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South Korea is the world’s second-largest heated tobacco product (HTP) market after Japan. HTP sales in South Korea have increased rapidly since May 2017, accounting for 10.6% of the total tobacco market in 2020. Despite this, little is known as to why HTP consumers who were current and former smokers started using HTPs and used them regularly. We analyzed cross-sectional data for 1815 adults (aged 19+) who participated in the 2020 International Tobacco Control (ITC) Korea Survey, of whom 1650 were HTP-cigarette consumers (those who reported smoking cigarettes and using HTPs ≥ weekly) and 165 were exclusive HTP consumers (using HTPs ≥ weekly) who were former or occasional smokers (smoking cigarette < weekly). Respondents were asked to report the reason(s) they used HTPs, with 25 possible reasons for HTP-cigarette consumers and 22 for exclusive HTP consumers. The most common reasons for initiating HTP use among all HTP consumers were out of curiosity (58.9%), family and friends use HTPs (45.5%), and they like the HTP technology (35.9%). The most common reasons for regularly using HTPs among all HTP consumers were that they were less smelly than cigarettes (71.3%), HTPs are less harmful to own health than cigarettes (48.6%), and stress reduction (47.4%). Overall, 35.4% of HTP-cigarette consumers reported using HTPs to quit smoking, 14.7% to reduce smoking but not to quit, and 49.7% for other reasons besides quitting or reducing smoking. In conclusion, several common reasons for initiating and regularly using HTPs were endorsed by all HTP consumers who were smoking, had quit smoking completely, or occasionally smoked. Notably, only about one-third of HTP-cigarette consumers said they were using HTPs to quit smoking, suggesting that most had no intention of using HTPs as an aid to quit smoking in South Korea.

Background: New tobacco products, particularly heated tobacco products (HTPs), have been introduced across several international markets as alternatives to combustible products, such as cigarettes. However, there are limited studies on physicians’ perceptions of HTPs. This study analyzes the awareness of HTPs among physicians and assesses their concerns and attitudes toward patients using HTPs.Methods: A self-administered questionnaire was sent to a sample of 7,500 member physicians (6,000 male and 1,500 female) from the Japan Medical Association (JMA). The smoking status of physicians was categorized as never smokers, ever HTP smokers, current HTP smokers, and ever and current smokers of other products. Physicians’ awareness and attitudes toward patients using HTPs were analyzed using descriptive statistics. The correlation between the outcomes was examined using logistic regression models, whereas physicians’ concerns about HTPs were analyzed using descriptive statistics.Results: Data were obtained from 5,492 physicians (21.9% women; aged 60.4±12.1 years) with a response rate of 74.6%. Overall, 76.7% of the physicians had awareness of HTP, and about half of whom asked patients about using HTPs. Physicians who took longer to discuss cessation were more likely to discourage patient use. Smoking status was associated with discouraging HTP use in patients. Physicians who had experience with HTP use were primarily concerned about the products’ long-term safety and less about product regulation.Conclusions: Japanese physicians do not have sufficient information and knowledge about HTPs. Therefore, evidence-based guidelines are required to support physicians in advising patients against HTP use.

Fall dormancy (FD) and forage yield (FY) are two key traits in alfalfa ( Medicago sativa L.) breeding programs. However, genetic progress has remained limited over the past decades, largely due to the complexity of alfalfa breeding and the reliance on labor-intensive phenotyping methods. High-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) represents a promising alternative for rapid and non-destructive crop evaluation. The objectives of this study were to i) estimate FD using UAV-derived canopy height and RGB vegetation indices and ii) evaluate the predictive performance of machine learning (ML) models for FY estimation. A total of 210 alfalfa populations with diverse genetic backgrounds were evaluated over two growing seasons (2023 to 2025) across seven harvests under Mediterranean conditions in central Chile. FY was measured manually, while FD was estimated using both manual and UAV-based approaches. A total of 19 RGB-derived indices (VIs) including plant height (PH) were extracted and used as predictor variables. Five complex predictive ML models were evaluated: PLS, PCR, SVM, ANN, and MLR. The results showed that UAV-derived FD was significantly correlated with FD obtained through conventional methods ( R 2  = 0.88). The automated UAV-based FD phenotyping framework demonstrated slightly higher precision ( R 2  = 0.92) and broad-sense heritability ( H 2  = 0.69) compared to manual measurements ( R 2  = 0.87–0.89; H 2  = 0.64), providing a more reliable selection tool for breeders. Among the tested ML models, SVM and ANN achieved the highest accuracy ( R 2 ≈ 0.73) for FY prediction. These findings demonstrate that integrating low-cost RGB imagery with complex modeling offers a promising avenue that could assist in refining future selection strategies for this genetically complex species.

