Torsion–rotation global analysis of the first three torsional states (νt = 0, 1, 2) and terahertz database for methanol

Stimulated by recent THz measurements of the methanol spectrum in one of our laboratories, undertaken in support of NASA programs related to the Herschel Space Observatory (HSO) and the Atacama Large Millimeter Array (ALMA), we have carried out a global analysis of available microwave and high-resolution infrared data for the first three torsional states (νt = 0, 1, 2), and for J values up to 30. This global fit of approximately 5600 frequency measurements and 19 000 Fourier transform far infrared (FTFIR) wavenumber measurements to 119 parameters reaches the estimated experimental measurement accuracy for the FTFIR transitions, and about twice the estimated experimental measurement accuracy for the microwave, submillimeter-wave, and terahertz transitions. The present fit is essentially a continuation of our earlier work, but we have greatly expanded our previous data set and have added a large number of new torsion–rotation interaction terms to the Hamiltonian in our previously used computer program. The results, together with a number of calculated (but unmeasured) transitions, including their line strength, estimated uncertainty, and lower state energy, are made available in the supplementary material as a database formatted to be useful for astronomical searches. Some discussion of several open spectroscopic problems, e.g., (i) an improved notation for the numerous parameters in the torsion–rotation Hamiltonian, (ii) possible causes of the failure to fit frequency measurements to the estimated measurement uncertainty, and (iii) pitfalls to be avoided when intercomparing apparently identical parameters from the internal axis method and the rho axis method are also given

Global proteomic characterization of microdissected estrogen receptor positive breast tumors

We here describe two proteomic datasets deposited in ProteomeXchange via PRIDE partner repository [1] with dataset identifiers PXD000484 (defined as "training") and PXD000485 (defined as "test") that have been used for the development of a tamoxifen outcome predictive signature [2]. Both datasets comprised 56 fresh frozen estrogen receptor (ER) positive primary breast tumor specimens derived from patients who received tamoxifen as first line therapy for recurrent disease. Patient groups were defined based on time to progression (TTP) after start of tamoxifen therapy (6 months cutoff): 32 good and 24 poor treatment outcome patients were comprised in the training set, respectively. The test set included 41 good and 15 poor treatment outcome patients. All specimens were subjected to laser capture microdissection (LCM) to enrich for epithelial tumor cells prior to high resolution mass spectrometric (MS) analysis. Protein identificat

The compositional and evolutionary logic of metabolism

Metabolism displays striking and robust regularities in the forms of modularity and hierarchy, whose composition may be compactly described. This renders metabolic architecture comprehensible as a system, and suggests the order in which layers of that system emerged. Metabolism also serves as the foundation in other hierarchies, at least up to cellular integration including bioenergetics and molecular replication, and trophic ecology. The recapitulation of patterns first seen in metabolism, in these higher levels, suggests metabolism as a source of causation or constraint on many forms of organization in the biosphere. We identify as modules widely reused subsets of chemicals, reactions, or functions, each with a conserved internal structure. At the small molecule substrate level, module boundaries are generally associated with the most complex reaction mechanisms and the most conserved enzymes. Cofactors form a structurally and functionally distinctive control layer over the small-molecule substrate. Complex cofactors are often used at module boundaries of the substrate level, while simpler ones participate in widely used reactions. Cofactor functions thus act as "keys" that incorporate classes of organic reactions within biochemistry. The same modules that organize the compositional diversity of metabolism are argued to have governed long-term evolution. Early evolution of core metabolism, especially carbon-fixation, appears to have required few innovations among a small number of conserved modules, to produce adaptations to simple biogeochemical changes of environment. We demonstrate these features of metabolism at several levels of hierarchy, beginning with the small-molecule substrate and network architecture, continuing with cofactors and key conserved reactions, and culminating in the aggregation of multiple diverse physical and biochemical processes in cells.Comment: 56 pages, 28 figure

