King University

Fear causes fleeing and thereby saves lives: this exemplifies a popular and common sense but increasingly untenable view that the direct causation of behavior is the primary function of emotion. Instead, the authors develop a theory of emotion as a feedback system whose influence on behavior is typically indirect. By providing feedback and stimulating retrospective appraisal of actions, conscious emotional states can promote learning and alter guidelines for future behavior. Behavior may also be chosen to pursue (or avoid) anticipated emotional outcomes. Rapid, automatic affective responses, in contrast to the full-blown conscious emotions, may inform cognition and behavioral choice and thereby help guide current behavior. The automatic affective responses may also remind the person of past emotional outcomes and provide useful guides as to what emotional outcomes may be anticipated in the present. To justify replacing the direct causation model with the feedback model, the authors review a large body of empirical findings.

We developed a model in which leader-member exchange mediated between perceived transformational leadership behaviors and followers' task performance and organizational citizenship behaviors. Our sample comprised 162 leader-follower dyads within organizations situated throughout the People's Republic of China. We showed that leader-member exchange fully mediated between transformational leadership and task performance as well as organizational citizenship behaviors. Implications for the theory and practice of leadership are discussed, and future research directions offered.

Microbes appear in every corner of human life, and microbes affect every aspect of human life. The human oral cavity contains a number of different habitats. Synergy and interaction of variable oral microorganisms help human body against invasion of undesirable stimulation outside. However, imbalance of microbial flora contributes to oral diseases and systemic diseases. Oral microbiomes play an important role in the human microbial community and human health. The use of recently developed molecular methods has greatly expanded our knowledge of the composition and function of the oral microbiome in health and disease. Studies in oral microbiomes and their interactions with microbiomes in variable body sites and variable health condition are critical in our cognition of our body and how to make effect on human health improvement.

MXenes are a growing family of two-dimensional transition metal carbides and/or nitrides that are densely stacked into macroscopically layered films and have been considered for applications such as flexible electromagnetic interference (EMI) shielding materials. However, the mechanical and electrical reliabilities of titanium carbide MXene films are affected by voids in their structure. We applied sequential bridging of hydrogen and covalent bonding agents to induce the densification of MXene films and removal of the voids, leading to highly compact MXene films. The obtained MXene films show high tensile strength, in combination with high toughness, electrical conductivity, and EMI shielding capability. Our high-performance MXene films are scalable, providing an avenue for assembling other two-dimensional platelets into high-performance films.

As organizational environments become increasingly dynamic, complex, and competitive, leaders are likely to face intensified contradictory, or seemingly paradoxical, demands. We develop the construct of “paradoxical leader behavior” in people management, which refers to seemingly competing, yet interrelated, behaviors to meet structural and follower demands simultaneously and over time. In Study 1, we develop a measure of paradoxical leader behavior in people management using five samples from China. Confirmatory factor analyses support a multidimensional measure of paradoxical leader behavior with five dimensions: (1) combining self-centeredness with other-centeredness; (2) maintaining both distance and closeness; (3) treating subordinates uniformly, while allowing individualization; (4) enforcing work requirements, while allowing flexibility; and (5) maintaining decision control, while allowing autonomy. In Study 2, we examine the antecedents and consequences of paradoxical leader behavior in people management with a field sample of 76 supervisors and 516 subordinates from 6 firms. We find that the extent to which supervisors engage in holistic thinking and have integrative complexity is positively related to their paradoxical behavior in managing people, which, in turn, is associated with increased proficiency, adaptivity, and proactivity among subordinates.

Programming language processing (similar to natural language processing) is a hot research topic in the field of software engineering; it has also aroused growing interest in the artificial intelligence community. However, different from a natural language sentence, a program contains rich, explicit, and complicated structural information. Hence, traditional NLP models may be inappropriate for programs. In this paper, we propose a novel tree-based convolutional neural network (TBCNN) for programming language processing, in which a convolution kernel is designed over programs' abstract syntax trees to capture structural information. TBCNN is a generic architecture for programming language processing; our experiments show its effectiveness in two different program analysis tasks: classifying programs according to functionality, and detecting code snippets of certain patterns. TBCNN outperforms baseline methods, including several neural models for NLP.

Severe acute respiratory syndrome coronavirus (SARS-CoV) is the pathogen of SARS, which caused a global panic in 2003. We describe here the screening of Chinese herbal medicine-based, novel small molecules that bind avidly with the surface spike protein of SARS-CoV and thus can interfere with the entry of the virus to its host cells. We achieved this by using a two-step screening method consisting of frontal affinity chromatography-mass spectrometry coupled with a viral infection assay based on a human immunodeficiency virus (HIV)-luc/SARS pseudotyped virus. Two small molecules, tetra-O-galloyl-beta-D-glucose (TGG) and luteolin, were identified, whose anti-SARS-CoV activities were confirmed by using a wild-type SARS-CoV infection system. TGG exhibits prominent anti-SARS-CoV activity with a 50% effective concentration of 4.5 microM and a selective index of 240.0. The two-step screening method described here yielded several small molecules that can be used for developing new classes of anti-SARS-CoV drugs and is potentially useful for the high-throughput screening of drugs inhibiting the entry of HIV, hepatitis C virus, and other insidious viruses into their host cells.

