Labeling Conditions Research Articles

How Promising Are "Ultraprocessed" Front-of-Package Labels? A Formative Study with US Adults.

High levels of food processing can have detrimental health effects independent of nutrient content. Experts and advocates have proposed adding information about food processing status to front-of-package labeling schemes, which currently exclusively focus on nutrient content. How consumers would perceive "ultraprocessed" labels has not yet been examined. To address this gap, we conducted a within-subjects online experiment with a convenience sample of 600 US adults. Participants viewed a product under three labeling conditions (control, "ultraprocessed" label, and "ultraprocessed" plus "high in sugar" label) in random order for a single product. The "ultraprocessed" label led participants to report thinking more about the risks of eating the product and discouraging them from wanting to buy the product more than the control, despite not grabbing more attention than the control. The "ultraprocessed" plus "high in sugar" labels grabbed more attention, led participants to think more about the risks of eating the product, and discouraged them from wanting to buy the product more than the "ultraprocessed" label alone. "Ultraprocessed" labels may constitute promising messages that could work in tandem with nutrient labels, and further research should examine how they would influence consumers' actual intentions and behaviors.

Can a health warning label diminish the persuasive effects of health-oriented nutrition advertising on ready-to-drink alcohol product packaging? A randomized experiment.

A health warning label (HWL) cautioning about the link between alcohol and cancer may be able to communicate alcohol risks to consumers and potentially counter health-oriented nutrition advertising on ready-to-drink alcoholic beverages. This study aimed to examine the independent and combined effects of nutrient content claims (e.g. 0g sugar) and a HWL on perceived product characteristics and intentions to consume, and whether these effects differed by gender and age. A between-subjects randomized experiment. Participants were randomized to view one of six experimental label conditions: nutrient content claims plus nutrition declaration (NCC + ND), ND only, NCC + ND + HWL, ND + HWL, HWL only and no NCC, ND or HWL, all on a ready-to-drink (RTD) vodka-based soda container. Alcohol consumers (n = 5063; 52% women) in Canada aged 18-64 recruited through a national online panel. Participants completed ratings of perceived product characteristics, perceived product health risks, and intentions to try, buy, binge and drink the product. Compared with the reference condition NCC + ND (current policy scenario in Canada), the other five experimental label conditions were associated with lower ratings for perceiving the product as healthy. All experimental conditions with a HWL were associated with lower product appeal, higher risk perceptions and reduced intentions to try, buy and binge. The experimental condition with a HWL only was associated with intentions to consume fewer cans in the next 7days (β = -0.72, 95% confidence interval [CI] = -1.37,-0.08) versus the reference. Few interactions were observed, suggesting that label effects on outcomes were similar by gender and age. Health warning labels on alcohol packaging appear to be associated with lower product appeal, higher perceived health risks and reduced consumption intentions, even in the presence of nutrient content claims.

Comparative Analysis of Fluorescent Labeling Techniques for Tracking Canine Amniotic Stem Cells.

The utmost aim of regenerative medicine is to promote the regeneration of injured tissues using stem cells. Amniotic mesenchymal stem cells (AmMSCs) have been used in several studies mainly because of their easy isolation from amniotic tissue postpartum and immunomodulatory and angiogenic properties and the low level of rejection. These cells share characteristics of both embryonic/fetal and adult stem cells and are particularly advantageous because they do not trigger tumorigenic activity when injected into immunocompromised animals. The large-scale use of AmMSCs for cellular therapies would greatly benefit from fluorescence labeling studies to validate their tracking in future therapies. This study evaluated the fluorophore positivity, fluorescence intensity, and longevity of canine AmMSCs. For this purpose, canine AmMSCs from the GDTI/USP biobank were submitted to three labeling conditions, two commercial fluorophores [CellTrace CFSE Cell Proliferation kit - CTrace, and CellTracker Green CMFDA - CTracker (CellTracker Green CMFDA, CT, #C2925, Molecular Probes®; Life Technologies)] and green fluorescent protein (GFP) expression after lentiviral transduction, to select the most suitable tracer in terms of adequate persistence and easy handling and analysis that could be used in studies of domestic animals. Fluorescence was detected in all groups; however, the patterns were different. Specifically, CTrace and CTracker fluorescence was detected 6 h after labeling, while GFP was visualized no earlier than 48 h after transduction. Flow cytometry analysis revealed more than 70% of positive cells on day 7 in the CTrace and CTracker groups, while fluorescence decreased significantly to 10% or less on day 20. Variations between repetitions were observed in the GFP group under the present conditions. Our results showed earlier fluorescence detection and more uniform results across repetitions for the commercial fluorophores. In contrast, fluorescence persisted for more extended periods in the GFP group. These results indicate a promising direction for assessing the roles of canine AmMSCs in regenerative medicine without genomic integration.

