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Fibroblast bioenergetics to classify amyotrophic lateral sclerosis patients
Csaba Konrad1,
Hibiki Kawamata1,
Kirsten G. Bredvik1,
Andrea J. Arreguin1,
Steven A. Cajamarca1,
Jonathan C. Hupf2,
John M. Ravits3,
Timothy M. Miller4,
Nicholas J. Maragakis5,
Chadwick M. Hales6,
Jonathan D. Glass6,
Steven Gross7,
Hiroshi Mitsumoto2 &
Giovanni Manfredi ORCID: orcid.org/0000-0003-3893-13481
Molecular Neurodegeneration volume 12, Article number: 76 (2017) Cite this article
The objective of this study was to investigate cellular bioenergetics in primary skin fibroblasts derived from patients with amyotrophic lateral sclerosis (ALS) and to determine if they can be used as classifiers for patient stratification.
We assembled a collection of unprecedented size of fibroblasts from patients with sporadic ALS (sALS, n = 171), primary lateral sclerosis (PLS, n = 34), ALS/PLS with C9orf72 mutations (n = 13), and healthy controls (n = 91). In search for novel ALS classifiers, we performed extensive studies of fibroblast bioenergetics, including mitochondrial membrane potential, respiration, glycolysis, and ATP content. Next, we developed a machine learning approach to determine whether fibroblast bioenergetic features could be used to stratify patients.
Compared to controls, sALS and PLS fibroblasts had higher average mitochondrial membrane potential, respiration, and glycolysis, suggesting that they were in a hypermetabolic state. Only membrane potential was elevated in C9Orf72 lines. ATP steady state levels did not correlate with respiration and glycolysis in sALS and PLS lines. Based on bioenergetic profiles, a support vector machine (SVM) was trained to classify sALS and PLS with 99% specificity and 70% sensitivity.
sALS, PLS, and C9Orf72 fibroblasts share hypermetabolic features, while presenting differences of bioenergetics. The absence of correlation between energy metabolism activation and ATP levels in sALS and PLS fibroblasts suggests that in these cells hypermetabolism is a mechanism to adapt to energy dissipation. Results from SVM support the use of metabolic characteristics of ALS fibroblasts and multivariate analysis to develop classifiers for patient stratification.
Amyotrophic lateral sclerosis (ALS) is the most common form of adult onset motor neuron disease, with a yearly incidence rate of 1–2.6 cases per 100,000. ALS leads to death within 3–5 years from disease onset [1]. Typical ALS is characterized by a rapidly progressive loss of upper and lower motor neurons. However, milder forms of the disease, such as primary lateral sclerosis (PLS), cause only upper motor neuron degeneration [2]. Unfortunately, most ALS clinical trials have been unsuccessful [3], and as a result there are only two currently approved drugs for ALS, Riluzole and Edaravone, both of which only prolong life by a few months. The ineffectiveness of candidate therapies, the heterogeneity of the disease phenotype, and the diversity of ALS-linked genes support the emerging concept that distinct pathogenic mechanisms may participate in the development of ALS. For this reason, research efforts are increasingly concentrated on finding biomarkers that allow stratifying patients into groups better suited for targeted clinical trials.
Recently, a number of candidate biomarkers have been proposed, including some obtained by neuroimaging [4, 5], electrical impedance myography [6], and proteomics of cerebrospinal fluid [7,8,9,10]. However, despite their potential link to disease pathogenesis, complex cellular functions have not yet been explored as ALS biomarkers. Clearly, functional measures in living cells from affected tissues, such as the spinal cord, could be problematic, but one could envision that more accessible cell types could serve as surrogate samples. In ALS, skin fibroblasts display numerous abnormalities [11,12,13,14], many of which are shared with motor neurons [15,16,17,18,19,20,21,22,23,24,25,26,27]. This suggests that these apparently unaffected cells may share common pathogenic pathways with motor neurons. Furthermore, fibroblasts can be propagated in culture, frozen, and stored almost indefinitely, and transformed in cell types that are severely affected by the disease, such as motor neurons and astrocytes [28]. Furthermore, fibroblasts derived from ALS patients were used to generate a tissue-engineered skin model, which recapitulated many of the skin alterations found in ALS [29, 30]. Therefore, studying complex functional measures in fibroblasts from ALS patients could provide a promising source of new classifiers.
Here, we have investigated a large cohort of ALS fibroblasts and characterized their bioenergetic properties. We used a battery of assays to study cellular energy metabolism, and found a hypermetabolic phenotype in ALS, involving both oxidative phosphorylation and glycolysis. Importantly, using a machine learning approach on bioenergetic profiles, we provide a proof of concept that fibroblast bioenergetic markers could differentiate between ALS and PLS, and could therefore be proposed as tools for discriminating among different forms of ALS.
All chemicals used were form Sigma (St. Louis, MO), unless otherwise specified.
Skin biopsy and fibroblast cultures
After informed consent, a punch skin biopsy was obtained from the volar part of the forearm. Skin biopsies were de-identified to protect patients' identity. Fibroblast samples were provided to our laboratory as coded samples. Some lines were obtained from the NINDS catalog of motor neuron disease fibroblasts. Skin fibroblasts were cultured as described previously [31] in Dulbecco's modified Eagle medium (DMEM) (Thermo Fisher Scientific, Waltham, MA) supplemented with 25 mM glucose, 4 mM glutamine, 1 mM pyruvate, and 10% fetal bovine serum (hereafter growth medium). All cultured fibroblast lines were studied at passages ranging between 5 and 10. We have not observed loss of contact inhibition in any of the lines or apparent differences in growth between any of the groups.
Measurements of TMRM and MTG fluorescence
Skin fibroblasts were seeded at the density of 1.5 × 104 cells/well in replicates of eights in 96-well tissue culture plates in growth medium and incubated at 37 °C in 5% CO2. The following day, cells were washed and loaded with 50 nM of the potentiometric dye Tetramethylrhodamine-methyl-ester (TMRM, 544ex, 590em; Thermo Fisher Scientific) and 450 nM MitoTracker Green (MTG, 490ex, 516em; Thermo Fisher Scientific) for 30 min at 37 °C in phenol-free DMEM containing 5 mM glucose, 4 mM glutamine, and 1 mM pyruvate. Samples were incubated in the absence or the presence of 2 μM cyanide p-trifluoromethoxyphenylhydrazone (FCCP) to completely depolarize mitochondria and obtain background TMRM and MTG fluorescence. After washing with DMEM, MTG and TMRM fluorescence were simultaneously recorded in a plate reader equipped with a polychromator (Spectramax M5; Molecular Devices Sunnyvale, CA). Background fluorescence was subtracted from the total fluorescence. MTG and TMRM fluorescence values were expressed as relative fluorescence units per milligram of total cellular proteins measured with the DC Protein Assay (BioRad, Hercules, CA).
Measurement of ATP content
Fibroblasts were seeded at the density of 1.5 × 104 cells/well in replicates of nines in 96-well tissue culture plates in growth medium incubated at 37 °C in 5% CO2. The next day cells were incubated in triplicates in DMEM containing 5 mM glucose, 4 mM glutamine, and 1 mM pyruvate (ATP baseline), or DMEM containing 4 mM glutamine, 1 mM pyruvate, and 5 mM 2-deoxy-D-glucose (2DG) to bock glycolysis (ATP 2DG), or DMEM containing 5 mM glucose, 4 mM glutamine, 1 mM pyruvate, and 1 μM oligomycin to block the mitochondrial ATPase (ATP Oligo). After 90 min incubation, cells were washed with phosphate buffered saline (PBS) and lysed in 30 μl tichloroacetic acid (2.5% W/V) on ice for 30 min. Following lysis, 20 μl aliquots were transferred into a separate plate for protein determination (DC Protein Assay). 45 μl Tris-acetate buffer (400 mM, pH = 8.0) was added to the remaining lysate. Cellular ATP content was measured after addition of 20 μl of luciferase reagent (Promega, Madison, WI) in a luminescence plate reader (Spectramax M5). Luminescence values were normalized against an ATP standard.
Measurements of oxygen consumption and extracellular acidification
Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were measured with a XF96 Extracellular Flux Analyzer (Agilent, Santa Clara, CA). Cell lines were seeded in 12 wells of a XF 96-well cell culture microplate (Agilent) at a density of 1 × 104 cells/well (cells reach confluency on the experimental day) in 200 μL of growth medium and incubated for 24 h at 37 °C in 5% CO2. After replacing the growth medium with 200 μL of XF Assay Medium (Agilent) supplemented with 5 mM glucose, 1 mM pyruvate and 4 mM glutamine, pre-warmed at 37 °C, cells were degassed for 1 h before starting the assay procedure, in a non-CO2 incubator. OCR and ECAR were recorded at baseline followed by sequential additions of 1 μM oligomycin, 2 μM FCCP and 0.5 μM Antimycin A plus 0.5 μM Rotenone. Non-mitochondrial oxygen consumption (in the presence of AA + Rot) was subtracted from all OCR values and technical replicates outside of two standard deviations of the means were discarded for both ECAR and OCR. Values were normalized by the mean protein value of each line. The measurement of OCR and ECAR in galactose medium was performed as described above, with the exception that the growth medium and assay medium contained no glucose and was supplemented with 5 mM galactose.
Lactate excretion rate measurement
Lactate production was measured using a kit (EnzyFluoTM L-lactate Assay Kit (EFLLC-100), BioAssay Systems) based on a fluorescent probe linked to NADH generated from lactate. Cell lines were seeded in XF 96-well cell culture microplates as described for OCR and ECAR measurements. On the experimental day, the growth medium was replaced with 200 μL of XF Assay Medium supplemented with 5 mM glucose, 1 mM pyruvate and 4 mM glutamine, pre-warmed at 37 °C. Cells were allowed to excrete lactate by incubation for 210 min at 37 °C in 5% CO2. Aliquots of the medium were then collected, and diluted 5 fold for the assay. Lactate standards diluted in Assay medium were used for quantification.
