Inhibition of RANKL-Induced Osteoclastogenesis by Novel Mutant RANKL

Background: Recently, it was reported that leucine-rich repeat-containing G-protein-coupled receptor 4 (LGR4, also called GPR48) is another receptor for RANKL and was shown to compete with RANK to bind RANKL and suppress canonical RANK signaling during osteoclast differentiation. The critical role of the protein triad RANK–RANKL in osteoclastogenesis has made their binding an important target for the development of drugs against osteoporosis. In this study, point-mutations were introduced in the RANKL protein based on the crystal structure of the RANKL complex and its counterpart receptor RANK, and we investigated whether LGR4 signaling in the absence of the RANK signal could lead to the inhibition of osteoclastogenesis.; Methods: The effects of point-mutated RANKL (mRANKL-MT) on osteoclastogenesis were assessed by tartrate-resistant acid phosphatase (TRAP), resorption pit formation, quantitative real-time polymerase chain reaction (qPCR), western blot, NFATc1 nuclear translocation, micro-CT and histomorphological assay in wild type RANKL (mRANKL-WT)-induced in vitro and in vivo experimental mice model. Results: As a proof of concept, treatment with the mutant RANKL led to the stimulation of GSK-3β phosphorylation, as well as the inhibition of NFATc1 translocation, mRNA expression of TRAP and OSCAR, TRAP activity, and bone resorption, in RANKL-induced mouse models; and Conclusions: The results of our study demonstrate that the mutant RANKL can be used as a therapeutic agent for osteoporosis by inhibiting RANKL-induced osteoclastogenesis via comparative inhibition of RANKL. Moreover, the mutant RANKL was found to lack the toxic side effects of most osteoporosis treatments.     

Magnetically Guidable Proteinaceous Adhesive Microbots for Targeted Locoregional Therapeutics Delivery in the Highly Dynamic Environment of the Esophagus

The esophagus is a tubular-shaped muscular organ where swallowed fluids and muscular contractions constitute a highly dynamic environment. The turbulent, coordinated processes that occur through the oropharyngeal conduit can often compromise targeted administration of therapeutic drugs to a lesion, significantly reducing therapeutic efficacy. Here, magnetically guidable drug vehicles capable of strongly adhering to target sites using a bioengineered mussel adhesive protein (MAP) to achieve localized delivery of therapeutic drugs against the hydrodynamic physiological conditions are proposed. A suite of highly uniform microparticles embedded with iron oxide (IO) nanoparticles (MAP@IO MPs) is microfluidically fabricated using the genipin-mediated covalent cross-linking of bioengineered MAP. The MAP@IO MPs are successfully targeted to a specific region and prolongedly retained in the tubular-structured passageway. In particular, orally administered MAP@IO MPs are effectively captured in the esophagus in vivo in a magnetically guidable manner. Moreover, doxorubicin (DOX)-loaded MAP@IO MPs exhibit a sustainable DOX release profile, effective anticancer therapeutic activity, and excellent biocompatibility. Thus, the magnetically guidable locomotion and robust underwater adhesive properties of the proteinaceous soft microbots can provide an intelligent modular approach for targeted locoregional therapeutics delivery to a specific lesion site in dynamic fluid-associated tubular organs such as the esophagus.     

A Lane-Level Road Marking Map Using a Monocular Camera

The essential requirement for precise localization of a self-driving car is a lane-level map which includes road markings (RMs). Obviously, we can build the lane-level map by running a mobile mapping system (MMS) which is equipped with a high-end 3D LiDAR and a number of high-cost sensors. This approach, however, is highly expensive and ineffective since a single high-end MMS must visit every place for mapping. In this paper, a lane-level RM mapping system using a monocular camera is developed. The developed system can be considered as an alternative to expensive high-end MMS. The developed RM map includes the information of road lanes (RLs) and symbolic road markings (SRMs). First, to build a lane-level RM map, the RMs are segmented at pixel level through the deep learning network. The network is named RMNet. The segmented RMs are then gathered to build a lane-level RM map. Second, the lane-level map is improved through loop-closure detection and graph optimization. To train the RMNet and build a lane-level RM map, a new dataset named SeRM set is developed. The set is a large dataset for lane-level RM mapping and it includes a total of 25157 pixel-wise annotated images and 21000 position labeled images. Finally, the proposed lane-level map building method is applied to SeRM set and its validity is demonstrated through experimentation.      

Solution‐Processable Photonic Inks of Mie‐Resonant Hollow Carbon–Silica Nanospheres

Hollow carbon–silica nanospheres that exhibit angle‐independent structural color with high saturation and minimal absorption are made. Through scattering calculations, it is shown that the structural color arises from Mie resonances that are tuned precisely by varying the thickness of the shells. Since the color does not depend on the spatial arrangement of the particles, the coloration is angle independent and vibrant in powders and liquid suspensions. These properties make hollow carbon–silica nanospheres ideal for applications, and their potential in making flexible, angle‐independent films and 3D printed films is explored.     

