POEMS Syndrome Diagnosed 10 Years after Disabling Peripheral Neuropathy

Peripheral neuropathy is characterized as a generalized, relatively homogeneous process affecting many peripheral nerves and predominantly affecting distal nerves. The epidemiology of peripheral neuropathy is limited since the disease presents with varying etiology, pathology, and severity. Toxic, inflammatory, hereditary, and infectious factors can cause damage to the peripheral nerves resulting in peripheral neuropathy. Peripheral neuropathy is most commonly caused by diabetes, alcohol, HIV infection, and malignancy. We report a case of a 42-year-old female with 10-year history of progressively worsening peripheral neuropathy, hypothyroidism, and skin changes who presents with dyspnea secondary to recurrent pleural and pericardial effusions. Prior to her arrival, her peripheral neuropathy was believed to be secondary to chronic demyelinating inflammatory polyneuropathy (CDIP) given elevated protein in the cerebral spinal fluid (CSF) which was treated with intravenous immunoglobulin (IVIG) and corticosteroids. Unfortunately, her peripheral neuropathy did not have any improvement. Incidentally, patient was found to have splenomegaly and papilledema on physical exam. Serum protein electrophoresis showed a monoclonal pattern of IgA lambda. Patient met the diagnostic criteria for POEMS (polyneuropathy, organomegaly, endocrinopathy, M-protein, and skin changes) syndrome. An underlying diagnosis of POEMS syndrome should be considered in patients with chronic debilitating neuropathy and an elevated protein in the CSF

Efficient and accurate calculation of exact exchange and RPA correlation energies in the Adiabatic-Connection Fluctuation-Dissipation theory

Recently there has been a renewed interest in the calculation of exact-exchange and RPA correlation energies for realistic systems. These quantities are main ingredients of the so-called EXX/RPA+ scheme which has been shown to be a promising alternative approach to the standard LDA/GGA DFT for weakly bound systems where LDA and GGA perform poorly. In this paper, we present an efficient approach to compute the RPA correlation energy in the framework of the Adiabatic-Connection Fluctuation-Dissipation formalism. The method is based on the calculation of a relatively small number of eigenmodes of RPA dielectric matrix, efficiently computed by iterative density response calculations in the framework of Density Functional Perturbation Theory. We will also discuss a careful treatment of the integrable divergence in the exact-exchange energy calculation which alleviates the problem of its slow convergence with respect to Brillouin zone sampling. As an illustration of the method, we show the results of applications to bulk Si, Be dimer and atomic systems.Comment: 12 pages, 6 figures. To appear in Phys. Rev.

Modelling of dishing for metal chemical mechanical polishing

In this paper, a physical model for the development of dishing during metal chemical mechanical polishing (CMP) is proposed. The main assumption of the model is that material removal occurs predominantly at the pad/wafer contacts. The distribution of pad/wafer contact size is studied first. This distribution is used as an input for a model of the dependence for the material removal rate on the line width. A relation that describes the development of dishing as a function of overpolish time will be presented. The model describes to a great accuracy the observed dishing effects, using one free paramete

Model-based Experimental Design in Electrochemistry

The following thesis applies an experimental design framework to investigate properties of electron transfer kinetics and homogeneous catalytic reactions. The approach is model-based and the classical Butler-Volmer description is chosen to describe the fundamental electrochemical reaction at a conductive interface. The methodology focuses on two significant design variables: the applied potential at the electrode and mass transport mode induced by physical arrangement. An important problem in electrochemistry is the recovery of model parameters from output current measurements. In this work, the identifiability function is proposed as a measure of correspondence between the parameters and output variable. Under diffusion-limit conditions, plain Monte Carlo optimization shows that the function is globally non-identifiable, or equivalently the correspondence is generally non-unique. However by selecting linear voltammetry as the applied potential, the primary parameters in the Butler-Volmer description are theoretically recovered from a single set of data. The result is accomplished via applications of Sobol ranking to reduce the parameter set and a sensitivity equation to inverse these parameters. The use of hydrodynamic tools for investigating electron transfer reactions is next considered. The work initially focuses on the rotating disk and its generalization - the rocking disk mechanism. A numerical framework is developed to analyze the latter, most notably the derivation of a Levich-like expression for the limiting current. The results are then used to compute corresponding identifiability functions for each of the above configurations. Potential effectiveness of each device in recovering kinetic parameters are straightforwardly evaluated by comparing the functional values. Furthermore, another hydrodynamic device - the rotating drum, which is highly suitable for viscous and resistive solvents, is theoretically analyzed. Combined with previous results, this rotating drum configuration shows promising potential as an alternative tool to traditional electrode arrangement. The final chapter illustrates the combination of modulated input signal and appro- priate mass transport regimes to express electro-catalytic effects. An AC voltammetry technique plays an important role in this approach and is discussed step-by-step from simple redox reaction to the complete EC′ catalytic mechanism. A general algorithm based on forward and inverse Fourier transform functions for extracting harmonic currents from the total current is presented. The catalytic effect is evaluated and compared for three cases: macro, micro electrodes under diffusion control condition and in micro fluidic environments. Experimental data are also included to support the simulated design results

Data-driven structural health monitoring using feature fusion and hybrid deep learning

Smart structural health monitoring (SHM) for large-scale infrastructures is an intriguing subject for engineering communities thanks to its significant advantages such as timely damage detection, optimal maintenance strategy, and reduced resource requirement. Yet, it is a challenging topic as it requires handling a large amount of collected sensors data continuously, which is inevitably contaminated by random noises. Therefore, this study developed a practical end-to-end framework that makes use of physical features embedded in raw data and an elaborated hybrid deep learning model, namely 1DCNN-LSTM, featuring two algorithms - Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM). In order to extract relevant features from sensory data, the method combines various signal processing techniques such as the autoregressive model, discrete wavelet transform, and empirical mode decomposition. The hybrid deep learning 1DCNN-LSTM is designed based on the CNN’s capacity of capturing local information and the LSTM network’s prominent ability to learn long-term dependencies. Through three case studies involving both experimental and synthetic datasets, it is demonstrated that the proposed approach achieves highly accurate damage detection, as accurate as the powerful two-dimensional CNN, but with a lower time and memory complexity, making it suitable for real-time SHM

The economic returns of sanitation interventions in Vietnam

Results of sanitation interventions in 9 rural and 8 urban sites have been evaluated, comparing open defecation with different range of sanitation facilities. Both quantitative and tangible benefits of sanitation and hygiene improvements versus averted costs of interventions were analyzed. Study results show improved sanitation is a socially profitable investment – pit latrines in rural areas have an economic return of at least 6 times the cost, and off-site treatment options in urban areas have an economic return of at least 3 times the cost. Net benefits from low-cost options are especially high, offering an affordable opportunity to poor households. Sanitation options that protect the environment are more costly to provide, but while environmental benefits are difficult to quantify in economic terms, the benefits are highly valued by households, tourists and businesses. Study results provide valuable information to allocate adequate resources for sanitation and hygiene improvement at central and local levels