Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources

To address the data scarcity issue in Conversational question answering (ConvQA), a dialog inpainting method, which utilizes documents to generate ConvQA datasets, has been proposed. However, the original dialog inpainting model is trained solely on the dialog reconstruction task, resulting in the generation of questions with low contextual relevance due to insufficient learning of question-answer alignment. To overcome this limitation, we propose a novel framework called Dialogizer, which has the capability to automatically generate ConvQA datasets with high contextual relevance from textual sources. The framework incorporates two training tasks: question-answer matching (QAM) and topic-aware dialog generation (TDG). Moreover, re-ranking is conducted during the inference phase based on the contextual relevance of the generated questions. Using our framework, we produce four ConvQA datasets by utilizing documents from multiple domains as the primary source. Through automatic evaluation using diverse metrics, as well as human evaluation, we validate that our proposed framework exhibits the ability to generate datasets of higher quality compared to the baseline dialog inpainting model.Comment: Accepted to EMNLP 2023 main conferenc

MP2D: An Automated Topic Shift Dialogue Generation Framework Leveraging Knowledge Graphs

Despite advancements in on-topic dialogue systems, effectively managing topic shifts within dialogues remains a persistent challenge, largely attributed to the limited availability of training datasets. To address this issue, we propose Multi-Passage to Dialogue (MP2D), a data generation framework that automatically creates conversational question-answering datasets with natural topic transitions. By leveraging the relationships between entities in a knowledge graph, MP2D maps the flow of topics within a dialogue, effectively mirroring the dynamics of human conversation. It retrieves relevant passages corresponding to the topics and transforms them into dialogues through the passage-to-dialogue method. Through quantitative and qualitative experiments, we demonstrate MP2D's efficacy in generating dialogue with natural topic shifts. Furthermore, this study introduces a novel benchmark for topic shift dialogues, TS-WikiDialog. Utilizing the dataset, we demonstrate that even Large Language Models (LLMs) struggle to handle topic shifts in dialogue effectively, and we showcase the performance improvements of models trained on datasets generated by MP2D across diverse topic shift dialogue tasks.Comment: 20 page

Spatial Uncertainty in Modeling Inhalation Exposure to Volatile Organic Compounds in Response to the Application of Consumer Spray Products.

(1) Background: Mathematical exposure modeling of volatile organic compounds (VOCs) in consumer spray products mostly assumes instantaneous mixing in a room. This well-mixed assumption may result in the uncertainty of exposure estimation in terms of spatial resolution. As the inhalation exposure to chemicals from consumer spray products may depend on the spatial heterogeneity, the degree of uncertainty of a well-mixed assumption should be evaluated under specific exposure scenarios. (2) Methods: A room for simulation was divided into eight compartments to simulate inhalation exposure to an ethanol trigger and a propellant product. Real-time measurements of the atmospheric concentration in a room-sized chamber by proton transfer reaction mass spectrometry were compared with mathematical modeling to evaluate the non-homogeneous distribution of chemicals after their application. (3) Results: The well-mixed model overestimated short-term exposure, particularly under the trigger spray scenario. The uncertainty regarding the different chemical proportions in the trigger did not significantly vary in this study. (4) Conclusions: Inhalation exposure to aerosol generating sprays should consider the spatial uncertainty in terms of the estimation of short-term exposure

A Modified Protocol of Diethylnitrosamine Administration in Mice to Model Hepatocellular Carcinoma

We aimed to create an animal model for hepatocellular carcinoma (HCC) with a short time, a high survival rate, as well as a high incidence of HCC in both males and females than previously reported. The Diethylnitrosamine (DEN) model has an age-related effect. A single dose of DEN treatment is not enough in young mice up to 50 weeks. The same pattern is shown in an adult with multiple-dose trials whether or not there is some promotion agent. In this study, two-week old C57BL6 mice were given a total of eight doses of DEN, initially 20mg/kg body weight, and then 30mg/kg in the third week, followed by 50mg/kg for the last six weeks. The first group is DEN treatment only and the other two groups received thioacetamide (TAA) treatment for four or eight weeks after one week of rest from the last DEN treatment. An autopsy was performed after 24 weeks of the initial dose of DEN in each group. The cellular arrangement of HCC in the entire group was well-differentiated carcinoma and tumor presence with no significant impact on the survival of mice. Increased levels of the biochemical markers in serum, loss of tissue architecture, hepatocyte death, and proliferation were highly activated in all tumor-induced groups. This finding demonstrates an improved strategy to generate an animal model with a high occurrence of tumors combined with cirrhosis in a short time regardless of sex for researchers who want to investigate liver cancer-related

