is necessary to counteract the open-fit design. An unsealed but semi-occluded ear canal can still register external sounds like the din of a train car, which can mask detail from your music. Bass notes are immediately affected by external masking, hence why Apple tuned them to sound twice as loud as certain midrange frequencies. In the real world, you're unlikely to register the bass notes from the AirPods as loud as the chart may suggest, but in a quiet environment, you're bound to notice the bass is a bit much, and instrumental detail is hard to hear.
If you have an Android phone, you can select from a menu of preset EQ profiles from the Galaxy Wearable app, but Apple keeps you locked into the default sound signature with the AirPods. You don't even get the benefits of Adaptive EQ which both the AirPods Pro and Apple AirPods (2nd generation) Max support.
Should you use the AirPods or Samsung Galaxy Buds Pro for phone calls?
If you have an iPhone, you may manually designate the left or right earbud for calls.
Samsung spared no effort in engineering the Galaxy Buds Pro microphone array. The advanced hardware effectively cancels out background noise and reduces wind noise, a benefit of the mesh hardware that covers the mic internals.
The Apple AirPods (2nd generation) microphone system is perfectly fine, but it's showing its age. When you speak with the AirPods, friends on the other side of the call might tell say you sound "hollow" or "distant." Both headsets support mono listening, and thereby mono calling, which is a must-have feature for the hearing-impaired population.
Take a moment to listen to our microphone samples and help other readers decide which sounds better.
Apple AirPods (2nd generation) microphone demo (old):
Samsung Galaxy Buds Pro microphone demo (old):
Which microphone do you think sound better?
Apple AirPods (2019)
Hold up! Something's different:
We've made a big improvement to how we demonstrate the microphone performance of products we review. We now use a standardized test setup that plays back pre-recorded phrases from a calibrated artificial mouth in our test chamber, either with or without simulated background noises, simulated reverberant spaces, or artificial wind. This means that samples from every product can be directly compared, which makes it far easier to make meaningful comparisons between products in terms of the raw speech quality or the product's ability to reject noise.
It will take a while to update our backlog of old test results, but we will update this review (and many others!) once we're able with improved microphone demos. These will be made obvious in each new sample which begins with the phrase, "This is a SoundGuys standardized microphone demonstration …"
Thank you for bearing with us, and we hope to see you again once we've sorted everything out.
Should you buy the Apple AirPods (2nd generation) or Samsung Galaxy Buds Pro?
The new Galaxy Buds Pro has active noise cancelling, and it rivals that of the Bose QuietComfort Earbuds and Sony WF-1000XM4.
The Apple AirPods (2nd generation) and Samsung Galaxy Buds Pro both restrict features to their respective hardware. If you want immediate access to customization and own a Samsung phone, get the Samsung Galaxy Buds Pro. You can access Bixby with just your voice, enjoy Samsung 360 Audio, and more. Even if you have a non-Samsung smartphone, you can access firmware updates and useful features through the Galaxy Wearable app.
$199.99 at Verizon
If you own an iPhone, the AirPods is the wiser earbuds to buy: your iPhone automatically receives updates for the AirPods, and you get hands-free access to Siri, among other things. Still, we recommend you save up for the AirPods Pro over the AirPods. The AirPods Pro seal to the ear, support Adaptive EQ, Spatial Audio, and more. Yes, the Pro variant is a bit pricier than the standard AirPods, but the expense is well worth it; plus, the AirPods Pro features an IPX4 rating whereas the AirPods lack an IP rating altogether.
Apple AirPods (2nd generation)
$149.00 at Crutchfield
$119.99 at Target
What should you get instead of the Apple AirPods (2nd generation) and Samsung Galaxy Buds Pro?
The Bose QuietComfort Earbuds features useful ANC, a sweat-resistant IPX4 rating, a comfortable fit, and good integration with both Apple and Android devices. These earphones are a great alternative, though a bit expensive.
If you want something with Microsoft 365 integration, pick up a pair of Microsoft Surface Earbuds. These earphones support SBC and aptX Bluetooth streaming and merit an IPX4 rating, so you can go for a run without worrying. The large, touch-sensitive panels are a bit polarizing design, so if you want to stick with something more traditional (and affordable), go with the Amazon Echo Buds (2nd Gen). These cost less than $150 USD, have very good noise cancelling, an IPX4 rating, and the case fast charges the buds.
The AirPods (3rd generation) will no doubt prove popular among iPhone owners, but there are far better, more comfortable options out there for less money.
iPhone owners may be tempted to spring for the newer Apple AirPods (3rd generation), but unless you want to hear everything going on around you, we recommend something with better sound quality and isolation like the Beats Studio Buds and Apple AirPods Pro. The Studio Buds has ANC and works just as well on Android as it does on iOS.
Frequently asked questions about the Apple AirPods (2nd generation) and Samsung Galaxy Buds
What's the difference between the older AirPods and the new Apple AirPods (3rd generation)?
The Apple AirPods (3rd generation) adds a more ergonomic and water-resistant build, spatial audio with head tracking, HD audio over Facetime calls, and MagSafe charging.
How does the Galaxy Buds Pro compare to the Galaxy Buds 2?
The Galaxy Buds Pro has slightly more features than the Galaxy Buds 2, such as IPX7 water-resistance, 360 Audio, and a better microphone.
What about the Samsung Galaxy Buds 2 Pro?
The Samsung Galaxy Buds 2 Pro costs a pretty penny, but features many incremental improvements over previous generations of Samsung true wireless earbuds. Most importantly, these earbuds feature significantly improved noise cancelling performance compared to the Galaxy Buds Pro.
Is the Apple AirPods Pro (2nd generation) worth the extra money?
Apple users should certainly consider spending the extra money to get the AirPods Pro (2nd generation). With improved performance compared to the original AirPods Pro, and significantly better performance than the regular AirPods, these earbuds are an obvious choice for Apple users.
AppleFeaturesSamsungTrue Wireless Earbuds


