Labeling Scheme Research Articles

To label or not? A choice experiment testing whether labelled green bonds matter to retail investors

Green bonds are an important sustainable finance tool that can help reorient financial flows and influence public policy in addressing climate action across the global financial markets. However, this market is still in its infancy for the retail investor segment, and it has not been sufficiently examined from a behavioural policy lens. We fill this research gap by examining whether labelling and environmental benefits framing of a green bond can influence retail investor decision-making. By employing 1105 Amazon Mechanical Turk workers across three choice scenarios, we test whether alignment of pro-environmental personal norms or having specific personal traits can have a mediating effect on their green bond preferences. Using a mix of quantitative analyses, we find that most retail investors are influenced by the presence of a ‘green label effect’. For most retail investors, we find that the presence of a green label matters more than the ‘greenness’ of a green bond or the higher financial return of a non-green bond. However, for a very small sub-set of our sample, the alignment of environmental performance-related framing with their pro-environmental personal norms, enables greater investment into enhanced performance green bonds, even at the cost of losing financial returns. Finally, personal traits like individual risk tolerance (high), or previous investment experience with investment products (bonds, stocks), gender (non-binary individuals) and those having employment experience with financial industry, are more likely to invest in a labelled green bond. Our findings have timely implications for sustainable finance public policy, as it relates to regulating the growth of such products through labelling schemes like green taxonomies as well as addressing greenwashing risks through improved regulatory oversight.

Trust in nutrition, subjective norms and urban consumers' purchase behavior of quinoa products: explanation based on preference heterogeneity.

With the increase of disposable income and the awakening of health consciousness of Chinese residents, higher requirements have been put forward for the nutritional value of food. To meet the market demand and promote the high-quality development of the whole quinoa industry chain, this study aimed to analyze the purchase behavior of quinoa from the aspects of nutritional trust and subjective norms. Based on 1,078 micro-survey data from 16 administrative districts in Shanghai, this paper empirically examined the influence of trust in nutrition and subjective norms on consumers' purchase behavior and willingness to pay of quinoa products, and further investigated the moderating effect of preference heterogeneity. Results show that 38.22% of respondents have purchased quinoa products. Trust in nutrition and subjective norms can not only promote their purchasing behavior, but also improve consumers' willingness to pay for quinoa products. The analysis of the moderating effect shows that both the purchase behavior and the willingness to pay are affected by the preference heterogeneity, and the cognitive preference will weaken the positive influence of subjective norms on the purchase behavior, while the nutritional preference of consumers can strengthen the positive influence of trust in nutrition on the willingness to pay. Therefore, this paper suggests that strengthens research and development of products market oriented, implements certification and labeling schemes for nutrition and other properties of products, intensifies publicity and promotion of products to enhancing consumers' awareness of healthy diet.

Land coverage prediction using convolutional neural network for enhanced wind loading estimation

PurposeThe wind loading on a building is likely to deviate further from the known wind loading due to the complexity of the real-world land coverage. To address this issue, research is needed in two separate areas. First, wind tunnel testing needs to be conducted for more complex terrains. Second, research is needed to classify real-world land coverage with high accuracy, specifically for wind engineering applications. This paper deals with this second area of research. The machine learning-based land cover prediction is a promising technique because it can remove subjectivity in human interpretation of upwind terrain.Design/methodology/approachThis paper presents a new deep neural network for land coverage prediction that can distinguish low- and mid-rise buildings in the built environment to enhance the estimation of surface roughness necessary in wind engineering. For the dataset, Landsat 8 satellite images were used. A patch-based convolutional neural network was employed and improved. The network predicted the land coverage at the center of the patch. Two different label schemes were used where the proposed network either achieved better accuracy than the conventional model or recognized additional building types while maintaining a similar level of accuracy.FindingsCompared to the validation accuracy of 78% in a previous study, the proposed method achieved the validation accuracy of 90% thanks to the improvements made in this study as well as the consolidation of labels with similar surface roughness. When additional building categories were added, the validation decreased to 80%, which is comparable to the previous study but is now able to predict different building types.Originality/valueThe improvement of the proposed method will depend on the site characteristics. For the sites tested in this paper, the error reduction in wind speed and pressure was up to about 55%. In addition to more accurate wind speed and pressure, better identification of buildings will benefit wind engineering research, as different building types cause different downwind effects. An example application would be automated recognition of areas that have a certain distance from the target building type to identify downwind areas affected by high winds.

