Vascular disrupting agent DMXAA enhances the antitumor effects generated by therapeutic HPV DNA vaccines

Antigen-specific immunotherapy using DNA vaccines has emerged as an attractive approach for the control of tumors. Another novel cancer therapy involves the employment of the vascular disrupting agent, 5,6-dimethylxanthenone-4-acetic acid (DMXAA). In the current study, we aimed to test the combination of DMXAA treatment with human papillomavirus type 16 (HPV-16) E7 DNA vaccination to enhance the antitumor effects and E7-specific CD8+ T cell immune responses in treated mice. We determined that treatment with DMXAA generates significant therapeutic effects against TC-1 tumors but does not enhance the antigen-specific immune responses in tumor bearing mice. We then found that combination of DMXAA treatment with E7 DNA vaccination generates potent antitumor effects and E7-specific CD8+ T cell immune responses in the splenocytes of tumor bearing mice. Furthermore, the DMXAA-mediated enhancement or suppression of E7-specific CD8+ T cell immune responses generated by CRT/E7 DNA vaccination was found to be dependent on the time of administration of DMXAA and was also applicable to other antigen-specific vaccines. In addition, we determined that inducible nitric oxide synthase (iNOS) plays a role in the immune suppression caused by DMXAA administration before DNA vaccination. Our study has significant implications for future clinical translation

Semantics-enhanced Temporal Graph Networks for Content Popularity Prediction

The surging demand for high-definition video streaming services and large neural network models (e.g., Generative Pre-trained Transformer, GPT) implies a tremendous explosion of Internet traffic. To mitigate the traffic pressure, architectures with in-network storage have been proposed to cache popular contents at devices in closer proximity to users. Correspondingly, in order to maximize caching utilization, it becomes essential to devise an effective popularity prediction method. In that regard, predicting popularity with dynamic graph neural network (DGNN) models achieve remarkable performance. However, DGNN models still suffer from tackling sparse datasets where most users are inactive. Therefore, we propose a reformative temporal graph network, named semantics-enhanced temporal graph network (STGN), which attaches extra semantic information into the user-content bipartite graph and could better leverage implicit relationships behind the superficial topology structure. On top of that, we customize its temporal and structural learning modules to further boost the prediction performance. Specifically, in order to efficiently aggregate the diversified semantics that a content might possess, we design a user-specific attention (UsAttn) mechanism for temporal learning module. Unlike the attention mechanism that only analyzes the influence of genres on content, UsAttn also considers the attraction of semantic information to a specific user. Meanwhile, as for the structural learning, we introduce the concept of positional encoding into our attention-based graph learning and adopt a semantic positional encoding (SPE) function to facilitate the analysis of content-oriented user-association analysis. Finally, extensive simulations verify the superiority of our STGN models and demonstrate the effectiveness in content caching

Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning

The age of information metric fails to correctly describe the intrinsic semantics of a status update. In an intelligent reflecting surface-aided cooperative relay communication system, we propose the age of semantics (AoS) for measuring semantics freshness of the status updates. Specifically, we focus on the status updating from a source node (SN) to the destination, which is formulated as a Markov decision process (MDP). The objective of the SN is to maximize the expected satisfaction of AoS and energy consumption under the maximum transmit power constraint. To seek the optimal control policy, we first derive an online deep actor-critic (DAC) learning scheme under the on-policy temporal difference learning framework. However, implementing the online DAC in practice poses the key challenge in infinitely repeated interactions between the SN and the system, which can be dangerous particularly during the exploration. We then put forward a novel offline DAC scheme, which estimates the optimal control policy from a previously collected dataset without any further interactions with the system. Numerical experiments verify the theoretical results and show that our offline DAC scheme significantly outperforms the online DAC scheme and the most representative baselines in terms of mean utility, demonstrating strong robustness to dataset quality.Comment: This work has been submitted to the IEEE for possible publicatio

An automated method for identifying an independent component analysis-based language-related resting-state network in brain tumor subjects for surgical planning

