Delivery of sorafenib by myofibroblast-targeted nanoparticles for the treatment of renal fibrosis.

Fibrosis is an excessive accumulation of the extracellular matrix within solid organs in response to injury and a common pathway that leads functional failure. No clinically approved agent is available to reverse or even prevent this process. Herein, we report a nanotechnology-based approach that utilizes a drug carrier to deliver a therapeutic cargo specifically to fibrotic kidneys, thereby improving the antifibrotic effect of the drug and reducing systemic toxicity. We first adopted in vitro-in vivo combinatorial phage display technology to identify peptide ligands that target myofibroblasts in mouse unilateral ureteral obstruction (UUO)-induced fibrotic kidneys. We then engineered lipid-coated poly(lactic-co-glycolic acid) nanoparticles (NPs) with fibrotic kidney-homing peptides on the surface and sorafenib, a potent antineoplastic multikinase inhibitor, encapsulated in the core. Sorafenib loaded in the myofibroblast-targeted NPs significantly reduced the infiltration of α-smooth muscle actin-expressing myofibroblasts and deposition of collagen I in UUO-treated kidneys and enhanced renal plasma flow measured by Technetium-99m mercaptoacetyltriglycine scintigraphy. This study demonstrates the therapeutic potential of the newly identified peptide fragments as anchors to target myofibroblasts and represents a strategic advance for selective delivery of sorafenib to treat renal fibrosis. SIGNIFICANCE STATEMENT: Renal fibrosis is a pathological feature accounting for the majority of issues in chronic kidney disease (CKD), which may progress to end-stage renal disease (ESRD). This manuscript describes a myofibroblast-targeting drug delivery system modified with phage-displayed fibrotic kidney-homing peptides. By loading the myofibroblast-targeting nanoparticles (NPs) with sorafenib, a multikinase inhibitor, the NPs could suppress collagen synthesis in cultured human myofibroblasts. When given intravenously to mice with UUO-induced renal fibrosis, sorafenib loaded in myofibroblast-targeting NPs significantly ameliorated renal fibrosis. This approach provides an efficient therapeutic option to renal fibrosis. The myofibroblast-targeting peptide ligands and nanoscale drug carriers may be translated into clinical application in the future.

Assistive Navigation Using Deep Reinforcement Learning Guiding Robot With UWB/Voice Beacons and Semantic Feedbacks for Blind and Visually Impaired People.

Facilitating navigation in pedestrian environments is critical for enabling people who are blind and visually impaired (BVI) to achieve independent mobility. A deep reinforcement learning (DRL)-based assistive guiding robot with ultrawide-bandwidth (UWB) beacons that can navigate through routes with designated waypoints was designed in this study. Typically, a simultaneous localization and mapping (SLAM) framework is used to estimate the robot pose and navigational goal; however, SLAM frameworks are vulnerable in certain dynamic environments. The proposed navigation method is a learning approach based on state-of-the-art DRL and can effectively avoid obstacles. When used with UWB beacons, the proposed strategy is suitable for environments with dynamic pedestrians. We also designed a handle device with an audio interface that enables BVI users to interact with the guiding robot through intuitive feedback. The UWB beacons were installed with an audio interface to obtain environmental information. The on-handle and on-beacon verbal feedback provides points of interests and turn-by-turn information to BVI users. BVI users were recruited in this study to conduct navigation tasks in different scenarios. A route was designed in a simulated ward to represent daily activities. In real-world situations, SLAM-based state estimation might be affected by dynamic obstacles, and the visual-based trail may suffer from occlusions from pedestrians or other obstacles. The proposed system successfully navigated through environments with dynamic pedestrians, in which systems based on existing SLAM algorithms have failed.

Diverse Substrate-Mediated Local Electric Field Enhancement of Metal Nanoparticles for Nanogap-Enhanced Raman Scattering.