Staple principles for the definition of front-of-pack nutritional labels.

Front-of-pack nutrition labels (FOPNLs) have been developed since 1989 to curb the increasing prevalence of obesity and non-communicable diseases (NCDs) and to promote healthy consumption choices. While several countries have introduced their own labeling schemes on a voluntary basis, the European Commission aims to harmonize a FOPNL system that will be mandatory for all member states. This paper summarizes a contribution to the current debate on FOPNLs from Italian and Spanish researchers working in the fields of human nutrition, nutritional epidemiology and public health education and communication policy before the final decision on FOPNLs to become mandatory in Europe is taken.

Universal Design in Sustainable Tourism Certification Schemes.

Universal design has played a modest role in Norwegian tourism policy, despite its potential for contributing to a more socially, economically and environmentally sustainable tourism industry. The increased interest in sustainability labeling schemes among tourism actors represents an opportunity to work towards universal design of tourism, provided these schemes encompass universal design criteria. Standards for universal design are important in creating recognizable solutions, not least for visitors who often consume services across different sectors in unfamiliar environments. Against this background, we investigated whether sustainable tourism labeling schemes include suggestions made in standards for universal design. Based on previous research on universal design in the transport sector, a coding structure was constructed and used to analyze a sample of 13 tourism-related standards for universal design and five sustainability labeling schemes used by Norwegian tourism actors. Findings show that labeling schemes for sustainable tourism to a very limited extent incorporate measures described in standards for universal design. Moreover, both labeling schemes and standards are primarily aimed at the physical environment rather than the organizational or social environments, and both dedicate little attention to employees. Nevertheless, standards for universal design comprise a much wider range of measures and user-groups. Integrating them into popular sustainability labeling schemes seems pertinent to raise awareness and foster practices that contribute to improve universal design of tourism.

Impacts of sugar and sweetener warning labels on substitution between sugar- and non-sugar-sweetened beverages in a non-hypothetical selection task

BackgroundFront-of-pack (FOP) warning labels have demonstrated effectiveness for reducing sugar-sweetened beverage (SSB) consumption and switching to water. However, an unintended consequence is that they may also increase switching to non-sugar-sweetened beverages (NSSBs). A non-hypothetical experimental study examined the effectiveness of combining sugar and sweetener FOP warning labels to reduce sugary drink consumption and prevent NSSB substitution. The study also examined potential integration with Australia and New Zealand’s existing Health Star Rating (HSR) system to determine suitability for local context and other jurisdictions with interpretive labelling schemes already in place.MethodsParticipants (N = 414) accessed an online convenience store app via an on-campus laptop to select one drink from an array of 10 beverages, on three occasions. Drink options included a variety of SSBs, 100% fruit juice, NSSBs, and water. Following an escalating exposure procedure, drinks were presented (1) without any additional labelling, (2) with warning labels added to sugary drinks or to both sugary drinks and NSSBs (according to allocated condition), then (3) with HSR icons added to all drinks. Participants were informed they would receive a complementary drink, based on their selections, following the completion of a brief questionnaire.ResultsBaseline results indicated that SSBs and water were the most and least popular drink choices, respectively. Placing FOP warning labels on sugary drinks decreased selection of SSBs and increased NSSB and water choices. Water became the most popular individual drink choice in response to warnings on sugary drinks. Placing FOP warning labels on both sugary drinks and NSSBs successfully avoided an increase in NSSB choices, whilst also increasing water selections, but did not significantly decrease selection of SSBs until HSR ratings were added. The incorporation of HSR icons consolidated warning label effects on NSSB and water selection across both conditions.ConclusionsResults demonstrate the potential of FOP sugar warning labels for addressing beverage consumption behaviours. The incorporation of sweetener warning labels may successfully avoid substitution towards NSSBs, whilst still promoting water choices, but may also dilute the impact of the sugary drinks warning labels. Warning labels were complementary to existing interpretive FOP labels.