Federated Pseudo-Sample Clustering Algorithm: A Label-Personalized Federated Learning Scheme Based on Image Clustering

In the realm of personalized federated learning, some current methods substitute shared parameters with shared samples created by Generative Adversarial Networks (GANs). This enables each client to independently design the architecture of their neural network model. However, this approach still fails to overcome the restriction of enforcing uniform labels on client models that partake in training. To tackle this problem, we suggest the Federated Pseudo-Sample Clustering Algorithm (LPFL-GD). This method allows clients to train cooperatively under personalized labeling conditions. The approach uses the local model as a discriminator and forms a GAN network with the generator to produce a sample set. This set is then uploaded to the central server. The uploaded shared samples are clustered and divided into several clusters by introducing the DBSCAN algorithm on the central server. When filtering client samples, we obtain the labels of shared samples from each client in a cluster and correct the label of the entire cluster. We then merge the corrected samples with the local dataset to extend it. Our approach improves model performance, even when different clients label the same type of data differently. Compared with their performance before participating in federated learning, our approach can improve client model accuracy by up to 13.4%. We replicated other methods in the same environment and found that the local model accuracy of those methods not only improved very little but even decreased by up to 34.5%.

Sulfonium-Based Activity-Based Probes for Lysine-Selective Protein Profiling under Alkaline Conditions

AbstractDue to their strong nucleophilicities, nucleophilic lysine and cysteine residues can be easily recognized and modified by electrophilic groups, thus, acting as the targets for covalent ligands or drugs. Therefore, the development of site-specific protein-modification chemistry for various nucleophilic residues has been explored to label proteins selectively for many biological and therapeutic applications. In this study, we constructed a series of sulfonium-based small molecules to react with the amine group of lysine residues by utilizing the strong electrophilicity of sulfonium, resulting in lysine-selective labeling via the formation of classical amide bonds under alkaline conditions (pH 9.0–11.0). After systematic optimization of the labeling conditions, this strategy was utilized for protein labeling across various bacteria’s lysates. Finally, combined with the activity-based protein profiling (ABPP) strategy, we successfully identified and analyzed hundreds of labeled lysine residues in the bacterial proteome.

Battery capacity estimation based on a co-learning framework with few-labeled and noisy data

Battery capacity estimation plays a crucial role in optimizing the performance and longevity of electric vehicles and stationary energy storage systems. However, accurately estimating battery capacity becomes challenging in real-world applications, particularly when dealing with unlabeled and noisy capacity data. To address this issue, this paper presents a co-learning framework incorporating both supervised and self-supervised learning for estimating battery capacity in the presence of few-labeled and noisy data. By training a shared encoder network with both self-supervised and supervised heads, the framework maximizes the agreement between the two heads in the latent space. The proposed approach demonstrates improved accuracy in battery capacity estimation in those challenging scenarios based on two public datasets. Comparative experiments show that the co-learning approach outperforms conventional end-to-end mapping methods, the average root mean square error of the proposed method is reduced at least by 36% and 19% under insufficient and noisy label conditions, respectively, leading to significant enhancements in estimation performance.

Impaired implicit emotion regulation in patients with panic disorder: An event-related potential study on affect labeling.

Panic disorder (PD) involves emotion dysregulation, but its underlying mechanisms remain poorly understood. Previous research suggests that implicit emotion regulation may play a central role in PD-related emotion dysregulation and symptom maintenance. However, there is a lack of studies exploring the neural mechanisms of implicit emotion regulation in PD using neurophysiological indicators. To study the neural mechanisms of implicit emotion regulation in PD with event-related potentials (ERP). A total of 25 PD patients and 20 healthy controls (HC) underwent clinical eva-luations. The study utilized a case-control design with random sampling, selecting participants for the case group from March to December 2018. Participants performed an affect labeling task, using affect labeling as the experimental condition and gender labeling as the control condition. ERP and behavioral data were recorded to compare the late positive potential (LPP) within and between the groups. Both PD and HC groups showed longer reaction times and decreased accuracy under the affect labeling. In the HC group, late LPP amplitudes exhibited a dynamic pattern of initial increase followed by decrease. Importantly, a significant group × condition interaction effect was observed. Simple effect analysis revealed a reduction in the differences of late LPP amplitudes between the affect labeling and gender labeling conditions in the PD group compared to the HC group. Furthermore, among PD patients under the affect labeling, the late LPP was negatively correlated with disease severity, symptom frequency, and intensity. PD patients demonstrate abnormalities in implicit emotion regulation, hampering their ability to mobilize cognitive resources for downregulating negative emotions. The late LPP amplitude in response to affect labeling may serve as a potentially valuable clinical indicator of PD severity.