The localized surface plasmon resonance of plasmonic nanoparticles (NPs) can be coupled with a noble metal substrate (S) to induce a localized augmented electric field (E-field) concentrated at the NP-S gap. Herein, we analyzed the fundamental near-field properties of metal NPs on diverse substrates numerically (using the 3D finite-difference time-domain method) and experimentally [using surface-enhanced Raman scattering (SERS)]. We systematically examined the effects of plasmonic NPs on noble metals (Ag and Au), non-noble metals (Al, Ti, Cu, Fe, and Ni), semiconductors (Si and Ge), and dielectrics (TiO2, ZnO, and SiO2) as substrates. For the AgNPs, the Al (11,664 times) and Si (3969 times) substrates produced considerable E-field enhancements, with Al in particular generating a tremendous E-field enhancement comparable in intensity to that induced by a Ag (28,224 times) substrate. Notably, we found that a superior metallic character of the substrate gave rise to easier induction of image charges within the metal substrate, resulting in a greater E-field at the NP-S gap; on the other hand, the larger the permittivity of the nonmetal substrate, the greater the ability of the substrate to store an image charge distribution, resulting in stronger coupling to the charges of localized surface plasmon resonance oscillation on the metal NP. Furthermore, we measured the SERS spectra of rhodamine 6G (a commonly used Raman spectral probe), histamine (a biogenic amine used as a food freshness indicator), creatinine (a kidney health indicator), and tert-butylbenzene [an extreme ultraviolet (EUV) lithography contaminant] on AgNP-immobilized Al and Si substrates to demonstrate the wide range of potential applications. Finally, the NP-S gap hotspots appear to be widely applicable as an ultrasensitive SERS platform (∼single-molecule level), especially when used as a powerful analytical tool for the detection of residual contaminants on versatile substrates.

Automatic Optimization of Wayfinding Design.

Wayfinding signs play an important role in guiding users to navigate in a virtual environment and in helping pedestrians to find their ways in a real-world architectural site. Conventionally, the wayfinding design of a virtual environment is created manually, so as the wayfinding design of a real-world architectural site. The many possible navigation scenarios, as well as the interplay between signs and human navigation, can make the manual design process overwhelming and non-trivial. As a result, creating a wayfinding design for a typical layout can take months to several years. In this paper, we introduce the Way to Go! approach for automatically generating a wayfinding design for a given layout. The designer simply has to specify some navigation scenarios; our approach will automatically generate an optimized wayfinding design with signs properly placed considering human agents' visibility and possibility of making mistakes during a navigation. We demonstrate the effectiveness of our approach in generating wayfinding designs for different layouts such as a train station, a downtown and a canyon. We evaluate our results by comparing different wayfinding designs and show that our optimized wayfinding design can guide pedestrians to their destinations effectively and efficiently. Our approach can also help the designer visualize the accessibility of a destination from different locations, and correct any "blind zone" with additional signs.

The roles of scene gist and spatial dependency among objects in the semantic guidance of attention in real-world scenes.

A previous study (Vision Research 51 (2011) 1192-1205) found evidence for semantic guidance of visual attention during the inspection of real-world scenes, i.e., an influence of semantic relationships among scene objects on overt shifts of attention. In particular, the results revealed an observer bias toward gaze transitions between semantically similar objects. However, this effect is not necessarily indicative of semantic processing of individual objects but may be mediated by knowledge of the scene gist, which does not require object recognition, or by known spatial dependency among objects. To examine the mechanisms underlying semantic guidance, in the present study, participants were asked to view a series of displays with the scene gist excluded and spatial dependency varied. Our results show that spatial dependency among objects seems to be sufficient to induce semantic guidance. Scene gist, on the other hand, does not seem to affect how observers use semantic information to guide attention while viewing natural scenes. Extracting semantic information mainly based on spatial dependency may be an efficient strategy of the visual system that only adds little cognitive load to the viewing task.

Predicting raters' transparency judgments of English and Chinese morphological constituents using latent semantic analysis.