Risk factors for surgical site infections using a data-driven approach

Objective The objective of this study was to identify risk factors for surgical site infection from digestive, thoracic and orthopaedic system surgeries using clinical and data-driven cut-off values. A second objective was to compare the identified risk factors in this study to risk factors identified in literature. Summary background data Retrospective data of 3 250 surgical procedures performed in large tertiary care hospital in The Netherlands during January 2013 to June 2014 were used. Methods Potential risk factors were identified using a literature scan and univariate analysis. A multivariate forward-step logistic regression model was used to identify risk factors. Standard medical cut-off values were compared with cut-offs determined from the data. Results For digestive, orthopaedic and thoracic system surgical procedures, the risk factors identified were preoperative temperature of �38˚C and antibiotics used at the time of surgery. Creactive protein and the duration of the surgery were identified as a risk factors for digestive surgical procedures. Being an adult (age �18) was identified as a protective effect for thoracic surgical procedures. Data-driven cut-off values were identified for temperature, age and CRP which can explain the SSI outcome up to 19.5% better than generic cut-off values. Conclusions This study identified risk factors for digestive, orthopaedic and thoracic system surgical procedures and illustrated how data-driven cut-offs can add value in the process. Future studies should investigate if data-driven cut-offs can add value to explain the outcome being modelled and not solely rely on standard medical cut-off values to identify risk factors

Altered mechanobiology of Schlemm’s canal endothelial cells in glaucoma

Increased flow resistance is responsible for the elevated intraocular pressure characteristic of glaucoma, but the cause of this resistance increase is not known. We tested the hypothesis that altered biomechanical behavior of Schlemm’s canal (SC) cells contributes to this dysfunction. We used atomic force microscopy, optical magnetic twisting cytometry, and a unique cell perfusion apparatus to examine cultured endothelial cells isolated from the inner wall of SC of healthy and glaucomatous human eyes. Here we establish the existence of a reduced tendency for pore formation in the glaucomatous SC cell—likely accounting for increased outflow resistance—that positively correlates with elevated subcortical cell stiffness, along with an enhanced sensitivity to the mechanical microenvironment including altered expression of several key genes, particularly connective tissue growth factor. Rather than being seen as a simple mechanical barrier to filtration, the endothelium of SC is seen instead as a dynamic material whose response to mechanical strain leads to pore formation and thereby modulates the resistance to aqueous humor outflow. In the glaucomatous eye, this process becomes impaired. Together, these observations support the idea of SC cell stiffness—and its biomechanical effects on pore formation—as a therapeutic target in glaucoma

4-protein signature predicting tamoxifen treatment outcome in recurrent breast cancer

Estrogen receptor (ER) positive tumors represent the majority of breast malignancies, and are effectively treated with hormonal therapies, such as tamoxifen. However, in the recurrent disease resistance to tamoxifen therapy is common and a major cause of death. In recent years, in-depth proteome analyses have enabled identification of clinically useful biomarkers, particularly, when heterogeneity in complex tumor tissue was reduced using laser capture microdissection (LCM). In the current study, we performed high resolution proteomic analysis on two cohorts of ER positive breast tumors derived from patients who either manifested good or poor outcome to tamoxifen treatment upon recurrence. A total of 112 fresh frozen tumors were collected from multiple medical centers and divided into two sets: an in-house training and a multi-center test set. Epithelial tumor cells were enriched with LCM and analyzed by nano-LC Orbitrap mass spectrometry (MS), which yielded >3000 and >4000 quantified proteins in the training and test sets, respectively. Raw data are available via ProteomeXchange with identifiers PXD000484 and PXD000485. Statistical analysis showed differential abundance of 99 proteins, of which a subset of 4 proteins was selected through a multivariate step-down to develop a predictor for tamoxifen treatment outcome. The 4-protein signature significantly predicted poor outcome patients in the test set, independent of predictive histopathological characteristics (hazard ratio [HR] = 2.17; 95% confidence interval [CI] = 1.15 to 4.17; multivariate Cox regression p value = 0.017). Immunohistochemical (IHC) staining of PDCD4, one of the signature proteins, on an independent set of formalin-fixed paraffin-embedded tumor tissues provided and independent technical validation (HR = 0.72; 95% CI = 0.57 to 0.92; multivariate Cox regression p value = 0.009). We hereby report the first validated protein predictor for tamoxifen treatment outcome in recurrent ER-positive breast cancer. IHC further showed that PDCD4 is an independent marker