Designing zeolite catalysts for shape-selective reactions: Chemical modification of surfaces for improved selectivity to dimethylamine in synthesis from methanol and ammonia

The relative contributions of external and intracrystalline acidic sites of small pore H-RHO zeolite for the selective synthesis of methylamines from methanol and ammonia have been studied. Nonselective surface reactions which produce predominantly trimethylamine can be eliminated by “capping” the external acidic sites with trimethylphosphite (TMP) and other reagents, thus improving the selectivity toward the formation of dimethylamine. For small pore zeolites, neither the zeolite pore size nor the internal acidic sites is significantly affected by this treatment. In situ infrared and MAS-NMR studies show that TMP reacts irreversibly with the zeolite acidic sites via a modified Arbusov rearrangement to form surface-bound dimethylmethylphosphonate

Bayesian meta-analytical methods to incorporate multiple surrogate endpoints in drug development process.

A number of meta-analytical methods have been proposed that aim to evaluate surrogate endpoints. Bivariate meta-analytical methods can be used to predict the treatment effect for the final outcome from the treatment effect estimate measured on the surrogate endpoint while taking into account the uncertainty around the effect estimate for the surrogate endpoint. In this paper, extensions to multivariate models are developed aiming to include multiple surrogate endpoints with the potential benefit of reducing the uncertainty when making predictions. In this Bayesian multivariate meta-analytic framework, the between-study variability is modelled in a formulation of a product of normal univariate distributions. This formulation is particularly convenient for including multiple surrogate endpoints and flexible for modelling the outcomes which can be surrogate endpoints to the final outcome and potentially to one another. Two models are proposed, first, using an unstructured between-study covariance matrix by assuming the treatment effects on all outcomes are correlated and second, using a structured between-study covariance matrix by assuming treatment effects on some of the outcomes are conditionally independent. While the two models are developed for the summary data on a study level, the individual-level association is taken into account by the use of the Prentice's criteria (obtained from individual patient data) to inform the within study correlations in the models. The modelling techniques are investigated using an example in relapsing remitting multiple sclerosis where the disability worsening is the final outcome, while relapse rate and MRI lesions are potential surrogates to the disability progression

Improving Pain Assessment Using Vital Signs and Pain Medication for Patients With Sickle Cell Disease: Retrospective Study

Background: Sickle cell disease (SCD) is the most common inherited blood disorder affecting millions of people worldwide. Most patients with SCD experience repeated, unpredictable episodes of severe pain. These pain episodes are the leading cause of emergency department visits among patients with SCD and may last for several weeks. Arguably, the most challenging aspect of treating pain episodes in SCD is assessing and interpreting a patient\u27s pain intensity level. Objective: This study aims to learn deep feature representations of subjective pain trajectories using objective physiological signals collected from electronic health records. Methods: This study used electronic health record data collected from 496 Duke University Medical Center participants over 5 consecutive years. Each record contained measures for 6 vital signs and the patient\u27s self-reported pain score, with an ordinal range from 0 (no pain) to 10 (severe and unbearable pain). We also extracted 3 features related to medication: medication type, medication status (given or applied, or missed or removed or due), and total medication dosage (mg/mL). We used variational autoencoders for representation learning and designed machine learning classification algorithms to build pain prediction models. We evaluated our results using an accuracy and confusion matrix and visualized the qualitative data representations. Results: We designed a classification model using raw data and deep representational learning to predict subjective pain scores with average accuracies of 82.8%, 70.6%, 49.3%, and 47.4% for 2-point, 4-point, 6-point, and 11-point pain ratings, respectively. We observed that random forest classification models trained on deep represented features outperformed models trained on unrepresented data for all pain rating scales. We observed that at varying Likert scales, our models performed better when provided with medication data along with vital signs data. We visualized the data representations to understand the underlying latent representations, indicating neighboring representations for similar pain scores with a higher resolution of pain ratings. Conclusions: Our results demonstrate that medication information (the type of medication, total medication dosage, and whether the medication was given or missed) can significantly improve subjective pain prediction modeling compared with modeling with only vital signs. This study shows promise in data-driven estimated pain scores that will help clinicians with additional information about the patient\u27s condition, in addition to the patient\u27s self-reported pain scores

