N-(Quinolin-8-yl)quinoline-2-carbox­amide

In the title compound, C19H13N3O, the dihedral angle between the two quinoline systems is 11.54 (3)°. The mol­ecular conformation is stabilized by intra­molecular N—H⋯N and C—H⋯O hydrogen bonds, with N—H⋯N being bifurcated towards the two N atoms of the two quinoline rings. In the crystal, there are weak intermolecular π–π inter­actions present involving the quinoline rings [centroid–centroid distance 3.7351 (14) Å]

Injection of botulinum toxin A in lateral pterygoid muscle as a novel method for prevention of traumatic temporomandibular joint ankylosis

AbstractTemporomandibular joint (TMJ) ankylosis can restrict the mandibular movement, followed by resulting in numerous problems. To understand the mechanism of TMJ ankylosis (TMJA) and prevent the generation of TMJA is urgent necessary. Although many factors contribute to it, trauma is the most common cause of TMJA. The mechanisms of TMJA are still unclear, and the distraction osteogenesis of the lateral pterygoid muscle (LPM) may play an important role. Injection of very small amounts of botulinum toxin type A (BTA) can temporarily block the muscle’s impulse and has been revealed to be an effective treatment method for many temporomandibular disorders. In this article, we make a hypothesis that LPM injection of BTA as a novel method for immobilization of mandible, followed by preventing the traumatic TMJA. Furthermore, the side effects of local injection of BTA also are minimal, temporary, reversible and self-limiting. If this strategy is validated, LPM injection of BTA will be a cost effective way to be administrated to prevent the traumatic TMJA

DeepSketchHair: Deep Sketch-based 3D Hair Modeling

We present sketchhair, a deep learning based tool for interactive modeling of 3D hair from 2D sketches. Given a 3D bust model as reference, our sketching system takes as input a user-drawn sketch (consisting of hair contour and a few strokes indicating the hair growing direction within a hair region), and automatically generates a 3D hair model, which matches the input sketch both globally and locally. The key enablers of our system are two carefully designed neural networks, namely, S2ONet, which converts an input sketch to a dense 2D hair orientation field; and O2VNet, which maps the 2D orientation field to a 3D vector field. Our system also supports hair editing with additional sketches in new views. This is enabled by another deep neural network, V2VNet, which updates the 3D vector field with respect to the new sketches. All the three networks are trained with synthetic data generated from a 3D hairstyle database. We demonstrate the effectiveness and expressiveness of our tool using a variety of hairstyles and also compare our method with prior art

Correlated Mutation Analysis on the Catalytic Domains of Serine/Threonine Protein Kinases

BACKGROUND:Protein kinases (PKs) have emerged as the largest family of signaling proteins in eukaryotic cells and are involved in every aspect of cellular regulation. Great progresses have been made in understanding the mechanisms of PKs phosphorylating their substrates, but the detailed mechanisms, by which PKs ensure their substrate specificity with their structurally conserved catalytic domains, still have not been adequately understood. Correlated mutation analysis based on large sets of diverse sequence data may provide new insights into this question. METHODOLOGY/PRINCIPAL FINDINGS:Statistical coupling, residue correlation and mutual information analyses along with clustering were applied to analyze the structure-based multiple sequence alignment of the catalytic domains of the Ser/Thr PK family. Two clusters of highly coupled sites were identified. Mapping these positions onto the 3D structure of PK catalytic domain showed that these two groups of positions form two physically close networks. We named these two networks as theta-shaped and gamma-shaped networks, respectively. CONCLUSIONS/SIGNIFICANCE:The theta-shaped network links the active site cleft and the substrate binding regions, and might participate in PKs recognizing and interacting with their substrates. The gamma-shaped network is mainly situated in one side of substrate binding regions, linking the activation loop and the substrate binding regions. It might play a role in supporting the activation loop and substrate binding regions before catalysis, and participate in product releasing after phosphoryl transfer. Our results exhibit significant correlations with experimental observations, and can be used as a guide to further experimental and theoretical studies on the mechanisms of PKs interacting with their substrates

