Three-dimensional visualization and a deep-learning model reveal complex fungal parasite networks in behaviorally manipulated ants.

Some microbes possess the ability to adaptively manipulate host behavior. To better understand how such microbial parasites control animal behavior, we examine the cell-level interactions between the species-specific fungal parasite Ophiocordyceps unilateralis sensu lato and its carpenter ant host (Camponotus castaneus) at a crucial moment in the parasite's lifecycle: when the manipulated host fixes itself permanently to a substrate by its mandibles. The fungus is known to secrete tissue-specific metabolites and cause changes in host gene expression as well as atrophy in the mandible muscles of its ant host, but it is unknown how the fungus coordinates these effects to manipulate its host's behavior. In this study, we combine techniques in serial block-face scanning-electron microscopy and deep-learning-based image segmentation algorithms to visualize the distribution, abundance, and interactions of this fungus inside the body of its manipulated host. Fungal cells were found throughout the host body but not in the brain, implying that behavioral control of the animal body by this microbe occurs peripherally. Additionally, fungal cells invaded host muscle fibers and joined together to form networks that encircled the muscles. These networks may represent a collective foraging behavior of this parasite, which may in turn facilitate host manipulation.

Meckel's Cartilage in Mandibular Development and Dysmorphogenesis.

The Fgfr2c C342Y/+ Crouzon syndrome mouse model carries a cysteine to tyrosine substitution at amino acid position 342 (Cys342Tyr; C342Y) in the fibroblast growth factor receptor 2 (Fgfr2) gene equivalent to a FGFR2 mutation commonly associated with Crouzon and Pfeiffer syndromes in humans. The Fgfr2c C342Y mutation results in constitutive activation of the receptor and is associated with upregulation of osteogenic differentiation. Fgfr2cC342Y/+ Crouzon syndrome mice show premature closure of the coronal suture and other craniofacial anomalies including malocclusion of teeth, most likely due to abnormal craniofacial form. Malformation of the mandible can precipitate a plethora of complications including disrupting development of the upper jaw and palate, impediment of the airway, and alteration of occlusion necessary for proper mastication. The current paradigm of mandibular development assumes that Meckel's cartilage (MC) serves as a support or model for mandibular bone formation and as a template for the later forming mandible. If valid, this implies a functional relationship between MC and the forming mandible, so mandibular dysmorphogenesis might be discerned in MC affecting the relationship between MC and mandibular bone. Here we investigate the relationship of MC to mandible development from the early mineralization of the mandible (E13.5) through the initiation of MC degradation at E17.7 using Fgfr2c C342Y/+ Crouzon syndrome embryos and their unaffected littermates (Fgfr2c +/+ ). Differences between genotypes in both MC and mandibular bone are subtle, however MC of Fgfr2c C342Y/+ embryos is generally longer relative to unaffected littermates at E15.5 with specific aspects remaining relatively large at E17.5. In contrast, mandibular bone is smaller overall in Fgfr2c C342Y/+ embryos relative to their unaffected littermates at E15.5 with the posterior aspect remaining relatively small at E17.5. At a cellular level, differences are identified between genotypes early (E13.5) followed by reduced proliferation in MC (E15.5) and in the forming mandible (E17.5) in Fgfr2c C342Y/+ embryos. Activation of the ERK pathways is reduced in the perichondrium of MC in Fgfr2c C342Y/+ embryos and increased in bone related cells at E15.5. These data reveal that the Fgfr2c C342Y mutation differentially affects cells by type, location, and developmental age indicating a complex set of changes in the cells that make up the lower jaw.

An irregular metal trace inpainting network for x-ray CT metal artifact reduction.

PURPOSE: Metal implants in the patient's body can generate severe metal artifacts in x-ray computed tomography (CT) images. These artifacts may cover the tissues around the metal implants in CT images and even corrupt the tissue regions, thus affecting disease diagnosis using these images. Previous deep learning metal trace inpainting methods used both valid pixels of uncorrupted areas and invalid pixels of corrupted areas to patch metal trace (i.e., the holes of removed metal-corrupted regions). Such methods cannot recover fine details well and often suffer information mismatch due to interference of invalid pixels, thus incurring considerable secondary artifacts. In this paper, we develop a new irregular metal trace inpainting network for reducing metal artifacts. METHODS: We develop a new deep learning network to patch irregular metal trace in metal-corrupted sinograms to reduce metal artifacts for isometric fan-beam CT. Our new method patches irregular metal trace in CT sinograms using only valid pixels, avoiding interference from invalid pixels. Furthermore, to enable the inpainting network to recover as many details as possible, we design an auxiliary inpainting network to suppress the probable secondary artifacts in CT images to assist fine detail restoration. The image produced by the auxiliary network is then projected onto a sinogram via a forward projection (FP) algorithm and is fused with the sinogram predicted by the inpainting network in order to predict the final recovered sinogram. Our entire network is trained end-to-end to extract cross-domain information between the sinogram domain and CT image domain. RESULTS: We compare our proposed method with two traditional and four deep learning-based metal trace inpainting methods, and with an iterative reconstruction method on four datasets: dental fillings (panoramic and local perspectives), hip prostheses, and spine fixations. We use both quantitative and qualitative indices to evaluate our method, and the analyses suggest that our method reduces the most metal artifacts and produces the best quality CT images. Additionally, our proposed method takes 0.1512 s on average to process a CT slice, which meets the clinical requirement. CONCLUSIONS: This paper proposes a new deep learning network to patch irregular metal trace in corrupted sinograms to reduce metal artifacts. Our method restores more fine details in irregular metal trace and has a superior capability on metal artifact reduction compared with state-of-the-art methods.