Open-cell polyvinylidene fluoride foams as carriers to promote biofilm growth for biological wastewater treatment

This article reports the design and fabrication of open-cell polyvinylidene fluoride (PVDF) foams as carriers that can promote biofilm growth and organic removal efficiency for biological wastewater treatment in attached growth bioreactors. Open-cell PVDF foams were fabricated by a manufacturing approach that integrated compression molding and particulate leaching. PVDF carriers were structured with two governing factors of leaching agent types (e.g., sodium chloride [NaCl] and sodium acetate [NaOAc]) and contents (e.g., 80 and 90 wt%). Open-cell PVDF foams possessed high porosity and high protected surface area (i.e., more than ×10 to ×20 of the areas of commercialized carriers), which promoted biofilm growth in these carriers. As a successful advantage, PVDF carriers used in the moving bed biofilm reactors (MBBR) were entirely covered by biofilm in both interior and exterior parts without clogging. This provides strong evidence of the bacterial compatibility of the fabricated open-cell PVDF foam carriers. Moreover, the specific morphology of the PVDF carriers in this article provided superior biofilm protection from the detachment in MBBR. Experimental results revealed that PVDF open-cell foams fabricated by 80 wt% of NaCl demonstrated higher mechanical strength with an organic removal efficiency of 77% ± 7% in the corresponding bioreactor containing them.     

Distilled Collections from Textual Image Queries

We present a distillation algorithm which operates on a large, unstructured, and noisy collection of internet images returned from an online object query. We introduce the notion of a distilled set, which is a clean, coherent, and structured subset of inlier images. In addition, the object of interest is properly segmented out throughout the distilled set. Our approach is unsupervised, built on a novel clustering scheme, and solves the distillation and object segmentation problems simultaneously. In essence, instead of distilling the collection of images, we distill a collection of loosely cutout foreground “shapes”, which may or may not contain the queried object. Our key observation, which motivated our clustering scheme, is that outlier shapes are expected to be random in nature, whereas, inlier shapes, which do tightly enclose the object of interest, tend to be well supported by similar shapes captured in similar views. We analyze the commonalities among candidate foreground segments, without aiming to analyze their semantics, but simply by clustering similar shapes and considering only the most significant clusters representing non‐trivial shapes. We show that when tuned conservatively, our distillation algorithm is able to extract a near perfect subset of true inliers. Furthermore, we show that our technique scales well in the sense that the precision rate remains high, as the collection grows. We demonstrate the utility of our distillation results with a number of interesting graphics applications.     

High quality voice conversion using prosodic and high-resolution spectral features

Voice conversion methods have advanced rapidly over the last decade. Studies have shown that speaker characteristics are captured by spectral feature as well as various prosodic features. Most existing conversion methods focus on the spectral feature as it directly represents the timbre characteristics, while some conversion methods have focused only on the prosodic feature represented by the fundamental frequency. In this paper, a comprehensive framework using deep neural networks to convert both timbre and prosodic features is proposed. The timbre feature is represented by a high-resolution spectral feature. The prosodic features include F0, intensity and duration. It is well known that DNN is useful as a tool to model high-dimensional features. In this work, we show that DNN initialized by our proposed autoencoder pretraining yields good quality DNN conversion models. This pretraining is tailor-made for voice conversion and leverages on autoencoder to capture the generic spectral shape of source speech. Additionally, our framework uses segmental DNN models to capture the evolution of the prosodic features over time. To reconstruct the converted speech, the spectral feature produced by the DNN model is combined with the three prosodic features produced by the DNN segmental models. Our experimental results show that the application of both prosodic and high-resolution spectral features leads to quality converted speech as measured by objective evaluation and subjective listening tests.     

Proteomics of plaques and novel sources of potential biomarkers for atherosclerosis

Cardiovascular disease (CVD) is the leading cause of death and loss of productive life years in the world. The underlying syndrome of CVD, atherosclerosis, is a complex disease process, which involves lipid metabolism, inflammation, innate and adaptive immunity, and many other pathophysiological aspects. Furthermore, CVD is influenced by genetic as well as environmental factors. Early detection of CVD and identification of patients at risk are crucial to reduce the burden of disease and to allow personalized treatment. As established risk factors fail to accurately predict which part of the population is likely to suffer from the disease, novel biomarkers are urgently needed. Proteomics can play a significant role in identifying these biomarkers. In this review, we describe the progress made in proteome profiling of the atherosclerotic plaque and several novel sources of potential biomarkers, including circulating cells and plasma extracellular vesicles. The importance of longitudinal biobanking in biomarker discovery is highlighted and exemplified by several plaque proteins identified in the biobank study Athero-Express. Finally, we discuss the PTMs of proteins that are involved in atherosclerosis, which may become one of the foci in the ongoing quest for biomarkers through proteomics of plaque and other matrices relevant to the progression of atherosclerosis.     