The article focuses on the effects of economic globalization on the field of management science. The continued growth of international business enterprises means that a corresponding importance must be placed on research into their management problems and practice. This obvious fact has been recognized, and there has been a large increase in research in this area published in the leading scholarly periodicals of the discipline. There is, however, a worrisome tendency for researchers to employ the paradigms of research into North American, primarily U.S. management. This ignores the differing cultural, social, and economic ideas and practices persons of different nations bring to the management of the same international operation. Research into these businesses requires a pluralistic approach if it is to accurately reflect their real world operations.

Accurate prediction of others' trajectories is essential for autonomous driving. Trajectory prediction is challenging because it requires reasoning about agents' past movements, social interactions among varying numbers and kinds of agents, constraints from the scene context, and the stochasticity of human behavior. Our approach models these interactions and constraints jointly within a novel Multi-Agent Tensor Fusion (MATF) network. Specifically, the model encodes multiple agents' past trajectories and the scene context into a Multi-Agent Tensor, then applies convolutional fusion to capture multiagent interactions while retaining the spatial structure of agents and the scene context. The model decodes recurrently to multiple agents' future trajectories, using adversarial loss to learn stochastic predictions. Experiments on both highway driving and pedestrian crowd datasets show that the model achieves state-of-the-art prediction accuracy.

What is the interrelationship among formal institutions, social networks, and new venture growth? Drawing on the theory of institutional polycentrism and social network theory, we examine this question using data on 637 entrepreneurs from four different countries. We find the confluence of weak and inefficient formal institutions to be associated with a larger number of structural holes in entrepreneurial social networks. While the effect of this institutional order on the revenue growth of new ventures is negative, a network's structural holes have a positive effect on revenue growth. Furthermore, the positive effect of structural holes on revenue growth is stronger in an environment with a more adverse institutional order (i.e., weaker and more inefficient institutions). The contributions and implications of these findings are discussed.

Bone, as a mineralized composite of inorganic (mostly carbonated hydroxyapatite) and organic (mainly type I collagen) phases, possesses a unique combination of remarkable strength and toughness. Its excellent mechanical properties are related to its hierarchical structures and precise organization of the inorganic and organic phases at the nanoscale: Nanometer-sized hydroxyapatite crystals periodically deposit within the gap zones of collagen fibrils during bone biomineralization process. This hierarchical arrangement produces nanomechanical heterogeneities, which enable a mechanism for high energy dissipation and resistance to fracture. The excellent mechanical properties integrated with the hierarchical nanostructure of bone have inspired chemists and material scientists to develop biomimetic strategies for artificial bone grafts in tissue engineering (TE). This critical review provides a broad overview of the current mechanisms involved in bone biomineralization, and the relationship between bone hierarchical structures and the deformation mechanism. Our goal in this review is to inspire the application of these principles toward bone TE.

Mesenchymal stem cell (MSC)-derived exosome plays a central role in the cell-free therapeutics involving MSCs and the contents can be customized under disease-associated microenvironments. However, optimal MSC-preconditioning to enhance its therapeutic potential is largely unknown. Here, we show that preconditioning of gingival tissue-derived MSCs (GMSCs) with tumor necrosis factor-alpha (TNF-α) is ideal for the treatment of periodontitis. TNF-α stimulation not only increased the amount of exosome secreted from GMSCs, but also enhanced the exosomal expression of CD73, thereby inducing anti-inflammatory M2 macrophage polarization. The effect of GMSC-derived exosomes on inflammatory bone loss were examined by ligature-induced periodontitis model in mice. Local injection of GMSC-derived exosomes significantly reduced periodontal bone resorption and the number of tartrate-resistant acid phosphatase (TRAP)-positive osteoclasts, and these effects were further enhanced by preconditioning of GMSCs with TNF-α. Thus, GMSC-derived exosomes also exhibited anti-osteoclastogenic activity. Receptor activator of NF-κB ligand (RANKL) expression was regulated by Wnt5a in periodontal ligament cells (PDLCs), and exosomal miR-1260b was found to target Wnt5a-mediated RANKL pathway and inhibit its osteoclastogenic activity. These results indicate that significant ability of the TNF-α-preconditioned GMSC-derived exosomes to regulate inflammation and osteoclastogenesis paves the way for establishment of a therapeutic approach for periodontitis.