Deterministic fault-tolerant connectivity labeling scheme

AbstractThe f-fault-tolerant connectivity labeling (f-FTC labeling) is a scheme of assigning each vertex and edge with a small-size label such that one can determine the connectivity of two vertices s and t under the presence of at most f faulty edges only from the labels of s, t, and the faulty edges. This paper presents a new deterministic f-FTC labeling scheme attaining $$O(f^2 \textrm{polylog}(n))$$ O ( f 2 polylog ( n ) ) -bit label size and a polynomial construction time, which settles the open problem left by Dory and Parter (in: Proceedings of the 2021 ACM symposium on principles of distributed computing (PODC), pp 445–455, 2021). The key ingredient of our construction is to develop a deterministic counterpart of the graph sketch technique by Ahn et al. (in: Proceedings of the 31st ACM SIGMOD-SIGACT-SIGAI symposium on principles of database systems (PODS), pp 5–14, 2012), via some natural connection with the theory of error-correcting codes. This technique removes one major obstacle in de-randomizing the Dory–Parter scheme. The whole scheme is obtained by combining this technique with a new deterministic graph sparsification algorithm derived from the seminal $$\epsilon $$ ϵ -net theory, which is also of independent interest. As byproducts, our result deduces the first deterministic fault-tolerant approximate distance labeling scheme with a non-trivial performance guarantee and an improved deterministic fault-tolerant compact routing. The authors believe that our new technique is potentially useful in the future exploration of more efficient FTC labeling schemes and other related applications based on graph sketches.

Comparison of front-of-package nutrition labelling schemes in Costa Rica: a multi-arm parallel-group randomised controlled trial assessing objective understanding and purchase intention

Different front-of-package labelling (FOPL) schemes have been developed worldwide to encourage informed and healthier food purchase decisions. However, few studies have formally compared different schemes, particularly in the Latin American context. This study aimed to assess the effects of four different FOPL schemes on the objective understanding of the nutritional content and intention to purchase products. This single-blinded multi-arm randomised controlled trial was conducted using a face-to-face survey with adult shoppers, recruited at supermarkets in Costa Rica (n=1350). Participants randomly assigned to intervention groups saw 12 mock-up products presented at random and balanced orders featuring one FOPL scheme or none: black octagonal warning labels (OWL), Nutri-Score (NUS), traffic-light labelling (TFL), guideline daily amounts (GDA), or no FOPL scheme (control group). Similar number of participants were analysed in each group: OWL (n=264), NUS (n=279), TFL (n=263), GDA (n=273), and control (n=271). Compared to the control group, the odds for correctly identifying the least harmful option more often were three times higher in the OWL group (OR 3.08; 95% CI, 2.26-4.20), and 89%, 57% and 19% higher in the TFL (1.89; 95% CI, 1.40-2.56), the GDA (1.57; 95% CI, 1.16-2.21) and the NUS (1.19; 95% CI 0.89-1.60), respectively. OWL also was more efficacious in helping participants to correctly identify a product with excessive amounts of sugars, sodium, and/or saturated fats, as well as in encouraging the intention to purchase the least harmful or the intention to choose none of the options in the choice task. OWL performed best in helping shoppers to correctly identify when a product contained excessive amounts of nutrients to limit, to correctly identify the least harmful option, and to intend to purchase the least harmful option, more often.

Checkmating the Nutri-Score: Will Food Patriotism Prevent the Harmonization of Front-of-Pack Labeling Schemes within the EU?

