Emergence, Evolution and Scaling of Online Social Networks

This work was partially supported by AFOSR under Grant No. FA9550-10-1-0083, NSF under Grant No. CDI-1026710, NSF of China under Grants Nos. 61473060 and 11275003, and NBRPC under Grant No. 2010CB731403. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

MicroRNAs in Human Pituitary Adenomas

MicroRNAs (miRNAs) are a class of recently identified noncoding RNAs that regulate gene expression at posttranscriptional level. Due to the large number of genes regulated by miRNAs, miRNAs play important roles in many cellular processes. Emerging evidence indicates that miRNAs are dysregulated in pituitary adenomas, a class of intracranial neoplasms which account for 10–15% of diagnosed brain tumors. Deregulated miRNAs and their targets contribute to pituitary adenomas progression and are associated with cell cycle control, apoptosis, invasion, and pharmacological treatment of pituitary adenomas. To provide an overview of miRNAs dysregulation and functions of these miRNAs in pituitary adenoma progression, we summarize the deregulated miRNAs and their targets to shed more light on their potential as therapeutic targets and novel biomarkers

3-(2-Pyrid­yl)-5-(4-pyrid­yl)-4-(p-tol­yl)-1H-1,2,4-triazole

In the mol­ecule of the title compound, C19H15N5, the dihedral angles formed by the plane of the triazole ring with those of the 2-pyridyl, 4-pyridyl and p-tolyl rings are 28.12 (10), 34.62 (10) and 71.43 (9)°, respectively. The crystal structure is consolidated by C—H⋯π hydrogen-bonding inter­actions and by π–π stacking inter­actions, with a centroid–centroid distance of 3.794 (4) Å

Nursing care in osteopetrosis treated by optic nerve decompression under image guidance system combined with endoscopic approach

AIM: To explore the nursing cooperation highlights of eight osteopetrosis patients underwent optic nerve decompression via transsphenoidal microsurgical approach instead of routine pathway, and to improve the quality of nursing cooperation. METHODS: We enrolled 8 cases(left eye in 3 cases, right eye in 5 cases)of osteopetrosis patients referred from the Eye Hospital of Wenzhou Medical University during February 2012 to November 2016. Patients received ophthalmic examinations including visual acuity and diagnostic imaging tests in pre-operation and post-operation. All eyes were performed surgical optic nerve decompression through endoscopic approach in assist of image guidance system. We retrospectively analyzed the clinical data and surgical cooperation procedure of these cases and summarized nursing cooperation experience. RESULTS: The operations of 8 patients were completed successfully without massive hemorrhage. Mean visual acuity improved from pre-operation(2.5±2.1)to post-operation(3.4±1.9). Cerebrospinal fluid leakage occurred in 1 patient and was instantly repaired during the operation. We performed the nursing strategy as postural drainage, condition monitoring and conscious assessment intra-and post-operation. CONCLUSION: It is the critical for this kind of surgery that both circulating nurse's high-skilled cooperation to the connection and operation of the navigation system, to treat with complication during the surgery, and scrub nurse's sufficient preparation of surgical instruments and consumables, proficient equipment delivery, meticulous management, use and maintenance of equipment

Statistically Evolving Fuzzy Inference System for Non-Gaussian Noises

Non-Gaussian noises always exist in the nonlinear system, which usually lead to inconsistency and divergence of the regression and identification applications. The conventional evolving fuzzy systems (EFSs) in common sense have succeeded to conquer the uncertainties and external disturbance employing the specific variable structure characteristic. However, non-Gaussian noises would trigger the frequent changes of structure under the transient criteria, which severely degrades performance. Statistical criterion provides an informed choice of the strategies of the structure evolution, utilizing the approximation uncertainty as the observation of model sufficiency. The approximation uncertainty can be always decomposed into model uncertainty term and noise term, and is suitable for the non-Gaussian noise condition, especially relaxing the traditional Gaussian assumption. In this paper, a novel incremental statistical evolving fuzzy inference system (SEFIS) is proposed, which has the capacity of updating the system parameters, and evolving the structure components to integrate new knowledge in the new process characteristic, system behavior, and operating conditions with non-Gaussian noises. The system generates a new rule based on the statistical model sufficiency which gives so insight into whether models are reliable and their approximations can be trusted. The nearest rule presents the inactive rule under the current data stream and further would be deleted without losing any information and accuracy of the subsequent trained models when the model sufficiency is satisfied. In our work, an adaptive maximum correntropy extend Kalman filter (AMCEKF) is derived to update the parameters of the evolving rules to cope with the non-Gaussian noises problems to further improve the robustness of parameter updating process. The parameter updating process shares an estimate of the uncertainty with the criteria of the structure evolving process to make the computation less of a burden dramatically. The simulation studies show that the proposed SEFIS has faster learning speed and is more accurate than the existing evolving fuzzy systems (EFSs) in the case of noise-free and noisy conditions

Grow and Merge: A Unified Framework for Continuous Categories Discovery

Although a number of studies are devoted to novel category discovery, most of them assume a static setting where both labeled and unlabeled data are given at once for finding new categories. In this work, we focus on the application scenarios where unlabeled data are continuously fed into the category discovery system. We refer to it as the {\bf Continuous Category Discovery} ({\bf CCD}) problem, which is significantly more challenging than the static setting. A common challenge faced by novel category discovery is that different sets of features are needed for classification and category discovery: class discriminative features are preferred for classification, while rich and diverse features are more suitable for new category mining. This challenge becomes more severe for dynamic setting as the system is asked to deliver good performance for known classes over time, and at the same time continuously discover new classes from unlabeled data. To address this challenge, we develop a framework of {\bf Grow and Merge} ({\bf GM}) that works by alternating between a growing phase and a merging phase: in the growing phase, it increases the diversity of features through a continuous self-supervised learning for effective category mining, and in the merging phase, it merges the grown model with a static one to ensure satisfying performance for known classes. Our extensive studies verify that the proposed GM framework is significantly more effective than the state-of-the-art approaches for continuous category discovery.Comment: This paper has already been accepted by 36th Conference on Neural Information Processing Systems (NeurIPS 2022

The Nematic Energy Scale and the Missing Electron Pocket in FeSe

Superconductivity emerges in proximity to a nematic phase in most iron-based superconductors. It is therefore important to understand the impact of nematicity on the electronic structure. Orbital assignment and tracking across the nematic phase transition prove to be challenging due to the multiband nature of iron-based superconductors and twinning effects. Here, we report a detailed study of the electronic structure of fully detwinned FeSe across the nematic phase transition using angle-resolved photoemission spectroscopy. We clearly observe a nematicity-driven band reconstruction involving dxz, dyz, and dxy orbitals. The nematic energy scale between dxz and dyz bands reaches a maximum of 50 meV at the Brillouin zone corner. We are also able to track the dxz electron pocket across the nematic transition and explain its absence in the nematic state. Our comprehensive data of the electronic structure provide an accurate basis for theoretical models of the superconducting pairing in FeSe