Kondo Holes in the Two-Dimensional Itinerant Ising Ferromagnet Fe3GeTe2.

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.

Mathematical modelling and deep learning algorithms to automate assessment of single and digitally multiplexed immunohistochemical stains in tumoural stroma.

Whilst automated analysis of immunostains in pathology research has focused predominantly on the epithelial compartment, automated analysis of stains in the stromal compartment is challenging and therefore requires time-consuming pathological input and guidance to adjust to tissue morphometry as perceived by pathologists. This study aimed to develop a robust method to automate stromal stain analyses using 2 of the commonest stromal stains (SMA and desmin) employed in clinical pathology practice as examples. An effective computational method capable of automatically assessing and quantifying tumour-associated stromal stains was developed and applied on cores of colorectal cancer tissue microarrays. The methodology combines both mathematical models and deep learning techniques with the former requiring no training data and the latter as many inputs as possible. The novel mathematical model was used to produce a digital double marker overlay allowing for fast automated digital multiplex analysis of stromal stains. The results show that deep learning methodologies in combination with mathematical modelling allow for an accurate means of quantifying stromal stains whilst also opening up new possibilities of digital multiplex analyses.

Controllable Modulation of the Electronic Properties of a Two-Dimensional Ambipolar Semiconductor by Interface Ferroelectric Polarization.

Precise control of charge carrier type and density of two-dimensional (2D) ambipolar semiconductors is the prerequisite for their applications in next-generation integrated circuits and electronic devices. Here, by fabricating a heterointerface between a 2D ambipolar semiconductor (hydrogenated germanene, GeH) and a ferroelectric substrate (PbMg1/3Nb2/3O3-PbTiO3, PMN-PT), fine-tuning of charge carrier type and density of GeH is achieved. Due to ambipolar properties, proper band gap, and high carrier mobility of GeH, by applying the opposite local bias (±8 V), a lateral polarization in GeH is constructed with a change of work function by 0.6 eV. Besides, the built-in polarization in GeH nanoflake could promote the separation of photoexcited electron-hole pairs, which lead to 4 times enhancement of the photoconductivity after poling by 200 V. In addition, a gradient regulation of the work function of GeH from 4.94 to 5.21 eV by adjusting the local substrate polarization is demonstrated, which could be used for data storage at the micrometer size by forming p-n homojunctions. This work of constructing such heterointerfaces provides a pathway for applying 2D ambipolar semiconductors in nonvolatile memory devices, photoelectronic devices, and next-generation integrated circuit.

Dual Consistency Enabled Weakly and Semi-Supervised Optic Disc and Cup Segmentation With Dual Adaptive Graph Convolutional Networks.

Glaucoma is a progressive eye disease that results in permanent vision loss, and the vertical cup to disc ratio (vCDR) in colour fundus images is essential in glaucoma screening and assessment. Previous fully supervised convolution neural networks segment the optic disc (OD) and optic cup (OC) from color fundus images and then calculate the vCDR offline. However, they rely on a large set of labeled masks for training, which is expensive and time-consuming to acquire. To address this, we propose a weakly and semi-supervised graph-based network that investigates geometric associations and domain knowledge between segmentation probability maps (PM), modified signed distance function representations (mSDF), and boundary region of interest characteristics (B-ROI) in three aspects. Firstly, we propose a novel Dual Adaptive Graph Convolutional Network (DAGCN) to reason the long-range features of the PM and the mSDF w.r.t. the regional uniformity. Secondly, we propose a dual consistency regularization-based semi-supervised learning paradigm. The regional consistency between the PM and the mSDF, and the marginal consistency between the derived B-ROI from each of them boost the proposed model's performance due to the inherent geometric associations. Thirdly, we exploit the task-specific domain knowledge via the oval shapes of OD & OC, where a differentiable vCDR estimating layer is proposed. Furthermore, without additional annotations, the supervision on vCDR serves as weakly-supervisions for segmentation tasks. Experiments on six large-scale datasets demonstrate our model's superior performance on OD & OC segmentation and vCDR estimation. The implementation code has been made available.https://github.com/smallmax00/Dual_Adaptive_Graph_Reasoning.

A proteasomal ß5 subunit of Haemonchus contortus with a role in the growth, development and life span.

