Interaction between Yeast Cdc6 Protein and B-Type Cyclin/Cdc28 Kinases

During purification of recombinant Cdc6 expressed in yeast, we found that Cdc6 interacts with the critical cell cycle, cyclin-dependent protein kinase Cdc28. Cdc6 and Cdc28 can be coimmunoprecipitated from extracts, Cdc6 is retained on the Cdc28-binding matrix p13-agarose, and Cdc28 is retained on an affinity column charged with bacterially produced Cdc6. Cdc6, which is a phosphoprotein in vivo, contains five Cdc28 consensus sites and is a substrate of the Cdc28 kinase in vitro. Cdc6 also inhibits Cdc28 histone H1 kinase activity. Strikingly, Cdc6 interacts preferentially with B-type cyclin/Cdc28 complexes and not Cln/Cdc28 in log-phase cells. However, Cdc6 does not associate with Cdc28 when cells are blocked at the restrictive temperature in a cdc34 mutant, a point in the cell cycle when the B-type cyclin/Cdc28 inhibitor p40Sic1 accumulates and purified p40Sic1 inhibits the Cdc6/Cdc28 interaction. Deletion of the Cdc28 interaction domain from Cdc6 yields a protein that cannot support growth. However, when overproduced, the mutant protein can support growth. Furthermore, whereas overproduction of wild-type Cdc6 leads to growth inhibition and bud hyperpolarization, overproduction of the mutant protein supports growth at normal rates with normal morphology. Thus, the interaction may have a role in the essential function of Cdc6 in initiation and in restraining mitosis until replication is complete

Incorporating Intra-Class Variance to Fine-Grained Visual Recognition

Fine-grained visual recognition aims to capture discriminative characteristics amongst visually similar categories. The state-of-the-art research work has significantly improved the fine-grained recognition performance by deep metric learning using triplet network. However, the impact of intra-category variance on the performance of recognition and robust feature representation has not been well studied. In this paper, we propose to leverage intra-class variance in metric learning of triplet network to improve the performance of fine-grained recognition. Through partitioning training images within each category into a few groups, we form the triplet samples across different categories as well as different groups, which is called Group Sensitive TRiplet Sampling (GS-TRS). Accordingly, the triplet loss function is strengthened by incorporating intra-class variance with GS-TRS, which may contribute to the optimization objective of triplet network. Extensive experiments over benchmark datasets CompCar and VehicleID show that the proposed GS-TRS has significantly outperformed state-of-the-art approaches in both classification and retrieval tasks.Comment: 6 pages, 5 figure

Control measures about vibration and noise of pipeline onboard marine vessels

The pipeline noise is an important part of the sources of vibration and noise for high performance marine vessels. It is vital to control the vibration and noise of the onboard pipeline to improve the acoustic stealth and life level in marine vessels. The vibration and noise caused by the onboard pipeline system was analyzed. Some methods and advices were put forwards to reduce the vibration and noise caused by the pipeline system, including the control of vibration, structural noise and fluid noise. In addition, a new concept “pressure-stabilized bladder” was created

TrojText: Test-time Invisible Textual Trojan Insertion

In Natural Language Processing (NLP), intelligent neuron models can be susceptible to textual Trojan attacks. Such attacks occur when Trojan models behave normally for standard inputs but generate malicious output for inputs that contain a specific trigger. Syntactic-structure triggers, which are invisible, are becoming more popular for Trojan attacks because they are difficult to detect and defend against. However, these types of attacks require a large corpus of training data to generate poisoned samples with the necessary syntactic structures for Trojan insertion. Obtaining such data can be difficult for attackers, and the process of generating syntactic poisoned triggers and inserting Trojans can be time-consuming. This paper proposes a solution called TrojText, which aims to determine whether invisible textual Trojan attacks can be performed more efficiently and cost-effectively without training data. The proposed approach, called the Representation-Logit Trojan Insertion (RLI) algorithm, uses smaller sampled test data instead of large training data to achieve the desired attack. The paper also introduces two additional techniques, namely the accumulated gradient ranking (AGR) and Trojan Weights Pruning (TWP), to reduce the number of tuned parameters and the attack overhead. The TrojText approach was evaluated on three datasets (AG's News, SST-2, and OLID) using three NLP models (BERT, XLNet, and DeBERTa). The experiments demonstrated that the TrojText approach achieved a 98.35\% classification accuracy for test sentences in the target class on the BERT model for the AG's News dataset. The source code for TrojText is available at https://github.com/UCF-ML-Research/TrojText.Comment: In The Eleventh International Conference on Learning Representations. 2023 (ICLR 2023

A novel folic acid-conjugated TiO2–SiO2 photosensitizer for cancer targeting in photodynamic therapy

In this paper, a novel folic acid-conjugated silica-coated titanium dioxide (TiO2–SiO2) photosensitizer was synthesized and characterized using various analytical instruments. The photosensitizer was further assessed with regards to its photoreactivity, cellular and hemocompatibility, cell internalization, and phototoxicity. Conjugating folic acid with TiO2–SiO2 has shown a significantly improved compatibility of the nanoparticles with the mouse fibroblast cells (L929) at 24 h. An improved compatibility with the human nasopharyngeal epidermoid cancer (KB) cells was also demonstrated, but to a slightly reduced degree. Enhanced cell internalization was well demonstrated in the TiO2–SiO2 folate nanoparticles. Upon exposure to UV light, TiO2–SiO2 folate nanoparticles maintained a high level photodynamic reactivity and yielded a 38–43% photo-killing of KB cells. The photo-killing effect increased with increasing dosage in the investigated concentration range of 50–100 μg ml−1

Research on A Novel Reliable MEMS Bistable Solid State Switch

As a result of the unpredictable nature of extreme environments (including temperature, humidity, impact, and other factors), micro-electro-mechanical systems (MEMS) solid-state fuze control modules have an urgent requirement for a MEMS solid-state switch (MEMS-S3). In particular, this switch must remain stable without any energy input after a state transition (i.e., it must be bistable). In this paper, a MEMS bistable solid-state switch (MEMS-bS3) is designed that is based on the concept of producing a micro-explosion. The reliable state switching of the MEMS-bS3 is studied via heat conduction theory and verified via both simulations and experimental methods. The experimental results show that these switches can produce micro-explosions driven by 33 V/47 μF pulse energy. However, the metal film bridge (MFB) structures used in this switch with smaller dimensions (80×20 μm2, 90×30 μm2, and 100×40 μm2) could not enable the switch to realize a reliable state transition, and the state transition rate was less than 40%. When the MFB dimensions reached 120×60 μm2 or 130×70 μm2, the state transition rate exceeded 80%, and the response time was on the μs-scale