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As intelligent driving vehicles came out of concept into people’s life, the combination of safe driving and artificial intelligence becomes the new direction of future transportation development. Autonomous driving technology is developing based on control algorithms and model recognitions. In this paper, a cloud-based interconnected multi-sensor fusion autonomous vehicle system is proposed that uses deep learning (YOLOv4) and improved ORB algorithms to identify pedestrians, vehicles, and various traffic signs. A cloud-based interactive system is built to enable vehicle owners to master the situation of their vehicles at any time. In order to meet multiple application of automatic driving vehicles, the environment perception technology of multi-sensor fusion processing has broadened the uses of automatic driving vehicles by being equipped with automatic speech recognition (ASR), vehicle following mode and road patrol mode. These functions enable automatic driving to be used in applications such as agricultural irrigation, road firefighting and contactless delivery under new coronavirus outbreaks. Finally, using the embedded system equipment, an intelligent car was built for experimental verification, and the overall recognition accuracy of the system was over 96%. Author
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Wild animals are considered reservoirs for emerging and reemerging viruses, such as the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Previous studies have reported that bats and ticks harbored variable important pathogenic viruses, some of which could cause potential diseases in humans and livestock, while viruses carried by reptiles were rarely reported. Our study first conducted snakes' virome analysis to establish effective surveillance of potential transboundary emerging diseases. Consequently, Adenoviridae, Circoviridae, Retroviridae, and Parvoviridae were identified in oral samples from Protobothrops mucrosquamatus, Elaphe dione, and Gloydius angusticeps based on sequence similarity to existing viruses. Picornaviridae and Adenoviridae were also identified in fecal samples of Protobothrops mucrosquamatus. Notably, the iflavirus and foamy virus were first reported in Protobothrops mucrosquamatus, enriching the transboundary viral diversity in snakes. Furthermore, phylogenetic analysis revealed that both the novel-identified viruses showed low genetic similarity with previously reported viruses. This study provided a basis for our understanding of microbiome diversity and the surveillance and prevention of emerging and unknown viruses in snakes.
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Background & Aim: The long-term effects of human mesenchymal stem cell (MSC) treatment on COVID-19 patients have not been fully characterized. The aim of this study was to evaluate the safety and efficacy of a MSC treatment administered to severe COVID-19 patients enrolled in a randomized, double-blind, placebo-controlled clinical trial (NCT 04288102). Methods, Results & Conclusion(s): A total of 100 patients experiencing severe COVID-19 received either MSC treatment (n = 65, 4x107 cells per infusion) or a placebo (n = 35) combined with standard of care on days 0, 3, and 6. Patients were subsequently evaluated 18 and 24 months after treatment to evaluate the long-term safety and efficacy of the MSC treatment. The outcomes measured included: 6-minute walking distance (6-MWD), lung imaging, quality of life according to the Short Form 36 questionnaire, COVID-19-related symptoms, titers of SARS-CoV-2 neutralizing antibodies, MSC-related adverse events (AEs), and tumor markers. Two years after treatment, a marginally smaller proportion of patients had a 6-MWD below the lower limit of the normal range in the MSC group than in the placebo group (OR = 0.19, 95% CI: 0.04-0.80, Fisher's exact test, p = 0.015). On the SF-36 questionnaire, a marginally higher general health score was received by the MSC group at month 18 compared with the placebo group (50.00 vs. 35.00;95% CI: 0.00-20.00, Wilcoxon rank sum test, p = 0.016). In contrast, there were no differences in the total severity score of lung imaging or the titer of neutralizing antibodies between the