Rifabutin corneal deposits localized to the deep stroma using anterior segment optical coherence tomography.

Purpose:To demonstrate that rifabutin-related corneal deposits are localized to the deep stroma using anterior segment optical coherence tomography (OCT) and confocal microscopy. Observations:A 55-year-old male with a history of human immunodeficiency virus (HIV) and disseminated mycobacterium avium complex on rifabutin treatment for 3 years presented with bilateral corneal deposits. Confocal microscopy and anterior segment OCT confirm that rifabutin-related corneal deposits are located in the deep stroma, rather than in the endothelium. Conclusions:And Importance: Rifabutin deposits localize to the deep corneal stroma, and can be seen with both confocal microscopy and anterior segment OCT. Anterior segment OCT is a widely available and easily used diagnostic tool, and can provide utility in the diagnosis of corneal deposits

Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue

Histological staining is a vital step used to diagnose various diseases and has been used for more than a century to provide contrast to tissue sections, rendering the tissue constituents visible for microscopic analysis by medical experts. However, this process is time-consuming, labor-intensive, expensive and destructive to the specimen. Recently, the ability to virtually-stain unlabeled tissue sections, entirely avoiding the histochemical staining step, has been demonstrated using tissue-stain specific deep neural networks. Here, we present a new deep learning-based framework which generates virtually-stained images using label-free tissue, where different stains are merged following a micro-structure map defined by the user. This approach uses a single deep neural network that receives two different sources of information at its input: (1) autofluorescence images of the label-free tissue sample, and (2) a digital staining matrix which represents the desired microscopic map of different stains to be virtually generated at the same tissue section. This digital staining matrix is also used to virtually blend existing stains, digitally synthesizing new histological stains. We trained and blindly tested this virtual-staining network using unlabeled kidney tissue sections to generate micro-structured combinations of Hematoxylin and Eosin (H&E), Jones silver stain, and Masson's Trichrome stain. Using a single network, this approach multiplexes virtual staining of label-free tissue with multiple types of stains and paves the way for synthesizing new digital histological stains that can be created on the same tissue cross-section, which is currently not feasible with standard histochemical staining methods.Comment: 19 pages, 5 figures, 2 table

L'acquisition des temps en français par les apprenants sinophones

Version du mémoire modifiée après soutenance.Ce mémoire a pour objectif de comparer les systèmes aspecto-temporels du mandarin et du français afin de trouver l'origine des difficultés des apprenants chinois dans l'acquisition des temps du passé du français. Ce travail cherche à montrer les différences dans le fonctionnement du système des temps en chinois mandarin et des temps en français dans une perspective contrastive

Aquatic landscape and the emergence of walled sites in late Neolithic Central Plains of China: Integrating archaeological and geoarchaeological evidence from the Guchengzhai site

The emergence of many late-Neolithic and early Bronze-Age walled sites on China's Central Plains coincided with some prominent Holocene climate events. Recent excavation and geoarchaeological investigation at one of the largest walled sites of Guchengzhai provide important data to examine some of the questions concerning the long-term relationship between the formation of aquatic landscape and social evolution in late prehistoric Central Plains. We collected fine-grained paleo-environmental and archaeological evidence from a range of on- and off-site contexts to reconstruct the late-Holocene paleo-environment surrounding the walled site, and examine the construction, maintenance and abandonment processes of its large-size moat. Our results show that there existed many small-to-large-sized waterbodies during the late Holocene, which, together with local rivers, were the main source of water to the site. The Guchengzhai population was drawn to the low-lying land near the river and other waterbodies with an optimal hydrological condition. During its use, the moat might have been linked to the nearby wetlands and/or rivers. The hydrological regime was dominated by gentle but relatively sediment-laden flow, being punctuated by several high-energy flood events. The sedimentation of light yellowish silt and sand with some anthropogenic inclusions during the use of the moat gave way to a quick siltation with the deposition of rich organic matter when the moat ceased to function as a main channel for water flow, although other land-use activities such as fire (land clearance?) continued to occur in the vicinity. The reconstructed ‘life-history’ of the moat demonstrates the increasingly acute challenge facing the growing population living at Guchengzhai as the climate was becoming drier. The construction and operation of the moat signified technological innovations and intensified water management at Guchengzhai, which led to the formation of distinctive aquatic landscape that featured large-scale hydraulic infrastructures in a hydrologically optimal environment. We contend that such was a common characteristic or trend shared by many contemporary or later-period walled sites on the Central Plains

Forward and inverse problems for Eikonal equation based on DeepONet

Seismic forward and inverse problems are significant research areas in geophysics. However, the time burden of traditional numerical methods hinders their applications in scenarios that require fast predictions. Machine learning-based methods also have limitations as retraining is required for every change in initial conditions. In this letter, we adopt deep operator network (DeepONet) to solve forward and inverse problems based on the Eikonal equation, respectively. DeepONet approximates the operator through two sub-networks, branch net and trunk net, which offers good generalization and flexibility. Different structures of DeepONets are proposed to respectively learn the operators in forward and inverse problems. We train the networks on different categories of datasets separately, so that they can deliver accurate predictions with different initial conditions for the specific velocity model. The numerical results demonstrate that DeepONet can not only predict the travel time fields with different sources for different velocity models, but also provide velocity models based on the observed travel time data.Comment: 5 pages, 4 figure