Bioenergetic features were tested for normality by D'Agostino-Pearson test (scipy v0.15.1; www.scipy.org), which combines skewness and kurtosis to produce an omnibus test of normality. Since none of the parameters passed the normality test, we used non-parametric tests. Differences amongst groups were compared using Kruskal–Wallis one-way ANOVA, followed by Dunn's multiple comparison test, as post hoc analysis (scipy v0.15.1). Correlations amongst bioenergetic features were tested using Spearman rank-order correlation coefficient. The p-values of correlations were adjusted for multiple comparisons by Benjamini-Hochberg correction with a false discovery rate set to <0.05. Data in the text are presented as % average (±95% confidence intervals of the differences). There were no correlations between any of the measured bioenergetics parameters and patient age, sex, or cell line passage number. Therefore, no adjustment for these parameters was necessary.
The complexity and performance of the SVM model is controlled by tunable parameters (class weights, kernels, penalties and gamma values). For each classification problem (i.e., control vs. disease, sALS vs. PLS), we tested an array of 1120 different sets of model parameters (grid search). A less complex model would have lower performance, but a more complex one would "overfit" the data, and the resulting decision boundary would follow the noise of the samples rather than inherent patterns that generalize well to the population. To find the best performing SVM that does not overfit, we used the well-established metod of k-fold cross-validation as a measure model performance: first the data was randomly divided into 10 sets, then for each set of model parameters the SVM was trained on 9 and validated on one set of the data. This step was repeated 10 times, using a different set as the validation set each time. Model performance was calculated as the average of the 10 validation performances (% accuracy). Fitting SVMs and generating receiver operating characteristic (ROC) curves were performed using scikit-learn v0.18 (www.scikit-learn.org). ROC curves are the averages of the 10 cross-validation sets.
Clinical features of study subjects
Table 1 summarizes the clinical characteristics of the sALS, PLS, C9Orf72, and control subjects whose de-identified fibroblasts were utilized for this study. sALS patients (n = 171) were clinically defined based on definite or probable ALS diagnosis. sALS patients did not have family history of ALS are were negative for SOD1 and C9orf72 mutations. PLS patients (n = 34) were clinically defined on the basis of pure upper motor neuron disease, >5 years after symptom onset, normal electromyogram, and no definable causes. As expected, there were more males in both the sALS and PLS groups, consistent with higher frequency of the disease in males [32]. Also predictably, the rate of progression (i.e., the rate at which the ALS Functional Rating Scale, ALSFRS, worsens) and the forced vital capacity (FVC) decline were significantly less severe in the PLS group than in sALS (mean: 17.7%, CI: 7.2 to 27.7%, p = 1.9E-18 and mean: 117.8%, CI: 107.1 to 128.1%, p = 2.9E-4 respectively), consistent with the milder phenotype in PLS. The age of disease onset was significantly earlier in the PLS group (mean: 89.6%, CI: 84.2 to 95.0%, p = 6.1E-05). We included in the study fibroblasts from patients with C9orf72 expansion who had ALS (n = 12) or PLS (n = 1). However, we did not compare the clinical features of the genetically defined C9orf72 group, because of the relatively low number of samples available.
Table 1 Clinical characteristics of study subjects
Bioenergetic characterization of sALS, PLS, and C9Orf72 fibroblasts
To generate a comprehensive bioenergetic profile of fibroblast lines in disease and control groups we measured the following parameters: mitochondrial membrane potential, cellular ATP content, cell respiration, and glycolysis. The fluorescence intensity of tetramethylrhodamine methyl ester (TMRM), an indicator of mitochondrial membrane potential, was significantly higher in all disease groups relative to controls (sALS mean: 127.6%, CI: 112.5 to 142.8%; PLS mean: 165.1%, CI: 142.1 to 189.3%; C9Orf72 mean: 155.1%, CI: 125.5 to 191.5%, Fig. 1a). To determine whether increased mitochondrial membrane potential could be attributed to differences in mitochondrial content, we measured the fluorescence of MitoTrackerGreen (MTG), a dye that is trapped and enriched in mitochondria, with minimal dependence on membrane potential [31], and can therefore be used as a readout of mitochondrial content. sALS and C9Orf72 lines showed no differences in MTG fluorescence relative to controls (Fig. 1b), whereas PLS had a significant decrease in MTG fluorescence (mean: 62.1%, CI: 36.3 to 88.8%). Since increased TMRM fluorescence in sALS and PLS was not matched by proportional increases in MTG fluorescence, we inferred that higher TMRM fluorescence was attributable to increased mitochondrial membrane potential, and not to mitochondrial content. Note that the number of samples tested for TMRM and MTG fluorescence was smaller than the total number of sALS lines available, because a subset of them were assessed for these two bioenergetic parameters in a previous study, which also indicated higher TMRM values in a smaller cohort of sALS and PLS lines [31].
Higher mitochondrial membrane potential in fibroblasts from patients with motor neuron disease. Scatter plots of TMRM a Control mean: 743.4, SD: 298.5; sALS mean: 948.3, SD: 380.3, PLS mean: 1227.3, SD: 456.7, C9Orf72 mean: 1153.0, SD: 397.5) and MTG b Control mean: 5080.9, SD: 2886.7, sALS mean: 5918.7, SD: 3939.6, PLS mean: 3153.6, SD: 2813.2; C9Orf72 mean: 5616.9, SD: 5911.8) values in sALS, PLS, C9Orf72, and control fibroblast lines. Middle bars represent the average values and error bars show standard deviations. RFU: relative fluorescence units. p-values are indicated where there was a significant difference between two groups. n.s.: no significant difference. n = 127 sALS, n = 33 PLS, n = 10 C9Orf72, n = 41 controls
Next, we measured mitochondrial OCR (Fig. 2a, blue curve) using flux analysis. In this experiment, baseline OCR is first measured, followed by the addition of the ATPase inhibitor oligomycin, which decreases OCR, as the proton motive force cannot be used for ATP production. The oligomycin sensitive OCR is calculated by subtracting oligomycin OCR rate from baseline. Then, the proton motive force is dissipated using the uncoupler FCCP, which allows the respiratory chain to consume oxygen at its fastest rate. The FCCP OCR rate is considered as maximal respiratory capacity, and the spare respiratory capacity is calculated as the difference between maximal and baseline OCR. Lastly, a mixture of the respiratory chain complex III and I inhibitors (Antimycin A and Rotenone, AA + Rot, respectively) are added to assess non-mitochondrial respiration, which is considered as background.
Higher oxygen consumption rates in sALS and PLS fibroblasts. a Schematic illustration of a typical flux experiment and the calculated metrics (1: OCR baseline, 2: oligomycin sensitive rate, 3: spare respiratory capacity, 4: maximal respiratory capacity, 5: ECAR baseline, 6: ECAR AA-Rot). B-E: scatter plots of OCR baseline b Control mean: 2606.9, SD: 1290.5; sALS mean: 3224.0, SD: 1864.5; PLS mean: 4504.6, SD: 3259.3; C9Orf72 mean: 3338.2, SD: 1450.7), oligomycin sensitive rate c Control mean: 2248.2, SD: 1193.9; sALS mean: 2707.6, SD: 1656.9; PLS mean: 3947.9, SD: 2892.3; C9Orf72 mean: 2900.4, SD: 1251.4), maximal respiratory capacity d Control mean: 3784.6, SD: 2166.9; sALS mean: 4445.7, SD: 3048.2; PLS mean: 6797.1, SD: 4247.2; C9Orf72 mean: 4443.5, SD: 2339.5), spare respiratory capacity e Control mean: 1177.7, SD: 1187.2; sALS mean: 1221.7, SD: 1476.5; PLS mean: 2292.5, SD: 1563.6; C9Orf72 mean: 1105.3, SD: 1195.8). Values are shown comparing sALS, PLS, C9Orf72, and control lines. Middle bars represent the average values and error bars show standard deviations. p-values are indicated where there was a significant difference between two groups. n.s.: no significant difference. n = 171 sALS; n = 34 PLS, n = 13 C9Orf72, n = 91 controls
Relative to controls, sALS and PLS had elevated baseline OCR (sALS mean: 123.7%, CI: 109.1 to 138.9%; PLS mean: 172.8%, CI: 131.6 to 216.4%, Fig. 2b), oligomycin sensitive OCR (sALS mean: 120.4%, CI: 105.3 to 136.0%; PLS mean: 175.6%, CI: 134.5 to 220.5%, Fig. 2c), and maximal respiratory capacity (sALS mean: 117.5%, CI: 100.8 to 135.0%; PLS mean: 179.6%, CI: 142.0 to 220.4%, Fig. 2d). Spare respiratory capacity was only significantly elevated in PLS lines (mean: 194.7%, CI: 149.4 to 243.7%, Fig. 2e). Comparing sALS, PLS and C9Orf72, there was no difference in baseline OCR and oligomycin sensitive respiration, but relative to PLS sALS had lower maximal capacity (mean: 65.4%, CI: 43.0 to 85.8%), and both sALS and C9Orf72 had lower spare respiratory capacity (sALS: mean: 53.3%, CI: 27.7 to 76.9%; C9Orf72: mean: 48.2%, CI: 12.9 to 83.3% Fig. 2e).
In parallel to OCR, the flux analyzer allows for measurement of ECAR (Fig. 2a, red curve). Relative to controls, both sALS and PLS cells had higher baseline ECAR (sALS: mean: 124.6%, CI: 111.0 to 138.0%; PLS: mean: 192.0%, CI: 157.4 to 230.5%, Fig. 3a). ECAR AA + Rot (i.e., maximal ECAR when the respiratory chain is fully inhibited) was also higher (sALS: mean: 118.8%, CI: 17.0 to 130.5%; PLS: mean: 186.1%, CI: 153.5 to 222.9%, Fig. 3b). Moreover, PLS had higher ECAR baseline (mean: 154.1%, CI: 126.5 to 185.1%, Fig. 3a) and ECAR AA + Rot (mean: 156.6%, CI: 129.9 to 187.3%, Fig. 3b) than sALS.
Higher extracellular acidification rates in sALS and PLS fibroblasts. Scatter plots of ECAR baseline a Control mean: 2035.9, SD: 987.4; sALS mean: 2536.0, SD: 1237.8; PLS mean: 3909.1, SD: 2175.4; C9Orf72 mean: 3381.9, SD: 2988.0), ECAR AA-Rot b Control mean: 2631.1, SD: 1164.4; sALS mean: 3126.2, SD: 1366.6; PLS mean: 4895.2, SD: 2661.5; C9Orf72 mean: 3426.1, SD: 2425.9), and OCR base/ECAR base c Control mean: 1.5, SD: 0.8; sALS mean: 1.4, SD: 0.7; PLS mean: 1.2, SD: 0.6; C9Orf72 mean: 1.8, SD: 1.7) values are shown comparing sALS, PLS, C9Orf72, and control lines. The method by which features were calculated is illustrated in Fig. 2a. Middle bars represent the average values and error bars show standard deviations. p-values are indicated where there was a significant difference between two groups. n.s.: no significant difference. n = 171 sALS; n = 34 PLS, n = 13 C9Orf72, n = 91 controls
