A Study of Social Chatbots Affordances Mitigating Loneliness

Loneliness is a significant concern and is linked to negative health outcomes such as depression and anxiety. Chatbots are gaining attention as potential companions to militate against loneliness. However, IS studies on the effects of human-AI relationships on mental wellness are limited, leaving unclear what enables humans to find companionship and intimate relationships with chatbots, and under what conditions human-chatbot interaction can alleviate loneliness. This study aims to develop a model of how chatbots alleviate loneliness and test it using a longitudinal study. Specifically, this research argues that shared identity affordance and social support affordance help mitigate loneliness directly and indirectly through enhanced intimacy feeling. The effects of chatbots’ affordances on loneliness and intimacy depend on users’ emotion regulation beliefs. Upon successful completion, this research has the potential to offer insight into the design of chatbots and how to leverage AI for social good

Comparative Transcriptome Analysis of Resistant and Susceptible Tomato Lines in Response to Infection by Xanthomonas perforans Race T3

Bacterial spot, incited by several Xanthomonas sp., is a serious disease in tomato (Solanum lycopersicum L.). Although genetics of resistance has been widely investigated, the interactions between the pathogen and tomato plants remain unclear. In this study, tanscriptomes of X. perforans race T3 infected tomato lines were compared to those of controls. An average of 7 million reads were generated with approximately 21,526 genes mapped in each sample post-inoculation at 6h (6 HPI) and 6d (6 DPI) using RNA-sequencing technology. Overall, the numbers of differentially expressed genes (DEGs) were higher in the resistant tomato line PI 114490 than in the susceptible line OH 88119, and the numbers of DEGs were higher at 6 DPI than at 6 HPI. Fewer genes (78 in PI 114490 and 15 in OH 88119) were up-regulated and most DEGs were down-regulated, suggesting that the inducible defense response might not be fully activated at 6 HPI. Accumulation expression levels of 326 co-up regulated genes in both tomato lines at 6 DPI might be involved in basal defense, while the specific and strongly induced genes at 6 DPI might be correlated with the resistance in PI114490. Most DEGs were involved in plant hormone signal transduction, plant-pathogen interaction and phenylalanine metabolism, and the genes significantly up-regulated in PI114490 at 6 DPI were associated with defense response pathways. DEGs containing NBS-LRR domain or defense-related WRKY transcription factors were also identified. The results will provide a valuable resource for understanding the interactions between X. perforans and tomato plants

Effect of Gelsemium elegans

Gelsemium elegans (GE) is a kind of well-known toxic plant. It can be detoxified by Mussaenda pubescens (MP), but the detoxification mechanism is still unclear. Thus, a detoxification herbal formula (GM) comprising GE and MP was derived. The Caco-2 cells monolayer model was used to evaluate GM effects on transporting six kinds of indole alkaloids of GE. The bidirectional transport studies demonstrated that absorbance percentage of indole alkaloids in GE increased linearly over time. But in GM, Papp (AP→BL) values of the most toxic members, gelsenicine, humantenidine, and gelsevirine, were lower than that of Papp (BL→AP) (P<0.05). The prominent analgesic effect members, gelsemine and koumine, were approximately 1.00 in γ values. Nowhere was this increasing efflux more pronounced than in the case of indole alkaloids with N-O structure. In the presence of verapamil, the γ values of humantenidine, gelsenicine, gelsevirine, and humantenine were decreased by 43.69, 41.42, 36.00, and 8.90 percent, respectively. The γ values in presence of ciclosporin were homologous with a decrease of 42.32, 40.59, 34.00, and 15.07 percent. It suggested that the efflux transport was affected by transporters. Taken together, due to the efflux transporters participation, the increasing efflux of indole alkaloids from GM was found in Caco-2 cells

NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is moving towards a robust perception age. However, LiDAR- and visual- SLAM may easily fail in adverse conditions (rain, snow, smoke and fog, etc.). In comparison, SLAM based on 4D Radar, thermal camera and IMU can work robustly. But only a few literature can be found. A major reason is the lack of related datasets, which seriously hinders the research. Even though some datasets are proposed based on 4D radar in past four years, they are mainly designed for object detection, rather than SLAM. Furthermore, they normally do not include thermal camera. Therefore, in this paper, NTU4DRadLM is presented to meet this requirement. The main characteristics are: 1) It is the only dataset that simultaneously includes all 6 sensors: 4D radar, thermal camera, IMU, 3D LiDAR, visual camera and RTK GPS. 2) Specifically designed for SLAM tasks, which provides fine-tuned ground truth odometry and intentionally formulated loop closures. 3) Considered both low-speed robot platform and fast-speed unmanned vehicle platform. 4) Covered structured, unstructured and semi-structured environments. 5) Considered both middle- and large- scale outdoor environments, i.e., the 6 trajectories range from 246m to 6.95km. 6) Comprehensively evaluated three types of SLAM algorithms. Totally, the dataset is around 17.6km, 85mins, 50GB and it will be accessible from this link: https://github.com/junzhang2016/NTU4DRadLMComment: 2023 IEEE International Intelligent Transportation Systems Conference (ITSC 2023

Prime–boost vaccination with plasmid and adenovirus gene vaccines control HER2/neu(+ )metastatic breast cancer in mice

INTRODUCTION: Once metastasis has occurred, the possibility of completely curing breast cancer is unlikely, particularly for the 30 to 40% of cancers overexpressing the gene for HER2/neu. A vaccine targeting p185, the protein product of the HER2/neu gene, could have therapeutic application by controlling the growth and metastasis of highly aggressive HER2/neu(+ )cells. The purpose of this study was to determine the effectiveness of two gene vaccines targeting HER2/neu in preventive and therapeutic tumor models. METHODS: The mouse breast cancer cell line A2L2, which expresses the gene for rat HER2/neu and hence p185, was injected into the mammary fat pad of mice as a model of solid tumor growth or was injected intravenously as a model of lung metastasis. SINCP-neu, a plasmid containing Sindbis virus genes and the gene for rat HER2/neu, and Adeno-neu, an E1,E2a-deleted adenovirus also containing the gene for rat HER2/neu, were tested as preventive and therapeutic vaccines. RESULTS: Vaccination with SINCP-neu or Adeno-neu before tumor challenge with A2L2 cells significantly inhibited the growth of the cells injected into the mammary fat or intravenously. Vaccination 2 days after tumor challenge with either vaccine was ineffective in both tumor models. However, therapeutic vaccination in a prime–boost protocol with SINCP-neu followed by Adeno-neu significantly prolonged the overall survival rate of mice injected intravenously with the tumor cells. Naive mice vaccinated using the same prime–boost protocol demonstrated a strong serum immunoglobulin G response and p185-specific cellular immunity, as shown by the results of ELISPOT (enzyme-linked immunospot) analysis for IFNγ. CONCLUSION: We report herein that vaccination of mice with a plasmid gene vaccine and an adenovirus gene vaccine, each containing the gene for HER2/neu, prevented growth of a HER2/neu-expressing breast cancer cell line injected into the mammary fat pad or intravenously. Sequential administration of the vaccines in a prime–boost protocol was therapeutically effective when tumor cells were injected intravenously before the vaccination. The vaccines induced high levels of both cellular and humoral immunity as determined by in vitro assessment. These findings indicate that clinical evaluation of these vaccines, particularly when used sequentially in a prime–boost protocol, is justified

Reservoir Computing with Sensitivity Analysis Input Scaling Regulation and Redundant Unit Pruning for Modeling Fed-Batch Bioprocesses