two groups. Meanwhile, there were no MSC-related AEs reported at the 18- or 24-month follow-ups. The serum levels of most of the tumor markers examined remained within normal ranges and were similar between the MSC and placebo groups. Long-term safety was observed for the COVID-19 patients who received MSC treatment. Yet few sustained efficacy of MSC treatment was observed at the end of the 2-year follow-up period. Funding(s): The National Key Research and Development Program of China (2022YFA1105604, 2020YFC0860900), the specific research fund of The Innovation Platform for Academicians of Hainan Province (YSPTZX202216) and the Fund of National Clinical Center for Infectious Diseases, PLA General Hospital (NCRCID202105,413FZT6). [Figure presented]Copyright © 2023 International Society for Cell & Gene Therapy
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The rise of Transportation Network Companies (TNCs) over the last decade has significantly disrupted the taxi industry. Studies have shown that taxi ridership has plummeted, and their capacity utilization rates are lower than 50% in five major U.S. cities. Additionally, the COVID-19 pandemic has dealt a severe blow to the already struggling taxi industry. To monitor the evolution of the taxi industry and its impacts on society, our study evaluates changes in the utilization rates, fuel consumption, and emissions among Chicago taxis, using taxi data with rich information on trip profiles from pre-pandemic and pandemic times. Our findings indicate that the taxi utilization rate decreased during the pandemic. While fuel consumption and emissions per kilometer decreased thanks to the reduced traffic during the pandemic, the overall fuel consumption and emissions increased due to increased deadhead travel. The methods developed in this study can be applied to monitor and evaluate the impact of future disruptive events on urban mobility and transportation systems more effectively. By utilizing mobility data to better understand transportation systems, we can develop more efficient, sustainable, and resilient mobility solutions for smart cities. © 2023 ACM.
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Background: Hepatocellular carcinoma (HCC) is the second leading cause of malignancy-related mortality and the fifth most common worldwide. Immuno-cancer microenvironment (ICME) was highlighted recently because scientists want to unlock the detailed mechanism in carcinogenesis pathway and find the novel interactions in ICME. Besides, single cell analysis could mitigate the interrupted signals between cells and tissues. On the other hand, COVID-19 angiotensin I converting enzyme (ACE) previously was reported associated with cancer. However, the robust association between COVID-19 and HCC ICME is still unaddressed. Aim(s): We plan to investigate the COVID-19 ACE relevant genes to HCC ICME regarding survival. Method(s): We used Reactome for COVID-19 ACE gene pathway mapping and explored the positive relevant gene expression. DISCO website was applied for single cell analyses using the above-collected genes from Reactome. Finally, we implanted the biomedical informatics into TIMER 2.0 for ICME survival analyses. Result(s): In Fig. 1, the gene-gene interaction mapping was shown. We collected 13 genes (CPB2, ACE2, AGT, MME, ANPEP, CPA3, ENPEP, GZMH, CTSZ, CTSD, CES1, ATP6AP2, and AOPEP) for further single cell relevant analyses, in Table 1, with detailed expression level (TPM). Among the above 13 genes, AGT, GZMH, CTSZ, CTSD, CES1, and ATP6AP2 were strongly expressed in liver tissue. We then applied the initial 13 genes to TIMER 2.0 for HCC ICME 2-year survival analyses. CPA3 and GZMH low expressions with high macrophage infiltration in HCC ICME showed significantly worse 2-year cumulative survival [hazard ratio (HR):CPA3 2.21, p-value 0.018;GZMH 2.07, p-value 0.0341]. ACE2, CPB2, AGT, MME, ANPEP, ENPEP, CTSZ, CTSD, CES1, and ATP6AP2 high expressions with high macrophage infiltration in HCC ICME revealed significantly worse 2-year cumulative survival. Conclusion(s): We demonstrate that ACE2 was strongly associated with HCC clinical survival with macrophage infiltration. However, the bidirectional translational roles about ACE2 relevant genes in HCC should be documented.