Taken together, these results showed that sALS and PLS fibroblasts upregulate both oxidative phosphorylation and glycolysis. Furthermore, the extent of relative OCR and ECAR increase was similar in the two groups, suggesting that both bioenergetic pathways are similarly upregulated. This was confirmed by calculating the ratio between OCR baseline and ECAR baseline, which was not different among the groups (Fig. 3c), indicating that there was no metabolic shift towards either glycolysis or oxidative phosphorylation. Of note, in highly aerobic states, CO2 production from mitochondria can contribute to ECAR. However, it was empirically determined that in cells with low baseline OCR/ECAR ratio (e.g., < 4) CO2 has a negligible contribution to ECAR [33]. Since in fibroblasts the average OCR/ECAR ratio was approximately 1.5, the CO2 contribution to ECAR was likely not significant, suggesting that glycolysis was the major contributor to ECAR. Furthermore, since under conditions of full respiratory chain inhibition CO2 production in the Krebs cycle is strongly attenuated, the interpretation of higher glycolysis is also supported by the higher ECAR values in sALS and PLS after AA + Rot addition. To test experimentally the assumption that ECAR reflects lactate excretion we measured lactate production rates in a subset of 12 sALS, 12 PLS, and 12 control lines, under baseline conditions. Lactate production rate and ECAR baseline were significantly correlated (p = 0.02, data not shown), indicating that medium acidification reflects lactate excretion. Collectively, these results suggest that sALS and PLS fibroblasts have a hypermetabolic phenotype involving both oxidative phosphorylation and anaerobic glycolysis.
Next, we measured cellular steady-state ATP levels at baseline and after 90 min treatment with either 2DG to inhibit glycolysis or oligomycin to inhibit mitochondrial ATP production. In sALS, we found higher baseline ATP content compared to controls (mean: 114.9%, CI: 13.6 to 125.8%, Fig. 4a). Interestingly, PLS fibroblasts did not show higher ATP content, despite having the highest average respiration and glycolytic fluxes among all groups. The decline in ATP content after 2DG (ATP 2DG delta) was greater in sALS relative to controls (mean: 117.5%, CI: 106.4 to 128.7%, Fig. 4b). However, neither PLS nor C9Orf72 lines had ATP 2DG delta greater than controls. When oxidative phosphorylation was inhibited (ATP Oligo delta), we observed no significant decline in ATP content in controls or sALS, while PLS showed a small but significant decline (−8.9%, Fig. 4c). Overall, these results suggest that sALS lines are more dependent on glucose utilization for ATP maintenance than controls, PLS or C9Orf72. Additionally, PLS is the only group that exhibits dependency on oxidative phosphorylation for ATP maintenance.
ATP content in sALS and PLS fibroblasts. Scatter plots of baseline ATP content a Control mean: 271.6, SD: 113.7; sALS mean: 312.1, SD: 122.2; PLS mean: 271.1, SD: 103.2; C9Orf72 mean: 296.1, SD: 103.8), ATP content lost after 2DG treatment b Control mean: 201.2, SD: 87.8; sALS mean: 236.4, SD: 92.1; PLS mean: 220.1, SD: 81.4; C9Orf72 mean: 234.7, SD: 94.6), and ATP content lost after oligomycin treatment c Control mean: -17.3, SD: 78.1; sALS mean: -16.6, SD: 72.4; PLS mean: 24.1, SD: 60.2; C9Orf72 mean: 18.6, SD: 55.1) are shown comparing sALS, PLS, C9Orf72, and control lines. Groups were compared using Kruskal–Wallis one-way analysis of variance followed by Dunn's post hoc analysis. Middle bars represent the average values and error bars show standard deviations. p-values are indicated where there was a significant difference between two groups. n.s.: no significant difference. n = 171 sALS; n = 34 PLS, n = 13 C9Orf72, n = 91 controls
We performed correlation analyses of bioenergetic parameters of fibroblasts to assess if the interdependence of the parameters differed among groups (Table 2). The underlying assumption was that parameters would correlate when they were co-regulated. First, these analyses confirmed that all groups were dependent on glycolysis for energy production, as baseline ATP and sensitivity to 2DG (ATP 2DG delta) were strongly correlated. Second, the maximal glycolytic rate (ECAR AA-Rot) significantly correlated with the maximal OCR rate (OCR Max) in both sALS and PLS. Similarly, there was a direct correlation between OCR baseline and ECAR baseline in sALS and PLS, but not in controls. Taken together, these correlations suggest that in sALS and PLS lines glycolytic and oxidative fluxes are co-regulated. Despite these similarities, there were also differences between sALS and PLS. For example, only in PLS there was a negative correlation between the OCR/ECAR ratio and ATP content. Furthermore, only PLS cells showed a negative correlation between OCR/ECAR ratio and ATP 2DG delta.
Table 2 Correlations among bioenergetics features
To test the ability of fibroblasts to respond to forced oxidative metabolism we grew a subset of lines in medium containing galactose instead of glucose for 24 h. In these conditions, fibroblasts are forced to oxidize glycolysis-derived pyruvate for energy production, because galactose is not converted to glucose-6P as efficiently as glucose [34]. We tested the 12 controls, 12 sALS, and 12 PLS lines that had ECAR and OCR values closest to the average of their respective groups. As expected, in galactose medium baseline OCR was faster than in glucose medium (compare Additional file 1: Figure S1A and Figure 2B), while ECAR was lower (compare Additional file 1: Figure S1E and Figure 3A). This was also apparent from the increase of the OCR/ECAR ratio from approximately 1.5 in glucose to 3.5 in galactose (compare Additional file 1: Figure S1F and Figure 3C). The differences in baseline OCR and ECAR between sALS and controls, PLS and controls, and sALS and PLS that was observed in glucose was not detected in galactose (Additional file 1: Figure S1A and S1E). These results suggest that control cells can upregulate OCR in galactose to match sALS and PLS. Interestingly, in galactose, the spare respiratory capacity was significantly lower in sALS than controls (Additional file 1: Figure D), while in PLS it was similar to controls. Since in glucose the spare respiratory capacity was higher in sALS and PLS than control (Fig. 2e), we interpret the result in galactose as an indication that ALS and PLS fibroblasts have respiration closer to maximal in glucose and cannot upregulate it much more when placed in galactose.
Stratification of ALS patients based on individual bioenergetic features
We separated our cohort of sALS fibroblast in two equally sized groups by the median values of key bioenergetic parameters: TMRM fluorescence (mitochondrial membrane potential), ECAR AA + Rot (maximal glycolytic activity), and oligomycin sensitive OCR (ATP synthesizing respiration). We then compared the two groups (i.e., with above median and below median values) for key clinical parameters: sex, age of disease onset, site of onset (i.e., bulbar vs. spinal), rate of disease progression, FVC at time of biopsy. We found that patients with high ECAR AA + Rot had significantly higher FVC (14%, p = 0.01) and more frequent spinal onset (25%, p = 0.02). We also found that patients with high TMRM had a faster rate of decline (18%, p = 0.04). Similar analyses were not performed for PLS or C9Orf72 lines, because the number of samples was too small to obtain adequately sized groups. Indeed, although interesting, the significant differences between upper and lower halves of the sALS lines were not large. Furthermore, significant liner correlations between individual key bioenergetic and clinical parameters, after correction for multiple correlations, were not found (not shown). Therefore, based on the available samples, we suggest that individual bioenergetic parameters in fibroblast lines may not be adequate to provide definite clinical classifications.
Supervised machine learning on bioenergetic profiles classifies sALS and PLS fibroblasts with high specificity
As no individual bioenergetic measure had sufficient sensitivity or specificity to be used as a tool for classification by itself (data not shown), we took advantage of the high dimensionality of the data gathered and performed multivariate analysis. The following 12 features from 301 records (control, sALS, PLS, and C9Orf72 combined) were used: TMRM, MTG, ECAR base, ECAR AA-Rot, OCR baseline, oligomycin sensitive respiration, spare respiratory capacity, maximal respiration, OCR baseline/ECAR baseline, ATP baseline, ATP 2DG delta and ATP Oligo delta. The goal was to determine if fibroblast groups could be clustered and predicted based purely on their bioenergetic features. Importantly, considering that such tool could have translational applications in helping to stratify patients, we wanted to establish proof of principle that multivariate analyses could distinguish between sALS and PLS. To this end, we utilized support vector machines (SVM), which are trained to fit non-linear decision boundaries to high dimensional data.
First we sought to classify control fibroblasts versus all disease groups combined (i.e., sALS, PLS and C9Orf72). Receiver operating characteristics (ROC) curves were generated based on the SVM classifier that yielded the highest accuracy (probability of correct assignment of samples to their respective groups). The best performing SVM yielded good sensitivity (88.5%, CI: 84.2 to 92.7%), but low specificity (38.1%, CI: 27.7 to 48.5%) (Fig. 5a). Other parameters of the performance of this SVM classifier included, positive predictive value (78.7%, CI: 73.6 to 83.8%), negative predictive value (56.1%, CI: 43.3 to 69.0%), false positive rate (61.9%, CI: 51.5 to 72.3%), false negative rate (11.5%, CI: 7.3 to 15.8%), and false discovery rate (21.3%, CI: 16.2 to 26.4%). Interestingly, our best performing SVM to classify sALS versus PLS (Fig. 5b) yielded good sensitivity (70.6%, CI: 55.3 to 85.9%), and high specificity (98.8%, CI: 97.2 to 100.0%), with a positive predictive value of 92.3%, CI: 82.1 to 100.0%, negative predictive value of 94.4%, CI: 91.0 to 97.8%, false positive rate of 1.2%, CI: 0.0 to 2.8%, false negative rate of 29.4%, CI: 14.1 to 44.7%, and false discovery rate of 7.7%, CI: 0.0 to 17.9%. In summary, the SVM analysis of fibroblast bioenergetic features was most effective in classifying the two forms of motor neurons disease, sALS and PLS, as indicated by the high area under the curve value (0.94) and the steep rise of true positive rate of the mean ROC curve (Fig 5b).
Receiver operating characteristic (ROC) curves of SVM classifiers distinguishes lines with motor neuron disease from controls (a), and sALS from PLS (b). SVMs were trained to distinguish between two groups, based on 12 bioenergetics features. Each continuous orange ROC curve represents the mean of 10 cross-validation curves, each shown as a dotted line. Values of the area under the curve for each ROC curve (0–9 fold ROC and mean ROC) are indicated in the boxes
In this study, we characterized the bioenergetics of a large number of sALS and PLS primary skin fibroblast lines with the goal of finding disease classifiers. In addition, we studied the bioenergetics of a smaller cohort of fibroblast lines from the most common genetic form of fALS, C9orf72, to assess if bioenergetic features generalized to this form of the disease. We found that compared to healthy controls, sALS, PLS, and C9orf72 shared higher mitochondrial membrane potential, and that sALS and PLS also shared features indicative of hypermetabolism, characterized by higher mitochondrial respiration and glycolytic fluxes.
