INTRODUCTION: The implementation of open posterior component separation (PCS) surgery has led to improved outcomes for complex hernias. While the PCS technique has been shown to decrease recurrence rates, and provide a feasible option to repair hernias in nontraditional locations, there is still significant postoperative pain associated with the laparotomy and extensive abdominal wall manipulation. Systemic opioids and thoracic epidural analgesia (TEA) are both commonly utilized, either together or independently, as postoperative analgesic regimens. The benefits of TEA have been studied following a variety of surgeries, however to date no study has been performed to investigate its efficacy in this particular surgery. The aim of this study is to evaluate the benefits of TEA following open PCS. We hypothesized that the incorporation of TEA in a patients postoperative analgesic regimen would show an advantage in time to bowel recovery. METHODS: An electronic medical record query was done to identify patients who had undergone an open PCS. Once this list was compiled, a retrospective chart review was performed and patients receiving TEA (either alone or combined with systemic opioids) were compared to patients receiving only systemic opioids. The primary endpoint compared time to resumption of a full diet, given by the patients postoperative day (POD). Secondarily, time to resumption of a liquid diet, postoperative length of stay (LOS), intensive care unit (ICU) admission rate, ICU LOS, and rates of several postoperative complications were all recorded and compared. A post-hoc analysis was also performed using the same endpoints. This analysis compared cohorts of patients receiving TEA and avoiding all systemic opioids, to patients who received systemic opioids (whether alone or combined with TEA). RESULTS: Based on inclusion parameters, 101 patients met criteria for analysis. In the initial analysis, 62 patients received TEA with or without systemic opioids, and 39 patients received only systemic opioids. In comparing these groups, there was no statistically significant difference in time to full diet (TEA 2.6 ± 1.7 vs Systemic opioids 3.1 ± 2.1 [mean POD ± SD]; P=0.21). In addition, no differences were found in the secondary outcomes of time to liquid diet, ICU admission, ICU LOS, or postoperative complications. In the post-hoc analysis, the 37 patients that received only TEA, were compared against 64 patients that received systemic opioids (either with or without TEA). In this comparison, the group receiving only TEA was found to have a statically shorter time to bowel recovery compared to patients receiving systemic opioids (TEA alone 2.2 ± 1.0 vs Systemic opioids 3.2 ± 2.2, P=0.0033). This subgroup (TEA only) also showed statically shorter time to liquid diet and a decreased postoperative LOS. CONCLUSION: For patients undergoing an open PCS, the inclusion of TEA in the postoperative analgesic regimen did not shorten return of bowel function. However, when TEA was utilized and systemic opioids were avoided, time to bowel recovery and hospital LOS were both significantly shortened.

Intensity mapping (IM) is a new observational technique to survey the large-scale structure of matter using emission lines, such as the 21 cm emission line of atomic hydrogen (HI) and the rotational lines of the carbon monoxide molecule (CO). Sensitive radio surveys have the potential to detect the HI power spectrum at low redshifts (z <1) in order to constrain the properties of dark energy and massive neutrinos. Observations of the HI signal will be contaminated by instrumental noise and, more significantly, by astrophysical foregrounds, such as the Galactic synchrotron emission, which is at least four orders of magnitude brighter than the HI signal. In this thesis, we study the ability of the Generalized Needlet Internal Linear Combination (GNILC) method to subtract radio foregrounds and to recover the cosmological HI signal for HI IM experiments. The GNILC method is a new technique that uses both frequency and spatial information to separate the components of the observed data. For simulated radio observations including HI emission, Galactic synchrotron, Galactic free-free, extragalactic point sources and thermal noise, we find that it can reconstruct the HI plus noise power spectrum with 7.0% accuracy for 0.13 <z <0.48 (960 - 1260 MHz) and l <400. In this work, GNILC is also applied to a particular CO IM experiment: the CO Mapping Array Pathfinder (COMAP). In this case, the simulated radio observations include CO emission, Galactic synchrotron, Galactic free-free, Galactic anomalous microwave emission, extragalactic point sources and thermal noise. We find that GNILC can reconstruct the CO plus noise power spectra with 7.3% accuracy for COMAP phase 1 (l <1800) and 6.3% for phase 2 (l <3000). In both cases, we have 2.4 <z <3.4 (26 - 34 GHz). In this work, we also forecast the uncertainties on cosmological parameters for the upcoming HI IM experiments BINGO (BAO from Integrated Neutral Gas Observations) and SKA (Square Kilometre Array) phase-1 dish array operating in auto-correlation mode. For the optimal case of BINGO with no foregrounds, the combination of the HI angular power spectra with Planck results allows w to be measured with a precision of 4%, while the combination of the BAO acoustic scale with Planck gives a precision of 7%. We consider a number of potentially complicating effects, including foregrounds and redshift dependent bias, which increase the uncertainty on w but not dramatically; in all cases the final uncertainty is found to be less than 8% for BINGO. For the combination of SKA-MID in auto-correlation mode (total-power) with Planck, we find that, in ideal conditions, w can be measured with a precision of 4% for the redshift range 0.35 <z <3 (350 - 1050 MHz) and 2% for 0 <z <0.49 (950 - 1421 MHz). Extending the model to include the sum of neutrino masses yields a 95% upper limit of less than 0.30 eV for BINGO and less than 0.12 eV for SKA phase 1, competitive with the current best constraints in the case of BINGO and significantly better in the case of SKA.