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Background/Aims: The clinical course of hepatitis B virus (HBV) infection in individuals with HIV-1 coinfection is marked by accelerated disease progression. A tenofovir-containing antiretroviral regimen is recommended in most people with HIV-1/HBV-coinfection, but there have not been randomized studies of tenofovir disoproxil fumarate (TDF) vs tenofovir alafenamide (TAF) in treatment- naive HIV-1/HBV-coinfected individuals. We report primary endpoint results from a Phase 3 study comparing bictegravir/emtricitabine/ TAF (B/F/TAF) vs dolutegravir + emtricitabine/TDF (DTG + F/TDF) at Week (W)48 in participants initiating treatment for both viruses. Method(s): Adults with HIV-1/HBV coinfection were randomized 1:1 to initiate blinded treatment with B/F/TAF or DTG + F/TDF (with placebo). Primary endpoints were the proportion of participants with HIV-1 RNA<50 copies/mL (FDA Snapshot) and plasma HBV DNA<29 IU/mL (missing = failure) at W48. Noninferiority was assessed with 95% CI (12% margin). Secondary and other endpoints included change from baseline cluster of differentiation 4 (CD4) count, proportion with hepatitis B surface antigen (HBsAg) and hepatitis B e antigen (HBeAg) loss/seroconversion, and alanine transaminase (ALT) normalization (AASLD criteria). Result(s): Participants (N = 243) were randomized and treated (B/F/ TAF [n = 121], DTG + F/TDF [n = 122]) from 11 countries in Asia, Europe, North, and Latin America. Baseline characteristics were median age of 32 years, 4.5% female, 88% Asian, 30% HIV-1 RNA>100,000 c/mL, 40% CD4<200 cells/lL, median HBV DNA 8.1 log10 IU/mL, 78% HBeAg+. At W48, B/F/TAF was noninferior to DTG + F/TDF at achieving HIV-1 RNA<50 copies/mL (95% vs 91%, difference 4.1%;95% CI -2.5%-10.8%;P = 0.21), with mean CD4 gains of + 200 and + 175 cells/lL, respectively. B/F/TAF was superior to DTG + F/TDF at achieving HBV DNA<29 IU/mL (63% vs 43%, difference 16.6%;95% CI 5.9%-27.3%;P = 0.0023). Participants treated with B/F/TAF vs DTG + F/TDF had numerically higher HBsAg loss (13% vs 6%;P = 0.059), HBeAg loss (26% vs 14%;P = 0.055), HBeAg seroconversion (23% vs 11%;P = 0.031), and ALT normalization (73% vs 55%;P = 0.066). The most frequent adverse events among participants treated with B/F/TAF vs DTG + F/TDF were upper respiratory tract infection (17% vs 11%), COVID- 19 (13% vs 11%), pyrexia (9% vs 12%), ALT increase (7% vs 11%), and nasopharyngitis (11% vs 4%). ALT flares (elevations at >= 2 consecutive postbaseline visits) occurred in 11 participants (7 B/F/ TAF, 4 DTG + F/TDF), and all resolved. Conclusion(s): Among adults with HIV-1/HBV-coinfection starting antiviral therapy, both B/F/TAF and DTG + F/TDF had high HIV-1 suppression at year 1, with B/F/TAF resulting in superior HBV DNA suppression and significantly more HBeAg seroconversion. Safety findings were similar between groups.
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The global epidemic of coronavirus disease 2019 (COVID-19) is still growing. The response to this emerging disease should be considered with the context of its clinical characteristics and pathophysiological mechanisms. Although available therapeutic options are still very limited, current experience has suggested that the choice of clinical strategies should be based upon the disease stage and immune functions of the patients. The present article reviews the clinical characteristics of COVID-19 and current evidence of various treatment approaches. Combined with first-line experience, we summarize the current clinical strategies for COVID-19 management based on disease progress and staging.Copyright © 2021, Peking Union Medical College Hospital. All rights reserved.