So far, studies on metabolic function in sALS and fALS fibroblasts have been conducted in much smaller cohorts. In a study of 6 sALS and 10 control fibroblast lines, it was reported that sALS had a lower average OCR baseline [34]. However, the number of the samples was thirty-fold smaller than the one studied here. We think that the sample size is important in this case, because of the variability observed among individual lines in all bioenergetic assays. Importantly, when they looked selectively at the older (≥70 years of age at onset) patients, they found that ECAR was significantly higher than controls. This is in agreement with our results showing increased ECAR in sALS, PLS. Therefore, except for the OCR baseline result, which could differ because of the difference in sample sizes, glycolytic flux increase appears to be a common finding in the two studies. Furthermore, mitochondrial function measurements performed in 3 sALS fibroblast lines and 10 controls identified a defect of cytochrome c oxidase in sALS, which correlated with a lower respiratory activity, but only when cells were forced to respire with succinate as substrate after blocking complex I with rotenone [35]. In this study, we did not assess individual respiratory chain complexes, and since fibroblasts do not naturally utilize succinate as a major respiratory substrate, we did not analyze this pathway because of relatively low physiological significance. Instead, we performed measurements of respiration in intact cells allowed to utilize glucose and NADH-generating substrates, such as glutamine and pyruvate. Therefore, the differences in the findings may be attributable to the different sample size, but also to the different approaches utilized. Lastly, a recent study of 4 C9orf72 and 4 control fibroblast lines found increased mitochondrial membrane potential in the C9orf72 lines [36] similar to that found in our C9orf72 cohort.
In our sALS and PLS fibroblast cohorts hypermetabolism was not accompanied by an increase in mitochondrial content or by a proportional increase in ATP content. Taken together, the data could be best interpreted as an adaptation to higher ATP demands, involving both oxidative and glycolytic pathways of energy generation. When cells were forced to maximize oxidative phosphorylation in galactose medium, the sALS and PLS were not capable of maintaining a faster respiratory rate than controls, suggesting that their capacity was close to maximal under glucose.
Further studies will be needed to dissect the mechanisms leading to hypermetabolism in ALS fibroblasts. However, we could hypothesize that several pathways may contribute to high ATP expenditure, including anabolic reactions, such as RNA and protein synthesis, catabolic reactions, such as protein degradation, vesicle acidification by V-ATPases, and ion homeostasis. The average values of several bioenergetic features were significantly different in PLS and sALS compared to controls, but also between sALS and PLS. The latter is a less aggressive form of motor neuron disease as compared to sALS, since it only affects the upper motor neurons and progresses more slowly. In light of these findings, it could be speculated that hypermetabolism could be a functional adaptation to increased ATP demands common to fibroblasts and neurons. Future studies utilizing neurons differentiated from fibroblast derived induced pluripotent cells or directly derived from fibroblasts will test the hypothesis that hypermetabolism is shared by fibroblasts and neurons.
We deemed that we could exploit the metabolic differences between disease and control lines and between sALS and PLS to identify disease classifiers. Despite the significant differences among groups in bioenergetic parameters, because of the large variability of the values, no single metric performed well enough to be used individually as a predictive classifier (e.g., to discriminate between controls, sALS and PLS). To overcome the constraints associated with single parameters, we opted to use multivariate analyses. We implemented a widely employed machine learning method of supervised multivariate analysis, the SVM, which takes all bioenergetics parameters from each cell line as input to make a prediction. This system generates its own hypothesis based on a learning process and produces a model for a decision boundary. The model was able to distinguish control and disease lines (sALS, PLS and C9Orf72 combined) with moderate accuracy, likely because the variability among the disease groups. Importantly, we found that sALS and PLS fibroblasts could be distinguished with high specificity using the machine learning model.
This result suggests that a machine learning model based on fibroblast bioenergetics may serve as a classifier to predict, prior to a definite clinical diagnosis, whether a patient will develop sALS or the milder motor neuron disease PLS, a prediction that would have clear prognostic implications. Admittedly, machine learning works best with high number of examples and more diverse features than the ones currently available to us. Although our collection of ALS fibroblast lines is likely one of the largest in existence, we predict that expanding the database with additional lines and features will increase the performance of the classification model. In addition, since in this work we did not have enough C9orf72 lines to be able to analyze them as a separate group, in the future it will be important increase the C9orf72 cohort. This would allow us to include them in the multivariate analyses of disease groups, for example to predict whether a C9orf72 patient will develop frontotemporal dementia, ALS, or both. Another development could include patients with different neuropathies, such as spinal muscular atrophy and hereditary spastic paraplegia, to assess whether the model's power of distinction between sALS from PLS can be also extended to other forms of lower and upper motor neuron degeneration.
We have identified bioenergetic markers of hypermetabolism in ALS fibroblasts. These findings will open new avenues of investigation of the molecular and biochemical mechanisms responsible for the bioenergetic modifications and their relationship to disease pathogenesis. We have also devised a novel approach that utilizes bioenergetic features to distinguish between fibroblast groups, which performed well in discriminating between sALS and PLS. Therefore, it is conceivable that analyses of fibroblasts bioenergetic features will help to stratify ALS patients into well-defined classes (e.g., hypermetabolic vs. normometabolic), to preselect patients entering clinical trials or to be used as post hoc criteria to interpret trial results. They could also help developing makers of prognosis and response to therapy, by implementing longitudinal studies on ALS fibroblast bioenergetics on subsequent skin biopsies obtained during disease progression and during the course of treatments.
2DG:
2-Deoxy-D-glucose
AA + Rot:
Antimycin A + Rotenone
ALSFRS:
ALS Functional Rating Scale
Dulbecco modified Eagle medium
ECAR:
Extracellular acidification rate
FCCP:
Cyanide p-trifluoromethoxyphenylhydrazone
FVC:
Forced vital capacity
MTG:
MitoTracker Green
OCR:
Oxygen consumption rate
PLS:
Primary lateral sclerosis
ROC:
Receiver operating characteristic
sALS:
SVM:
TMRM:
Tetramethylrhodamine-methyl-ester
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Kirk K, Gennings C, Hupf JC, Tadesse S, D'Aurelio M, Kawamata H, Valsecchi F, Mitsumoto H, Groups APCS, Manfredi G. Bioenergetic markers in skin fibroblasts of sporadic amyotrophic lateral sclerosis and progressive lateral sclerosis patients. Ann Neurol. 2014;76:620–4.
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This study used fibroblast samples from the NINDS Repository, as well as clinical data related to the samples. NINDS Repository sample numbers corresponding to the samples used are: (ND39022, ND39023, ND29422, ND29774, ND29149, AG13244, AG12598, AG13285, AG14284, AG11482, AG13150, AG13968, AG12657, AG12597, AG12851, AG11489, AG12964, AG12850, AG13144, AG13994, AG14048, AG13348, AG13990).
This work was funded by NIH R01NS062055 and by ALS Association grant 15-IIP-195 to GM.
All data generated or analyzed during this study are included in this published article.
Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, 407 East 61st Street, RR507, New York, NY, 10065, USA
Csaba Konrad
, Hibiki Kawamata
, Kirsten G. Bredvik
, Andrea J. Arreguin
, Steven A. Cajamarca
& Giovanni Manfredi
Department of Neurology, Columbia University, New York, NY, USA
Jonathan C. Hupf
& Hiroshi Mitsumoto
Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
John M. Ravits
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
Timothy M. Miller
Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Nicholas J. Maragakis
Department of Neurology, Emory School of Medicine, Atlanta, GA, USA
Chadwick M. Hales
& Jonathan D. Glass
Department of Pharmacology, Weill Cornell Medicine, New York, NY, USA
Steven Gross
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Key contributors: CK, HK, KGB, AJA, SAC, MS, SG, HM, GM. Data acquisition contributors: JCH, JMR, TMM, NJM, CMH, JDG. All authors read and approved the final manuscript.
Correspondence to Giovanni Manfredi.
Flux analysis of control, sALS and PLS fibroblasts under forced oxidative metabolism in galactose medium. Scatter plots of OCR baseline (A; Control mean: 3850.0, SD: 1936.7; sALS mean: 3307.5, SD: 1430.7; PLS mean: 2989.2, SD: 1164.7), oligomycin sensitive rate (B; Control mean: 3630.0, SD: 1772.0; sALS mean: 2835.0, SD: 1135.4; PLS mean: 2707.5, SD: 1138.9), maximal respiratory capacity (C; Control mean: 5325.8, SD: 2993.3; sALS mean: 3613.3, SD: 1662.5; PLS mean: 3741.7, SD: 2102.3), spare respiratory capacity (D; Control mean: 1478.8, SD: 1385.2; sALS mean: 306.8, SD: 589.2; PLS mean: 754.5, SD: 1120.1), ECAR baseline (E; Control mean: 1114.8, SD: 349.3; sALS mean: 1063.3, SD: 573.2; PLS mean: 1007.8, SD: 351.7), and OCR base/ECAR base (F; Control mean: 3.6, SD: 1.3; sALS mean: 3.5, SD: 1.4; PLS mean: 3.3, SD: 1.4). Values are shown comparing sALS, PLS, and control lines. Middle bars represent the average values and error bars show standard deviations. p-values are indicated where there was a significant difference between groups. n.s.: no significant difference. n = 12 sALS; n = 12 PLS, n = 12 controls. (DOCX 132 kb)
Konrad, C., Kawamata, H., Bredvik, K.G. et al. Fibroblast bioenergetics to classify amyotrophic lateral sclerosis patients. Mol Neurodegeneration 12, 76 (2017) doi:10.1186/s13024-017-0217-5
Received: 12 June 2017