Most conditional generation tasks expect diverse outputs given a single conditional context. However, conditional generative adversarial networks (cGANs) often focus on the prior conditional information and ignore the input noise vectors, which contribute to the output variations. Recent attempts to resolve the mode collapse issue for cGANs are usually task-specific and computationally expensive. In this work, we propose a simple yet effective regularization term to address the mode collapse issue for cGANs. The proposed method explicitly maximizes the ratio of the distance between generated images with respect to the corresponding latent codes, thus encouraging the generators to explore more minor modes during training. This mode seeking regularization term is readily applicable to various conditional generation tasks without imposing training overhead or modifying the original network structures. We validate the proposed algorithm on three conditional image synthesis tasks including categorical generation, image-to-image translation, and text-to-image synthesis with different baseline models. Both qualitative and quantitative results demonstrate the effectiveness of the proposed regularization method for improving diversity without loss of quality.

Abstract Most photocrosslinkable hydrogels have inadequacy in either mechanical performance or biodegradability. This issue is addressed by adopting a novel hydrogel design by introducing two different chitosan chains (catechol‐modified methacryloyl chitosan, CMC; methacryloyl chitosan, MC) via the simultaneous crosslinking of carbon–carbon double bonds and catechol‐Fe 3+ chelation. This leads to an interpenetrating network of two chitosan chains with high crosslinking‐network density, which enhances mechanical performance including high compressive modulus and high ductility. The chitosan polymers not only endow the hydrogels with good biodegradability and biocompatibility, they also offer intrinsic antibacterial capability. The quinone groups formed by Fe 3+ oxidation and protonated amino groups of chitosan polymer further enhance antibacterial property of the hydrogels. Serving as one of the two types of crosslinking mechanisms, the catechol‐Fe 3+ chelation can covalently link with amino, thiol, and imidazole groups, which substantially enhance the hydrogel's adhesion to biological tissues. The hydrogel's adhesion to porcine skin shows a lap shear strength of 18.1 kPa, which is 6‐time that of the clinically established Fibrin Glue's adhesion. The hydrogel also has a good hemostatic performance due to the superior tissue adhesion as demonstrated with a hemorrhaging liver model. Furthermore, the hydrogel can remarkably promote healing of bacteria‐infected wound.

OBJECTIVE: The authors retrospectively examined a spectrum of childhood traits that reflect obsessive-compulsive personality in adult women with eating disorders and assessed the predictive value of the traits for the development of eating disorders. METHOD: In a case-control design, 44 women with anorexia nervosa, 28 women with bulimia nervosa, and 28 healthy female comparison subjects were assessed with an interview instrument that asked them to recall whether they had experienced various types of childhood behavior suggesting traits associated with obsessive-compulsive personality. The subjects also completed a self-report inventory of obsessive-compulsive disorder (OCD) symptoms. RESULTS: Childhood obsessive-compulsive personality traits showed a high predictive value for development of eating disorders, with the estimated odds ratio for eating disorders increasing by a factor of 6.9 for every additional trait present. Subjects with eating disorders who reported perfectionism and rigidity in childhood had significantly higher rates of obsessive-compulsive personality disorder and OCD comorbidity later in life, compared with eating disorder subjects who did not report those traits. CONCLUSIONS: Childhood traits reflecting obsessive-compulsive personality appear to be important risk factors for the development of eating disorders and may represent markers of a broader phenotype for a specific subgroup of patients with anorexia nervosa.

Nasopharyngeal carcinoma (NPC) is a malignant epithelial tumor originating in the nasopharynx and has a high incidence in Southeast Asia and North Africa. To develop these comprehensive guidelines for the diagnosis and management of NPC, the Chinese Society of Clinical Oncology (CSCO) arranged a multi-disciplinary team comprising of experts from all sub-specialties of NPC to write, discuss, and revise the guidelines. Based on the findings of evidence-based medicine in China and abroad, domestic experts have iteratively developed these guidelines to provide proper management of NPC. Overall, the guidelines describe the screening, clinical and pathological diagnosis, staging and risk assessment, therapies, and follow-up of NPC, which aim to improve the management of NPC.

Abstract The solution of the eigenvalue problem for large structures is often the most costly phase of a dynamic response analysis. In this paper, the need for the exact solution of this large eigenvalue problem is eliminated. A new algorithm, based on error minimization, is presented for the generation of a sequence of Ritz vectors. These orthogonal vectors are used to reduce the size of the system. Only Ritz vectors with a large participation factor are used in the subsequent mode superposition analysis. In all examples studied, the superposition of Ritz vectors yields more accurate results, with fewer vectors, than if the exact eigenvectors are used. The proposed method not only reduces computer time requirements significantly but provides an error estimation for the dynamic analysis. The approach automatically includes the advantages of the proven numerical techniques of static condensation, Guyan reduction and static correction due to higher mode truncation.