Evaluating source credibility effects in health labelling using vending machines in a hospital setting.

Providing advice to consumers in the form of labelling may mitigate the increased availability and low cost of foods that contribute to the obesity problem. Our objective was to test whether making the source of the health advice on the label more credible makes labelling more effective. Vending machines in different locations were stocked with healthy and unhealthy products in a hospital. Healthy products were randomly assigned to one of three conditions (i) a control condition in which no labelling was present (ii) a low source credibility label, "Lighter choices", and (iii) a high source credibility label that included the UK National Health Service (NHS) logo and name, "NHS lighter choices". Unhealthy products received no labelling. The outcome measure was sales volume. There were no main effects of labelling. However, there were significant interactions between labelling, vending machine location and payment type. For one location and payment type, sales of products increased in the high credibility label condition compared to control, particularly for unhealthy products, contrary to expectations. Our findings suggest that high source credibility health labels (NHS endorsement) on food either have little effect, or worse, can "backfire" and lead to effects opposite to those intended. The primary limitations are the limited range of source credibility labels and the scale of the study.

Facile one-pot strategy for radiocoplexation of [99mTc]-nitrido-nebracetam for brain imaging: Biological evaluation, Optimized chromatographic separation and labeling conditions.

Facile one-pot strategy for radiocoplexation of [99mTc]-nitrido-nebracetam for brain imaging: Biological evaluation, Optimized chromatographic separation and labeling conditions.

Large-scale pied-piping in the labeling theory and conditions on weak heads

Abstract This paper discusses the concept of weak head in Chomsky’s (2015. Problems of projection: Extensions. In Elisa Di Domenico, Cornelia Hamann & Simona Matteini (eds.), Structures, strategies and beyond: Studies in honour of Adriana Belletti, 3–16. Amsterdam: John Benjamins) sense from a typological perspective. This paper first establishes a novel generalization that large-scale pied-piping is available in a language only if the language has indeterminate pronouns in Kuroda’s (1965. Generative grammatical studies in the Japanese language. Cambridge, MA: Massachusetts Institute of Technology dissertation) sense and the clause to be pied-piped is head-final. To deduce this generalization, this paper first offers a labeling theoretic account of large-scale pied-piping. It then proposes that weak heads are (i) heads that have unvalued features and (ii) morpho-phonologically weak. It is shown that the generalization is deduced from this conception of weak heads, given Inaba’s (2011. The morphosyntax of constituent ordering patterns. The Hiroshima University Studies, Graduate School of Letters 71. 43–72) generalization that head-final complementizers are generally affixal. This paper further argues that the proposed conception of weak heads also allows us to deduce Agree from Minimal Search, which is a third factor principle according to Chomsky (2013. Problems of projection. Lingua 130. 33–49), hence minimizes UG.

Magnetic Bead-Based Workflow for Sensitive and Streamlined Cell Surface Proteomics.

Cell surface proteins represent an important class of molecules for therapeutic targeting and cellular phenotyping. However, their enrichment and detection via mass spectrometry-based proteomics remains challenging due to low abundance, post-translational modifications, hydrophobic regions, and processing requirements. To improve their identification, we optimized a Cell-Surface Capture (CSC) workflow that incorporates magnetic bead-based processing. Using this approach, we evaluated labeling conditions (biotin tags and catalysts), enrichment specificity (streptavidin beads), missed cleavages (lysis buffers), nonenzymatic deamidation (digestion and deglycosylation buffers), and data acquisition methods (DDA, DIA, and TMT). Our findings support the use of alkoxyamine-PEG4-biotin plus 5-methoxy-anthranilic acid, SDS/urea-based lysis buffers, single-pot solid-phased-enhanced sample-preparation (SP3), and streptavidin magnetic beads for maximal surfaceome coverage. Notably, with semiautomated processing, sample handling was simplified and between ∼600 and 900 cell surface N-glycoproteins were identified from only 25-200 μg of HeLa protein. CSC also revealed significant differences between in vitro monolayer cultures and in vivo tumor xenografts of murine CT26 colon adenocarcinoma samples that may aid in target identification for drug development. Overall, the improved efficiency of the magnetic-based CSC workflow identified both previously reported and novel N-glycosites with less material and high reproducibility that should help advance the field of surfaceomics by providing insight in cellular phenotypes not previously documented.