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The global pandemic Corona Virus Disease 2019 (COVID-19) has become one of the deadliest epidemics in human history, bringing enormous harm to human society. To help health policymakers respond to the threat of COVID-19, prediction of outbreaks is needed. Research on COVID-19 prediction usually uses data-driven models and mechanism models. However, in the early stages of the epidemic, there were not enough data to establish a data-driven model. The inadequate understanding of the virus that causes COVID-19, SARS-COV-2, has also led to the inaccuracies of the mechanism model. This has left the government with the toughest Non-pharmaceutical interventions (NPIs) to curb the spread of the virus, such as the lockdown of Wuhan in 2020. Yet man is a social animal, and social relations and interactions are necessary for his existence. The novel coronavirus and containment measures have challenged human and community interactions, affecting the lives of individuals and collective societies. To help governments take appropriate and necessary actions in the early stages of an epidemic, and to mitigate its impact on people's psychology and lives, we used the COVID-19 pandemic as an example to develop a model that uses surveillance data from one epidemic to predict the development trend of another. Based on the fact that both influenza and COVID-19 are transmitted through infectious respiratory droplets, we hypothesized that they may have the same underlying contact structure, and we proposed the influenza data-based COVID-19 prediction (ICP) model. In this model, the underlying contact pattern is firstly inferred by using a singular value decomposition method from influenza surveillance data. Then the contact matrix was used to simulate the influenza virus transmission through close contact of people, and the influenza virus transmission model was established. In order to be able to simulate the spread of COVID-19 virus using influenza transmission models, we used influenza contact matrix and COVID-19 infection data to estimate the risk of a population contracting COVID-19, i.e. force of infection of COVID-19. Finally, we used force of infection and influenza virus transmission model to simulate and predict the spread of COVID-19 in the population. We obtained age-disaggregated influenza and COVID-19 infection data for the United States in 2020, as well as data for Europe, which was not disaggregated by age. We use correlation coefficients as an evaluation indicator, and the final results prove that the predicted value and the actual value are positively correlated. So, the development trend of COVID-19 can be predicted using influenza surveillance data. © 2022 IEEE.
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Background: Omicron lineages, including BA.1 and BA.2, emerged following mass COVID-19 vaccination campaigns, displaced previous SARS-CoV-2 variants of concern worldwide, and gave rise to sublineages that continue to spread among humans. Previous research has shown that Omicron lineages exhibit a decreased propensity for lower respiratory tract (lung) infection compared to ancestral SARS-CoV-2, which may explain the decreased pathogenicity associated with Omicron infections. Nonetheless, Omicron lineages exhibit an unprecedented transmissibility in humans, which until now has been solely attributed to escape from vaccine-induced neutralizing antibodies. Method(s): We comprehensively analyzed BA1 and BA2 infection in primary human nasal epithelial cells cultured at the air-liquid interface, which recapitulates the physiological architecture of the nasal epithelium in vivo. Meanwhile we also took advantage of the VSV-based pseudovirus decorated with different Spike variants. Result(s): In primary human nasal epithelial cells cultured at the air-liquid interface, which recapitulates the physiological architecture of the nasal epithelium in vivo, BA.1 and BA.2 exhibited enhanced infectivity relative to ancestral SARS-CoV-2. Using VSV-based pseudovirus decorated with different Spike variants, we found that increased infectivity conferred by Omicron Spike is due to superior attachment and entry into nasal epithelial cells. In contrast to ancestral SARS-CoV-2, invasion of nasal epithelia by Omicron occurred via the cell surface and endosomal routes of entry and was accompanied by elevated induction of type-I interferons, indicative of a robust innate immune response. Furthermore, BA.1 was less sensitive to inhibition by the antiviral state elicited by type-I and type-III interferons, and this was recapitulated by pseudovirus bearing BA.1 and BA.2 Spike proteins. Conclusion(s): Our results suggest that the constellation of Spike mutations unique to Omicron allow for increased adherence to nasal epithelia, flexible usage of virus entry pathways, and interferon resistance. These findings inform our understanding of how Omicron evolved elevated transmissibility between humans despite a decreased propensity to infect the lower respiratory tract. Additionally, the interferon insensitivity of the Omicron Spike-mediated entry process may explain why Omicron lineages lost the capacity to antagonize interferon pathways compared to ancestral SARS-CoV-2.