Clustering of Pain Dynamics in Sickle Cell Disease from Sparse, Uneven Samples

Irregularly sampled time series data are common in a variety of fields. Many typical methods for drawing insight from data fail in this case. Here we attempt to generalize methods for clustering trajectories to irregularly and sparsely sampled data. We first construct synthetic data sets, then propose and assess four methods of data alignment to allow for application of spectral clustering. We also repeat the same process for real data drawn from medical records of patients with sickle cell disease -- patients whose subjective experiences of pain were tracked for several months via a mobile app. We find that different methods for aligning irregularly sampled sparse data sets can lead to different optimal numbers of clusters, even for synthetic data with known properties. For the case of sickle cell disease, we find that three clusters is a reasonable choice, and these appear to correspond to (1) a low pain group with occasionally acute pain, (2) a group which experiences moderate mean pain that fluctuates often from low to high, and (3) a group that experiences persistent high levels of pain. Our results may help physicians and patients better understand and manage patients\u27 pain levels over time, and we expect that the methods we develop will apply to a wide range of other data sources in medicine and beyond

Predictors of Employee Involvement in a Worksite Health Promotion Program

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67001/2/10.1177_109019819001700404.pd

THE PERFORMANCE OF BASE-FORM ION EXCHANGERS FOR pH CONTROL AND REMOVAL OF RADIOISOTOPES FROM A PRESSURIZED WATER REACTOR SYSTEM

Laboratory experiments and in-pile loop tests designed to evaluate, explain, and predict the performance of mixedbed ion exchange columns in the base form for the control of radioisotopes in reactor coolants are summarized. The results of these tests are evaluated with the aid of a simple theory of column performance for absorption of radioactivity decaying ions, based on an approximate model for an ion exchange column. It is concluded that LiOH form resin will perform satisfactorily for both pH control and activity removal and that it is more effective than either KOH resin or NH/sub 4/OH resin for these purposes. (auth

The Impact of Initial-Final Mass Relations on Black Hole Microlensing

Uncertainty in the initial-final mass relation (IFMR) has long been a problem in understanding the final stages of massive star evolution. One of the major challenges of constraining the IFMR is the difficulty of measuring the mass of non-luminous remnant objects (i.e. neutron stars and black holes). Gravitational wave detectors have opened the possibility of finding large numbers of compact objects in other galaxies, but all in merging binary systems. Gravitational lensing experiments using astrometry and photometry are capable of finding compact objects, both isolated and in binaries, in the Milky Way. In this work we improve the PopSyCLE microlensing simulation code in order to explore the possibility of constraining the IFMR using the Milky Way microlensing population. We predict that the Roman Space Telescope's microlensing survey will likely be able to distinguish different IFMRs based on the differences at the long end of the Einstein crossing time distribution and the small end of the microlensing parallax distribution, assuming the small ($\pi_E \lesssim 0.02$) microlensing parallaxes characteristic of black hole lenses are able to be measured accurately. We emphasize that future microlensing surveys need to be capable of characterizing events with small microlensing parallaxes in order to place the most meaningful constraints on the IFMR.Comment: 24 pages, 17 figures Accepted to Ap

Hypoxia-induced bacterial translocation in the puppy

Because hypoxia is one of the most common major stresses to which a neonate is exposed, we postulated that it alone might be the cause of intestinal bacterial translocation, which could be the underlying etiology of neonatal sepsis. An animal model, in which hypoxia is the sole stress, was developed in our laboratory and tested in 18 puppies to determine the effect of hypoxia and reoxygenation on intestinal bacterial translocation. In group I (n = 8), following laparotomy and cannulation of the superior mesenteric vein (SMV), the FIO2 was decreased from 21% to 9% for 90 minutes followed by reoxygenation at 21% for 120 minutes. The abdomen was closed and the animals were allowed to recover. After 24 hours the mesenteric lymph nodes (MLNs), spleen, and liver were harvested for bacterial determination and the ileum and jejunum for histological evaluation. Group II (n = 7) was treated the same as group I with the FIO2 maintained at 21%. Group III (n = 3) animals were killed, without intervention, for bacterial analysis. In group I, the systemic PO2 decreased by 75%, SMV PO2 decreased by 64%, and oxygen delivery to the small bowel decreased by 80% in comparison with group II. The mean arterial pressure and cardiac output were not significantly different between group I and group II; however, the mucosal blood flow was decreased by 60% (P P P < .001). This study demonstrates that severe systemic hypoxia and subsequent reoxygenation does not initiate oxidant-mediated, lipid peroxidation injury to the small bowel mucosa, but does allow bacterial translocation to the MLNs. Thus, hypoxia-induced bacterial translocation could serve as a model for neonatal sepsis without apparent bowel injury.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30364/1/0000766.pd