Pembelajaran Bahasa Arab Berbasis Media IPAD (I-Learning)

The information technology plays an urgent role in our everyday life and in the practice of education as well. When it is well-planned and prepared, it has effective functions as the media of learning. Therefore, for the sake of making an active and dynamic process of learning and accomplishing the learning objectives, the Arabic lecturers/teachers must create an interesting, inovative, effective and creative learning practices. The technology, media of learning, as well as learning strategy the lectures/teachers take and implement will seriousely influence the output of students\u27 learning. Electronic Learning and i-learning (iPad learning) are two medias of Arabic learning that use internet in learning process. If it is well-prepared it will automatically raise the learning output

The effect of cooling rate on the wear performance of a ZrCuAlAg bulk metallic glass

In the present work, the local atomic ordering and the wear performance of ZrCuAlAg bulk metallic glass (BMG) samples with different diameters have been studied using transmission electron microscopy (TEM) plus autocorrelation function analysis, and pin-on-disc dry sliding wear experiments. Differential scanning calorimetry and TEM studies show that smaller diameter BMG sample has higher free volume and less local atomic ordering. The wear experiments demonstrate that with the same chemical composition, the smaller BMG sample exhibits higher coefficient of friction, higher wear rate, and rougher worn surface than those of the larger ones. Compared with larger BMG sample, the faster cooling rate of the smaller sample results in looser atomic configuration with more free volume, which facilitates the formation of the shear bands, and thus leads to larger plasticity and lower wear resistance. The results provide more quantitative understanding on the relationship among the cooling rate, the local atomic ordering, and the wear performance of BMGs

A New Neural Distinguisher Considering Features Derived from Multiple Ciphertext Pairs

Neural aided cryptanalysis is a challenging topic, in which the neural distinguisher (N D) is a core module. In this paper, we propose a new N D considering multiple ciphertext pairs simultaneously. Besides, multiple ciphertext pairs are constructed from different keys. The motivation is that the distinguishing accuracy can be improved by exploiting features derived from multiple ciphertext pairs. To verify this motivation, we have applied this new N D to five different ciphers. Experiments show that taking multiple ciphertext pairs as input indeed brings accuracy improvement. Then, we prove that our new N D applies to two different neural aided key recovery attacks. Moreover, the accuracy improvement is helpful for reducing the data complexity of the neural aided statistic attack. The code is available at https://github.com/AI-Lab-Y/ND_mc

A Deep Learning aided Key Recovery Framework for Large-State Block Ciphers

In the seminal work published by Gohr in CRYPTO 2019, neural networks were successfully exploited to perform differential attacks on Speck32/64, the smallest member in the block cipher family Speck. The deep learning aided key-recovery attack by Gohr achieves considerable improvement in terms of time complexity upon the state-of-the-art result from the conventional cryptanalysis method. A further question is whether the advantage of deep learning aided attacks can be kept on large-state members of Speck and other primitives. Since there are several key points in Gohr’s key-recovery frameworks that seem not fit for large-state ciphers, this question stays open for years. This work provides an answer to this question by proposing a deep learning aided multi-stage key-recovery framework. To apply this key-recovery framework on large-state members of Speck, multiple neural distinguishers (NDs) are trained and carefully combined into groups. Employing the groups of NDs under the multi-stage key-recovery framework, practical attacks are designed and trialed. Experimental results show the effectiveness of the framework. The practical attacks are then extended into theoretical attacks that cover more rounds. To do that, multi-round classical differentials (CDs) are used together with the NDs. To find the CDs’ neutral bits to boost signals from the distinguishers, an efficient algorithm is proposed. As a result, considerable improvement in terms of both time and data complexity of differential key-recovery attacks on round-reduced Speck with the largest, i.e., the 128-bit state, is obtained. Besides, efficient differential attacks are achieved on round-reduced Speck with 96-bit and 64-bit states. Since most real-world block ciphers have a state size of no less than 64 bits, this work paves the way for performing cryptanalysis using deep learning on more block ciphers. The code is available at https://github.com/AI-Lab-Y/NAAF