text:
Q: Magento 2: Widget/Block not working with caching enabled I have a widget which displays on the main page and the product category pages. Based on the user's selection in the widget the product results should filter. After that if we navigate to the category pages, the category filter should apply in addition to the widget selection.
Widget uses php COOKIE in the controller to retain the selection information.
Problem: When caching is disabled the widget works fine. But when caching is enabled it is not getting the selection information from main page to category pages or vice versa. I tried setting the block caching to false then it works partially. Do php cookies not work with caching in Magento? How to get it to work?

redpajama_set_name: RedPajamaStackExchange


text:
package com.newwordpress.hum;

import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.boot.web.support.SpringBootServletInitializer;
import org.springframework.context.annotation.Bean;
import org.springframework.web.context.request.RequestContextListener;

@SpringBootApplication

public class WebApplication extends SpringBootServletInitializer {

    public static void main(String[] args) {
        SpringApplication.run(WebApplication.class, args);
    }

    @Bean
    public RequestContextListener requestContextListener() {
        return new RequestContextListener();
    }

}

redpajama_set_name: RedPajamaGithub


text:
First Art Museum in California …Timken Museum of Art Partners with EnChroma® to Enhance Experience for Those with Color Blindness
First Art Museum in California …
Timken Museum of Art Partners with EnChroma® to
Enhance Experience for Those with Color Blindness
EnChroma® Glasses open up a whole new world for color blind visitors to vividly see colors never before possible!
Timken Museum of Art of San Diego and EnChroma, creators of glasses for color blindness, today announced the first Southern California collaboration to revolutionize the museum experience for color-blind visitors. When the Timken reopens, visitors who are red-green color blind will have free access to special EnChroma glasses to experience the Museum's collection in vibrant color for the first time.
"The Timken is honored to partner with EnChroma as we take this important step—as the first art museum in the state of California—to reach a new audience previously denied the opportunity to fully experience great artworks in all of their beauty," stated Megan Pogue, Executive Director of the Timken Museum of Art. "To assist those with color blindness, the EnChroma glasses are revolutionary, and we are so proud to be on the cutting edge of this exciting new and inclusive technology. We estimate over 10,000 color blind visitors per year could benefit from these glasses."
An estimated 13 million people in the United States, and 350 million worldwide are impacted by color blindness. While those with normal color vision see more than one million shades of color, those with color vision deficiency only see an estimated 10 percent of hues and shades. As a result, colors can appear dull, indistinct and difficult to discern. Here is an example of how Old Master artworks from the Timken's Collection currently appear to color vision deficient visitors.
"The mission of EnChroma is to enable those with color vision deficiencies access to more of life's colorful experiences through our specially engineered eyewear," said Erik Ritchie, Chief Executive Officer of EnChroma. "We are thrilled to collaborate with the Timken Museum of Art to make its extensive collection of artwork accessible to those with color blindness."
EnChroma's patented lens technology is engineered with special optical filters that enable people with color blindness to see an expanded range of colors more vibrantly, clearly and distinctly. A recent study by the University of California, Davis, and France's INSERM Stem Cell and Brain Research Institute published in the scholarly journal Current Biology, demonstrated the effectiveness of EnChroma glasses.
This partnership with EnChroma is just the beginning of a larger, more robust Accessibility Program that the Timken is developing for those with sensory needs.
About The Timken Museum of Art
Founded in 1965, the Timken Museum of Art preserves the Putnam Foundation Collection of European Old Masters, American art and Russian icons for the education and benefit of present and future generations of San Diego residents and visitors. Featuring world-class exhibitions that are open free to the public, the Timken is one of the leading art museums in San Diego. The Timken strives to expand art appreciation by serving the community through year-round tours both in-person and online. The museum exhibits the only Rembrandt on public display in San Diego County - a great attraction for those new to the art world and the art connoisseur. Though currently closed for renovation, the Timken will reopen in early 2022.
About EnChroma
Based in Berkeley, Calif., EnChroma produces leading-edge eyewear for color blindness and low vision, and other solutions for color vision, sold online and through Authorized Retailers worldwide. Invented in 2010, EnChroma's patented eyewear for color blindness combines the latest in color perception, neuroscience and lens innovation to improve the lives of people with color vision deficiency around the world. EnChroma received an SBIR grant from the National Institutes of Health (NIH). It earned the 2016 Tibbetts Award from the U.S. Small Business Administration in recognition of the firm's innovative impact on the human experience through technology and the 2020 Innovation Award in Life Sciences from the Bay Area's East Bay Economic Development Alliance.
EnChroma Color Accessibility Program
The EnChroma Color Accessibility Program helps public venues, schools, state parks, libraries, museums, and other organizations purchase and loan EnChroma glasses to color blind students and guests to help make schoolwork that involves color, colorful exhibits, attractions and/or experiences accessible to the color blind. Email accessibility@enchroma.com for more.
Timken Museum of Art
Stephen Kougias
skougias@timkenmuseum.org
Kent Streeb
Vice President of Communications and Partnerships
kent@enchroma.com
CLICK HERE FOR PRESS RELEASE