Front-of-pack labeling schemes are an effective but contested regulatory approach to nudge consumers toward healthy food choices. The Nutri-Score, being implemented by eight European countries, is one of the most elaborated and evidence-based examples. Therefore, the Nutri-Score has been deemed as the front-runner within the European Commission's attempt to harmonize front-of-pack labeling among European Union (EU) member states under its Farm-to-Fork strategy by the end of 2022. However, the endeavor is on the brink of failure because of massive resistance by Mediterranean member states and parts of the food industry capitalizing on patriotic narratives (e.g., “Made in Italy”). This article investigates the Nutri-Score saga from a political and commercial determinants of health lens. It argues that an EU-wide rollout of the label hinges on the specific interplay between political structures and stakeholder agency. As shown, the EU's weak decision-making power has been exploited by the No-Nutri-Score alliance.

Study of high-order non-equal probability QAM for visible light communication systems.

In this Letter, we propose and experimentally demonstrate a high-order non-equal probability quadrature amplitude modulation (NEP-QAM) scheme for visible light communication (VLC) systems, where NEP-72QAM is considered as an example. Nonlinear coding is introduced to generate the NEP-18QAM signal in the first quadrant, which is then jointly mapped with the other two bits to obtain the four-quadrant symmetric NEP-72QAM signal. Moreover, a bit-to-symbol labeling scheme is proposed to achieve thorough Gray mapping. The experimental results show that the NEP-72QAM scheme achieves better performance than the traditional 64QAM scheme regardless of whether the light-emitting diode (LED) operates in the linear or nonlinear range. Compared with the probabilistically shaped 72QAM scheme, the available range of the driving peak-to-peak voltage of the NEP-72QAM scheme is only reduced by 20 mV when 0.92 is used as the normalized generalized mutual information threshold, but the complexity is much lower.

Enhancing solution structural analysis of large molecular proteins through optimal stereo array isotope labeling of aromatic amino acids

The observation of side-chain peaks of aromatic amino acids is the prerequisite for a high-resolution three-dimensional structure determination of proteins by NMR. However, it becomes difficult with increasing molecular size due to an increased transverse relaxation and the control of the relaxation pathway is needed to achieve the observation. We demonstrated that even for the large molecular size of 82 kDa Malate synthase G (MSG), the aromatic 13C-1H (CH) peaks of Tryptophan (Trp) and Phenylalanine (Phe) residues can be observed with high quality using a systematic stable isotope labeling scheme, Stereo-Array Isotope Labeling (SAIL) method. However, the sequence specific assignments of these peaks relied on the use of amino acid substitutions, employing an inefficient method that required many isotopes labeled samples. In this study, we developed novel SAIL amino acids that allow for the observation of the aromatic ring δ,ζ and the aliphatic β position peak of Phe residues. The application of TROSY-based experiment to the isolated CH moieties resulted in the successful observation of discernible and resolved CH peaks in Phe residues in MSG. In MSG, the sequence-specific assignments of the backbone and Cβ positions have already been confirmed. Therefore, using this labeling method, the δ and β position peaks of Phe residues can be clearly assigned in a sequence-specific and stereospecific manner through experiments based on intra-residue NOE. Furthermore, the NOESY experiment also allows for the acquisition of information pertaining to the conformation of Phe residues, such as the χ1 dihedral angle, providing valuable insights for the determination of accurate protein structures and in dynamic analysis. This new SAIL amino acids open an avenue to achieve a variety of NMR analysis of large molecular proteins, including a high-resolution structure determination and dynamics and interaction analysis.

Assessing the Validity of Front-of-Pack Nutrition Labels for Evaluating the Healthiness of Mediterranean Food Choices: A Global Comparison.

