Concha bullosa is a rather common condition of the nasal turbinates, rarely reported in archaeological skeletal collections. This paper examines a case of concha bullosa as seen in a female cranium from a burial in central Italy, dated to the Longobard domination in the Peninsula (mid-7th- early 8th century CE).
 
  The individual under investigation (T86/17) comes from the funerary area of Selvicciola, located near the town of Viterbo in northern Latium, Italy.
 
  The skeleton was macroscopically examined. We analyzed the CT-scans of the defect by applying innovative R-based virtual tools.
 
  It was possible to calculate the inner volume of the concha bullosa and to provide a 3D visual assessment of its shape.
 
  Its size and shape suggest that the individual had this condition for a considerable period of time, during which its presence may have had affected her daily activities and health status.
 
  An under-represented paleopathological defect is examined for the first time through a virtual approach aimed at visualizing its shape and the assessment of its volume. New methods of 3D based virtual assessment can increase the informative value of defects.
 
  Techniques used in this assessment should be considered as an evaluative tool for other conditions when macroscopic and radiographic imaging are limited.
 Techniques used in this assessment should be considered as an evaluative tool for other conditions when macroscopic and radiographic imaging are limited.Current chemodynamic therapy (CDT) has been restricted by the requirement of strongly acidic conditions, insufficient endogenous H2O2 and upregulated cellular antioxidant defense. To overcome these obstacles, the carrier-free Fe(III)-ART nanoparticle is developed via coordination driven self-assembly of Fe3+ and hydrolyzed ART and evaluated as a redox-triggered C-centered free radicals nanogenerator for self-enhanced magnetic resonance imaging and chemodynamic therapy. The carrier-free Fe(III)-ART NPs can be triggered by intracellular GSH to release ART and Fe3+, which is further reduced to Fe2+ that catalyzed the endoperoxide of ART to generate C-centered free radicals. Notably, unlike current CDT, such a free radical generation process is without reliance on pH or endogenous H2O2. Meanwhile, the concurrent GSH depletion can diminish the antioxidation of tumors and enhance CDT. The C-centered free radicals-mediated apoptosis and GSH depletion-induced ferrotosis act in synergy, leading to potent tumor growth inhibition and superior anticancer efficacy in vitro and in vivo. Moreover, Fe(III)-ART NPs exhibit redox-triggered T2 relaxivity and contribute to activatable MRI-guided CDT. The development of biodegradable Fe(III)-ART NPs with superior anticancer efficacy, favorable pharmacokinetics and good biocompatibility provides a promising strategy to break through the bottlenecks of traditional CDT and greatly promotes the development of next-generation cancer theranostics.Mesenchymal stem cells are the focus of intense research in bone development and regeneration. The potential of microparticles as modulating moieties of osteogenic response by utilizing their architectural features is demonstrated herein. Topographically textured microparticles of varying microscale features are produced by exploiting phase-separation of a readily soluble sacrificial component from polylactic acid. The influence of varying topographical features on primary human mesenchymal stem cell attachment, proliferation and markers of osteogenesis is investigated. In the absence of osteoinductive supplements, cells cultured on textured microparticles exhibit notably increased expression of osteogenic markers relative to conventional smooth microparticles. They also exhibit varying morphological, attachment and proliferation responses. Significantly altered gene expression and metabolic profiles are observed, with varying histological characteristics in vivo. This study highlights how tailoring topographical design offers cell-instructive 3D microenvironments which allow manipulation of stem cell fate by eliciting the desired downstream response without use of exogenous osteoinductive factors.Recent research shows that speakers of most languages find smells difficult to abstract and name. Can verbal labels enhance the human capacity to learn smell categories? Few studies have examined how verbal labeling might affect non-visual cognitive processes, and thus far very little is known about word-assisted odor category learning. To address these gaps, we tested whether different types of training change learning gains in odor categorization. After four intensive days of training to categorize odors that were co-presented with arbitrary verbal labels, people who learned odor categories with odor-label pairs that were more consistent were significantly more accurate than people with the same perceptual experience but who had odor-label pairs that were less consistent. Both groups' accuracy scores improved, but the learning curves differed. The context of consistent linguistic cuing supported an increase in correct responses from the third day of training. However, inconsistent linguistic cuing delayed the start of approximating to target odor categorization until after the fourth day. These results show that associations formed between odors and novel verbal labels facilitate the formation of odor categories. We interpret this as showing a causal link between language and olfactory perceptual processing in supporting categorization.Eye movements are vital for human vision, and it is therefore important to understand how observers decide where to look. Meaning maps (MMs), a technique to capture the distribution of semantic information across an image, have recently been proposed to support the hypothesis that meaning rather than image features guides human gaze. MMs have the potential to be an important tool far beyond eye-movements research. Here, we examine central assumptions underlying MMs. First, we compared the performance of MMs in predicting fixations to saliency models, showing that DeepGaze II - a deep neural network trained to predict fixations based on high-level features rather than meaning - outperforms MMs.  https://www.selleckchem.com/products/Gefitinib.html  Second, we show that whereas human observers respond to changes in meaning induced by manipulating object-context relationships, MMs and DeepGaze II do not. Together, these findings challenge central assumptions underlying the use of MMs to measure the distribution of meaning in images.