In nature, the deconstruction of plant carbohydrates is carried out by carbohydrate-active enzymes (CAZymes). A high-throughput (HTP) strategy was used to isolate and clone 1476 genes obtained from a diverse library of recombinant CAZymes covering a variety of sequence-based families, enzyme classes, and source organisms. All genes were successfully isolated by either PCR (61%) or gene synthesis (GS) (39%) and were subsequently cloned into Escherichia coli expression vectors. Most proteins (79%) were obtained at a good yield during recombinant expression. A significantly lower number (p < 0.01) of proteins from eukaryotic (57.7%) and archaeal (53.3%) origin were soluble compared to bacteria (79.7%). Genes obtained by GS gave a significantly lower number (p = 0.04) of soluble proteins while the green fluorescent protein tag improved protein solubility (p = 0.05). Finally, a relationship between the amino acid composition and protein solubility was observed. Thus, a lower percentage of non-polar and higher percentage of negatively charged amino acids in a protein may be a good predictor for higher protein solubility in E. coli. The HTP approach presented here is a powerful tool for producing recombinant CAZymes that can be used for future studies of plant cell wall degradation. Successful production and expression of soluble recombinant proteins at a high rate opens new possibilities for the high-throughput production of targets from limitless sources.

The airtightness of heated tobacco product (HTP) packs is a very important indicator for the product quality and is also of great importance during the conditioning process. A method for evaluation of the airtightness was developed based on the air pressure difference in a constant pumping configuration. The essential feature of this method is that the pressure difference between the inside and the outside of the HTP packs during the deflation process is used to characterize the sealing quality of HTP packs. The detailed setup, the principle as well as the determination procedure are described. The accuracy and the repeatability of the method were assessed, and the effect of airtightness on the conditioning process was also investigated. The developed method is proven to be reliable with a standard deviation less than 0.09 kPa and repeatability less than 0.30 kPa. In addition, it was found that, although the transmission of moisture between HTPs and atmosphere could not be entirely prevented by the packs, airtightness still plays a significant role during the conditioning process, especially if the airtightness was at a relatively low level (e.g., lower than 1.5 kPa under a pumping flow rate of 200 mL/min). The method provides a promising way to assess and monitor the sealing quality of HTP packs, and it is suggested that the airtightness of the pack should not be lower than 2 kPa under a pumping flow rate of 200 mL/min. [Contrib. Tob. Nicotine Res. 32 (2023) 140–145]

Integrating high-throughput phenotyping (HTP) based traits into phenomic and genomic selection (GS) can accelerate the breeding of high-yielding and climate-resilient wheat cultivars. In this study, we explored the applicability of Unmanned Aerial Vehicles (UAV)-assisted HTP combined with deep learning (DL) for the phenomic or multi-trait (MT) genomic prediction of grain yield (GY), test weight (TW), and grain protein content (GPC) in winter wheat. Significant correlations were observed between agronomic traits and HTP-based traits across different growth stages of winter wheat. Using a deep neural network (DNN) model, HTP-based phenomic predictions showed robust prediction accuracies for GY, TW, and GPC for a single location with R 2 of 0.71, 0.62, and 0.49, respectively. Further prediction accuracies increased (R 2 of 0.76, 0.64, and 0.75) for GY, TW, and GPC, respectively when advanced breeding lines from multi-locations were used in the DNN model. Prediction accuracies for GY varied across growth stages, with the highest accuracy at the Feekes 11 (Milky ripe) stage. Furthermore, forward prediction of GY in preliminary breeding lines using DNN trained on multi-location data from advanced breeding lines improved the prediction accuracy by 32% compared to single-location data. Next, we evaluated the potential of incorporating HTP-based traits in multi-trait genomic selection (MT-GS) models in the prediction of GY, TW, and GPC. MT-GS, models including UAV data-based anthocyanin reflectance index (ARI), green chlorophyll index (GCI), and ratio vegetation index 2 (RVI_2) as covariates demonstrated higher predictive ability (0.40, 0.40, and 0.37, respectively) as compared to single-trait model (0.23) for GY. Overall, this study demonstrates the potential of integrating HTP traits into DL-based phenomic or MT-GS models for enhancing breeding efficiency.

