Synthesis of Oxygenated Boronic Acid Substituted a-Cyanostilbenes For Use as Antibacterial

Stilbenes are naturally occurring compounds that have exhibited antibacterial activity, although the biological effect of various stilbenes differs for Gram-positive and Gram-negative strains of bacteria. In prior research, cyanostilbenes have shown slight antibacterial activity (Brownlee, et al. 1943). This project aims to explore whether hybrid oxygenated a-cyanostilbenes possessing a boronic acid pharmacophore exhibits significant antibacterial activity (Das et al. 2013). The biological activity against Staphylocococcus aureus (Gram-positive) and Escherichia coli (Gram-negative) was tested using the Kirby-Bauer text. No inhibition of E. coli growth was shown while S. aureus was partially inhibited

P-14 Synthesis and Antibacterial Activity of Oxygenated Boronic Acid Substituted a-Cyanostilbenes

Stilbenes are naturally occurring compounds that have exhibited antibacterial activity, although the biological effect of various stilbenes is different for Gram-positive and Gram-negative strains of bacteria. In prior research, cyanostilbenes have shown “slight” antibacterial activity (Brownlee, et al. 1943). This project aims to explore whether hybrid oxygenated a-cyanostilbenes possessing a boronic acid pharmacophore exhibits significant antibacterial activity (Das, et al., 2013). The biological activity against both Gram-positive and Gram-negative strains will be tested

Towards Explainable AI Writing Assistants for Non-native English Speakers

We highlight the challenges faced by non-native speakers when using AI writing assistants to paraphrase text. Through an interview study with 15 non-native English speakers (NNESs) with varying levels of English proficiency, we observe that they face difficulties in assessing paraphrased texts generated by AI writing assistants, largely due to the lack of explanations accompanying the suggested paraphrases. Furthermore, we examine their strategies to assess AI-generated texts in the absence of such explanations. Drawing on the needs of NNESs identified in our interview, we propose four potential user interfaces to enhance the writing experience of NNESs using AI writing assistants. The proposed designs focus on incorporating explanations to better support NNESs in understanding and evaluating the AI-generated paraphrasing suggestions.Comment: CHI In2Writing Workshop 2023 camera-ready versio

RPLKG: Robust Prompt Learning with Knowledge Graph

Large-scale pre-trained models have been known that they are transferable, and they generalize well on the unseen dataset. Recently, multimodal pre-trained models such as CLIP show significant performance improvement in diverse experiments. However, when the labeled dataset is limited, the generalization of a new dataset or domain is still challenging. To improve the generalization performance on few-shot learning, there have been diverse efforts, such as prompt learning and adapter. However, the current few-shot adaptation methods are not interpretable, and they require a high computation cost for adaptation. In this study, we propose a new method, robust prompt learning with knowledge graph (RPLKG). Based on the knowledge graph, we automatically design diverse interpretable and meaningful prompt sets. Our model obtains cached embeddings of prompt sets after one forwarding from a large pre-trained model. After that, model optimizes the prompt selection processes with GumbelSoftmax. In this way, our model is trained using relatively little memory and learning time. Also, RPLKG selects the optimal interpretable prompt automatically, depending on the dataset. In summary, RPLKG is i) interpretable, ii) requires small computation resources, and iii) easy to incorporate prior human knowledge. To validate the RPLKG, we provide comprehensive experimental results on few-shot learning, domain generalization and new class generalization setting. RPLKG shows a significant performance improvement compared to zero-shot learning and competitive performance against several prompt learning methods using much lower resources

A Lightweight Block Cipher Algorithm for Secure SDN Environment

Software Defined Network is a next-generation networking technology that transforms a closed network environment based on existing network vendors into a flexible, software-based, centralized management environment that can be simplified by abstracting and programming. Although these advantages can be applied to some security problems rather than existing networks, most of the security problems and vulnerabilities of existing networks are present and various attacks are taking place. In this paper, we propose a structure to enhance the security function of SDN by checking how to implement the network security function using SDN technology and lightening the existing block cipher algorithm for this security problem. Lightweight-AES algorithm, which is a lightweight block cipher algorithm based on the AES-256 algorithm, which can simultaneously satisfy the quality of high level of security. In the case of simply reducing the number of round operations of the AES algorithm, the difference diffusion effect of the KeySchedule function generating the round key is reduced, and the security of the encryption algorithm is degraded due to the related key attack using the related key difference characteristic. The Lightweight-AES algorithm proposed in this paper improves the rate of cancellation and decryption by reducing the number of round operations, and the round internal function is supplemented to increase the differential diffusion effect of the KeySchedule function. In order to evaluate the performance of the Lightweight-AES algorithm proposed in this paper, a comparison simulation is performed with the existing AES algorithm. As a result, we confirmed that the Lightweight-AES algorithm can provide SDN content security equal to the encryption / decryption rate and algorithm security strength of the AES-128 algorithm. Therefore, it is considered that the proposed Lightweight-AES algorithm can provide better security service in SDN environment quality and security than the existing AES-128 algorithm

