(E)-2-(2H-Benzotriazol-2-yl)-4-methyl-6-(phenyl­imino­meth­yl)phenol

In the title compound, C20H16N4O, the non-H atoms of the benzotriazole ring system and those of the methyl­phenol group are essentially coplanar, with an r.m.s. deviation of 0.004 (2) Å. The mean plane of these atoms forms a dihedral angle of 60.9 (2)° with the phenyl ring. There is an intra­molecular O—H⋯N hydrogen bond between the phenol and benzotriazole groups

Paxillin facilitates timely neurite initiation on soft-substrate environments by interacting with the endocytic machinery.

Neurite initiation is the first step in neuronal development and occurs spontaneously in soft tissue environments. Although the mechanisms regulating the morphology of migratory cells on rigid substrates in cell culture are widely known, how soft environments modulate neurite initiation remains elusive. Using hydrogel cultures, pharmacologic inhibition, and genetic approaches, we reveal that paxillin-linked endocytosis and adhesion are components of a bistable switch controlling neurite initiation in a substrate modulus-dependent manner. On soft substrates, most paxillin binds to endocytic factors and facilitates vesicle invagination, elevating neuritogenic Rac1 activity and expression of genes encoding the endocytic machinery. By contrast, on rigid substrates, cells develop extensive adhesions, increase RhoA activity and sequester paxillin from the endocytic machinery, thereby delaying neurite initiation. Our results highlight paxillin as a core molecule in substrate modulus-controlled morphogenesis and define a mechanism whereby neuronal cells respond to environments exhibiting varying mechanical properties

Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay

Few-shot class-incremental learning (FSCIL) has been proposed aiming to enable a deep learning system to incrementally learn new classes with limited data. Recently, a pioneer claims that the commonly used replay-based method in class-incremental learning (CIL) is ineffective and thus not preferred for FSCIL. This has, if truth, a significant influence on the fields of FSCIL. In this paper, we show through empirical results that adopting the data replay is surprisingly favorable. However, storing and replaying old data can lead to a privacy concern. To address this issue, we alternatively propose using data-free replay that can synthesize data by a generator without accessing real data. In observing the the effectiveness of uncertain data for knowledge distillation, we impose entropy regularization in the generator training to encourage more uncertain examples. Moreover, we propose to relabel the generated data with one-hot-like labels. This modification allows the network to learn by solely minimizing the cross-entropy loss, which mitigates the problem of balancing different objectives in the conventional knowledge distillation approach. Finally, we show extensive experimental results and analysis on CIFAR-100, miniImageNet and CUB-200 to demonstrate the effectiveness of our proposed one.Comment: Accepted by ECCV 202

2-{5-[N-(2-Pyridyl)carbamo­yl]pentan­amido}pyridinium hexa­fluoro­phosphate

In the crystal structure of the title compound, C16H19N4O2 +·PF6 −, the cations and anions are situated on centres of inversion. Thus, the N—H H atom is disordered over both N atoms due to symmetry. In the crystal, mol­ecules are connected via N—H⋯F and N—H⋯O hydrogen bonds. The cation adopts the ⋯AAA⋯ trans conformation in the solid state

Bis{1-[(E)-o-tolyl­diazen­yl]-2-naphtho­l­ato}copper(II)

In the title complex, [Cu(C17H13N2O)2], the CuII atom is tetra­coordinated by two N atoms and two O atoms from two bidentate 1-[(E)-o-tolyl­diazen­yl]-2-naphtho­late ligands, forming a slightly distorted square-planar environment. The two N atoms and two O atoms around the CuII atom are trans to each other, with an O—Cu—O bond angle of 177.00 (9)° and an N—Cu—N bond angle of 165.63 (10)°. The average distances between the CuII atom and the coordinated O and N atoms are 1.905 (2) and 1.995 (2)Å, respectively

HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer

Learning expressive representations for high-dimensional yet sparse features has been a longstanding problem in information retrieval. Though recent deep learning methods can partially solve the problem, they often fail to handle the numerous sparse features, particularly those tail feature values with infrequent occurrences in the training data. Worse still, existing methods cannot explicitly leverage the correlations among different instances to help further improve the representation learning on sparse features since such relational prior knowledge is not provided. To address these challenges, in this paper, we tackle the problem of representation learning on feature-sparse data from a graph learning perspective. Specifically, we propose to model the sparse features of different instances using hypergraphs where each node represents a data instance and each hyperedge denotes a distinct feature value. By passing messages on the constructed hypergraphs based on our Hypergraph Transformer (HyperFormer), the learned feature representations capture not only the correlations among different instances but also the correlations among features. Our experiments demonstrate that the proposed approach can effectively improve feature representation learning on sparse features.Comment: Accepted by SIGIR 202

The relationship between preoperative American Society of Anesthesiologists Physical Status Classification scores and functional recovery following hip-fracture surgery

Abstract Background Little is known about the relationship of the American Society of Anesthesiologists Physical Status Classification scores (ASA scores) on patient outcomes following hip fracture surgery in Asian countries. Therefore, this study explored the association of patients’ preoperative ASA scores on trajectories of recovery in physical functioning and health outcomes during the first year following postoperative discharge for older adults with hip-fracture surgery in Taiwan. Methods The data for this study was generated from three prior studies. Participants (N = 226) were older hip-fracture patients from an observational study (n = 86) and two clinical trials (n = 61 and n = 79). Participants were recruited from the trauma wards of one medical center in northern Taiwan and data was collected prior to discharge and at 1, 3, 6, and 12 months after hospital discharge. Participants were grouped as ASA class 1–2 (50.5%; ASA Class 1, n = 7; ASA Class 2, n = 107) and ASA class 3 (49.5%, n = 112). Measures for mortality, service utilization, activities of daily living (ADL), measured by the Chinese Barthel Index, and health related quality of life, measured by Medical Outcomes Study Short Form-36, were assessed for the two groups. Generalized estimating equations (GEE) were used to analyze the changes over time for the two groups. Results During the first year following hip-fracture surgery, ASA class 1–2 participants had significantly fewer rehospitalizations (6%, p = .02) and better scores for mental health (mean = 70.29, standard deviation = 19.03) at 6- and 12-months following discharge than those classified as ASA 3. In addition, recovery of walking ability (70%, p = .001) and general health (adjusted mean = 58.31, p = .003) was also significantly better than ASA 3 participants. Conclusions There was a significant association of hip-fracture patients classified as ASA 1–2 with better recovery and service utilization during the first year following surgery. Interventions for hip fractured patients with high ASA scores should be developed to improve recovery and quality of life.https://deepblue.lib.umich.edu/bitstream/2027.42/138818/1/12891_2017_Article_1768.pd