redpajama_set_name: RedPajamaCommonCrawl


The first 450SX Class race held in Dallas-Fort Worth market was on February 28, 1975. Jimmy Ellis won on a Can-Am. It was Can-am''s first 450SX Class victory.
This will be the 35th time the gate will drop for a 450SX Class race in Dallas-Fort Worth market.
This will be the sixth race in AT&T Stadium.
Three riders won their last 450SX Class race in Dallas-Fort Worth market. They are: Mike Bell in 1983, David Bailey in 1986, and Jeff Emig in 1997.
Four riders won their first 450SX Class race in Dallas-Fort Worth market. They are: Ellis in 1975, J.M. Bayle in 1990, Doug Henry in 1995, and James Stewart in 2005.
At the 1977 Dallas-Fort Worth market race, there were nine different brands in the top 20: Honda, Yamaha, Kawasaki, Maico, Suzuki, Penton, Husqvarna, Bultaco, and Ossa. Can-am''s Ellis did not qualify for the race.
Chad Reed has the most 450SX Class wins in Dallas-Fort Worth market with four. Stewart and Rick Johnson are tied in second with three Dallas-Fort Worth market wins apiece.
Seven riders have won back-to-back 450SX Class races in the Dallas-Fort Worth market. Ellis, Johnson, Damon Bradshaw, Kevin Windham, Ricky Carmichael, Reed, and Ryan Villopoto are the riders to have done this.
Ryan Dungey and Trey Canard will be looking for their second 450SX Class wins in the Dallas-Fort Worth market. They won in 2011 and 2012 respectively.
Mike LaRocco, McGrath, Henry, Windham, Stewart, and Villopoto have all won in the Dallas-Fort Worth market in the 250SX and 450SX Classes.
Yamaha has not won in AT&T Stadium in either class.
Canard earned his fifth career 450SX Class victory in San Diego. He tied Mike Kiedrowski, Kent Howerton, and Davi Millsaps on the all-time 450SX Class win list. Canard now has 15 career 450SX Class podiums.
Can Canard continue to reel in Ken Roczen and Dungey in the points standings? He sits only 18 points behind the top spot.
Ken Roczen earned his 13th career 450SX Class podium with a second place finish in San Diego. It was his fifth career runner-up finish and second this season.
Roczen was able to gain two points on Dungey with his second place finish in San Diego. Can he chip away at that now nine point lead next week?
Dungey earned his 62nd career 450SX Class podium with a third place in San Diego. It was his fifth podium in six races this season. Dungey is one podium away from tying his career best six race podium streak, which he did from races seven to 12 in 2014. He also did it in the same races in 2010.
Dungey is holding onto the points lead by just nine points over Roczen. He also had Eli Tomac within striking distance. How can Dungey handle the pressure?
Reed earned his second top-five finish of the season with a fourth. Reed now has 145 career 450SX Class top-five finishes in 176 starts.
Can Reed finally break through and get a victory? He currently has a third place finish on his 2015 resume and is within striking distance of Cole Seely in fifth for the points.
Tomac earned his fifth straight top-five finish with a fifth. He is one of just two riders to have a top-five finish in every race since the opener, where he finished 20th. Dungey is the other rider to do this.
Can Tomac reaffirm his position in the championship? He sits 27 points out of first place.
After a career-best second-place finish last week, Seely earned yet another top-10 finish with a sixth place. It was his fourth straight top-10 finish of the season.
Blake Baggett tied his career high seventh-place finish from last week. He has now reeled off four straight top-10 finishes.
After missing the third Anaheim last week, Broc Tickle earned his third top-10 finish of the season with a ninth place. It was his 28th career 450SX Class top-10.
With a tenth place finish in San Diego, Jason Anderson continues to be just one of two 450SX Class riders to have a top-10 finish in each race this season. Dungey is the other rider.
Anderson also lead Seely for the rookie championship by only three points. Anderson is fifth with 85 points and Seely is sixth with 82 points.
Who will be the first rookie get a win in 2015? Anderson? Seely? Baggett?
The first 250SX Class race held in the Dallas-Fort Worth market was on May 4, 1985. Bobby More won the race on a Suzuki.
This will be the 30th time the gate will drop for a 250SX Class race in the Dallas-Fort Worth market.
KTM won their first ever 250SX Class race in the Dallas-Fort Worth market in 2001 with Grant Langston''s victory.
Villopoto won his first career 250SX Class race in the Dallas-Fort Worth market in 2006.
Baggett also earned his first 250SX Class victory in Dallas-Fort Worth market in 2010.
Only one time has there been a back-to-back winner the 250SX Class in the Dallas-Fort Worth market. McGrath won the 250SX Class races in the Dallas-Fort Worth market in ''91-''92. Since Adam Cianciarulo will not be racing in the Dallas-Fort Worth market, McGrath will remain the only rider to have done this for at least one more season.
Yamaha has not won a 250SX Class race in the Dallas-Fort Worth market since 2000. Greg Schnell was victorious that evening.
Cooper Webb earned his fourth career 250SX Class victory with his win in San Diego. It was his eighth career podium and 14th top-10 finish in 15 250SX Class starts.
The victory was Yamaha''s first 250SX Class win in San Diego since Jason Lawrence in 2008.
When the Western Regional 250SX Class Championship returns in Houston, will Webb be able to hold onto his 30-point lead in the standings?
Shane McElrath earned his first career 250SX Class podium with a second place finish. He now has seven career top-10 finishes in 15 250SX Class starts.
Can McElrath take the confidence of his first career podium onto the top step in one of the final three remaining races of the Western Regional 250SX Class?
Tyler Bowers earned his third podium of the season with a third-place finish. Bowers now has four career 250SX Class podiums and 15 top-10 finishes in 17 career 250SX Class starts.
Bowers is one of three riders to have three podium finishes through the first six races of the Western Regional 250SX Class. Webb and Jessy Nelson are the other two.
Josh Hansen made his 63rd career 250SX Class start and earned his 28th career 250SX Class top-five finish with a fourth.
Can Hansen earn a podium in one of the final three Western Regional 250SX Class races?
San Diego was Hansen''s best finish of the season and his first top-five finish in the 250SX Class since February 5, 2011 when he won the second Anaheim. He did have two top-five finishes in the 450SX Class during that time span.
Alex Martin made his 33rd career 250SX Class start and earned his second career top-five finish with a fifth. He is one of eight riders to have five top-10 finishes in the Western Regional 250SX Class and he has 17 in his career.
Zach Osborne earned his sixth top-10 finish of the season with a sixth- place finish in San Diego. He now has 26 top-10 finishes in 36 career 250SX Class starts. He is also one of just three riders to have a top-10 finish in each race this season. Webb and Nelson are the other two.
Zach Bell earned his third top-10 finish of the season with a seventh place. It was Bell''s fifth career 250SX Class top-10 finish in 11 career 250SX Class starts.
Nelson earned his 18th career top-10 finish in just 21 career 250SX Class starts with an eighth place finish.
Can Nelson close in on Webb''s 30 point lead in the standings?
Tommy Hahn tied a season best-ninth place finish in San Diego. Hahn now has 23 career top-10 250SX Class finishes in 31 career 250SX Class starts.
Chris Alldredge earned a career best 10th-place finish in San Diego.


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Canadian Dina Gilbert kicks off symphony conductor try-out concerts at the Hult on Thursday
December 5, 2016 / Bob Keefer / Comments Off on Canadian Dina Gilbert kicks off symphony conductor try-out concerts at the Hult on Thursday
It might have been inevitable that Dina Gilbert would become a conductor. The animated young French Canadian woman talks as much with her hands as she does with her voice.
That became clear in a half-hour interview this morning at the offices of the Eugene Symphony, where Gilbert is one of three finalists to replace music director Danail Rachev when he leaves his post at the end of this season. She will guest conduct the orchestra Thursday in its regular season concert.
As with the last three conductor searches, this one has drawn a lot of interest around the country and around the world. Basically, everyone out there who owns a baton wants to work with the orchestra that propelled Marin Alsop to fame and glory with the Baltimore Symphony Orchestra, followed by Miguel Harth-Bedoya (who went on to become music director at the Fort Worth Symphony Orchestra) and Giancarlo Guerrero (now at the Nashville Symphony ).
Gilbert – pronounce her last name as the French do, "jeel-BAIR" – is perfectly positioned for the youth vote, just in case any young people get a say in the selection process. At 32 she's an advocate for new music who likes to conduct soundtracks for video games and film. In person she's so animated she seems hyperactive, her hands flying constantly as she speaks.
She's premiered more than 30 works by young Canadian composers – including one, Alexandre David, who was one of the artists I met in October during my residency at Brush Creek Ranch in Wyoming. (David spoke highly of her as a conductor and as an advocate for new music. "Hire her!" he said.)
Gilbert arrived in Eugene last last week, got to spend a day exploring the community, and then plunged right in on Saturday – her birthday – by meeting the orchestra for a first rehearsal for this week's concert, which will feature works by Mozart, Korngold, Stravinsky and Dukas. (The symphony board later sang her "Happy Birthday.")
She said she's amazed to find an orchestra of this caliber in a city the size of Eugene. "Nothing like this would exist in Canada," she said. "No regular orchestra in a small community."
Gilbert just wrapped up a three-year non-renewable appointment as assistant conductor at the Orchestre symphonique de Montréal, whose previous music directors have included Zubin Mehta and Charles Dutoit.
She was often called on to step in for current music director Kent Nagano in rehearsals. The first time she said, he called her to the podium to conduct parts of the first movement of Beethoven's third symphony while he stepped out into the hall. Finally, without warning, he asked her to conduct all other movements as well.
Fortunately she had prepared to conduct every piece of music the orchestra might play while she was there – even if that meant studying scores late every night after work. "It's normal for me to have just four or five hours of sleep each night," she said. "That's OK. When I'm in a rehearsal I'm giving everything, but I'm receiving a lot back from the musicians, too."
In fact, when I asked her what the public least understands about the job of a conductor, she said it was the amount of work that goes on behind the scenes. "Our job is 99 percent done when the concert starts," she said. "I spend 70 percent of my time reading scores. The public doesn't see all the preparation we need to do before getting there."
Gilbert grew up in a small town in Quebec, the fifth of six daughters in an all-girl family. She started playing piano, switched to clarinet, and took up conducting when a teacher told her she had "an intuitive way of communicating" with her hands.
She is founder and artistic director of Ensemble Arkea, a chamber orchestra in Montreal that performs innovative interpretations of orchestral music.
She has a PhD from the Université de Montréal; her work for the degree was on syncing orchestral music to film. She also has a bachelor's in clarinet performance and a master's in conducting.
The program for Thursday's concert is:
Mozart: Overture to The Magic Flute
Korngold: Violin Concerto (Elena Urioste, violin)
Stravinsky: Petrouchka (1947 version)
Dukas: The Sorceror's Apprentice
Two other finalists for the music director job have also been named.
Ryan McAdams will be here to conduct the January 26 concert, and Francesco Lecce-Chong will conduct on March 16. Read more about all three finalists here.
Annie Get Your Gun at the Shedd: A fun night's entertainment, if culturally uncomfortable
Dina Gilbert makes a nervous debut as conductor job candidate