Hierarchical sampling optimization of particle filter for global robot localization in pervasive network environment

This paper presents a hierarchical framework for managing the sampling distribution of a particle filter (PF) that estimates the global positions of mobile robots in a large‐scale area. The key concept is to gradually improve the accuracy of the global localization by fusing sensor information with different characteristics. The sensor observations are the received signal strength indications (RSSIs) of Wi‐Fi devices as network facilities and the range of a laser scanner. First, the RSSI data used for determining certain global areas within which the robot is located are represented as RSSI bins. In addition, the results of the RSSI bins contain the uncertainty of localization, which is utilized for calculating the optimal sampling size of the PF to cover the regions of the RSSI bins. The range data are then used to estimate the precise position of the robot in the regions of the RSSI bins using the core process of the PF. The experimental results demonstrate superior performance compared with other approaches in terms of the success rate of the global localization and the amount of computation for managing the optimal sampling size.     

Succinct Palette and Color Model Generation and Manipulation Using Hierarchical Representation

We propose a new method to obtain the representative colors and their distributions of an image. Our intuition is that it is possible to derive the global model from the local distributions. Beginning by sampling pure colors, we build a hierarchical representation of colors in the image via a bottom‐up approach. From the resulting hierarchy, we can obtain satisfactory palettes/color models automatically without a predefined size. Furthermore, we provide interactive operations to manipulate the results which allow the users to reflect their intention directly. In our experiment, we show that the proposed method produces more succinct results that faithfully represent all the colors in the image with an appropriate number of components. We also show that the proposed interactive approach can improve the results of applications such as recoloring and soft segmentation.     

Automated delineation of non-small cell lung cancer: A step toward quantitative reasoning in medical decision science

Quantitative reasoning in medical decision science relies on the delineation of pathological objects. For example, evidence-based clinical decisions regarding lung diseases require the segmentation of nodules, tumors, or cancers. Non-small cell lung cancer (NSCLC) tends to be large sized, irregularly shaped, and grows against surrounding structures imposing challenges in the segmentation, even for expert clinicians. An automated delineation tool based on spatial analysis was developed and studied on 25 sets of computed tomography scans of NSCLC. Manual and automated delineations were compared, and the proposed method exhibited robustness in terms of the tumor size (5.32–18.24 mm), shape (spherical or irregular), contouring (lobulated, spiculated, or cavitated), localization (solitary, pleural, mediastinal, endobronchial, or tagging), and laterality (left or right lobe) with accuracy between 80% and 99%. Small discrepancies observed between the manual and automated delineations may arise from the variability in the practitioners' definitions of region of interest or imaging artifacts that reduced the tissue resolution.     

Brief Review of Precipitated Iron-Based Catalysts for Low-Temperature Fischer–Tropsch Synthesis

Topics in Catalysis - Fischer–Tropsch synthesis (FTS) is a promising way to produce clean liquid fuels and high value-added chemicals from low-value carbon-containing resources such as coal,...     

Computer-based engineering of thermostabilized antibody fragments

We used the molecular modeling program Rosetta to identify clusters of amino acid substitutions in antibody fragments (scFvs and scAbs) that improve global protein stability and resistance to thermal deactivation. Using this methodology, we increased the melting temperature (T_m) and resistance to heat treatment of an antibody fragment that binds to the Clostridium botulinum hemagglutinin protein (anti-HA33). Two designed antibody fragment variants with two amino acid replacement clusters, designed to stabilize local regions, were shown to have both higher T_m compared to the parental scFv and importantly to retain full antigen binding activity after 2 hr of incubation at 70°C. The crystal structure of one thermostabilized scFv variants was solved at 1.6 Å and shown to be in close agreement with the RosettaAntibody model prediction.     

D-limonene Inhibits Pentylenetetrazole-Induced Seizure via Adenosine A2A Receptor Modulation on GABAergic Neuronal Activity

Background: Epilepsy is a chronic neurological disorder characterized by the recurrence of seizures. One-third of patients with epilepsy may not respond to antiseizure drugs. Purpose: We aimed to examine whether D-limonene, a cyclic monoterpene, exhibited any antiseizure activity in the pentylenetetrazole (PTZ)-induced kindling mouse model and in vitro. Methods: PTZ kindling mouse model was established by administering PTZ (30 mg/kg) intraperitoneally to mice once every 48 h. We performed immunoblot blots, immunohistochemistry (IHC), and high-performance liquid chromatography (HPLC) analysis after the behavioral study. Results: An acute injection of PTZ (60 mg/kg) induced seizure in mice, while pretreatment with D-limonene inhibited PTZ-induced seizure. Repeated administration of PTZ (30 mg/kg) increased the seizure score gradually in mice, which was reduced in D-limonene (10 mg/kg)-pretreated group. In addition, D-limonene treatment increased glutamate decarboxylase-67 (GAD-67) expression in the hippocampus. Axonal sprouting of hippocampal neurons after kindling was inhibited by D-limonene pretreatment. Moreover, D-limonene reduced the expression levels of Neuronal PAS Domain Protein 4 (Npas4)-induced by PTZ. Furthermore, the adenosine A2A antagonist {"type":"entrez-protein","attrs":{"text":"SCH58261","term_id":"1052882304","term_text":"SCH58261"}}SCH58261 and ZM241385 inhibited anticonvulsant activity and gamma-aminobutyric acid (GABA)ergic neurotransmission-induced by D-limonene. Conclusion: These results suggest that D-limonene exhibits anticonvulsant activity through modulation of adenosine A2A receptors on GABAergic neuronal function.