A comprehensive LC-MS based study of factors influencing biotinylation of critical reagents.

Aim: Critical reagents (CR) are applied in ligand binding assays (LBA) and biotinylation is a widely conjugation method used for critical reagents. However, insufficient characterization and inconsistent biotinylation can lead to LBA failures and necessitate extensive troubleshooting. This publication developed the detection of biotinylated CR and evaluates efficiency of biotinylation conditions to ensure the reliability of reagents and accuracy when implemented in LBA.Materials & methods: Intact mass analysis was applied to characterize a CR with complex glycosylation and biotinylation patterns. Peptide mapping was developed to identify the biotinylation sites.Results: Biotinylation degrees and sites were clearly illustrated.Conclusion: A CR and its biotinylation were successfully characterized. The relationship between biotinylation efficiency and labeling conditions was clearly illustrated.

Deep-learning optical flow for measuring velocity fields from experimental data.

Deep learning-based optical flow (DLOF) extracts features in adjacent video frames with deep convolutional neural networks. It uses those features to estimate the inter-frame motions of objects. We evaluate the ability of optical flow to quantify the spontaneous flows of microtubule (MT)-based active nematics under different labeling conditions, and compare its performance to particle image velocimetry (PIV). We obtain flow velocity ground truths either by performing semi-automated particle tracking on samples with sparsely labeled filaments, or from passive tracer beads. DLOF produces more accurate velocity fields than PIV for densely labeled samples. PIV cannot reliably distinguish contrast variations at high densities, particularly along the nematic director. DLOF overcomes this limitation. For sparsely labeled samples, DLOF and PIV produce comparable results, but DLOF gives higher-resolution fields. Our work establishes DLOF as a versatile tool for measuring fluid flows in a broad class of active, soft, and biophysical systems.

Reaction rate constant: a theoretical description from local temperature.

Application of various descriptors based on electron density and its associated quantities to quantify chemical reactivity within the conceptual density functional theory has recently come into spotlight. Among others and particularly relevant to our study, local temperature based on electron density as well as kinetic energy density, as a measure of the kinetic energy of an electron moving in the Kohn-Sham potential of systems, should be mentioned. In this work, we propose to use the local temperature for describing the reaction rate constant, where our main idea originates from the point that the smaller the local temperature at the reaction center, the easier the electron removal, leading to a larger rate constant. On the basis of theoretical considerations, it is proved that the rate constant variations caused by the substituent effects can well be proportional to the local temperature at the reaction center. In order to numerically validate our proposed approach, we have taken the phenol derivatives with the available experimental rate constants of their O-methylation reaction as working models. The reason for this choice is that one of the most versatile approaches for labeling biologically active compounds with the 11C nuclide for positron emission tomography (PET) is methylation by methyl iodide including 11C nuclide, [11C]MeI, where methylation of phenolic oxygen with [11C]MeI is utilized to label some important tracers for PET studies. Our results unveil that the local temperature changes at the reaction center of the aforementioned reaction are reasonably correlated with the rate constant variations. Hopefully, incorporating the proposed correlations between the local temperature and the kinetics data into a computer control algorithm not only provides a simple tool for predicting the rate constant of the O-methylation reaction for other substituted phenols, but also, as a part of the chemical artificial intelligence, the optimum [11C]MeI labeling conditions for a wide variety of phenol derivatives can be controlled.

Managing a policy paradox? Responses to textual warning labels on E-cigarette advertisements among U.S. national samples of youth overall and adults who smoke or vape