In response to growing public health concerns, governments worldwide have implemented various nutrition labelling schemes to promote healthier eating habits. This study aimed to assess the consistency and effectiveness of these labels in an out-of-home context, specifically focusing on restaurant, hospitality, and institutional food service settings. In total, 178 different dishes from Spain were analysed using labels from the Mazocco method, the UK's traffic light system, the Health Star Rating (Australia), Nutri-Score (France), multiple traffic lights (Ecuador), and warning labels (Chile and Uruguay). The results demonstrated a generally low level of agreement among these labels (K < 0.40), indicating notable variability and a lack of consensus, which could hinder consumers' ability to make informed food choices in out-of-home settings. Nutri-Score classified the highest number of dishes as unhealthy (38%). This study underscores the need for an easy-to-understand labelling system tailored to each country's culinary and socio-cultural contexts to improve consumer decision-making in various dining environments. Future research should focus on developing and testing qualitative methods to more accurately gauge the nutritional quality of cooked dishes in diverse out-of-home settings, thereby enhancing public health outcomes. By addressing the specific needs of the home, restaurants, hospitality, and institutional food services, tailored labelling schemes could significantly improve consumers' ability to make healthier food choices.

PIS-Net: Efficient weakly supervised instance segmentation network based on annotated points for rice field weed identification

Weed damage in rice fields is one of the main causes of reduced rice yields and quality. Accurate and efficient weed identification is the prerequisite for realizing intelligent and precise weeding in paddies. Recently, Vision Transformers (ViTs) have emerged with superior performance on computer vision tasks compared to the convolutional neural network (CNN)-based models. However, the lack of fully labeled weed datasets hinders the potential application of deep learning models in weed identification. To address the above issues, this study customizes a novel point-supervised instance segmentation network (PIS-Net) for weakly supervised instance segmentation of weeds in rice fields. More correctly, we first propose a novel instance segmentation point labeling scheme that utilizes randomly generated annotation points within each instance, aiming to decrease both labeling time and difficulty. Additionally, to make optimal use of point labels, this study puts forth a mask generation strategy based on the adaptive selection of pyramid levels. In this sense, the network model can flexibly choose the pyramid level expected to generate the most suitable instance mask based on the network's reliability. Finally, we establish the pseudo label refinement network (PLR-Net) to refine rough instance masks. The proposed PIS-Net utilizes 13 randomly generated annotation points for each instance, yet achieving an AP of 38.5 and an AP50 of 68.3, which is superior to the baseline mask-R-CNN with an AP of 8.2 and AP50 of 6.9, achieving 90 % fully supervised performance. This method effectively utilizes point labels, annotated with high efficiency, as a robust source of weak supervision to address challenges in weed data annotation and the low accuracy of existing weakly supervised models. Experiments show that the point annotation scheme of the PIS-Net is faster than full-object mask annotation, and the AP is also higher than the current semi-supervised weed segmentation model, enjoying high potentials in practical paddy fields.

Determining the Optimal Window Duration to Enhance Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography Signals

Automatic emotion recognition using portable sensors is gaining attention due to its potential use in real-life scenarios. Existing studies have not explored Galvanic Skin Response and Photoplethysmography sensors exclusively for emotion recognition using nonlinear features with machine learning (ML) classifiers such as Random Forest, Support Vector Machine, Gradient Boosting Machine, K-Nearest Neighbor, and Decision Tree. In this study, we proposed a genuine window sensitivity analysis on a continuous annotation dataset to determine the window duration and percentage of overlap that optimize the classification performance using ML algorithms and nonlinear features, namely, Lyapunov Exponent, Approximate Entropy, and Poincaré indices. We found an optimum window duration of 3 s with 50% overlap and achieved accuracies of 0.75 and 0.74 for both arousal and valence, respectively. In addition, we proposed a Strong Labeling Scheme that kept only the extreme values of the labels, which raised the accuracy score to 0.94 for arousal. Under certain conditions mentioned, traditional ML models offer a good compromise between performance and low computational cost. Our results suggest that well-known ML algorithms can still contribute to the field of emotion recognition, provided that window duration, overlap percentage, and nonlinear features are carefully selected.

