The exploration of less expensive materials for the direct synthesis of hydrogen peroxide

The research presented in this thesis describes the direct synthesis of hydrogen peroxide from H2 and O2 using supported palladium based catalysts. The direct synthesis of hydrogen peroxide offers a more straightforward and sustainable alternative to the current industrial anthraquinone autoxidation (AO) process. Au-Pd bimetallic catalysts have been proved to be highly active for the direct synthesis process. The work presented in this thesis attempted to produce less expensive catalysts through adding cheap secondary metal to Pd as an effective substitute to Au or using an effective preparation for a low metal loading of Au-Pd nanoparticles. In addition, a comprehension of the actual active sites over bimetallic and Pd monometallic particles for H2O2 direct synthesis was also attempted. The first part of this work aims to explain an interesting phenomenon – an increase of activity for H2O2 direct synthesis and a decrease of hydrogenation of H2O2 over carbon supported Ni-Pd bimetallic and Pd only catalysts after both hydrogen peroxide synthesis and storage under ambient conditions. Based on the results of XPS, XRD and CO-chemisorption integrated with previous publications, it was concluded that (i) both the reaction of hydrogen peroxide direct synthesis and catalyst storage led to an decrease of particle dispersion; (ii) relative to the active sites on high energy surfaces/small particles of Pd (0), those on low energy surfaces/large particles are more selective for H2O2 synthesis, as the latter demonstrates lower activity of dissociative adsorption of O2 and H2O2. The role of secondary metal-Ni added to Pd was also investigated for H2O2 direct synthesis in the thesis. For carbon supported Ni/Pd catalysts (including Ni monometallic, Pd monometallic and Ni-Pd bimetallic catalysts), the addition of Ni to Pd enhanced catalytic activity and selectivity for H2O2 synthesis. The results of MP-AES, XPS, XRD and TPR implied that metallic Pd may sit on the top of Ni oxides with a dissolution of metallic Ni in Pd to some degree. Electron transfer from Ni to Pd probably also occurred which was inferred by XPS analysis. The role of Ni in Pd for H2O2 direct synthesis was Preface III also investigated over TiO2 supported catalysts which led to an enhancement of H2O2 productivity, H2 conversion rate and H2O2 selectivity relative to Pd only catalyst. Based on the results of XPS, TPR and STEM, it was concluded that inactive Ni species diluted Pd sites as individual Pd atoms which are the selective active sites for H2O2 direct formation. The next part of the study addressed a modified impregnation method (MIm) for the preparation of Au-Pd nanoparticles. These nanoparticles have been proved previously by STEM which are well dispersed homogeneous particles because of excess amount of Cl- ions in the preparation. As a consequence, the resulted catalyst demonstrated a superior activity than conventional impregnation method (CIm) analogues even the latter loaded with a quintuple metal loading. Through tuning Pd metal loading in 1 wt% Au-Pd and Pd only catalysts for H2O2 direct synthesis, two typical phenomena were observed in general: (i) an enhanced synergistic effect of Au and Pd by MIm than CIm and (ii) a rise of H2O2 productivity based on the mass of Pd loading with the addition of Au in 1 wt% Au-Pd MIm catalysts. As the possible formation of homogeneous Au-Pd alloy, an increase of H2O2 productivity based on Pd with the increase of Au content is probably out of the ensemble effect from the secondary metal

Unsupervised Echocardiography Registration through Patch-based MLPs and Transformers