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Numerous vulnerabilities are detected every day. That's a good thing and a key benefit of using open source products. At m6web we don't want to be exposed to known vulnerabilities, so we use a service provided by Sensiolabs in our continuous integration tool (Jenkins) to check it.
And automatically check your composer.lock againts vulnerabilities. Your build will fail if something wrong is detected.
You can contribute to the vulnerabilities database and the checker via Github.com.


text:
Q: email.message_from_string() and imaplib adding '<' '>' Alright so I load the email in from gmail with imaplib and then when I'm trying to parse the email it does not separate anything out in a usable format. I suspect this is because somewhere in the process '<' or '>' are being added to the raw email.
Here is what the debugger is showing me after I have called the method:

As you can see it hasn't really parsed anything into a usable format.
Here is the code I'm using: (NOTE: the .replace('>', '') seems to have no effect on the end result.)
mail = imaplib.IMAP4_SSL('imap.gmail.com')
mail.login('myEmail@gmail.com', 'password')
mail.list()
mail.select('inbox')
typ, data = mail.search(None, 'ALL')
ids = data[0]
id_list = ids.split()

# get the most recent email id
latest_email_id = int( id_list[-1] )

# iterate through 15 messages in descending order starting with latest_email_id
# the '-1' dictates reverse looping order

for i in range( latest_email_id -10, latest_email_id-15, -1 ):
    typ, data = mail.fetch( str(i), '(RFC822)' )

    for response_part in data:
        if isinstance(response_part, tuple):
            msg = str(response_part[1]).replace('<', '')
            msg = msg.replace('>', '')
            msg = email.message_from_string(msg)
            #msg = feedparser.parse(response_part[1])
            varSubject = msg['subject']
            varFrom = msg['from']

python email.message_from_string() parse problems and Parsing email with Python both had very similar and identical problems to me (I think) and they solved it by altering their email, however I'm reading my email straight from Google's servers so I'm not sure exactly what to do to the email to fix it up since removing all '<' and '>' obviously won't work.
So, how do I fix the email that is read from imaplib so that it can be easily read with email.message_from_string()? (Or any other improvements/possible solutions as I'm not 100% certain the '<' and '>' are actually the problem, I'm only guessing based off of those other questions asked.)
Cheers

A: You shouldn't parse <, > and data between them - it is like parsing HTML, but much more complicated. There are existing solutions to do it.
Here is my code that can read mail with attachments, extract data that can be used for further use and process it to human and code readable format. As you can see, all tasks are being made by third-party modules.
from datetime import datetime
import imaplib
import email
import html2text
from os import path

class MailClient(object):
    def __init__(self):
        self.m = imaplib.IMAP4_SSL('your.server.com')
        self.Login()

    def Login(self):
        result, data = self.m.login('login@domain.com', 'p@s$w0rd')
        if result != 'OK':
            raise Exception("Error connecting to mailbox: {}".format(data))

    def ReadLatest(self, delete = True):
        result, data = self.m.select("inbox")
        if result != 'OK':
            raise Exception("Error reading inbox: {}".format(data))
        if data == ['0']:
            return None
        latest = data[0].split()[-1]
        result, data = self.m.fetch(latest, "(RFC822)")
        if result != 'OK':
            raise Exception("Error reading email: {}".format(data))
        if delete:
            self.m.store(latest, '+FLAGS', '\\Deleted')

        message = email.message_from_string(data[0][1])
        res = {
            'From' : email.utils.parseaddr(message['From'])[1],
            'From name' : email.utils.parseaddr(message['From'])[0],
            'Time' : datetime.fromtimestamp(email.utils.mktime_tz(email.utils.parsedate_tz(message['Date']))),
            'To' : message['To'],
            'Subject' : email.Header.decode_header(message["Subject"])[0][0],
            'Text' : '',
            'File' : None
        }

        for part in message.walk():
            if part.get_content_maintype() == 'multipart':
                continue
            if part.get_content_maintype() == 'text':
                # reading as HTML (not plain text)

                _html = part.get_payload(decode = True)
                res['Text'] = html2text.html2text(_html)

            elif part.get_content_maintype() == 'application' and part.get_filename():
                fname = path.join("your/folder", part.get_filename())
                attachment = open(fname, 'wb')
                attachment.write(part.get_payload(decode = True))
                attachment.close()
                if res['File']:
                    res['File'].append(fname)
                else:
                    res['File'] = [fname]

        return res

    def __del__(self):
        self.m.close()


Welcome to the Parliamentary Deployment Votes Database!
the Parliamentary Deployment Votes Database (PDVD) collects and shares data on parliamentary votes on the deployment of armed forces.
Since the end of the Cold War, democracies have been participating in military missions of various kinds, ranging from peacekeeping to war. These deployments have raised questions about their democratic legitimacy and parliamentary control in particular. In many democracies, deployments decisions are the exclusive competence of the government, and parliament is only informed. In a growing number of cases, however, such decisions have been put to a vote in parliament. PDVD documents and analyzes these votes.
PDVD serves a dual purpose: As scholars of political science, we are interested in an analysis of deployment votes. By sharing the data we collected, we hope to stimulate further analyses. As citizens of countries that have participated in military missions, we consider the publication of deployment votes to be a contribution to the democratic control of security and defense policy.
At the moment, in version 1 of the dataset, PDVP documents 183 votes in plenaries for the period between 1991 and August 2016 in France, Germany, Spain and the United Kingdom. This work is based on the contributions of the founding members of the project, Anna Herranz-Surrallés, Juliet Kaarbo, Falk Ostermann, and Wolfgang Wagner.


