No abstract

No abstract

BackgroundThe TGF-β signalling pathway plays a key role in regulating dauer formation in the free-living nematode Caenorhabditis elegans, and previous work has shown that TGF-β receptors are involved in parasitic nematodes. Here, we explored the structure and function of a TGF-β type II receptor homologue in the TGF-β signalling pathway in Haemonchus contortus, a highly pathogenic, haematophagous parasitic nematode.Methodology/Principal findingsAmino acid sequence and phylogenetic analyses revealed that the protein, called Hc-TGFBR2 (encoded by the gene Hc-tgfbr2), is a member of TGF-β type II receptor family and contains conserved functional domains, both in the extracellular region containing cysteine residues that form a characteristic feature (CXCX4C) of TGF-β type II receptor and in the intracellular regions containing a serine/threonine kinase domain. The Hc-tgfbr2 gene was transcribed in all key developmental stages of H. contortus, with particularly high levels in the infective third-stage larvae (L3s) and male adults. Immunohistochemical results revealed that Hc-TGFBR2 was expressed in the intestine, ovary and eggs within the uterus of female adults, and also in the testes of male adults of H. contortus. Double-stranded RNA interference (RNAi) in this nematode by soaking induced a marked decrease in transcription of Hc-tgfbr2 and in development from the exsheathed L3 to the fourth-stage larva (L4) in vitro.Conclusions/SignificanceThese results indicate that Hc-TGFBR2 plays an important role in governing developmental processes in H. contortus via the TGF-β signalling pathway, particularly in the transition from the free-living to the parasitic stages.

Heavy Fermion (HF) states emerge in correlated quantum materials due to the intriguing interplay between localized magnetic moments and itinerant electrons but rarely appear in 3d-electron systems due to high itinerancy of d-electrons. Here, an anomalous enhancement of Kondo screening is observed at the Kondo hole of local Fe vacancies in Fe3GeTe2 which is a recently discovered 3d-HF system featuring Kondo lattice and two-dimensional itinerant ferromagnetism. An itinerant Kondo–Ising model is established to reproduce the experimental results and provides insight into the competition between Ising ferromagnetism and Kondo screening. Our work explains the microscopic origin of the d-electron HF states in Fe3GeTe2 and inspires future studies of the enriched quantum many-body effects with Kondo holes.

Semi-supervised approaches for crowd counting attract attention, as the fully supervised paradigm is expensive and laborious due to its request for a large number of images of dense crowd scenarios and their annotations. This paper proposes a spatial uncertainty-aware semi-supervised approach via regularized surrogate task (binary segmentation) for crowd counting problems. Different from existing semisupervised learning-based crowd counting methods, to exploit the unlabeled data, our proposed spatial uncertaintyaware teacher-student framework focuses on high confident regions' information while addressing the noisy supervision from the unlabeled data in an end-to-end manner. Specifically, we estimate the spatial uncertainty maps from the teacher model's surrogate task to guide the feature learning of the main task (density regression) and the surrogate task of the student model at the same time. Besides, we introduce a simple yet effective differential transformation layer to enforce the inherent spatial consistency regularization between the main task and the surrogate task in the student model, which helps the surrogate task to yield more reliable predictions and generates high-quality uncertainty maps. Thus, our model can also address the task-level perturbation problems that occur spatial inconsistency between the primary and surrogate tasks in the student model. Experimental results on four challenging crowd counting datasets demonstrate that our method achieves superior performance to the state-of-the-art semi-supervised methods. Code is available at : https://github.com/ smallmax00/SUA_crowd_counting