text:
Morning business round-up: South Korea-UK in trade deal
What made the business news in Asia and Europe this morning? Here's our daily business round-up:
A trade deal has been agreed between the UK and South Korea, the UK Deputy Prime Minister Nick Clegg has announced.
The UK has approved a Free Trade Agreement between the EU and South Korea, which could bring £500m a year into the British economy, he said.
Mr Clegg, attending a nuclear summit in Seoul, said the UK aimed to foster good trade links with expanding economies.
In Europe, the German Chancellor Angela Merkel has said it would be a huge political mistake if debt-stricken Greece was allowed to leave the euro.
In an exclusive interview with the BBC, she said Germany would do everything it could to keep the eurozone together.
She also calmed fears of further bailouts for eurozone countries, saying important lessons had been learned.
And she applauded the UK government's austerity programme, saying "no country can live beyond its means".
Greece recently won approval for a second bailout of 130bn euros ($173bn; £110bn) intended to help keep it afloat until 2014.
Meanwhile in Germany itself, German business confidence has risen for the fifth month in a row.
According to the Ifo business climate index, which is based on a survey of 7,000 executives, edged higher to 109.8 in March, up from 109.6 in February.
Recent data on the German economy has been mixed. Last week, a report showed that German manufacturing activity contracted in March.
Biz Heads
In other news, the upmarket bank Coutts, whose clients include the Queen, was fined £8.75m by the regulator.
The private bank has been fined £8.75m ($13.9m) by the Financial Services Authority (FSA) for not taking adequate measures to prevent money laundering.
The FSA said the failings "resulted in an unacceptable risk of Coutts handling the proceeds of crime".
The findings related to high-risk people, especially those whose political positions meant they were vulnerable to corruption.
The low cost airline, Easyjet predicted smaller losses.
The company said its six-month losses will be lower than previously forecast.
The low-cost airline is predicting losses for the six months to the end of March of between £110m and £120m, down from its previous forecast of £140m to £160m. In the first half of the previous year, it lost £153m.
Easyjet saved money because a mild winter meant there was less disruption to flights.
And still with airlines, Easyjet and its rivals have a new competitor after Spain's Iberia launched a low-cost airline.
Iberia Express will begin business on Sunday with prices starting at 25 euros (£21) for a one-way ticket.
It will cover Spanish cities including Madrid and the islands such as Ibiza, Fuerteventura and Lanzarote.
The latest edition of the BBC's Business Daily programme is all about jobs.
It goes to the place where everyone wants to work in the public sector - and look at whether there is a better way to cope with unemployment.