Social network,social trust and shared goals in organizational knowledge sharing

The aim of our study was to further develop an understanding of social capital in organizational-knowledge-sharing. We first developed a measurement tool and then a theoretical framework in which three social capital factors (social network, social trust, and shared goals) were combined with the theory of reasoned action; their relationships were then examined using confirmatory factoring analysis. We then surveyed of 190 managers from Hong Kong firms, we confirm that a social network and shared goals significantly contributed to a person's volition to share knowledge, and directly contributed to the perceived social pressure of the organization. The social trust has however showed no direct effect on the attitude and subjective norm of sharing knowledge.     

A change management framework for macroergonomic field research

With the proliferation of macroergonomic field research, it is time to carefully examine how such research should be managed and implemented. We argue that the importance of attending to high-quality implementation of field research is equal to that of methodological rigor. One way to systematically manage the implementation process is to adopt a change management framework, wherein the research project is conceptualized as an instance of organization-level change. Consequently, principles for successful organization-level change from the literature on change management can be used to guide successful field research implementation. This paper briefly reviews that literature, deriving 30 principles of successful change management, covering topics such as political awareness, assembling the change team, generating buy-in, and management support. For each principle, corresponding suggestions for macroergonomic field research practice are presented. We urge other researchers to further develop and adopt frameworks that guide the implementation of field research.     

The Electrochemical Response of Single Crystalline Copper Nanowires to Atmospheric Air and Aqueous Solution

In this paper, single crystalline copper nanowires (CuNWs) have been electrochemically grown through anodic aluminum oxide template. The environmental stability of the as‐obtained CuNWs in both 40% relative humidity (RH) atmosphere and 0.1 m NaOH aqueous solution has been subsequently studied. In 40% RH atmosphere, a uniform compact Cu₂O layer is formed as a function of exposure time following the logarithmic law and epitaxially covers the CuNW surfaces. It is also found that the oxide layers on CuNWs are sequentially grown when subjected to the cyclic voltammetry measurement in 0.1 m NaOH solution. An epitaxially homogeneous Cu₂O layer is initially formed over the surface of the CuNW substrates by solid‐state reaction (SSR). Subsequently, the conversion of Cu₂O into epitaxial CuO based on the SSR takes place with the increase of applied potential. This CuO layer is partially dissolved in the solution forming Cu(OH)₂, which then redeposited on the CuNW surfaces (i.e., dissolution‐redeposition (DR) process) giving rise to a mixed polycrystalline CuO/Cu(OH)₂ layer. The further increase of applied potential allows the complete oxidation of Cu₂O into CuO to form a dual‐layer structure (i.e., CuO inner layer and Cu(OH)₂ outer layer) with random orientations through an enhanced DR process.     

Lyme disease--on the basis of our observations

We biologically assessed functions of several reconstituted surfactants with the same minimum surface tension (2-3 mN/m) as "complete" porcine pulmonary surfactant (natural surfactant) but with longer surface adsorption times. Administration of natural surfactant (adsorption time 0.29 s) into the lungs of surfactant-deficient immature rabbits brought a tidal volume of 16.1 +/- 4.4 (SD) ml/kg during mechanical ventilation with 40 breaths/min and 20 cmH2O insufflation pressure. In static pressure-volume recordings, these animals showed a lung volume of 62.4 +/- 9.7 ml/kg at 30 cmH2O airway pressure and maintained 55% of this volume when the pressure decreased to 5 cmH2O. With two reconstituted surfactants consisting of synthetic lipids or isolated lipids from porcine lungs plus surfactant-associated hydrophobic proteins (adsorption times 0.57 and 0.78 s, respectively), tidal volumes were < 38% of that with natural surfactant (P < 0.05), but static pressure-volume recordings were not different. Care is therefore needed in estimating the in vivo function of surfactant preparations from minimum surface tension or static pressure-volume measurements.