Content: In this study, different acid proteases, which were produced from Aspergillus and Bacillus, were applied for wet-blue bating and their properties and bating effects were observed. The results showed that the acid protease produced Aspergillus had better bating effect and higher chromium tolerance than that of produced by Bacillus. Furthermore, how the acid protease influenced wet-blue microstructure was analyzed by SEM and Micro-CT. The enzymatic properties of acid protease was studied firstly.Zeta potential analysis showed that the isoelectric point (pI) of the protease was consistent with its pH value, which was at 3.0. By particle size analysis, it found that its particle size was 700 nm. In order to obtain the functional components, the molecular weight of the acidic protease was analyzed by Polyacrylamide gel Electrophoresis (SDS-PAGE). Different molecular weight components were obtained by separating the acidic protease with Tangential Flow Filtration (TFF) Technology. The characteristics of these components were determined such as enzyme types and their proportion. Afterwards, these different molecular weight components were used for wet-blue bating. The bating effluent was collected, and then, contents of Hydroxyproline (Hyp), Hyaluronic acid (HA), Desmosine (Des) and Chondroitin sulfate (CS) were analysed, which could be directly corresponding with the degradation of different proteins in wet-blue. Therefore, by characterizing and comparing the bating effect influenced with these different molecular weight components, the functional components of protease could be identified and further be separated and purified. Based on these results, this research is helpful to the development and study of the action of acid protease in the wet-blue bating process. Take-Away: Micro-CT as a new way to characterize the microstructure of leather; Identification and Separation of Effective Components of Acid Protease； Degradation Analysis of Main Components of Wet-Blue during bating process.

No description available.

L’imagerie optique par colorant potentiométrique est une méthode d’enregistrement de l’activité corticale prometteuse, mais dont le potentiel réel est limité par la présence d’artefacts et d’interférences dans les acquisitions. À partir de modèles existant dans la littérature, nous proposons un modèle génératif du signal basé sur un mélange additif de composantes, chacune contrainte dans une union d’espaces linéaires déterminés par son origine biophysique. Motivés par le problème de séparation de composantes qui en découle, qui est un problème inverse linéaire sous-déterminé, nous développons : (1) des régularisations convexes structurées spatialement, favorisant en particulier des solutions parcimonieuses ; (2) un nouvel algorithme proximal de premier ordre pour minimiser efficacement la fonctionnelle qui en résulte ; (3) des méthodes statistiques de sélection de paramètre basées sur l’estimateur non biaisé du risque de Stein. Nous étudions ces outils dans un cadre général, et discutons leur utilité pour de nombreux domaines des mathématiques appliqués, en particulier pour les problèmes inverses ou de régression en grande dimension. Nous développons par la suite un logiciel de séparation de composantes en présence de bruit, dans un environnement intégré adapté à l’imagerie optique par colorant potentiométrique. Finalement, nous évaluons ce logiciel sur différentes données, synthétiques et réelles, montrant des résultats encourageants quant à la possibilité d’observer des dynamiques corticales complexes. / Voltage-sensitive dye optical imaging is a promising recording modality for the cortical activity, but its practical potential is limited by many artefacts and interferences in the acquisitions. Inspired by existing models in the literature, we propose a generative model of the signal, based on an additive mixtures of components, each one being constrained within an union of linear spaces, determined by its biophysical origin. Motivated by the resulting component separation problem, which is an underdetermined linear inverse problem, we develop: (1) convex, spatially structured regularizations, enforcing in particular sparsity on the solutions; (2) a new rst-order proximal algorithm for minimizing e›ciently the resulting functional; (3) statistical methods for automatic parameters selection, based on Stein’s unbiased risk estimate.We study thosemethods in a general framework, and discuss their potential applications in variouselds of applied mathematics, in particular for large scale inverse problems or regressions. We develop subsequently a soŸware for noisy component separation, in an integrated environment adapted to voltage-sensitive dye optical imaging. Finally, we evaluate this soŸware on dišerent data set, including synthetic and real data, showing encouraging perspectives for the observation of complex cortical dynamics.