Phenomics has emerged as important tool to bridge the genotype-phenotype gap. To dissect complex traits such as highly dynamic plant growth, and quantification of its component traits over a different growth phase of plant will immensely help dissect genetic basis of biomass production. Based on RGB images, models have been developed to predict biomass recently. However, it is very challenging to find a model performing stable across experiments. In this study, we recorded RGB and NIR images of wheat germplasm and Recombinant Inbred Lines (RILs) of Raj3765xHD2329, and examined the use of multimodal images from RGB, NIR sensors and machine learning models to predict biomass and leaf area non-invasively. The image-based traits (i-Traits) containing geometric features, RGB based indices, RGB colour classes and NIR features were categorized into architectural traits and physiological traits. Total 77 i-Traits were selected for prediction of biomass and leaf area consisting of 35 architectural and 42 physiological traits. We have shown that different biomass related traits such as fresh weight, dry weight and shoot area can be predicted accurately from RGB and NIR images using 16 machine learning models. We applied the models on two consecutive years of experiments and found that measurement accuracies were similar suggesting the generalized nature of models. Results showed that all biomass-related traits could be estimated with about 90% accuracy but the performance of model BLASSO was relatively stable and high in all the traits and experiments. The R of BLASSO for fresh weight prediction was 0.96 (both year experiments), for dry weight prediction was 0.90 (Experiment 1) and 0.93 (Experiment 2) and for shoot area prediction 0.96 (Experiment 1) and 0.93 (Experiment 2). Also, the RMSRE of BLASSO for fresh weight prediction was 0.53 (Experiment 1) and 0.24 (Experiment 2), for dry weight prediction was 0.85 (Experiment 1) and 0.25 (Experiment 2) and for shoot area prediction 0.59 (Experiment 1) and 0.53 (Experiment 2). Based on the quantification power analysis of i-Traits, the determinants of biomass accumulation were found which contains both architectural and physiological traits. The best predictor i-Trait for fresh weight and dry weight prediction was Area_SV and for shoot area prediction was projected shoot area. These results will be helpful for identification and genetic basis dissection of major determinants of biomass accumulation and also non-invasive high throughput estimation of plant growth during different phenological stages can identify hitherto uncovered genes for biomass production and its deployment in crop improvement for breaking the yield plateau.

Controlled environment agriculture (CEA) is essential for resilient crop production but faces high energy demands and operational costs. While high-throughput phenotyping (HTP) provides critical biological feedback to optimize these systems, conventional HTP platforms remain prohibitively expensive, infrastructure-heavy, and technically complex for widespread adoption. This review examines the emerging shift toward “affordable phenomics”, an approach integrating low-cost, open-source microcontrollers and Internet-of-Things (IoT) devices to continuously capture dynamic plant physiological data. By utilizing customizable tools such as modular chlorophyll fluorometers and wearable sensors, researchers and commercial growers can non-destructively monitor key traits like photosynthetic efficiency and water status in real time. Coupling these accessible sensing networks with artificial intelligence (AI)-driven analytics allows static environmental controls to transition into dynamic, plant-centered feedback systems. We synthesize recent advancements in affordable sensor technologies and review how temporal AI modeling extracts biologically meaningful features from longitudinal datasets to direct adaptive lighting and irrigation strategies. Furthermore, we critically assess current technological limitations, including sensor calibration, signal noise, cross-platform data standardization, and edge-versus-cloud computation tradeoffs. Finally, we highlight essential future research directions, particularly the development of robust edge-computing frameworks and predictive crop digital twins, demonstrating how affordable phenomics offers a scalable, data-driven pathway to improve resource-use efficiency in modern agriculture.

The tobacco industry promotes electronic nicotine delivery systems (ENDS) and heated tobacco products (HTP) as a safer alternative to conventional cigarettes with misleading marketing sustained by studies with conflict of interest. As a result, these devices sell without regulations and warnings about their adverse effects on health, with a growing user base targeting young people. This systematic review aimed to describe the adverse effects on the respiratory system in consumers of these devices. We conducted a systematic review and bibliometric analysis of 79 studies without conflict of interest evaluating ENDS and HTP effects in the respiratory system in experimental models, retrieved from the PubMed database. We found that the damage produced by using these devices is involved in pathways related to pulmonary diseases, involving mechanisms previously reported in conventional cigarettes as well as new mechanisms particular to these devices, which challenges that the tobacco industry’s claims. The present study provides significant evidence to suggest that these devices are an emerging public health problem and that they should be regulated or avoided.

The P1/HC-Pro viral suppressor of potyvirus suppresses posttranscriptional gene silencing (PTGS). The fusion protein of P1/HC-Pro can be cleaved into P1 and HC-Pro through the P1 self-cleavage activity, and P1 is necessary and sufficient to enhance PTGS suppression of HC-Pro. To address the modulation of gene regulatory relationships induced by turnip mosaic virus (TuMV) P1/HC-Pro (P1/HC-ProTu), a comparative transcriptome analysis of three types of transgenic plants (P1Tu, HC-ProTu, and P1/HC-ProTu) were conducted using both high-throughput (HTP) and low-throughput (LTP) RNA-Seq strategies. The results showed that P1/HC-ProTu disturbed the endogenous abscisic acid (ABA) accumulation and genes in the signaling pathway. Additionally, the integrated responses of stress-related genes, in particular to drought stress, cold stress, senescence, and stomatal dynamics, altered the expressions by the ABA/calcium signaling. Crosstalk among the ABA, jasmonic acid, and salicylic acid pathways might simultaneously modulate the stress responses triggered by P1/HC-ProTu. Furthermore, the LTP network analysis revealed crucial genes in common with those identified by the HTP network in this study, demonstrating the effectiveness of the miniaturization of the HTP profile. Overall, our findings indicate that P1/HC-ProTu-mediated suppression in RNA silencing altered the ABA/calcium signaling and a wide range of stress responses.