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text:
Das Naturschutzgebiet Wälder bei Blomberg befindet sich im Kreis Lippe und hat eine Fläche von insgesamt rund 1.168 Hektar. Das Gebiet wird mit den beiden Nummern LIP-054 (Größenangabe 552,5569 Hektar) und LIP-064 (Größenangabe 605,4911 Hektar) geführt und erstreckt sich über Teile der Städte Blomberg, Barntrup, Lügde und Schieder-Schwalenberg.

Es wurde insbesondere zum großflächigen Schutz von Hainsimsen-Buchenwald, Waldmeister-Buchenwald, Erlen-Eschen-Weichholzauenwald und des Rotmilans sowie des Mittelspechts und des Schwarzspechts ausgewiesen.

Weblinks

Schutzgebiet (Umwelt- und Naturschutz) in Europa
Naturschutzgebiet im Kreis Lippe
Geographie (Blomberg)
Geographie (Lügde)
Geographie (Schieder-Schwalenberg)
Geographie (Barntrup)

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It just goes to show that most religious aren't really that religious.
Oh they're pretty religious, they're just ignorant of their own teachings.
One way to do it, would be to offer alternative choices that include common misconceptions. That way you get an effect that could lead people to be more likely to choose the wrong answer than a pure chance guess.
To reinforce Stu's point about the results being driven by educational level, I'll point out that the Pew survey threw in a few questions on general knowledge of non-religious topics. The atheists did best on those, too. So the interesting issue (missed by the mainstream media reports) now becomes one of causality.
LOL I got a 100%! Awesome! I really did de-convert after I had time to actually study the Bible so I think the claim that reading and studying the Bible makes Atheists is very correct.
I'm always amazed at how stupid Christians are.Truth is the only thing that sets one free.
I agree with Penn that atheist are generally more educated than religious people, but I am always skeptical of how these surveys are conducted. I wonder HOW this survey was conducted? Was it done at a well respected university? Would the results be different if the survey was conducted at a Walmart parking lot?
If I had a book which I believed explained the universe, was an infallible moral guide and guaranteed an eternity of bliss, I'd read it. It's amazing how many christians have just had the nice bits explained to them when they were kids and are happy to leave it at that.


As part of our commitment to provide the highest standards of care, Arun Veterinary Group are pleased to announce that we are now able to provide medical laser treatment.
This form of treatment has been available for some years and is now becoming more popular as we understand in more detail how this treatment can work for the body and the many different conditions that can benefit from its use.
The laser we are using is the most powerful available for clinical use and is so much more than the "red light" treatments that have been popular amongst proponents of physical therapies for many years. Indeed, it is so powerful that it has the ability to burn and so has to be used with great care.
The most popular use and where we see quite remarkable improvement is in the treatment of chronic pain, particularly that associated with osteoarthritis. In some cases, improvements in mobility can be seen within a day or so of starting treatment. Using therapeutic laser as part of the treatment program for osteoarthritis has become a significant part of our daily routine. A typical treatment plan for osteoarthritis is six treatment sessions over a three week period to provide an initial "boost" followed by one session per month. We charge £30 per session.
There are many other conditions that have been shown to benefit from the use of medical laser therapy, particularly musculoskeletal injuries and surgical conditions such as fracture repair. Wounds have been shown to heal faster and we shall be starting to provide this as a routine therapy following surgery. Infections and diseases of the skin such as ear infections can be brought under control faster with the aid of laser therapy too.
The laser we use is approved by many pet insurance companies but we would recommend that you contact your provider in the first instance to ensure your policy covers this treatment.
Please contact one of our nurses to see if your pet would be eligible.
Hi would it assist a dog with canine degenerative myopathy ?
I would suggest phoning Becky on 01798 872089 and she can discuss the condition in more detail with you.