Spatial Uncertainty in Modeling Inhalation Exposure to Volatile Organic Compounds in Response to the Application of Consumer Spray Products.

(1) Background: Mathematical exposure modeling of volatile organic compounds (VOCs) in consumer spray products mostly assumes instantaneous mixing in a room. This well-mixed assumption may result in the uncertainty of exposure estimation in terms of spatial resolution. As the inhalation exposure to chemicals from consumer spray products may depend on the spatial heterogeneity, the degree of uncertainty of a well-mixed assumption should be evaluated under specific exposure scenarios. (2) Methods: A room for simulation was divided into eight compartments to simulate inhalation exposure to an ethanol trigger and a propellant product. Real-time measurements of the atmospheric concentration in a room-sized chamber by proton transfer reaction mass spectrometry were compared with mathematical modeling to evaluate the non-homogeneous distribution of chemicals after their application. (3) Results: The well-mixed model overestimated short-term exposure, particularly under the trigger spray scenario. The uncertainty regarding the different chemical proportions in the trigger did not significantly vary in this study. (4) Conclusions: Inhalation exposure to aerosol generating sprays should consider the spatial uncertainty in terms of the estimation of short-term exposure

Spatial Uncertainty in Modeling Inhalation Exposure to Volatile Organic Compounds in Response to the Application of Consumer Spray Products

(1) Background: Mathematical exposure modeling of volatile organic compounds (VOCs) in consumer spray products mostly assumes instantaneous mixing in a room. This well-mixed assumption may result in the uncertainty of exposure estimation in terms of spatial resolution. As the inhalation exposure to chemicals from consumer spray products may depend on the spatial heterogeneity, the degree of uncertainty of a well-mixed assumption should be evaluated under specific exposure scenarios. (2) Methods: A room for simulation was divided into eight compartments to simulate inhalation exposure to an ethanol trigger and a propellant product. Real-time measurements of the atmospheric concentration in a room-sized chamber by proton transfer reaction mass spectrometry were compared with mathematical modeling to evaluate the non-homogeneous distribution of chemicals after their application. (3) Results: The well-mixed model overestimated short-term exposure, particularly under the trigger spray scenario. The uncertainty regarding the different chemical proportions in the trigger did not significantly vary in this study. (4) Conclusions: Inhalation exposure to aerosol generating sprays should consider the spatial uncertainty in terms of the estimation of short-term exposure

PySupercharge: a python algorithm for enabling ABC transporter bacterial secretion of all proteins through amino acid mutation

Abstract Background The process of producing proteins in bacterial systems and secreting them through ATP-binding cassette (ABC) transporters is an area that has been actively researched and used due to its high protein production capacity and efficiency. However, some proteins are unable to pass through the ABC transporter after synthesis, a phenomenon we previously determined to be caused by an excessive positive charge in certain regions of their amino acid sequence. If such an excessive charge is removed, the secretion of any protein through ABC transporters becomes possible. Results In this study, we introduce ‘linear charge density’ as the criteria for possibility of protein secretion through ABC transporters and confirm that this criterion can be applied to various non-secretable proteins, such as SARS-CoV-2 spike proteins, botulinum toxin light chain, and human growth factors. Additionally, we develop a new algorithm, PySupercharge, that enables the secretion of proteins containing regions with high linear charge density. It selectively converts positively charged amino acids into negatively charged or neutral amino acids after linear charge density analysis to enable protein secretion through ABC transporters. Conclusions PySupercharge, which also minimizes functional/structural stability loss of the pre-mutation proteins through the use of sequence conservation data, is currently being operated on an accessible web server. We verified the efficacy of PySupercharge-driven protein supercharging by secreting various previously non-secretable proteins commonly used in research, and so suggest this tool for use in future research requiring effective protein production