BitE : Accelerating Learned Query Optimization in a Mixed-Workload Environment

Although the many efforts to apply deep reinforcement learning to query optimization in recent years, there remains room for improvement as query optimizers are complex entities that require hand-designed tuning of workloads and datasets. Recent research present learned query optimizations results mostly in bulks of single workloads which focus on picking up the unique traits of the specific workload. This proves to be problematic in scenarios where the different characteristics of multiple workloads and datasets are to be mixed and learned together. Henceforth, in this paper, we propose BitE, a novel ensemble learning model using database statistics and metadata to tune a learned query optimizer for enhancing performance. On the way, we introduce multiple revisions to solve several challenges: we extend the search space for the optimal Abstract SQL Plan(represented as a JSON object called ASP) by expanding hintsets, we steer the model away from the default plans that may be biased by configuring the experience with all unique plans of queries, and we deviate from the traditional loss functions and choose an alternative method to cope with underestimation and overestimation of reward. Our model achieves 19.6% more improved queries and 15.8% less regressed queries compared to the existing traditional methods whilst using a comparable level of resources.Comment: This work was done when the first three author were interns in SAP Labs Korea and they have equal contributio

Neuropathological Responses to Chronic NMDA in Rats Are Worsened by Dietary n-3 PUFA Deprivation but Are Not Ameliorated by Fish Oil Supplementation

Background Dietary long-chain n-3 polyunsaturated fatty acid (PUFA) supplementation may be beneficial for chronic brain illnesses, but the issue is not agreed on. We examined effects of dietary n-3 PUFA deprivation or supplementation, compared with an n-3 PUFA adequate diet (containing alpha-linolenic acid [18:3 n-3] but not docosahexaenoic acid [DHA, 22:6n-3]), on brain markers of lipid metabolism and excitotoxicity, in rats treated chronically with NMDA or saline. Methods Male rats after weaning were maintained on one of three diets for 15 weeks. After 12 weeks, each diet group was injected i.p. daily with saline (1 ml/kg) or a subconvulsive dose of NMDA (25 mg/kg) for 3 additional weeks. Then, brain fatty acid concentrations and various markers of excitotoxicity and fatty acid metabolism were measured. Results Compared to the diet-adequate group, brain DHA concentration was reduced, while n-6 docosapentaenoic acid (DPA, 22:5n-6) concentration was increased in the n-3 deficient group; arachidonic acid (AA, 20:4n-6) concentration was unchanged. These concentrations were unaffected by fish oil supplementation. Chronic NMDA increased brain cPLA2 activity in each of the three groups, but n-3 PUFA deprivation or fish oil did not change cPLA2 activity or protein compared with the adequate group. sPLA2 expression was unchanged in the three conditions, whereas iPLA2 expression was reduced by deprivation but not changed by supplementation. BDNF protein was reduced by NMDA in N-3 PUFA deficient rats, but protein levels of IL-1β, NGF, and GFAP did not differ between groups. Conclusions N-3 PUFA deprivation significantly worsened several pathological NMDA-induced changes produced in diet adequate rats, whereas n-3 PUFA supplementation did not affect NMDA induced changes. Supplementation may not be critical for this measured neuropathology once the diet has an adequate n-3 PUFA content

Novel Smart N95 Filtering Facepiece Respirator with Real-time Adaptive Fit Functionality and Wireless Humidity Monitoring for Enhanced Wearable Comfort

The widespread emergence of the COVID-19 pandemic has transformed our lifestyle, and facial respirators have become an essential part of daily life. Nevertheless, the current respirators possess several limitations such as poor respirator fit because they are incapable of covering diverse human facial sizes and shapes, potentially diminishing the effect of wearing respirators. In addition, the current facial respirators do not inform the user of the air quality within the smart facepiece respirator in case of continuous long-term use. Here, we demonstrate the novel smart N-95 filtering facepiece respirator that incorporates the humidity sensor and pressure sensory feedback-enabled self-fit adjusting functionality for the effective performance of the facial respirator to prevent the transmission of airborne pathogens. The laser-induced graphene (LIG) constitutes the humidity sensor, and the pressure sensor array based on the dielectric elastomeric sponge monitors the respirator contact on the face of the user, providing the sensory information for a closed-loop feedback mechanism. As a result of the self-fit adjusting mode along with elastomeric lining, the fit factor is increased by 3.20 and 5 times at average and maximum respectively. We expect that the experimental proof-of-concept of this work will offer viable solutions to the current commercial respirators to address the limitations.Comment: 20 pages, 5 figures, 1 table, submitted for possible publicatio

Network analysis, in vivo, and in vitro experiments identified the mechanisms by which Piper longum L. [Piperaceae] alleviates cartilage destruction, joint inflammation, and arthritic pain

Osteoarthritis (OA) is characterized by irreversible joint destruction, pain, and dysfunction. Piper longum L. [Piperaceae] (PL) is an East Asian herbal medicine with reported anti-inflammatory, analgesic, antioxidant, anti-stress, and anti-osteoporotic effects. This study aimed to evaluate the efficacy of PL in inhibiting pain and progressive joint destruction in OA based on its anti-inflammatory activity, and to explore its potential mechanisms using in vivo and in vitro models of OA. We predicted the potential hub targets and signaling pathways of PL through network analysis and molecular docking. Network analysis results showed that the possible hub targets of PL against OA were F2R, F3, MMP1, MMP2, MMP9, and PTGS2. The molecular docking results predicted strong binding affinities for the core compounds in PL: piperlongumine, piperlonguminine, and piperine. In vitro experiments showed that PL inhibited the expression of LPS-induced pro-inflammatory factors, such as F2R, F3, IL-1β, IL-6, IL-17A, MMP-1, MMP-2, MMP-3, MMP-9, MMP-13, NOS2, PTGS2, PGE2, and TNF-β. These mechanisms and effects were dose-dependent in vivo models. Furthermore, PL inhibited cartilage degradation in an OA-induced rat model. Thus, this study demonstrated that multiple components of PL may inhibit the multilayered pathology of OA by acting on multiple targets and pathways. These findings highlight the potential of PL as a disease-modifying OA drug candidate, which warrants further investigation