Although reservoir computing (RC) is an effective approach to designing and training recurrent neural networks, the optimization of neural network systems involves a number of manual, tweaking or brute-force searching parameters, such as network size, input scaling parameters, and spectral radius. To create an optimal echo state network (ESN), we propose a modified RC that combines sensitivity analysis input scaling regulation (SAISR) and redundant unit pruning algorithm (RUPA). SAISR is first employed to obtain the optimal input scaling parameters. In SAISR, an ESN without tuning is established, and then its input scaling parameters are tuned based on the Sobol’ sensitivity analysis. Second, RUPA is employed to prune out the redundant readout connections. A fed-batch penicillin cultivation process is chosen to demonstrate the applicability of the modified RC. The results show that the input scaling parameter has a more important influence than other parameters in ESN, and SAISR-ESN outperforms ESN without tuning. The RUPA method improves the generalization performance and simplifies the size of ESN. The prediction performance of RUPA-SAISR-ESN is compared with those of the existing methods, and the results indicate the superiority of RUPA-SAISR-ESN in the fed-batch penicillin cultivation process

Effect of Gelsemium elegans and Mussaenda pubescens, the Components of a Detoxification Herbal Formula, on Disturbance of the Intestinal Absorptions of Indole Alkaloids in Caco-2 Cells

Gelsemium elegans (GE) is a kind of well-known toxic plant. It can be detoxified by Mussaenda pubescens (MP), but the detoxification mechanism is still unclear. Thus, a detoxification herbal formula (GM) comprising GE and MP was derived. The Caco-2 cells monolayer model was used to evaluate GM effects on transporting six kinds of indole alkaloids of GE. The bidirectional transport studies demonstrated that absorbance percentage of indole alkaloids in GE increased linearly over time. But in GM, Papp (AP→BL) values of the most toxic members, gelsenicine, humantenidine, and gelsevirine, were lower than that of Papp (BL→AP) (P<0.05). The prominent analgesic effect members, gelsemine and koumine, were approximately 1.00 in γ values. Nowhere was this increasing efflux more pronounced than in the case of indole alkaloids with N-O structure. In the presence of verapamil, the γ values of humantenidine, gelsenicine, gelsevirine, and humantenine were decreased by 43.69, 41.42, 36.00, and 8.90 percent, respectively. The γ values in presence of ciclosporin were homologous with a decrease of 42.32, 40.59, 34.00, and 15.07 percent. It suggested that the efflux transport was affected by transporters. Taken together, due to the efflux transporters participation, the increasing efflux of indole alkaloids from GM was found in Caco-2 cells

Bearing-Fault Diagnosis with Signal-to-RGB Image Mapping and Multichannel Multiscale Convolutional Neural Network

Deep learning bearing-fault diagnosis has shown strong vitality in recent years. In industrial practice, the running state of bearings is monitored by collecting data from multiple sensors, for instance, the drive end, the fan end, and the base. Given the complexity of the operating conditions and the limited number of bearing-fault samples, obtaining complementary fault features using the traditional fault-diagnosis method, which uses statistical characteristic in time or frequency, is difficult and relies heavily on prior knowledge. In addition, intelligent bearing-fault diagnosis based on a convolutional neural network (CNN) has several deficiencies, such as single-scale fixed convolutional kernels, excessive dependence on experts&rsquo; experience, and a limited capacity for learning a small training dataset. Considering these drawbacks, a novel intelligent bearing-fault-diagnosis method based on signal-to-RGB image mapping (STRIM) and multichannel multiscale CNN (MCMS-CNN) is proposed. First, the signals from three different sensors are converted into RGB images by the STRIM method to achieve feature fusion. To extract RGB image features effectively, the proposed MCMS-CNN is established, which can automatically learn complementary and abundant features at different scales. By increasing the width and decreasing the depth of the network, the overfitting caused by the complex network for a small dataset is eliminated, and the fault classification capability is guaranteed simultaneously. The performance of the method is verified through the Case Western Reserve University&rsquo;s (CWRU) bearing dataset. Compared with different DL approaches, the proposed approach can effectively realize fault diagnosis and substantially outperform other methods