BACKGROUND: The proteasome in eukaryotic cells can degrade a variety of proteins and plays an important role in regulating the cell cycle, cell survival and apoptosis. The proteasome receives much attention as a potential chemotherapeutic target for treatment of a variety of infectious parasitic diseases, but few studies of proteasomes have been done on parasitic nematodes. METHODS: A proteasomal ß5 subunit encoding gene (named Hc-pbs-5) and its inferred product (Hc-PBS-5) in Haemonchus contortus were identified and characterized in this study. Then, the transcriptional profiles and anatomical expression were studied using an integrated molecular approach. Finally, a specific proteasome inhibitor bortezomib (BTZ), together with RNA interference (RNAi), was employed to assess the function of Hc-PBS-5. RESULTS: Bioinformatic analysis revealed that the coding sequence of Hc-pbs-5 was 855 bp long and encoded 284 amino acids (aa). The predicted protein (Hc-PBS-5) had core conservative sequences (65-250 aa) belonging to N-terminal nucleophile (Ntn) family of hydrolases. Real-time PCR results revealed that Hc-pbs-5 was continuously transcribed in eight developmental stages with higher levels at the infective third-stage larvae (L3s) and adult males of H. contortus. Immunohistochemical results revealed that Hc-PBS-5 was expressed in intestine, outer cuticle, muscle cells under the outer cuticle, cervical glands and seminal vesicles of male adults and also in intestine, outer cuticle, cervical glands, uterine wall, eggs and ovaries of female adults of H. contortus. BTZ could reduce proportions of egg hatching, and the fourth-stage larvae (L4s) developed from the exsheathed L3s (xL3s) of H. contortus. In addition, silencing Hc-pbs-5 by soaking the specific double-stranded RNA (dsRNA) could decrease the transcription of Hc-pbs-5 and result in fewer xL3s developing to L4s in vitro. CONCLUSIONS: These results indicate that proteasomal ß5 subunit plays an important role in the growth, development and life span of H. contortus.

Topological Flat Bands in 2D Breathing-Kagome Lattice Nb3 TeCl7.

Flat bands (FBs) can appear in two-dimensional (2D) geometrically frustrated systems caused by quantum destructive interference (QDI). However, the scarcity of pure 2D frustrated crystal structures in natural materials makes FBs hard to be identified, let alone modulate FBs relating to electronic properties. Here, the experimental evidence of the complete electronic QDI induced FB contributed by the 2D breathing-kagome layers of Nb atoms in Nb3 TeCl7 (NTC) is reported. An identical chemical state and 2D localization characteristics of the Nb breathing-kagome layers are experimentally confirmed, based on which NTC is demonstrated to be a superior concrete candidate for the breathing-kagome tight-binding model. Furthermore, it theoretically establishes the tunable roles of the on-site energy over Nb sites on bandwidth, energy position, and topology of FBs in NTC. This work opens an aveanue to manipulate FB characteristics in these 4d transition-metal-based breathing-kagome materials.

Graph-Based Region and Boundary Aggregation for Biomedical Image Segmentation.

Segmentation is a fundamental task in biomedical image analysis. Unlike the existing region-based dense pixel classification methods or boundary-based polygon regression methods, we build a novel graph neural network (GNN) based deep learning framework with multiple graph reasoning modules to explicitly leverage both region and boundary features in an end-to-end manner. The mechanism extracts discriminative region and boundary features, referred to as initialized region and boundary node embeddings, using a proposed Attention Enhancement Module (AEM). The weighted links between cross-domain nodes (region and boundary feature domains) in each graph are defined in a data-dependent way, which retains both global and local cross-node relationships. The iterative message aggregation and node update mechanism can enhance the interaction between each graph reasoning module's global semantic information and local spatial characteristics. Our model, in particular, is capable of concurrently addressing region and boundary feature reasoning and aggregation at several different feature levels due to the proposed multi-level feature node embeddings in different parallel graph reasoning modules. Experiments on two types of challenging datasets demonstrate that our method outperforms state-of-the-art approaches for segmentation of polyps in colonoscopy images and of the optic disc and optic cup in colour fundus images. The trained models will be made available at: https://github.com/smallmax00/Graph_Region_Boudnary.

Piloting a Deep Learning Model for Predicting Nuclear BAP1 Immunohistochemical Expression of Uveal Melanoma from Hematoxylin-and-Eosin Sections.

Background: Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. Monosomy 3 and BAP1 mutation are strong prognostic factors predicting metastatic risk in UM. Nuclear BAP1 (nBAP1) expression is a close immunohistochemical surrogate for both genetic alterations. Not all laboratories perform routine BAP1 immunohistochemistry or genetic testing, and rely mainly on clinical information and anatomic/morphologic analyses for UM prognostication. The purpose of our study was to pilot deep learning (DL) techniques to predict nBAP1 expression on whole slide images (WSIs) of hematoxylin and eosin (H&E) stained UM sections. Methods: One hundred forty H&E-stained UMs were scanned at 40 × magnification, using commercially available WSI image scanners. The training cohort comprised 66 BAP1+ and 74 BAP1- UM, with known chromosome 3 status and clinical outcomes. Nonoverlapping areas of three different dimensions (512 × 512, 1024 × 1024, and 2048 × 2048 pixels) for comparison were extracted from tumor regions in each WSI, and were resized to 256 × 256 pixels. Deep convolutional neural networks (Resnet18 pre-trained on Imagenet) and auto-encoder-decoders (U-Net) were trained to predict nBAP1 expression of these patches. Trained models were tested on the patches cropped from a test cohort of WSIs of 16 BAP1+ and 28 BAP1- UM cases. Results: The trained model with best performance achieved area under the curve values of 0.90 for patches and 0.93 for slides on the test set. Conclusions: Our results show the effectiveness of DL for predicting nBAP1 expression in UM on the basis of H&E sections only. Translational Relevance: Our pilot demonstrates a high capacity of artificial intelligence-related techniques for automated prediction on the basis of histomorphology, and may be translatable into routine histology laboratories.

