A newly developed band-target entropy minimization (BTEM) algorithm was tested on experimental FTIR data of Rh 4 (CO) 12 /Rh 6 (CO) 16 mixtures in order to recover the pure component spectra of the constituent complexes. Bands in the nonoverlapping bridging carbonyl region as well as bands in the highly overlapping terminal carbonyl region were targeted for retention. The bands are identified in the vector-space decomposition of the observations, a crucial first step in untangling the superposition of the pure component spectra. In both cases, the targeted band was retained, and exceptionally accurate whole spectral estimates of Rh 4 (CO) 12 and Rh 6 (CO) 16 were obtained. Due to the constructs used in BTEM, enhanced signal-to-noise characteristics result, and spectral nonlinearities arising from changing band positions and changing band shapes are essentially eliminated. For the experimentalist, the utility of BTEM arises from its direct one-spectrum-at-a-time spectral reconstruction approachswhich is guided by the choice of the targeted region. As such, BTEM appears particularly applicable to spectroscopy possessing highly localized features: i.e., FTIR, Raman, etc. The BTEM algorithm is so useful that the spectral pattern from the minute presence of suspended particles of Rh 6 (CO) 16 could be reconstructed. Indeed, the integrated absorbance of Rh 4 (CO) 12 , Rh 6 (CO) 16 , and Rh 6 (CO) 16 solids account for only ca. 0.3, 0.09, and 0.04% of the experimental observations. The new BTEM algorithm was compared to other algorithms such as SIMPLISMA, IPCA, and OPA-ALS. The latter either fail with the present data set or are unable to produce reconstructed spectra of similar quality to BTEM. This new algorithm holds considerable promise for the analysis of in-situ spectroscopic reaction data such as those arising in complex organometallic and organic syntheses, where absolutely no a priori information about the constituents/intermediates is available.

A newly developed self-modeling curve resolution method, band-target entropy minimization (BTEM), is described. This method starts with the data decomposition of a set of spectroscopic mixture data using singular value decomposition. It is followed by the transformation of the orthonormal basis vectors/loading vectors into individual pure component spectra one at a time. The transformation is based in part on some seminal ideas borrowed from information entropy theory with the desire to maximize the simplicity of the recovered pure component spectrum. Thus, the proper estimate is obtained via minimization of the proposed information entropy function or via minimization of derivative and area of the spectral estimate. Nonnegativity constraints are also imposed on the recovered pure component spectral estimate and its corresponding concentrations. As its name suggests, in this method, one targets a spectral feature readily observed in loading vectors to retain, and then combinations of the loading vectors are searched to achieve the global minimum value of an appropriate objective function. The major advantage of this method is its one spectrum at a time approach and its capability of recovering minor components having low spectroscopic signals. To illustrate the application of BTEM, spectral resolution was performed on FT-IR measurements of very highly overlapping mixture spectra containing six organic species with a two-component background interference (air). BTEM estimates were also compared with the estimates obtained using other self-modeling curve resolution techniques, i.e., SIMPLISMA, IPCA, OPA-ALS, and SIMPLISMA-ALS.

Solid-state fast ionic conductors are of great interest due to their application potential enabling the development of safer high-performance energy and conversion systems ranging from batteries through supercapacitors to fuel cells, electrolyzers, and novel neuromorphic devices. However, identifying fast ion conductors has remained a slow trial-and-error search process. High-throughput computational screening methods such as our bond valence site energy method can significantly accelerate this materials design, but their implementation not only needs to be computationally efficient and dependable but also simple to be used by experimentalists in order to find widespread usage for guiding experimental efforts to promising classes of candidate materials. To bridge the current gap between computational method developers and application-oriented users, we combine the computationally low-cost bond valence site energy calculations in our softBV software tool using a new automated pathway analysis toolthe bond valence pathway analyzer (BVPA). The integration of BVPA gives rapid comprehensive access to and simplifies the visualization of the desired information on the characteristics of ion transport properties in candidate materials. Examples for the main application of identifying suitable structure types for fast ion transport as solid electrolytes or mixed conducting electrode materials with high-rate capability are given. A new dopant predictor further simplifies defect engineering of the candidate systems by automatically suggesting suitable substitutional dopants for each site in the structure based on a new machine-learned approach.

BACKGROUND. Sphingolipids (SPs) are ubiquitous, structurally diverse molecules that include ceramides, sphingomyelins (SMs), and sphingosines. They are involved in various pathologies, including obesity and type 2 diabetes mellitus (T2DM). Therefore, it is likely that perturbations in plasma concentrations of SPs are associated with disease. Identifying these associations may reveal useful biomarkers or provide insight into disease processes. METHODS. We performed a lipidomics evaluation of molecularly distinct SPs in the plasma of 2302 ethnically Chinese Singaporeans using electrospray ionization mass spectrometry coupled with liquid chromatography. SP profiles were compared to clinical and biochemical characteristics, and subjects were evaluated with follow-up visits for 11 years. RESULTS. We found that ceramides correlated positively but hexosylceramides correlated negatively with BMI and homeostatic model assessment of insulin resistance (HOMA-IR). Furthermore, SPs with a d16:1 sphingoid backbone correlated more positively with BMI and HOMA-IR, while d18:2 SPs correlated less positively, relative to canonical d18:1 SPs. We also found that higher concentrations of 2 distinct SMs were associated with a higher risk of T2DM (HR 1.45 with 95% CI 1.18-1.78 for SM d16:1/18:0 and HR 1.40 with 95% CI 1.17-1.68 for SM d18:1/18:0). CONCLUSIONS. We identified significant associations between SPs and obesity/T2DM characteristics, specifically, those of hexosylceramides, d16:1 SPs, and d18:2 SPs. This suggests that the balance of SP metabolism, rather than ceramide accumulation, is associated with the pathology of obesity. We further identified 2 specific SPs that may represent prognostic biomarkers for T2DM.

Since the promotion of Process Analytical Technology (PAT) by the U.S. Food and Drug Administration (FDA), there has been a flurry of activities happening across related fields. This excitement permeates regulatory agencies, professional societies, academia and industry worldwide. This review surveys the PAT related developments that have taken place in the period 2004-2009. It serves as an introduction to PAT, with highlights on the parallel advances and convergence points across various fields and applications. From this review, five common threads are identified from the underlying trends of the recent global PAT endeavor, namely, organisational objectives, enabling sciences, economic outlook, collaborative efforts and emerging trends. There are also six potential gaps that require further efforts to bridge. The overall PAT venture is promising for delivering an integrated systems approach for quality design, process analyses, understanding and control, continuous improvement, knowledge and risk-based management within the FDA 21st century pharmaceutical cGMP initiative.