Therapeutic antibodies that target T-cell co-inhibitory molecules display potent antitumor effects in multiple types of cancer. LSECtin is a cell surface lectin of the DC-SIGN family expressed in dendritic cells that inhibits T-cell responses. LSECtin limits T-cell activity in infectious disease, but it has not been studied in cancer. Here we report the finding that LSECtin is expressed commonly in melanomas where it blunts tumor-specific T-cell responses. When expressed in B16 melanoma cells, LSECtin promoted tumor growth, whereas its blockade slowed tumor growth in either wild-type or LSECtin-deficient mice. The tumor-promoting effects of LSECtin were abrogated in Rag1(-/-) mice or in response to CD4(+) or CD8(+) T-cell depletion. Mechanistic investigations determined that LSECtin inhibited the proliferation of tumor-specific effector T cells by downregulating the cell cycle kinases CDK2, CDK4, and CDK6. Accordingly, as expressed in B16, tumor cells LSECtin inhibited tumor-specific T-cell responses relying upon proliferation in vitro and in vivo. Notably, LSECtin interacted with the co-regulatory molecule LAG-3, the blockade of which restored IFNγ secretion that was reduced by melanoma-derived expression of LSECtin. Together, our findings reveal that common expression of LSECtin in melanoma cells engenders a mechanism of immune escape, with implications for novel immunotherapeutic combination strategies.

OBJECTIVES: The present study aimed to investigate whether exosomes derived from miR-375-overexpressing human adipose mesenchymal stem cells (hASCs) could enhance bone regeneration. MATERIALS AND METHODS: Exosomes enriched with miR-375 (Exo [miR-375]) were generated from hASCs stably overexpressing miR-375 after lentiviral transfection and identified with transmission electron microscopy, nanosight and western blotting. The construction efficiency of Exo (miR-375) was evaluated with qRT-PCR and incubated with human bone marrow mesenchymal stem cells (hBMSCs) to optimize the effective dosage. Then, the osteogenic capability of Exo (miR-375) was investigated with ALP and ARS assays. Furthermore, dual-luciferase reporter assay and western blotting were conducted to reveal the underlying mechanism of miR-375 in osteogenic regulation. Finally, Exo (miR-375) were embedded with hydrogel and applied to a rat model of calvarial defect, and μ-CT analysis and histological examination were conducted to evaluate the therapeutic effects of Exo (miR-375) in bone regeneration. RESULTS: miR-375 could be enriched in exosomes by overexpressing in the parent cells. Administration of Exo (miR-375) at 50 μg/mL improved the osteogenic differentiation of hBMSCs. With miR-375 absorbed by hBMSCs, insulin-like growth factor binding protein 3 (IGFBP3) was inhibited by binding to its 3'UTR, and recombinant IGFBP3 protein reduced the osteogenic effects triggered by Exo (miR-375). After incorporated with hydrogel, Exo (miR-375) displayed a slow and controlled release, and further in vivo analysis demonstrated that Exo (miR-375) enhanced the bone regenerative capacity in a rat model of calvarial defect. CONCLUSIONS: Taken together, our study demonstrated that exosomes derived from miR-375-overexpressing hASCs promoted bone regeneration.

Rendering synthetic data (e.g., 3D CAD-rendered images) to generate annotations for learning deep models in vision tasks has attracted increasing attention in recent years. However, simply applying the models learnt on synthetic images may lead to high generalization error on real images due to domain shift. To address this issue, recent progress in cross-domain recognition has featured the Mean Teacher, which directly simulates unsupervised domain adaptation as semi-supervised learning. The domain gap is thus naturally bridged with consistency regularization in a teacher-student scheme. In this work, we advance this Mean Teacher paradigm to be applicable for cross-domain detection. Specifically, we present Mean Teacher with Object Relations (MTOR) that novelly remolds Mean Teacher under the backbone of Faster R-CNN by integrating the object relations into the measure of consistency cost between teacher and student modules. Technically, MTOR firstly learns relational graphs that capture similarities between pairs of regions for teacher and student respectively. The whole architecture is then optimized with three consistency regularizations: 1) region-level consistency to align the region-level predictions between teacher and student, 2) inter-graph consistency for matching the graph structures between teacher and student, and 3) intra-graph consistency to enhance the similarity between regions of same class within the graph of student. Extensive experiments are conducted on the transfers across Cityscapes, Foggy Cityscapes, and SIM10k, and superior results are reported when comparing to state-of-the-art approaches. More remarkably, we obtain a new record of single model: 22.8% of mAP on Syn2Real detection dataset.