Image registration is an essential but challenging task in medical image computing, especially for echocardiography, where the anatomical structures are relatively noisy compared to other imaging modalities. Traditional (non-learning) registration approaches rely on the iterative optimization of a similarity metric which is usually costly in time complexity. In recent years, convolutional neural network (CNN) based image registration methods have shown good effectiveness. In the meantime, recent studies show that the attention-based model (e.g., Transformer) can bring superior performance in pattern recognition tasks. In contrast, whether the superior performance of the Transformer comes from the long-winded architecture or is attributed to the use of patches for dividing the inputs is unclear yet. This work introduces three patch-based frameworks for image registration using MLPs and transformers. We provide experiments on 2D-echocardiography registration to answer the former question partially and provide a benchmark solution. Our results on a large public 2D echocardiography dataset show that the patch-based MLP/Transformer model can be effectively used for unsupervised echocardiography registration. They demonstrate comparable and even better registration performance than a popular CNN registration model. In particular, patch-based models better preserve volume changes in terms of Jacobian determinants, thus generating robust registration fields with less unrealistic deformation. Our results demonstrate that patch-based learning methods, whether with attention or not, can perform high-performance unsupervised registration tasks with adequate time and space complexity. Our codes are available https://gitlab.inria.fr/epione/mlp\_transformer\_registratio

Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy

Posterior sampling, i.e., exponential mechanism to sample from the posterior distribution, provides $\varepsilon$-pure differential privacy (DP) guarantees and does not suffer from potentially unbounded privacy breach introduced by $(\varepsilon,\delta)$-approximate DP. In practice, however, one needs to apply approximate sampling methods such as Markov chain Monte Carlo (MCMC), thus re-introducing the unappealing $\delta$-approximation error into the privacy guarantees. To bridge this gap, we propose the Approximate SAample Perturbation (abbr. ASAP) algorithm which perturbs an MCMC sample with noise proportional to its Wasserstein-infinity ($W_\infty$) distance from a reference distribution that satisfies pure DP or pure Gaussian DP (i.e., $\delta=0$). We then leverage a Metropolis-Hastings algorithm to generate the sample and prove that the algorithm converges in W$_\infty$ distance. We show that by combining our new techniques with a careful localization step, we obtain the first nearly linear-time algorithm that achieves the optimal rates in the DP-ERM problem with strongly convex and smooth losses

Intracellular Accumulation of Linezolid and Florfenicol in OptrA-Producing Enterococcus faecalis and Staphylococcus aureus

The optrA gene, which confers transferable resistance to oxazolidinones and phenicols, is defined as an ATP-binding cassette (ABC) transporter but lacks transmembrane domains. The resistance mechanism of optrA and whether it involves antibiotic efflux or ribosomal protection remain unclear. In this study, we determined the MIC values of all bacterial strains by broth microdilution, and used ultra-high performance liquid chromatography-tandem quadrupole mass spectrometry to quantitatively determine the intracellular concentrations of linezolid and florfenicol in Enterococcus faecalis and Staphylococcus aureus. Linezolid and florfenicol both accumulated in susceptible strains and optrA-carrying strains of E. faecalis and S. aureus. No significant differences were observed in the patterns of drug accumulation among E. faecalis JH2-2, E. faecalis JH2-2/pAM401, and E. faecalis JH2-2/pAM401+optrA, but also among S. aureus RN4220, S. aureus RN4220/pAM401, and S. aureus RN4220/pAM401+optrA. ANOVA scores also suggested similar accumulation conditions of the two target compounds in susceptible strains and optrA-carrying strains. Based on our findings, the mechanism of optrA-mediated resistance to oxazolidinones and phenicols obviously does not involve active efflux and the OptrA protein does not confer resistance via efflux like other ABC transporters

GC/MS ANALYSIS OF COAL TAR COMPOSITION PRODUCED FROM COAL PYROLYSIS

Coal tar is a significant product generated from coal pyrolysis. A detailed analytical study on its composition and chemical structure will be of great advantage to its further processing and utilization. Using a combined method of planigraphy-gas chromatograph/mass spectroscopy (GC/MS), this work presents a composition analysis on the coal tar generated in the experiment. The analysis gives a satisfactory result, which offers a referable theoretical foundation for the further processing and utilization of coal tar. KEY WORDS: Coking-coals, Coal pyrolysis, Coal tar, GC/MS Bull. Chem. Soc. Ethiop. 2007, 21(2), 229-240

EVIL: Evidential Inference Learning for Trustworthy Semi-supervised Medical Image Segmentation