Multi-temporal orthophoto and digital surface model registration produced from UAV imagery over an agricultural field

Correcting the three-dimensional geometric error is essential to effectively use the multi-temporal unmanned aerial vehicle (UAV) orthophoto and digital surface model (DSM) acquired from the agricultural field. Although ground control points (GCPs) obtained through field surveys are usually used to calibrate geometrical errors establishing/maintaining GCPs and surveying them in the field are time-consuming and inefficient. Therefore, we propose a simple and efficient methodology to improve the geometric registration of multi-temporal orthophotos and DSMs without GCPs. In the proposed method, coarse to fine image registration is performed first, which corrects severe to slight errors by sequential feature and area-based matching methods. Subsequently, we extract height-invariant regions in multi-temporal DSM pairs, called elevation invariant feature (EIF), using the EIFs to register DSMs by estimating a linear regression model. Various experiments were conducted to analyze the absolute and relative accuracies using ten multi-temporal orthophotos and DSMs, and the robustness of the proposed method was evaluated using data obtained from another site. The experimental results demonstrate that the geometric quality of registered orthophotos and DSMs was significantly improved

Healthy lifestyle interventions for childhood and adolescent cancer survivors: a systematic review and meta-analysis

Purpose This study investigated the effects of healthy lifestyle interventions (HLSIs) on health-related quality of life (HR-QoL) in childhood and adolescent cancer survivors (CACS). Methods Major databases were searched for English-language original articles published between January 1, 2000 and May 2, 2021. Randomized controlled trials (RCTs) and non-RCTs were included. Quality was assessed using the revised Cochrane risk-of-bias tool, and a meta-analysis was conducted using RevMan 5.3 software. Results Nineteen studies were included. Significant effects on HR-QoL were found for interventions using a multi-modal approach (exercise and education) (d=-0.46; 95% confidence interval [CI]=-0.84 to -0.07, p=.02), lasting not less than 6 months (d=-0.72; 95% CI=-1.15 to -0.29, p=.0010), and using a group approach (d=-0.46; 95% CI=-0.85 to -0.06, p=.02). Self-efficacy showed significant effects when HLSIs provided health education only (d=-0.55; 95% CI=-0.92 to -0.18; p=.003), lasted for less than 6 months (d=-0.40; 95% CI=-0.69 to -0.11, p=.006), and were conducted individually (d=-0.55; 95% CI=-0.92 to -0.18, p=.003). The physical outcomes (physical activity, fatigue, exercise capacity-VO2, exercise capacity-upper body, body mass index) revealed no statistical significance. Conclusion Areas of HLSIs for CACS requiring further study were identified, and needs and directions of research for holistic health management were suggested

Gut microbial and clinical characteristics of individuals with autism spectrum disorder differ depending on the ecological structure of the gut microbiome

Understanding the relationship between the gut microbiome and autism spectrum disorder (ASD) is challenging due to the heterogeneous nature of ASD. Here, we analyzed the microbial and clinical characteristics of individuals with ASD using enterotypes. A total of 456 individuals participated in the study, including 249 participants with ASD, 106 typically developing siblings, and 101 controls. The alpha and beta diversities of the ASD, sibling, and control groups did not show significant differences. Analysis revealed a negative association between the Bifidobacterium longum group and the Childhood Autism Rating Scale, as well as a negative association between the Streptococcus salivarus group and the Social Responsiveness Scale (SRS) within the ASD group. When clustered based on microbial composition, participants with ASD exhibited two distinct enterotypes, E1 and E2. In the E2 group, the SRS score was significantly higher, and the Vineland Adaptive Behavior Scale score was significantly lower compared to the E1 group. Machine learning results indicated that the microbial species predicting SRS scores were distinct between the two enterotypes. Our study suggests that the microbial composition in individuals with ASD exhibits considerable variability, and the patterns of associations between the gut microbiome and clinical symptoms may vary depending on the enterotype.N