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In recent years, on-site visitation has been strictly restricted in many scenic areas due to the global spread of the COVID-19 pandemic. "Cloud tourism”, also called online travel, uses high-resolution photographs taken by unmanned aerial vehicles (UAVs) as the dominant data source and has attracted much attention. Due to the differences between ground and aerial observation perspectives, the landscape elements that affect the beauty of colored-leaved forests are quite different. In this paper, Qixia National Forest Park in Nanjing, China, was chosen as the case study area, and the best viewpoints were selected by combining tourists' preferred viewing routes with a field survey, followed by a scenic beauty evaluation (SBE) of the forests with autumn-colored leaves in 2021 from the aerial and ground perspectives. The results show that (1) the best viewpoints can be obtained through the spatial overlay of five landscape factors: elevation, surface runoff, slope, aspect, and distance from the road;(2) the dominant factors influencing the beauty of colored-leaved forests from the aerial perspective are terrain changes, forest coverage, landscape composition, landscape contrast, the condition of the human landscape, and recreation frequency;and (3) the beauty of the ground perspective of the colored-leaved forests is strongly influenced by the average diameter at breast height (DBH), the dominant color of the leaves, the ratio of the colored-leaved tree species, the canopy width, and the fallen leaf coverage. The research results can provide scientific reference for the creation of management measures for forests with autumn-colored leaves. © 2023 by the authors.
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The area of clinical decision support systems (CDSS) is facing a boost in research and development with the increasing amount of data in clinical analysis together with new tools to support patient care. This creates a vibrant and challenging environment for the medical and technical staff. This chapter presents a discussion about the challenges and trends of CDSS considering big data and patient-centered constraints. Two case studies are presented in detail. The first presents the development of a big data and AI classification system for maternal and fetal ambulatory monitoring, composed by different solutions such as the implementation of an Internet of Things sensors and devices network, a fuzzy inference system for emergency alarms, a feature extraction model based on signal processing of the fetal and maternal data, and finally a deep learning classifier with six convolutional layers achieving an F1-score of 0.89 for the case of both maternal and fetal as harmful. The system was designed to support maternal–fetal ambulatory premises in developing countries, where the demand is extremely high and the number of medical specialists is very low. The second case study considered two artificial intelligence approaches to providing efficient prediction of infections for clinical decision support during the COVID-19 pandemic in Brazil. First, LSTM recurrent neural networks were considered with the model achieving R2=0.93 and MAE=40,604.4 in average, while the best, R2=0.9939, was achieved for the time series 3. Second, an open-source framework called H2O AutoML was considered with the "stacked ensemble” approach and presented the best performance followed by XGBoost. Brazil has been one of the most challenging environments during the pandemic and where efficient predictions may be the difference in saving lives. The presentation of such different approaches (ambulatory monitoring and epidemiology data) is important to illustrate the large spectrum of AI tools to support clinical decision-making. © 2022 Elsevier Inc. All rights reserved.
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In this article's Data Availability Statement, the URL to the replication code was missing. Find the full Data Availability Statement below along with the link to the openly available code on GitHub. Data Availability Statement. If possible, results of computed indicators or aggregated statistics will be made available through the website of the Gambia Bureau of Statistics (GBoS) or the Public Utilities Regulatory Authority (PURA). Details of methodologies employed for computing indicators can be found on the World Bank COVID19 Mobility Task Force Github repository. Code adjusted for running a system under PURA is maintained on the University of Tokyo's Spatial Data Commons Github repository and can be found here: https://github.com/SpatialDataCommons/CDR-wb-indicators-package. © The Author(s), 2023. Published by Cambridge University Press on behalf of Applied Probability Trust.
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We present a novel DenseNet framework with attention mechanisms (AM-DenseNet) to extract lung feature of 1 COVID-19 patient. In AM-DenseNet, a lightweight Efficient Channel Attention (ECA) structure is added at the end of each dense connection to introduce an attention mechanism to discovery local lesion domain. We compare our AM-DenseNet to VGG-16, ResNet-50 and DenseNet-121 on CT image dataset of COVID-19 patients. According to the experimental results, we conclude that the classification performance of AM-DensNet framework can be significantly enhanced under the effect of attention mechanism. The AM-DensNet shows better classification performance than the compared models. © 2022 IEEE.