Recently, uncertainty-aware methods have attracted increasing attention in semi-supervised medical image segmentation. However, current methods usually suffer from the drawback that it is difficult to balance the computational cost, estimation accuracy, and theoretical support in a unified framework. To alleviate this problem, we introduce the Dempster-Shafer Theory of Evidence (DST) into semi-supervised medical image segmentation, dubbed Evidential Inference Learning (EVIL). EVIL provides a theoretically guaranteed solution to infer accurate uncertainty quantification in a single forward pass. Trustworthy pseudo labels on unlabeled data are generated after uncertainty estimation. The recently proposed consistency regularization-based training paradigm is adopted in our framework, which enforces the consistency on the perturbed predictions to enhance the generalization with few labeled data. Experimental results show that EVIL achieves competitive performance in comparison with several state-of-the-art methods on the public dataset

Robust Split Federated Learning for U-shaped Medical Image Networks

U-shaped networks are widely used in various medical image tasks, such as segmentation, restoration and reconstruction, but most of them usually rely on centralized learning and thus ignore privacy issues. To address the privacy concerns, federated learning (FL) and split learning (SL) have attracted increasing attention. However, it is hard for both FL and SL to balance the local computational cost, model privacy and parallel training simultaneously. To achieve this goal, in this paper, we propose Robust Split Federated Learning (RoS-FL) for U-shaped medical image networks, which is a novel hybrid learning paradigm of FL and SL. Previous works cannot preserve the data privacy, including the input, model parameters, label and output simultaneously. To effectively deal with all of them, we design a novel splitting method for U-shaped medical image networks, which splits the network into three parts hosted by different parties. Besides, the distributed learning methods usually suffer from a drift between local and global models caused by data heterogeneity. Based on this consideration, we propose a dynamic weight correction strategy (\textbf{DWCS}) to stabilize the training process and avoid model drift. Specifically, a weight correction loss is designed to quantify the drift between the models from two adjacent communication rounds. By minimizing this loss, a correction model is obtained. Then we treat the weighted sum of correction model and final round models as the result. The effectiveness of the proposed RoS-FL is supported by extensive experimental results on different tasks. Related codes will be released at https://github.com/Zi-YuanYang/RoS-FL.Comment: 11 pages, 5 figure

Measurement of uterine natural killer cell percentage in the periimplantation endometrium from fertile women and women with recurrent reproductive failure: establishment of a reference range

Background Uterine natural killer cells are the major leukocytes present in the periimplantation endometrium. Previous studies have found controversial differences in uterine natural killer cell percentage in women with recurrent reproductive failure compared with fertile controls. Objective We sought to compare the uterine natural killer cell percentage in women with recurrent reproductive failure and fertile controls. Study Design This was a retrospective study carried out in university hospitals. A total of 215 women from 3 university centers participated in the study, including 97 women with recurrent miscarriage, 34 women with recurrent implantation failure, and 84 fertile controls. Endometrial biopsy samples were obtained precisely 7 days after luteinization hormone surge in a natural cycle. Endometrial sections were immunostained for CD56 and cell counting was performed by a standardized protocol. Results were expressed as percentage of positive uterine natural killer cell/total stromal cells. Results The median uterine natural killer cell percentage in Chinese ovulatory fertile controls in natural cycles was 2.5% (range 0.9-5.3%). Using 5th and 95th percentile to define the lower and upper limits of uterine natural killer cell percentage, the reference range was 1.2-4.5%. Overall, the groups with recurrent reproductive failure had significantly higher uterine natural killer cell percentage than the controls (recurrent miscarriage: median 3.2%, range 0.6-8.8%; recurrent implantation failure: median 3.1%, range 0.8-8.3%). However, there was a subset of both groups (recurrent miscarriage: 16/97; recurrent implantation failure: 6/34) that had lower uterine natural killer cell percentage compared to fertile controls. Conclusion A reference range for uterine natural killer cell percentage in fertile women was established. Women with recurrent reproductive failure had uterine natural killer cell percentages both above and below the reference range