Quasi-Equilibrium Feature Pyramid Network for Salient Object Detection.

Modern saliency detection models are based on the encoder-decoder framework and they use different strategies to fuse the multi-level features between the encoder and decoder to boost representation power. Motivated by recent work in implicit modelling, we propose to introduce an implicit function to simulate the equilibrium state of the feature pyramid at infinite depths. We question the existence of the ideal equilibrium and thus propose a quasi-equilibrium model by taking the first-order derivative into the black-box root solver using Taylor expansion. It models more realistic convergence states and significantly improves the network performance. We also propose a differentiable edge extractor that directly extracts edges from the saliency masks. By optimizing the extracted edges, the generated saliency masks are naturally optimized on contour constraints and the non-deterministic predictions are removed. We evaluate the proposed methodology on five public datasets and extensive experiments show that our method achieves new state-of-the-art performances on six metrics across datasets.

The diagnostic value of metagenomic next-generation sequencing for identifying Pneumocystis jirovecii infection in non-HIV immunocompromised patients.

Background: Pneumocystis jirovecii pneumonia (PJP) remains an important cause of morbidity and mortality in non-HIV immunocompromised patients especially in transplant recipients. But its diagnosis remains challenging due to the insuffificient performance of conventional methods for diagnosing Pneumocystis jirovecii(P. jirovecii) infection. Therefore, the auxiliary diagnostic function of metagenomics next-generation sequencing (mNGS) in clinical practice is worth of exploring. Method: 34 non-HIV immunocompromised patients who were diagnosed as PJP by clinical manifestations, imaging findings, immune status of the host, and Methenamine silver staining were tested by mNGS from October 2018 to December 2020 in Sichuan Provincial People's Hospital. The clinical performances of mNGS for P. jirovecii infection diagnosis were also evaluated with genome reads abundance and comparing with other traditional diagnostic methods. Results: We diagnosed a total of 34 non-HIV PJP patients by the clinical composite diagnosis. Our data shows that, compared with the clinical microbiological test, the detection rate of mNGS for P. jirovecii in non-HIV infected PJP patients is significantly higher than that of Methenamine silver staining and serum 1-3-ß-D-glucan. mNGS can be used as an auxiliary diagnostic tool to help diagnosis. The number of reads mapped to the genome of P. jirovecii and the duration of patients from onset to sampling collection were statistically significant between the two groups (Reads>100 and Reads ≤ 100) (8days vs. 23days, p=0.020). In addition, univariate analysis showed that C-reactive protein (15.8mg/L vs.79.56mg/L, p=0.016), lactate dehydrogenase (696U/l vs. 494U/l, p=0.030) and procalcitonin (0.09ng/ml vs. 0.59ng/ml, p=0.028) was also statistically significant between the two groups. Conclusions: An effective detection rate was achieved in PJP patients using mNGS testing of bronchoalveolar lavage fluid (BALF) or blood. The study also confirmed that the abundance of reads of P. jirovecii is related to the interval between the onset and sample collection. And the inflammation status during simultaneous mNGS detection might determine the abundance of pathogens. Hence, we conclude that the mNGS strategy could benefit disease diagnosis as well as treatment when complicated clinical infections appeared.

Olfactory exposure to late-pregnant and lactating mice causes stress-induced analgesia in male mice.

In an attempt to improve reproducibility, more attention is being paid to potential sources of stress in the laboratory environment. Here, we report that the mere proximity of pregnant or lactating female mice causes olfactory-mediated stress-induced analgesia, to a variety of noxious stimuli, in gonadally intact male mice. We show that exposure to volatile compounds released in the urine of pregnant and lactating female mice can themselves produce stress and associated pain inhibition. This phenomenon, a novel form of female-to-male chemosignaling, is mediated by female scent marking of urinary volatiles, such as n-pentyl-acetate, and likely signals potential maternal aggression aimed at defending against infanticide by stranger males.

Matching biomedical ontologies based on formal concept analysis.

BACKGROUND: The goal of ontology matching is to identify correspondences between entities from different yet overlapping ontologies so as to facilitate semantic integration, reuse and interoperability. As a well developed mathematical model for analyzing individuals and structuring concepts, Formal Concept Analysis (FCA) has been applied to ontology matching (OM) tasks since the beginning of OM research, whereas ontological knowledge exploited in FCA-based methods is limited. This motivates the study in this paper, i.e., to empower FCA with as much as ontological knowledge as possible for identifying mappings across ontologies. METHODS: We propose a method based on Formal Concept Analysis to identify and validate mappings across ontologies, including one-to-one mappings, complex mappings and correspondences between object properties. Our method, called FCA-Map, incrementally generates a total of five types of formal contexts and extracts mappings from the lattices derived. First, the token-based formal context describes how class names, labels and synonyms share lexical tokens, leading to lexical mappings (anchors) across ontologies. Second, the relation-based formal context describes how classes are in taxonomic, partonomic and disjoint relationships with the anchors, leading to positive and negative structural evidence for validating the lexical matching. Third, the positive relation-based context can be used to discover structural mappings. Afterwards, the property-based formal context describes how object properties are used in axioms to connect anchor classes across ontologies, leading to property mappings. Last, the restriction-based formal context describes co-occurrence of classes across ontologies in anonymous ancestors of anchors, from which extended structural mappings and complex mappings can be identified. RESULTS: Evaluation on the Anatomy, the Large Biomedical Ontologies, and the Disease and Phenotype track of the 2016 Ontology Alignment Evaluation Initiative campaign demonstrates the effectiveness of FCA-Map and its competitiveness with the top-ranked systems. FCA-Map can achieve a better balance between precision and recall for large-scale domain ontologies through constructing multiple FCA structures, whereas it performs unsatisfactorily for smaller-sized ontologies with less lexical and semantic expressions. CONCLUSIONS: Compared with other FCA-based OM systems, the study in this paper is more comprehensive as an attempt to push the envelope of the Formal Concept Analysis formalism in ontology matching tasks. Five types of formal contexts are constructed incrementally, and their derived concept lattices are used to cluster the commonalities among classes at lexical and structural level, respectively. Experiments on large, real-world domain ontologies show promising results and reveal the power of FCA.