DORec: Decomposed Object Reconstruction Utilizing 2D Self-Supervised Features

Decomposing a target object from a complex background while reconstructing is challenging. Most approaches acquire the perception for object instances through the use of manual labels, but the annotation procedure is costly. The recent advancements in 2D self-supervised learning have brought new prospects to object-aware representation, yet it remains unclear how to leverage such noisy 2D features for clean decomposition. In this paper, we propose a Decomposed Object Reconstruction (DORec) network based on neural implicit representations. Our key idea is to transfer 2D self-supervised features into masks of two levels of granularity to supervise the decomposition, including a binary mask to indicate the foreground regions and a K-cluster mask to indicate the semantically similar regions. These two masks are complementary to each other and lead to robust decomposition. Experimental results show the superiority of DORec in segmenting and reconstructing the foreground object on various datasets

Patients With Obsessive-Compulsive Disorder Exhibit Deficits in Consummatory but Not Anticipatory Pleasure

Background: Reward dysfunctions have been reported in obsessive-compulsive disorder (OCD), which implicates a high possibility of anhedonia for this disease. However, several components of anhedonia, such as consummatory and anticipatory pleasure, has not been substantially studied in OCD patients.Methods: The Chinese version of the Temporal Experience of Pleasure Scale (CV-TEPS) was used to evaluate both the consummatory and anticipatory pleasure in 130 OCD patients, 89 major depressive disorder (MDD) patients, and 95 healthy controls (HCs). The Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) and the Beck Depression Inventory (BDI) were scored for assessing the severity of obsessive and compulsive symptoms and depressive symptoms, respectively. Analyses of covariance (ANCOVA) were used to compare the differences of anhedonia among the three groups with the severity of depression controlled. Regression analyses were also used to analyze the relationship between consummatory and anticipatory pleasure and clinical variables in OCD patients.Results: After controlling for the effect of depression, there were significant differences in TEPS scores among the three groups (p < 0.05). Compared with HCs, OCD patients had lower scores on the consummatory subscale, but not the anticipatory subscale, of the TEPS. MDD patients had lower scores on both the consummatory and anticipatory subscales than HCs.Conclusion: OCD patients exhibit deficits in consummatory but not anticipatory pleasure, which is distinct from MDD patients

Cyclo­oxygenase-1-selective inhibitor SC-560

In the title compound, 5-(4-chloro­phen­yl)-1-(4-methoxy­phen­yl)-3-(trifluoro­meth­yl)-1H-pyrazole (SC-560), C17H12ClF3N2O, a COX-1-selective inhibitor, the dihedral angles between the heterocycle and the chlorobenzene and methoxybenzene rings are 41.66 (6) and 43.08 (7)°, respectively. The dihedral angle between the two phenyl rings is 59.94 (6)°. No classic hydrogen bonds are possible in the crystal, and intermolecular interactions must be mainly of the dispersion type. This information may aid the identification of dosage formulations with improved oral bioavailability

The secreted FolAsp aspartic protease facilitates the virulence of Fusarium oxysporum f. sp. lycopersici

Pathogens utilize secretory effectors to manipulate plant defense. Fusarium oxysporum f. sp. lycopersici (Fol) is the causal agent of Fusarium wilt disease in tomatoes. We previously identified 32 secreted effector candidates by LC-MS analysis. In this study, we functionally identified one of the secreted proteins, FolAsp, which belongs to the aspartic proteases (Asp) family. The FolAsp was upregulated with host root specifically induction. Its N-terminal 1–19 amino acids performed the secretion activity in the yeast system, which supported its secretion in Fol. Phenotypically, the growth and conidia production of the FolAsp deletion mutants were not changed; however, the mutants displayed significantly reduced virulence to the host tomato. Further study revealed the FolAsp was localized at the apoplast and inhibited INF1-induced cell death in planta. Meanwhile, FolAsp could inhibit flg22-mediated ROS burst. Furthermore, FolAsp displayed protease activity on host protein, and overexpression of FolAsp in Fol enhanced pathogen virulence. These results considerably extend our understanding of pathogens utilizing secreted protease to inhibit plant defense and promote its virulence, which provides potential applications for tomato improvement against disease as the new drug target

Preoperative Non-Invasive Prediction of Breast Cancer Molecular Subtypes With a Deep Convolutional Neural Network on Ultrasound Images

PurposeThis study aimed to develop a deep convolutional neural network (DCNN) model to classify molecular subtypes of breast cancer from ultrasound (US) images together with clinical information.MethodsA total of 1,012 breast cancer patients with 2,284 US images (center 1) were collected as the main cohort for training and internal testing. Another cohort of 117 breast cancer cases with 153 US images (center 2) was used as the external testing cohort. Patients were grouped according to thresholds of nodule sizes of 20 mm and age of 50 years. The DCNN models were constructed based on US images and the clinical information to predict the molecular subtypes of breast cancer. A Breast Imaging-Reporting and Data System (BI-RADS) lexicon model was built on the same data based on morphological and clinical description parameters for diagnostic performance comparison. The diagnostic performance was assessed through the accuracy, sensitivity, specificity, Youden’s index (YI), and area under the receiver operating characteristic curve (AUC).ResultsOur DCNN model achieved better diagnostic performance than the BI-RADS lexicon model in differentiating molecular subtypes of breast cancer in both the main cohort and external testing cohort (all p < 0.001). In the main cohort, when classifying luminal A from non-luminal A subtypes, our model obtained an AUC of 0.776 (95% CI, 0.649–0.885) for patients older than 50 years and 0.818 (95% CI, 0.726–0.902) for those with tumor sizes ≤20 mm. For young patients ≤50 years, the AUC value of our model for detecting triple-negative breast cancer was 0.712 (95% CI, 0.538–0.874). In the external testing cohort, when classifying luminal A from non-luminal A subtypes for patients older than 50 years, our DCNN model achieved an AUC of 0.686 (95% CI, 0.567–0.806).ConclusionsWe employed a DCNN model to predict the molecular subtypes of breast cancer based on US images. Our model can be valuable depending on the patient’s age and nodule sizes