Protargol Synthesis: An In-House Protocol

The protargol staining method has proved to be indispensable for revealing the cellular structures of a variety of protozoa, especially the flagellates and ciliates. Protargol provides permanent stains of a variety of cellular structures: nuclei, extrusomes, basal bodies, and microfilamentous constituents of cells. Together with the older silver nitrate methods, protargol impregnations have provided the basis for the detailed descriptions of nearly all ciliates to date. The performance of commercially available preparations has varied widely. Recently, suppliers have stopped stocking the effective forms of protargol resulting in a worldwide shortage. Thus, it has become necessary for protistologists to explore on-site synthesis of this critically important agent. An optimum protocol for synthesis of protargol should be rapid, relatively inexpensive, simple enough to be done by non-chemists, and achievable without specialized equipment. In this article, the authors briefly review the interesting history of protargol and describe a protocol, based on the early studies of neuroanatomists, that yields a protargol producing impregnations of ciliates comparable to those obtained with previously available commercial preparations

Attenuating Immune Response of Macrophage by Enhancing Hydrophilicity of Ti Surface

Immune responses can determine the in vivo fate of implanted materials. The strategy for developing implants has shifted towards using materials with immunomodulatory activity. However, the immunoregulatory effect of hydrophilicity of titanium surface on the macrophage behavior and its underlying mechanism remain poorly understood. Here, the Ti surface hydrophilicity-dependent behavior of murine RAW264.7 macrophages was investigated in vitro. Two laboratory models with significantly different surface hydrophilicity and similar roughness were established with Ti-polished and Ti-H2O2 surfaces. The results of cell morphology observation showed that the Ti-H2O2 surface yielded enhanced cell adhesion and less multinucleated cell formation. CCK-8 assay indicated that the growth rate of macrophage on Ti-H2O2 surface is higher than that of Ti-polished. ELISA assay result revealed lower level of proinflammatory factor TNF-α and higher level of anti-inflammatory factor IL-10 on the Ti-H2O2 surface compared to Ti-polished. Subsequently, immunofluorescence and western blotting analysis showed that activation of the NF-κB-TNF-α pathway might be involved in the modulation of the immune response by surface hydrophilicity. Together, these results suggested that relative high hydrophilic Ti surface might attenuate the immune response of macrophage by activating NF-κB signaling. These findings could provide new insights into designing implant devices for orthopedic applications

Nitrogen Doped Carbon Nanosheets Encapsulated in situ Generated Sulfur Enable High Capacity and Superior Rate Cathode for Li-S Batteries

Lithium-sulfur batteries (LSBs), with large specific capacity (1,675 mAh g−1), are regarded as the most likely alternative to the traditional Lithium-ion batteries. However, the intrinsical insulation and dramatic volume change of sulfur, as well as serious shuttle effect of polysulfides hinder their practical implementation. Herein, we develop three-dimensional micron flowers assembled by nitrogen doped carbon (NC) nanosheets with sulfur encapsulated (S@NC-NSs) as a promising cathode for Li-S to overcome the forementioned obstacles. The in situ generated S layer adheres to the inner surface of the hollow and micro-porous NC shell with fruitful O/N containing groups endowing both efficient physical trapping and chemical anchoring of polysulfides. Meanwhile, such a novel carbon shell helps to bear dramatic volume change and provides a fast way for electron transfer during cycling. Consequently, the S@NC-NSs demonstrate a high capacity (1,238 mAh g−1 at 0.2 C; 1.0 C = 1,675 mA g−1), superior rate performance with a capacity retention of 57.8% when the current density increases 25 times from 0.2 to 5.0 C, as well as outstanding cycling performance with an ultralow capacity fading of only 0.064% after 200 cycles at a high current density of 5.0 C

Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits.

Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal remains challenging. In this study, we describe a deep learning (DL) method to analyze the impact of sequence variants on enhancers. Focusing on pancreatic islets, a T2D relevant tissue, we show that our model learns islet-specific transcription factor (TF) regulatory patterns and can be used to prioritize candidate causal variants. At 101 genetic signals associated with T2D and related glycemic traits where multiple variants occur in linkage disequilibrium, our method nominates a single causal variant for each association signal, including three variants previously shown to alter reporter activity in islet-relevant cell types. For another signal associated with blood glucose levels, we biochemically test all candidate causal variants from statistical fine-mapping using a pancreatic islet beta cell line and show biochemical evidence of allelic effects on TF binding for the model-prioritized variant. To aid in future research, we publicly distribute our model and islet enhancer perturbation scores across ~67 million genetic variants. We anticipate that DL methods like the one presented in this study will enhance the prioritization of candidate causal variants for functional studies

Comparison of 2D and 3D prediction models for environmental vibration induced by underground railway with two types of tracks

Two-dimensional (2D) and three-dimensional (3D) prediction models for environmental vibration induced by underground railway with direct fixation track and steel spring floating slab track are developed and verified. The responses of ground surface calculated by 2D prediction models with various equivalent forces are compared to those calculated by 3D prediction models. The numerical results show that (a) the computational time for each case calculated by 2D prediction models is more than 500 times less than that calculated by 3D prediction models, however, the accuracy of 2D prediction models is relatively lower than 3D prediction models, so 3D prediction models are required for absolute prediction due to their higher accuracy and applicability to a wider range of complex problems; and (b) a suitable equivalent force transfer method for 2D prediction models can improve the prediction accuracy of 2D prediction models, the equivalent forces in 2D prediction models are respectively recommended to use the equivalent wheel-rail force and the equivalent steel spring force averaged over a vehicle length for underground direct fixation track and steel spring floating slab trac