Life can’t be too sweet: the effects of sugar-sweetened beverage labelling on the government’s health battle

Singapore’s recent mandatory labelling regulations for sugar-sweetened beverages (SSBs) aim to guide consumer choices towards healthier options, which mandates labels on SSBs regarded as less healthy and encourages voluntary labelling for healthier SSBs. We focus on market competition between two SSB firms under different labelling schemes. Surprisingly, we find that mandatory labelling increases the consumption of less-healthy SSBs while reducing healthier SSBs consumption when the health risk is low. This counterintuitive result is attributed to the revelation of the lower-than-expected health risk, triggering a heterogeneity-reducing effect that heightens competition and price sensitivity. The lower retail price of less-healthy SSBs increases the demand. The heterogeneity-reducing effect also enables voluntary labelling of healthier SSBs to alleviate the adverse impact of mandatory labelling on consumer surplus and social welfare. Additionally, the imposition of SSB taxes effectively reduces consumption but challenges consumer surplus and compromises the efficacy of mandatory labelling.

Taking a Consumer-Led Approach to Identify Key Characteristics of an Effective Ecolabelling Scheme

A gap exists between consumer adoption of sustainable purchasing habits and the behavioural changes required to significantly decrease planetary environmental impact. Current ecolabels do not enable purchasing based on environmental impact, and an effective labelling scheme is required to change purchasing priorities. Using focus groups, ecolabels with components varying in the depth of environmental information conveyed, visual features employed, as well as product comparability and scalability of design, were evaluated by consumers with differing levels of environmental awareness. Independent, credible information accessible to consumers of varying environmental awareness was a participant priority. In-depth data communicated through a bar or pie chart with an interpretable holistic message across all environmental factors was preferred. Colour-coded components offering product comparisons and scalable to the size of the packaging, product value, and lifetime were most likely to empower environmentally motivated purchasing. Insights can inform the development of an effective ecolabelling scheme that equips consumers to adopt more sustainable purchasing habits.

Thigh-worn accelerometry: a comparative study of two no-code classification methods for identifying physical activity types

BackgroundThe more accurate we can assess human physical behaviour in free-living conditions the better we can understand its relationship with health and wellbeing. Thigh-worn accelerometry can be used to identify basic activity types as well as different postures with high accuracy. User-friendly software without the need for specialized programming may support the adoption of this method. This study aims to evaluate the classification accuracy of two novel no-code classification methods, namely SENS motion and ActiPASS.MethodsA sample of 38 healthy adults (30.8 ± 9.6 years; 53% female) wore the SENS motion accelerometer (12.5 Hz; ±4 g) on their thigh during various physical activities. Participants completed standardized activities with varying intensities in the laboratory. Activities included walking, running, cycling, sitting, standing, and lying down. Subsequently, participants performed unrestricted free-living activities outside of the laboratory while being video-recorded with a chest-mounted camera. Videos were annotated using a predefined labelling scheme and annotations served as a reference for the free-living condition. Classification output from the SENS motion software and ActiPASS software was compared to reference labels.ResultsA total of 63.6 h of activity data were analysed. We observed a high level of agreement between the two classification algorithms and their respective references in both conditions. In the free-living condition, Cohen’s kappa coefficients were 0.86 for SENS and 0.92 for ActiPASS. The mean balanced accuracy ranged from 0.81 (cycling) to 0.99 (running) for SENS and from 0.92 (walking) to 0.99 (sedentary) for ActiPASS across all activity types.ConclusionsThe study shows that two available no-code classification methods can be used to accurately identify basic physical activity types and postures. Our results highlight the accuracy of both methods based on relatively low sampling frequency data. The classification methods showed differences in performance, with lower sensitivity observed in free-living cycling (SENS) and slow treadmill walking (ActiPASS). Both methods use different sets of activity classes with varying definitions, which may explain the observed differences. Our results support the use of the SENS motion system and both no-code classification methods.