text:
India: Reject attempts to reinterpret and subvert the FRA. Deepen the struggles
Statement from All India Forum of Forest Movements (AIFFM), 28 February, 2019.
India. Ph: Ridan Sun/Focus on the Global South
Today the Supreme Court of India has put on hold its 13 February order, which directed various state governments to evict forest dwellers whose claims under Forest Rights Act have been rejected. This latest order follows the Government of India's appeal for a stay. The Supreme Court has reportedly asked the states to verify within four months whether due process of law has been followed while rejecting claims.
While the present order by the Court brings some modicum of relief for the forest communities of India, this in no way dissipates the threat. The events leading to the eviction order show definitively that the Indian state can not be expected to defend the FRA, and the persecuted and harassed forest dwellers of the country shouldn't expect justice from state and its organs–the governments at the centre and states, the judiciary and majority of the big political parties. It is also clear that if voted back to power, the present BJP-led government at the center would start afresh their systematic and deliberate attempts to scuttle the law. The attack on FRA and the autonomy and dignity of millions of forest communities across
the country would continue, beyond the ultimate outcomes of the case in the Supreme Court.
FRA and people's militant mobilisations around it impede and thwart the plans to hand over India's forests to the big private companies so that they could earn profits at the cost of people and their environments. State would try to ensure that the act is scuttled or suitably amended and diluted so that forest dwellers and their Gram Sabhas no longer have the power to protect their forests from mining, big dams, big tourism, commercial plantations and many such threats.
It's up to the people and their Movements to finally determine whether state and its corporate allies would be allowed to destroy FRA. In the face of widespread outrage and afraid of backlash during the election season, state has been forced to go defensive for the time being. It would try to come back and we need to be prepared for that.
AIFFM calls upon all forest dwellers of India, their Movements and Movements alliances, and the civil society at large to unite in the defense of FRA. We already called the law a site of struggle and let us all reiterate it together. Let us deepen and prolong the struggle.
We do not think that governments and courts could determine the extent of people's rights over forests. Nor do we believe that the question of rights can be settled within the four months as ordered by the top court or within any given time period. We reject such arbitrary and unjust interpretation of FRA, which call for replacing people with state, and all attempts to enclose the law.
We will fight for our sovereignty, autonomy and dignity. The fight shall continue.
On behalf of All India Forum of Forest Movements (AIFFM)
Devjit Nandi, Pravin Mote and Lalsing Bhujel


Well, you asked for them, and now we've got them. And just in time for summer, no less.
Response to our hand-made Italian leather and suede straps has continued to be amazing, with certain colors often selling out the same day we restock them. But one question we kept getting was "When are you going to start carrying NATOs?" The answer: Today.
Well, you asked for them, and now we've got them. NATO straps. And just in time for summer, no less.
When you visit our online shop, you'll notice a whole new category of NATO straps available on the front page. Clicking through you'll find a variety of basic colors, military inspired colors (these are military straps after all), and a few stripes as well. One reason we waited so long to bring you NATOs is that we didn't want to offer anything but the best - and we think we finally found the best.
All HODINKEE NATOs are 11 inches long, which means they will fit most wrists from 6 inches up to 9 inches. The 100% nylon webbing is tightly woven and slightly stiff when you pull the strap out of the packaging, but it softens up immediately and is extremely comfortable to wear. The hardware is all stainless steel with a soft brushed finish on all surfaces. And at 20mm these should fit most sport watches, both vintage and modern.
Because you don't need to remove any springbars to swap out NATOs, you can grab a few and swap them amongst your watches as you wish. These are extremely hard wearing and breathable in the summer heat.
Check out the full range here and let us know in the comments if there are colors or patterns you'd like to see in the future.


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