As a noninvasive and "task-free" technique, resting-state functional magnetic resonance imaging (rs-fMRI) has been gradually applied to pre-surgical functional mapping. Independent component analysis (ICA)-based mapping has shown advantage, as no a priori information is required. We developed an automated method for identifying language network in brain tumor subjects using ICA on rs-fMRI. In addition to standard processing strategies, we applied a discriminability-index-based component identification algorithm to identify language networks in three different groups. The results from the training group were validated in an independent group of healthy human subjects. For the testing group, ICA and seed-based correlation were separately computed and the detected language networks were assessed by intra-operative stimulation mapping to verify reliability of application in the clinical setting. Individualized language network mapping could be automatically achieved for all subjects from the two healthy groups except one (19/20, success rate = 95.0%). In the testing group (brain tumor patients), the sensitivity of the language mapping result was 60.9%, which increased to 87.0% (superior to that of conventional seed-based correlation [47.8%]) after extending to a radius of 1 cm. We established an automatic and practical component identification method for rs-fMRI-based pre-surgical mapping and successfully applied it to brain tumor patients

Inventarisasi Tanaman Peneduh Jalan Penjerap Timbal di Purwokerto

Tanaman peneduh jalan adalah tanaman yang berada di tepi jalan. Tanaman peneduh jalan memiliki dua fungsi yaitu sebagai estetika dan ekologis. Salah satu fungsi ekologis tanaman peneduh jalan adalah mengakumulasi bahan pencemar. Jenis pencemaran yang memerlukan penanganan secara sistematis dan komprehensif adalah pencemaran timbal (Pb). Pb banyak dihasilkan oleh aktivitas pembakaran bahan bakar minyak kendaraan bermotor. Jenis tanaman peneduh jalan yang berpotensi mengakumulasi Pb belum tereksplorasi sehingga dilakukan riset yang dapat menghasilkan database jenis spesies yang mampu mengurangi Pb di lingkungan. Tujuan penelitian adalah menginventarisasi jenis tanaman peneduh jalan penjerap Pb. Manfaat penelitian adalah mendapatkan jenis tanaman peneduh jalan yang berpotensi penjerap Pb. Metode penelitian yang digunakan adalah survai di 8 (delapan) jalan di wilayah Purwokerto. Sampel daun tanaman peneduh jalan diambil secara acak terpilih di sepanjang jalan tersebut. Hasil penelitian menunjukkan jenis-jenis tanaman peneduh jalan yang berpotensi menjerap Pb adalah Glodogan (Polyalthea longifolia), Angsana (Pterocarpus indicus), Filicium (Filicium decipiends), Ketapang (Terminalia catappa), Beringin (Ficus benjamina), Kupu-kupu (Bauhinia tomentosa), Puspa (Schima wallichii), Kenari (Canarium ovatum) dan Genitu (Chrysophyllum cainito)

Indigo: a natural molecular passivator for efficient perovskite solar cells

Organic–inorganic hybrid lead halide perovskite solar cells have made unprecedented progress in improving photovoltaic efficiency during the past decade, while still facing critical stability challenges. Herein, the natural organic dye Indigo is explored for the first time to be an efficient molecular passivator that assists in the preparation of high-quality hybrid perovskite film with reduced defects and enhanced stability. The Indigo molecule with both carbonyl and amino groups can provide bifunctional chemical passivation for defects. In-depth theoretical and experimental studies show that the Indigo molecules firmly binds to the perovskite surfaces, enhancing the crystallization of perovskite films with improved morphology. Consequently, the Indigo-passivated perovskite film exhibits increased grain size with better uniformity, reduced grain boundaries, lowered defect density, and retarded ion migration, boosting the device efficiency up to 23.22%, and ˜21% for large-area device (1 cm2). Furthermore, the Indigo passivation can enhance device stability in terms of both humidity and thermal stress. These results provide not only new insights into the multipassivation role of natural organic dyes but also a simple and low-cost strategy to prepare high-quality hybrid perovskite films for optoelectronic applications based on Indigo derivatives.Peer ReviewedPostprint (author's final draft