I've not been following this thread closely cuz the discussions seem to go way far into the workings of the chip and beyond my grasp of how it all works. Still... seems like a significant upgrade over the P1.
So... I have no idea of what I would use the P2 for, but I feel I gotta have one. Any Idea of a ballpark of the price for the critter once it comes out?
Yes I know there is a lot up in the air yet, but... some speculation?
Not sure, but probably $8..$10, qty 1. Maybe $6 at 1k.
Interesting. How much will P1 cost once P2 comes out?
I've thought the same but there's no point in substantially reducing the cost of P1 since legacy designs always have to pay a premium or upgrade. So eventually the P1 will be obsolete as there is no point in further production runs just as there is no point in doing QFN for instance at present. In that case if you want to use them it will be like so many other obsolete parts where you can buy them from those "sources" for many more dollars than you care. This won't happen overnight but it will happen. Anybody want to buy some SX48 chips?
I'm pretty sure that Parallax has committed to supporting P1 for the long term even after P2 comes out. However, I suppose there is no rule that says P1 can't be more expensive than P2. It just seems a bit nonintuitive.
Support does not mean price competitive of course. If the P1 was price competitive with the P2 then they would be selling P1s for less than 50 cents apiece! Of course too Parallax will be trying to keep the price of the P2 low enough for marketing reasons, much as they lowered the original $20 price of P1 for marketing reasons in the hope that the lower price would be offset by a much larger volume. So when P2 comes out then P1 volumes will drop so it is unlikely that P1 prices will drop much or if at all.
Nor should they. The two devices play in different enough niches.
Won't it be hard to sell P1 if it is at a higher price or the same as P2? I suppose they can keep selling to people who have already designed P1 into products or for people who need a lower-power Propeller. If I recall, P2 won't be as low-power as P1.
Not when they have different use cases.
Want low power operation, for example?
If it were me, I would drop P1 a little bit, but not very much. It's not really warranted.
I would have P2 come in at higher than P1 as well. Does not need to be a lot, but there is a significant investment there. It's reasonable to ask for some to begin to pay that down.
Is there any other reason besides low power that you would want to stick with P1? I guess one possibility might be the packages. Only P1 will be available in a DIP package.
P1 employs PLL, P2 employs NCO too. Those differences may matter to some people.
IMHO, low power is a big one. Enough to keep the parts differentiated.
DIP was already "deprecated" when the P1 came out in 2006, how much more so now? Besides, putting the P2 or even the P1 onto a plug-in module along with support chips and caps etc makes much more sense for prototyping. I noticed that octal sockets are getting harder to get DIP is a bit like that too, time to move on plus SMD is actually very very easy to solder onto pcbs manually with just a simple iron if need be.
IMHO there will be a continuing market for both chips.
The packing is *the* huge difference between the P1 and the P2. I as a hobbyist bought Propeller 1 chips, in addition to the occasional Quickstart and the like. So the chip price mattered a bit. I will never buy P2 chips, as there's absolutely no way for me to use them as-is. I will have to limit myself to pre-made boards, so I will look exclusively at board prices, not chip prices.
Is there any other reason besides low power that you would want to stick with P1?
Simple, elegant design, no interrupts, and PLL w/ asynchronous counter outputs are P1 wins for me.
I will be very surprised if the P2 sells for $8 to $10. Given the amount of time, effort and money that Parallax has put into the development it seems like they will need to sell it for much more to recoup their costs. Also, isn't the P2 die considerably larger than the P1 die. It seems like it would be more expensive to make than the P1. The P1 came out in 2006. The P2 will hopefully come out in 2018, 12 years later. Inflation alone should account for a $1 to $2 increase. So I would think the P2 would sell for more than $12. In the near term I would hope that the price of the P1 will drop slightly. As the P1 reaches EOL the price will increase as it will be harder to get the P1.
At first glance if seems nonintuitive, but partly that's because Parallax have such a small line-up.
Other vendors generally do not lower the price of older lines, when a new line is released, and that means their mature parts commonly have a worst price metric than the hot-new-device.
P1 will come into play in the education (formal or informal) market still, I think. My college used (probably still uses) NXP 8051s back in 2013 and they were something like $4 each! Crazy price compared to far more powerful ARMs and the like. But... legacy. These old chips (8051 and P1) are far, far simpler to program than newer ones like ARM and P2 on top of the DIP form factor.
I expect Parallax to have reasonable ideas of the wafer-based price content, but I also think they have far less idea, right now, about the other important final-price elements of Testing Time and Yields.
Cost recovery is a Volume * Margin equation, so you can make more money from a lower price point, if you sell more.
Getting early design wins is going to be very important for P2 to hit critical mass.
One simple marketing technique I see Altera use, is to have just one disti price.
Pop over to Digikey and search for CPLD, and you find the MAX-V parts from 5M40ZE64C5N	have a single price of $0.90, no volume columns.
That is a simple way to break some psychological price barrier, and encourage design wins.
Certainly the price and early availability of good P2 eval board(s) is going to be critical.
No one is going to be able to knock up a quick test on a bare P2 chip, and other vendors have Eval modules even sub $10, not to mention RaspPi Zeros at $5 & $10 price points.
Some eval boards are $20-$30, but usually those have 'something extra' - eg SiLabs add a Graphics LCD & good Debug, the Parallax FLiP adds some comprehensive power supply and protection features.
Supply and demand. Don't forget supply and demand. The price is not entirely up to Parallax to dictate.
At a 100 dollars they would never sell any P2. At ten dollars they might start to move. At one dollar they might fly off the shelf.
Of course at either extreme it's not worth doing. No sales, no return. Or many sales. no return.
Then there is the small issue of all the billions of other devices competing to do whatever it is people mind think of using a P2 for. Available at ever decreasing cost.
The most expensive stocked MCU at Digikey, (many hundred dollars!) is a Ceramic 8051, designed for high temperature systems.
More mainstream... show the 8051 has both old and new offerings.
SiLabs offer 8051 from 33c to $17.57/1k for more mature, larger package ones.
& Asian website docs indicate 0.60 yuan (sub US$0.10) for upcoming 8-pin 8051 offerings.
The newest 8051's are looking very much the lowest-cost 8 bit leaders.
"How many Stocked MCUs in 100-176 pins are > $6/1k" ?
ie close to 70% of the stocked parts in that package area, are above $6.00, and even a P2 price point of ~$7.50/1k would see ~50% above that.
I happen to have a copy of the August 2006 (#193) issue of Circuit Cellar magazine with an article on the new-born Propeller by Tom Cantrell (I've discarded most of my periodicals but have held on to that issue). Anyway, on the back of that magazine, there's a Parallax cover ad that lists the Propeller for $25 in all three variants in QTY 1. Today, we are sitting just a tad south of one-third that price. I'm sure glad that the price came down.
As for whether to charge $8/10/12, Chip and Ken and team will decide, and the market will chime in indirectly. But while the Prop 2 is certainly worth an extra buck or two, if not ten, for hobbyists and designers integrating it into low-volume projects, there's no telling what "damage" such a higher price could do to the Propeller long-term, as it might put off some from considering the chip. That in turn might prevent it from ever getting a "critical mass" of users and applications. Yes, the price is certainly justifiable for many of us, but any price increase comes at a cost and with a big risk, the risk being that it might not catch on nearly as much as it has the potential to do so. Of course, Ken has already identified the primary audience for Propellers (and it's usually not the users that need hundreds of thousands or millions of chips), so it can bear a higher price than your standard ARM chip, but not without limit. Obviously, Parallax already figured this out for the Prop 1 when they lowered the price (and if they hadn't, it's possible that the Prop 1 wouldn't even be available). So, while I can certainly see a $12 price, it increases the risk of the Prop 2 not catching on. As such, I'm encouraged that Parallax is considering quite reasonable pricing. That is, they could charge more, even gouge those that definitely need the chip, but they don't plan on doing so, nor would it likely be in their interest to do so. And aside from making money and covering the engineering/design costs--which is a necessity for the Prop 2's long-term survival--I think Chip wants to have an impact on the world, directly and indirectly fostering invention. After all, when it's all said and done, we don't take the money with us (though it can be passed on to loved ones in a will), but its nice to have had an impact on the world for the better. That's likely a secondary or tertiary consideration in a pricing discussion, if it's relevant at all, but, again, if the chip is priced out of the market, the impact of the chip will be limited, as will the proceeds that can be derived from the chip from sales.
You know, word of mouth advertising travels best of all, and a person that uses the Prop 2 in a personal design (paying whatever for it in single or small quantities) is sometimes a person who will recommend it at work or tell a friend. A $15 price, for example, has a much higher likelihood of scaring off such a potential buyer, or, such a person might not have heard of the chip at that price due to a small critical mass, such a critical mass including not just the number of chips out in the field but also the range of products/applications using the chip and type and number of code objects available for it. Again, I really think Chip/Ken/Team have a good handle on this already. They've figured it out, because, as I recall, both of them have mentioned a similar pricing range on multiple occasions. They've identified, or at least zeroed in on, the sweet spot, at least as much as is possible prior to the actual release of silicon. So, I think I might think twice about encouraging them to feel free to increase the price (not that our pricing suggestions are the deciding factor), and the same thing could be said for lowering the estimated price. I like the range they're already in, and they're there for a reason (well, many reasons, in fact).
Parallax sells current P1 chips for 7.99 in singles and 5.19 each for 1000. That's lower than the prices Chip mentioned for the P2.
The price has two main components, actual cost to manufacture and R&D cost. The first determines the minimum price, and it was my understanding that this will not be all that high for P2. The second combined with how fast they want to recover costs and their projected volume determine the actual price.
If they project a decent volume, then they can afford a lower price and still recover R&D costs in a reasonable time frame.
In any case, the singles price is somewhat less important than the volume cost as far as R&D recovery is concerned. So it's probably beneficial to make that as low as feasible, while picking a good balance for the volume prices.
For P3 I want just want a faster P1 with PortB (64 pins), some smartpins, eeprom or flash, and at less than US $1.
As that might not be possible, actually the only requirement is to be less than $1.
I want a holographic propeller beanie logo in color on the package body On second thought, I'll settle for a P2 and continued supply of the P1.
Raw cost to Parallax for the die only is more than double this. Only way is to produce tens of millions pa.
Then they don't need the P3, they can just start buying any recent low cost FPGA (like Max 10, Cyclone 10 LP, or ICE40) with embedded RAM, internal DDR3 controller, multipliers, ADC, and on and on ... starting from around $2 to $30.
I just only need a P1 at < $1. But I guess that in the next 3 years we will have a 'Max 12' at $2.50 (single quantities) with all the above things and I no longer will need the P1 anymore.
A Max 10 10M02 is never going to do what a P1 does!!!


text:
New DIY 3M Window Film Kits Provide Cost and Energy Savings
In honor of National Home Improvement Month and just in time for summer, 3M today introduces Ultra Clear Energy Saving Window Film Kits to its line of Home Energy Savings solutions.
This Smart News Release features an interactive multimedia capsule. View the full release here:
3M Ultra Clear Energy Saving Window Films come in three heat reduction shades: light, medium and dark.
During the dog days of summer, you may feel the sun's harsh rays in more than one way. Not only do they shine through windows and potentially cause upholstery and flooring to fade, they can also drive up energy consumption. While temperatures soar this summer, so might the electric bill. Air conditioners use about five percent of all the electricity produced in the United States at an annual cost of more than $11 billion to homeowners.1
3M's new do-it-yourself Ultra Clear Energy Saving Window Films apply 3M's proprietary, non-metalized engineered film technology to help block at least 98 percent of solar UVA and UVB rays and at least 35 percent of the sun's heat. The result is less work for your air conditioning unit, reduced window glare and protection for furniture and flooring.
Calculate your potential cost savings after installing 3M Ultra Clear Energy Saving Window Films here.
"For years, 3M has been a leader in commercial-, automotive- and residential-installed window films," said Mike Kuhl, global business unit manager of energy and new product platforms in 3M's Construction and Home Improvement Markets Division. "Our recent research and consumer insights show that many homeowners are looking for an affordable, energy-saving window film that they can install themselves. By applying our renewable energy technology, we developed a high-quality, heat-blocking film that helps save energy and is easy to apply with just water."
Innovative features and benefits include:
- Comes in three heat reduction shades: Light, Medium and Dark
- Easy do-it-yourself installation with no mess or special tools — squeegee and cutting tool are included in the kit
- Applies with water or non-aerosol glass cleaner
- Blocks at least 35 percent of the sun's heat, depending on the shade
- Stops at least 98 percent of UVA and UVB rays, helping to protect furniture and flooring from fading
- Minimal reflectivity and glare for a clear view
- Helps save energy by reducing air conditioning use
Truong Duy