Looking for Environmental Scoring: A Case Study of a Portuguese Cotton White T-Shirt Made with Recycled Fiber

Promoting sustainable consumer behavior is now an obligation under new European legislation, requiring life cycle assessment (LCA) for accurate environmental impact evaluation. Portugal is a key textile producer with an edge in competitiveness in sustainable textile production, driven by electricity-reduced carbon footprints and closed-loop manufacturing. Additionally, while simple spreadsheets can estimate a product’s carbon footprint, openLCA v1.11.0 software, combined with the ecoinvent database, greatly enhances environmental footprint calculations by integrating diverse impact categories that are otherwise difficult to estimate. In this study, openLCA is used to evaluate the environmental footprint of a white T-shirt made in Portugal with 50% recycled cotton from post-industrial wastes combined with 50% organic cotton from Turkey to assist in the design of environmental key performance indicators (KPI). The RECIPE and EF methods (adapted) are used to calculate the environmental impacts and allow aggregation into a single score. The KPI related to the global warming impact is validated using a spreadsheet calculator. We propose an “Envi-Score” based on an A-to-E classification for benchmarking and better communication with the buyers. E is set as the normalized environmental impact of the European benchmark for a mixture of material T-shirts encompassing cradle-to-gate boundaries. The introduction of recycled cotton produced in Portugal proves to be environmentally beneficial over organic and conventional cotton. Organic cotton proves to be beneficial in comparison with conventional cotton for most environmental categories, except for the ones affected by the lower production yield, for example, land use. The hotspots for the main impact categories are identified, and finally, a labeling scheme is proposed to clearly inform about the environmental performance of the products and avoid greenwashing with the “Envi-Score” rate, carbon footprint, land use, and water depletion.

Identification of De Novo Dividing Stem Cells.

The ability to alternate between quiescent and proliferating states is a remarkable feature of many types of somatic stem cells. The balance between quiescent and proliferating states is vital for maintenance of stem cells over the lifespan, and its disturbance may lead to premature depletion of the stem cell pool and loss of the tissue regenerative or renewal capacity at later stages of life. The question on how this balance is regulated is of critical importance in stem cell research and biology of aging. Assessment of the balance between quiescent and proliferating states has remained challenged until recently due to the lack of approaches for robust determination of the rate at which stem cells exit reversible cell cycle arrest. Here, we propose a simple method for detection of those stem cells that have entered the division cycle after a prolonged period of quiescence.The method combines cumulative and pulse labeling with thymidine analogues 5-bromo-2'-deoxyuridine (BrdU) and 5-ethynyl-2'-deoxyuridine (EdU). In the discussed labeling scheme, cells that have incorporated only the second label, EdU, are de novo dividing cells. The suggested double labeling method provides quantitative assessment of the rate at which stem cells exit the quiescent state and allows the fates of de novo dividing stem cells to be traced.

Sustainability certification for renewable hydrogen: An international survey of energy professionals

Hydrogen produced from renewable energy is being promoted to decarbonise global energy systems. To support this energy transition, standards, certification, and labelling schemes (SCLs) aim to differentiate hydrogen products based on their system-wide carbon emissions and method of production characteristics. However, being certified as low-carbon, clean, or green hydrogen does not guarantee broader sustainability across economic, environmental, social, or governance dimensions. Through an international survey of energy-sector and sustainability professionals (n = 179), we investigated the desirable sustainability features for renewable hydrogen SCLs and the perceived advantages and disadvantages of sustainability certification. Our mixed-method study revealed general accordance on the feasible inclusion of diverse sustainability criteria in SCLs, albeit with varying degrees of perceived essentiality. Within the confines of the data, some differences in viewpoints emerged based on respondents’ geographical and supply chain locations, which were associated with the sharing of costs and benefits. Qualitatively, respondents found the idea of SCL harmonisation attractive but weighed this against the risks of duplication, complicated administrative procedures, and contradictory regulation. The implications of this research centre on the need for further studies to inform policy recommendations for an overarching SCL sustainability framework that embodies the principles of harmonisation in the context of multistakeholder governance.