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Quaternary ammonium compounds (QACs) are commonly used in a variety of consumer and commercial products, typically as a component of disinfectants. During the COVID-19 pandemic, QACs became one of the primary agents utilized to inactivate the SARS-CoV-2 virus on surfaces. However, the ecotoxicological effects of QACs upon aquatic organisms have not been fully assessed. In this study, we examined the effects of a widely used QAC (benzalkonium chloride-C14, BAC-14) on two toxigenic Microcystis strains and one non-toxigenic freshwater Microcystis strain and carried out an analysis focused on primary, adaptive and compensatory stress responses at apical (growth and photosynthesis) and metabolic levels. This analysis revealed that the two toxic Microcystis strains were more tolerant than the non-toxic strain, with 96 hr-EC50 values of 0.70, 0.76, and 0.38 mg/L BAC-14 for toxigenic M. aeruginosa FACHB-905, toxigenic M. aeruginosa FACHB-469, and non-toxigenic M. wesenbergii FACHB-908, respectively. The photosynthetic activities of the Microcystis, assessed via Fv/Fm values, were significantly suppressed under 0.4 mg/L BAC-14. Furthermore, this analysis revealed that BAC-14 altered 14, 12, and 8 metabolic pathways in M. aeruginosa FACHB-905, M. aeruginosa FACHB-469, and M. wesenbergii FACHB-908, respectively. It is noteworthy that BAC-14 enhanced the level of extracellular microcystin production in the toxigenic Microcystis strains, although cell growth was not significantly affected. Collectively, these data show that BAC-14 disrupted the physiological and metabolic status of Microcystis cells and stimulated the production and release of microcystin, which could result in damage to aquatic systems. © 2022
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The corona virus disease 2019 (COVID-19) pandemic continues to severely impact healthcare systems around the world, and patients with cancer are even worse affected owing to compromised immune status and greater exposure risk. In the present review, we retrieved the relevant literature including guidelines and consensuses directly related to the purpose of this study from the PubMed database, and then summarized the research data on cancer and COVID-19, aiming to discuss the personal protection, systemic anti-cancer therapy, outcome of co-infection, and the clinical management strategy in this population. We found that patients with malignant tumors had a higher chance of suffering COVID-19, co-infection of whom had an even worse clinical prognosis, especially for those with lung cancer or hematologic cancers. Systemic chemotherapy may delay the clearance of severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) virus of human body, and thus have a negative impact on the clinical outcomes of COVID-19, while certain endocrine therapy and targeted drugs having limited or no impact. There has been no sufficient evidence for the impact of immune checkpoint therapy on the outcomes of COVID-19 till now. It is of great value to strengthen the personal protection of patients, adjust the anti-tumor treatments rationally and optimize the clinical management processes.Copyright © 2022, Editorial Office of China Oncology. All rights reserved.
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In this paper, we propose a new novel coronavirus pneumonia image classification model based on the combination of Transformer and convolutional network(VQ-ViCNet), and present a vector quantization feature enhancement module for the inconspicuous characteristics of lung medical image data. This model extracts the local latent layer features of the image through the convolutional network, and learns the deep global features of the image data through the Transformer's multi-head self attention algorithm. After the calculation of convolution and attention, the features learned by the Transformer Encoder are enhanced by the vector quantization feature enhancement module and able to better complete the final downstream tasks. This model performs better than convolutional architectures, pure attention architectures and generative models on all 6 public datasets. © 2022 IEEE.
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Self-disclosure to a social robot is a mental health intervention that can decrease stress for adolescents. Online digital robots provide the potential to scale this intervention especially in COVID-19 social distancing situations. However, self-disclosure interactions with digital social robots remain relatively unexplored. We conducted two online self-disclosure studies with adolescents (13-19 years old): our Active Listening Study compared experiences sharing positive, negative, and neutral feelings with a social robot, while our Journaling Study explored differences in sharing stressors by speaking with and without a social robot and by writing. We found that positive prompt tone improved mood while neutral prompt decreased stress, and less negative attitudes toward robots correlate with more qualitatively positive experiences with robot interactions.We also found robot disclosure interactions hold promising potential as a preferred method of self-disclosure over solo speaking, moderated by negative attitudes toward robots. This paper outlines limitations and future work from these studies. © 2023 IEEE Computer Society. All rights reserved.