Electrocatalysis for Chemical and Fuel Production: Investigating Climate Change Mitigation Potential and Economic Feasibility.

The manufacture of goods from oil, coal, or gas to everyday consumer products comprises in more or less all cases at least one catalytic step. Compared to conventional hydrothermal catalysis, electrocatalysis possesses the advantage of mild operational conditions and high selectivity, yet the potential energy savings and climate change mitigation have rarely been assessed. This study conducted a life cycle assessment (LCA) for the electrocatalytic oxidation of crude glycerol to produce lactic acid, one of the most common platform chemicals. The LCA results demonstrated a 31% reduction in global warming potential (GWP) compared to the benchmark (bio- and chemocatalytic) processes. Additionally, electrocatalysis yielded a synergetic potential to mitigate climate change depending on the scenario. For example, electrocatalysis combined with a low-carbon-intensity grid can reduce GWP by 57% if the process yields lactic acid and lignocellulosic biofuel as compared to a conventional fossil-based system with functionally equivalent products. This illustrates the potential of electrocatalysis as an important contributor to climate change mitigation across multiple industries. A technoeconomic analysis (TEA) for electrocatalytic lactic acid production indicated considerable challenges in economic feasibility due to the significant upfront capital cost. This challenge could be largely addressed by enabling dual redox processing to produce separate streams of renewable chemicals and biofuels simultaneously.

Measurement of cell-bound complement activation products enhances diagnostic performance in systemic lupus erythematosus.

OBJECTIVE: To determine the value of cell-bound complement activation products in combination with antinuclear antibody (ANA), anti-double-stranded DNA antibody (anti-dsDNA), and anti-mutated citrullinated vimentin antibody (anti-MCV) for the diagnosis of systemic lupus erythematosus (SLE). METHODS: This was a multicenter cross-sectional study in which 593 subjects were enrolled (210 SLE patients, 178 patients with other rheumatic diseases, and 205 healthy subjects). Complement receptor 1 levels on erythrocytes (ECR1) together with complement C4d levels on erythrocytes (EC4d), platelets (PC4d), and B cells (BC4d) were determined using fluorescence-activated cell sorting. Serologic markers were measured by enzyme-linked immunosorbent assay. Statistical analyses were performed using area under the curve (AUC), logistic regression, and calculations of diagnostic sensitivity and specificity. RESULTS: Anti-dsDNA was an insensitive (30%) but specific (>95%) marker for SLE. Levels of EC4d, BC4d, and PC4d were several times higher, and levels of ECR1 lower, in SLE patients compared to patients with other rheumatic diseases and healthy subjects. Among 523 anti-dsDNA-negative subjects, multivariate logistic regression analysis revealed that SLE was associated with ANA positivity (≥20 units), anti-MCV negativity (≤70 units), and elevated levels of both EC4d and BC4d (AUC 0.918, P < 0.001). A positive index score corresponding to the weighted sum of these 4 markers correctly categorized 72% of SLE patients. Specificity in relation to patients with other rheumatic diseases and healthy controls was >90%. The combination of anti-dsDNA and index score positivity yielded 80% sensitivity for SLE and 87% specificity against other rheumatic diseases. CONCLUSION: An assay panel combining anti-dsDNA, ANA, anti-MCV, EC4d, and BC4d is sensitive and specific for the diagnosis of SLE.

Biomarker discovery across annotated and unannotated microarray datasets using semi-supervised learning.

The growing body of DNA microarray data has the potential to advance our understanding of the molecular basis of disease. However annotating microarray datasets with clinically useful information is not always possible, as this often requires access to detailed patient records. In this study we introduce GLAD, a new Semi-Supervised Learning (SSL) method for combining independent annotated datasets and unannotated datasets with the aim of identifying more robust sample classifiers. In our method, independent models are developed using subsets of genes for the annotated and unannotated datasets. These models are evaluated according to a scoring function that incorporates terms for classification accuracy on annotated data, and relative cluster separation in unannotated data. Improved models are iteratively generated using a genetic algorithm feature selection technique. Our results show that the addition of unannotated data into training, significantly improves classifier robustness.

Amplification patterns of three genomic regions predict distant recurrence in breast carcinoma.

Currently used clinical and histopathological parameters imprecisely define the risk of distant recurrence in breast cancer, underscoring the need for more informative prognostic markers. In the present fluorescence in situ hybridization study of archived surgical specimens, we derived an algorithm for computing a prognostic index (PI) from DNA copy numbers of three genomic regions (CYP24, PDCD6IP, and BIRC5) for estrogen/progesterone receptor-positive (ER/PR+) cancers and a distinct PI (based on NR1D1, SMARCE1, and BIRC5) for estrogen/progesterone receptor-negative (ER/PR-) cancers. Among independent test cases stratified by PI, recurrence rates were significantly higher among high-risk patients than low-risk patients for both ER/PR+ (odds ratio = 9.52, 95% confidence interval >2.12, P = 0.0024) and ER/PR- (odds ratio = 12.3, 95% confidence interval >1.45, P = 0.0188) cancers. Among the entire population, recurrences were significantly more prevalent for cases with PI above the medians for both ER/PR+ (Fisher's exact, P = 1.19 x 10(-5)) and ER/PR- (P = 0.0025) patients and for the node-negative subsets (ER/PR+ node-negative, P = 0.042 and ER/PR- node-negative, P = 0.039). In conclusion, these markers perform well in comparison with other criteria for recurrence risk assessment and can be used with routinely formalin-fixed, paraffin-embedded surgical specimens.