The morphological constituents of English compounds (e.g., "butter" and "fly" for "butterfly") and two-character Chinese compounds may differ in meaning from the whole word. Subjective differences and ambiguity of transparency make judgments difficult, and a computational alternative based on a general model might be a way to average across subjective differences. In the present study, we propose two approaches based on latent semantic analysis (Landauer & Dumais in Psychological Review 104:211-240, 1997): Model 1 compares the semantic similarity between a compound word and each of its constituents, and Model 2 derives the dominant meaning of a constituent from a clustering analysis of morphological family members (e.g., "butterfingers" or "buttermilk" for "butter"). The proposed models successfully predicted participants' transparency ratings, and we recommend that experimenters use Model 1 for English compounds and Model 2 for Chinese compounds, on the basis of differences in raters' morphological processing in the different writing systems. The dominance of lexical meaning, semantic transparency, and the average similarity between all pairs within a morphological family are provided, and practical applications for future studies are discussed.

Using singular value decomposition to investigate degraded Chinese character recognition: evidence from eye movements during reading.

Previous research indicates that removing initial strokes from Chinese characters makes them harder to read than removing final or internal ones. In the present study, we examined the contribution of important components to character configuration via singular value decomposition. The results indicated that when the least important segments, which did not seriously alter the configuration (contour) of the character, were deleted, subjects read as fast as when no segments were deleted. When the most important segments, which are located in the left side of a character and written first, were deleted, reading speed was greatly slowed. These results suggest that singular value decomposition, which has no information about stroke writing order, can identify the most important strokes for Chinese character identification. Furthermore, they also suggest that contour may be correlated with stroke writing order.

The attraction of visual attention to texts in real-world scenes.

When we look at real-world scenes, attention seems disproportionately attracted by texts that are embedded in these scenes, for instance, on signs or billboards. The present study was aimed at verifying the existence of this bias and investigating its underlying factors. For this purpose, data from a previous experiment were reanalyzed and four new experiments measuring eye movements during the viewing of real-world scenes were conducted. By pairing text objects with matching control objects and regions, the following main results were obtained: (a) Greater fixation probability and shorter minimum fixation distance of texts confirmed the higher attractiveness of texts; (b) the locations where texts are typically placed contribute partially to this effect; (c) specific visual features of texts, rather than typically salient features (e.g., color, orientation, and contrast), are the main attractors of attention; (d) the meaningfulness of texts does not add to their attentional capture; and (e) the attraction of attention depends to some extent on the observer's familiarity with the writing system and language of a given text.

Semantic guidance of eye movements in real-world scenes.

The perception of objects in our visual world is influenced by not only their low-level visual features such as shape and color, but also their high-level features such as meaning and semantic relations among them. While it has been shown that low-level features in real-world scenes guide eye movements during scene inspection and search, the influence of semantic similarity among scene objects on eye movements in such situations has not been investigated. Here we study guidance of eye movements by semantic similarity among objects during real-world scene inspection and search. By selecting scenes from the LabelMe object-annotated image database and applying latent semantic analysis (LSA) to the object labels, we generated semantic saliency maps of real-world scenes based on the semantic similarity of scene objects to the currently fixated object or the search target. An ROC analysis of these maps as predictors of subjects' gaze transitions between objects during scene inspection revealed a preference for transitions to objects that were semantically similar to the currently inspected one. Furthermore, during the course of a scene search, subjects' eye movements were progressively guided toward objects that were semantically similar to the search target. These findings demonstrate substantial semantic guidance of eye movements in real-world scenes and show its importance for understanding real-world attentional control.

Estimating the effect of word predictability on eye movements in Chinese reading using latent semantic analysis and transitional probability.

Latent semantic analysis (LSA) and transitional probability (TP), two computational methods used to reflect lexical semantic representation from large text corpora, were employed to examine the effects of word predictability on Chinese reading. Participants' eye movements were monitored, and the influence of word complexity (number of strokes), word frequency, and word predictability on different eye movement measures (first-fixation duration, gaze duration, and total time) were examined. We found influences of TP on first-fixation duration and gaze duration and of LSA on total time. The results suggest that TP reflects an early stage of lexical processing while LSA reflects a later stage.