A TGF-ß type II receptor that associates with developmental transition in Haemonchus contortus in vitro.

BACKGROUND: The TGF-ß signalling pathway plays a key role in regulating dauer formation in the free-living nematode Caenorhabditis elegans, and previous work has shown that TGF-ß receptors are involved in parasitic nematodes. Here, we explored the structure and function of a TGF-ß type II receptor homologue in the TGF-ß signalling pathway in Haemonchus contortus, a highly pathogenic, haematophagous parasitic nematode. METHODOLOGY/PRINCIPAL FINDINGS: Amino acid sequence and phylogenetic analyses revealed that the protein, called Hc-TGFBR2 (encoded by the gene Hc-tgfbr2), is a member of TGF-ß 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-ß 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/SIGNIFICANCE: These results indicate that Hc-TGFBR2 plays an important role in governing developmental processes in H. contortus via the TGF-ß signalling pathway, particularly in the transition from the free-living to the parasitic stages.

A TGF-ß type I receptor-like molecule with a key functional role in Haemonchus contortus development.

Here we investigated the gene of a transforming growth factor (TGF)-ß type I receptor-like molecule in Haemonchus contortus, a highly pathogenic and economically important parasitic nematode of small ruminants. Designated Hc-tgfbr1, this gene is transcribed in all developmental stages of H. contortus, and the encoded protein has glycine-serine rich and kinase domains characteristic of a TGF-ß family type I receptor. Expression of a GFP reporter driven by the putative Hc-tgfbr1 promoter localised to two intestinal rings, the anterior-most intestinal ring (int ring I) and the posterior-most intestinal ring (int ring IX) in Caenorhabditis elegans in vivo. Heterologous genetic complementation using a plasmid construct containing Hc-tgfbr1 genomic DNA failed to rescue the function of Ce-daf-1 (a known TGF-ß type I receptor gene) in a daf-1-deficient mutant strain of C. elegans. In addition, a TGF-ß type I receptor inhibitor, galunisertib, and double-stranded RNA interference (RNAi) were employed to assess the function of Hc-tgfbr1 in the transition from exsheathed L3 (xL3) to the L4 of H. contortus in vitro, revealing that both galunisertib and Hc-tgfbr1-specific double-stranded RNA could retard L4 development. Taken together, these results provide evidence that Hc-tgfbr1 is involved in developmental processes in H. contortus in the transition from the free-living to the parasitic stage.

Diagnoses of coaxial probes in shock compression.

A miniature diagnostic apparatus, which consists of a target, a guiding tube, a nail-gun, and a simple base, has been developed in the proposed research to estimate batches of pinlike coaxial probes with selectable collision speeds, 198.3, 361.0, and 420.6 mps, corresponding to thrust capabilities of certain minibullets. This work aims at filling the gap between typical two stage light gas gun (>500 mps) and pendulum machine (<100 mps) with low cost and risk in realistic shock compressions. As a part of the experiment, the kappa coefficient and the statistical agreement and reliability are all evaluated.

[Study on acupuncture treatment of diabetic neurogenic bladder].

OBJECTIVE: To probe into a better therapy for diabetic neurogenic bladder. METHODS: The patients were randomly divided into a treatment group and a control group, 35 cases in each group. The control group were treated with intramuscular injection of Methycobal 250 microg, once every other day; the treatment group were treated with intramuscular injection of Methycobal 250 microg, once every other day, and acupuncture at Guanyuan (CV 4), Shenshu (BL 23), Ciliao (BL 32), Huiyang (BL 35), once every day. The residual urine were compared before and after treatment in the two groups; the effective rate for improvement of symptoms were compared between the two groups. The mental and healthy nursing were conducted for the patients. RESULTS: After treatment, the improving rate for the urgency of urination, frequency of micturition, dribbling urination, urinary incontinence and dysuria in the treatment group was significantly better than that in the control group, but with no significant difference between the two groups in prolongation of urination time. After treatment, the residual urine in the bladder significantly improved in the two groups with more significantly improved in the treatment group than in the control group. CONCLUSION: Methycobal plus acupuncture has a better result than the simple Methyeobal for treatment of diabetic neurogenic bladder, and strengthening nursing care in the treatment can significantly enhance life quality of the patient.