A range extension for Haplomitrium mnioides (Lindb.) R.M.Schust.

Haplomitrium mnioides (Lindb.) R.M.Schust. is reported as new to Hainan Island. A continuous distribution of H. mnioides from west (Thailand) to east (Japan) is confirmed. Habitat pictures and a distribution map are provided

Quantum simulation of exotic PT-invariant topological nodal loop bands with ultracold atoms in an optical lattice

Since the well-known PT symmetry has its fundamental significance and implication in physics, where PT denotes the combined operation of space-inversion P and time-reversal T, it is extremely important and intriguing to completely classify exotic PT-invariant topological metals and to physically realize them. Here we, for the first time, establish a rigorous classification of topological metals that are protected by the PT symmetry using KO-theory. As a physically realistic example, a PT-invariant nodal loop (NL) model in a 3D Brillouin zone is constructed, whose topological stability is revealed through its PT-symmetry-protected nontrivial Z2 topological charge. Based on these exact results, we propose an experimental scheme to realize and to detect tunable PT-invariant topological NL states with ultracold atoms in an optical lattice, in which atoms with two hyperfine spin states are loaded in a spin-dependent 3D OL and two pairs of Raman lasers are used to create out-of-plane spin-flip hopping with site-dependent phase. Such a realistic cold-atom setup can yield topological NL states, having a tunable ring-shaped band-touching line with the two-fold degeneracy in the bulk spectrum and non-trivial surface states. The states are actually protected by the combined PT symmetry even in the absence of both P and T symmetries, and are characterized by a Z2-type invariant (a quantized Berry phase). Remarkably, we demonstrate with numerical simulations that (i) the characteristic NL can be detected by measuring the atomic transfer fractions in a Bloch-Zener oscillation; (ii) the topological invariant may be measured based on the time-of-flight imaging; and (iii) the surface states may be probed through Bragg spectroscopy. The present proposal for realizing topological NL states in cold atom systems may provide a unique experimental platform for exploring exotic PT-invariant topological physics.Comment: 11 pages, 6 figures; accepted for publication in Phys. Rev.

Identification and characterization of gastrointestinal hormone immunoreactive cells in the skin and parotoids of Chinese toad Bufo gargarizans

The skin and skin secretion of Chinese toad Bufo gargarizans have long been used in traditional Chinese medicine. However, the exact types and location of bioactive substances in Bufo gargarizans skin still have not been fully elucidated. The aim of the study was to investigate the distribution and density of six types of gastrointestinal (GI) hormone immunoreactive (IR) cells in the skin and parotoids of Bufo gargarizans. Immunohistochemistry was used for qualitative and semiquantitative analysis of GI hormone presence in the dorsal and ventral skin, and parotoids of eight adult Chinese toads. Six types of IR cells were found: serotonin (5-HT), glucagon (GLU), gastrin (GAS), somatostatin (SS), pancreatic polypeptide (PP) and neuropeptide Y(NPY) IR cells. They were mainly present in the epidermis and skin glands. 5-HT-IR cells were distributed in all layers of epidermis and glands, with higher density in the glands. Glucagon was prominently expressed in the epidermis and the bottle-shaped glands of parotoids; however, it was not present in the granular glands of skin and parotoids. The distributions of GAS and SS-IR cells were similar since they were present mainly in mucous, granular and bottle-shaped glands, while these cell types were absent in the differentiated glands of parotoids. PP-IR cells were predominant in the granular glands and the bottle-shaped glands. The expression of NPY was high in epidermal stratum granulosum and mucous glands of the dorsal skin, the bottle-shaped glands and differentiated glands of parotoids, while NPY-IR was rarely seen in the granular glands of ventral skin, and not present in the granular glands of dorsal skin and parotoids. The expression of several types of GI hormones in the skin and parotoids of Bufo gargarizans varies depending on tissue and type of glands

Matched Shrunken Cone Detector (MSCD): Bayesian Derivations and Case Studies for Hyperspectral Target Detection

Hyperspectral images (HSIs) possess non-negative properties for both hyperspectral signatures and abundance coefficients, which can be naturally modeled using cone-based representation. However, in hyperspectral target detection, cone-based methods are barely studied. In this paper, we propose a new regularized cone-based representation approach to hyperspectral target detection, as well as its two working models by incorporating into the cone representation l2-norm and l1-norm regularizations, respectively. We call the new approach the matched shrunken cone detector (MSCD). Also important, we provide principled derivations of the proposed MSCD from the Bayesian perspective: we show that MSCD can be derived by assuming a multivariate half-Gaussian distribution or a multivariate half-Laplace distribution as the prior distribution of the coefficients of the models. In the experimental studies, we compare the proposed MSCD with the subspace methods and the sparse representation-based methods for HSI target detection. Two real hyperspectral data sets are used for evaluating the detection performances on sub-pixel targets and full-pixel targets, respectively. Results show that the proposed MSCD can outperform other methods in both cases, demonstrating the competitiveness of the regularized cone-based representation

Locally-Enriched Cross-Reconstruction for Few-Shot Fine-Grained Image Classification

Few-shot fine-grained image classification has attracted considerable attention in recent years for its realistic setting to imitate how humans conduct recognition tasks. Metric-based few-shot classifiers have achieved high accuracies. However, their metric function usually requires two arguments of vectors, while transforming or reshaping three-dimensional feature maps to vectors can result in loss of spatial information. Image reconstruction is thus involved to retain more appearance details: the test images are reconstructed by different classes and then classified to the one with the smallest reconstruction error. However, discriminative local information, vital to distinguish sub-categories in fine-grained images with high similarities, is not well elaborated when only the base features from a usual embedding module are adopted for reconstruction. Hence, we propose the novel local content-enriched cross-reconstruction network (LCCRN) for few-shot fine-grained classification. In LCCRN, we design two new modules: the local content-enriched module (LCEM) to learn the discriminative local features, and the cross-reconstruction module (CRM) to fully engage the local features with the appearance details obtained from a separate embedding module. The classification score is calculated based on the weighted sum of reconstruction errors of the cross-reconstruction tasks, with weights learnt from the training process. Extensive experiments on four fine-grained datasets showcase the superior classification performance of LCCRN compared with the state-of-the-art few-shot classification methods. Codes are available at: https://github.com/lutsong/LCCRN