ContextCurrent use and potential future uptake of e-cigarettes among youth remain public health concerns in the U.S., even as people who smoke combustible cigarettes could benefit from switching completely to e-cigarettes. The U.S. Food and Drug Administration (FDA) is considering alternative warning messages, but warnings that discourage youth from use may also deter people who smoke from switching. This study tests ten pre-registered hypotheses on effects of warning messages with national samples of youth overall and adults who smoke and/or vape. MethodsNORC recruited 1639 adults (ages 18+) who smoke, vape, or use both products, from their probability-sampled AmeriSpeak Panel and augmented their AmeriSpeak Teen Panel with Lucid's nonprobability opt-in panel to recruit 1217 youth (ages 14–17) to participate in a web-based survey experiment. We randomly assigned respondents to view one of five warning label conditions and respond to measures of their e-cigarette risk beliefs, willingness to use e-cigarettes, and (among people who smoke or vape) considerations to quit these products. FindingsRelative to the current FDA warning about nicotine, warning messages about the harms of e-cigarette use for youth brain development did not influence risk beliefs or reduce willingness to use these products among youth. Brain development warning messages did increase beliefs about these harms among adults but did not increase quit considerations among people who vape, relative to the FDA warning. Warning messages with information about chemical constituents of vaping products and the harm of these chemicals produced higher e-cigarette quit considerations than did the FDA warning among adults who vape. ConclusionPotential alternative warning label messages were largely ineffective relative to the current FDA warning about nicotine, though limited evidence suggests some potential for chemical + harm messaging to encourage people who use both e-cigarettes and cigarettes to consider quitting both.

Signaling sustainability: Can it entice business angels' willingness to invest?

AbstractDespite increasing attention to sustainability as a source of new business opportunities, current research on new ventures has hardly considered this factor with respect to access to funding. This paper examines the role of new venture sustainability signals on business angels' willingness to invest. Specifically, it considers the effects of third‐party sustainability certification and the self‐proclaimed label of sustainable entrepreneurship as congruent reputational signals for inducing angel investors' willingness to invest. Three studies were conducted using a 2 × 2 between‐subject experimental design on certification and label conditions, respectively, in two samples from the Amazon MTurk crowdsourcing marketplace and one from business angels. Results show that early‐stage ventures would use such signals in their sustainability exemplification strategy to convey quality and prestige and eventually improve their chances of raising business angel funding. Implications for theory and practice are discussed.

Self-prioritization effect in the attentional blink paradigm: Attention-based or familiarity-based effect?

The self-prioritization effect (SPE) refers to the advantage in processing stimuli associated with oneself. Here, we addressed the SPE in an attentional blink (AB) task. In Experiment 1, shapes associated to you, friend, or stranger served as T1, and letter X as T2. The AB effect was larger for you than the other label conditions, and larger for friend condition than for stranger condition. We suggest that self-associated shape increased its perceptual salience, producing greater attentional capture. In Experiment 2 participants trained with a shape-label matching task to increase familiarity with the shape-label associations before performing the AB task. The difference between friend and stranger conditions disappeared, suggesting that the difference between the two conditions observed in Experiment 1 was mainly due to differences in familiarity or frequency of use. Importantly, the advantage of you over friend and stranger conditions remained, suggesting that the SPE is a genuine effect.

CGIR: Conditional Generative Instance Reconstruction Attacks Against Federated Learning

Data reconstruction attack has become an emerging privacy threat to Federal Learning (FL), inspiring a rethinking of FL's ability to protect privacy. While existing data reconstruction attacks have shown some effective performance, prior arts rely on different strong assumptions to guide the reconstruction process. In this work, we propose a novel Conditional Generative Instance Reconstruction Attack (CGIR attack) that drops all these assumptions. Specifically, we propose a batch label inference attack in non-IID FL scenarios, where multiple images can share the same labels. Based on the inferred labels, we conduct a “coarse-to-fine” image reconstruction process that provides a stable and effective data reconstruction. In addition, we equip the generator with a label condition restriction so that the contents and the labels of the reconstructed images are consistent. Our extensive evaluation results on two model architectures and five image datasets show that without the auxiliary assumptions, the CGIR attack outperforms the prior arts, even for complex datasets, deep models, and large batch sizes. Furthermore, we evaluate several existing defense methods. The experimental results suggest that pruning gradients can be used as a strategy to mitigate privacy risks in FL if a model tolerates a slight accuracy loss.

Effects of cigarette package colors and warning labels on marlboro smokers’ risk beliefs, product appraisals, and smoking behavior: a randomized trial

ObjectivePlain packaging and graphic warning labels are two regulatory strategies that may impact cigarette risk beliefs and reduce consumption, but data are needed to better understand how smokers respond to such regulations.MethodsAdult, daily, Marlboro non-menthol smokers (Red [n = 141] or Gold [n = 43]) completed a mixed factorial randomized trial. Participants smoked their usual cigarettes during baseline (5-days) and were randomized to receive cigarette packs with a warning label manipulation (graphic vs. text-only). Within each warning label condition, participants completed three within-subjects pack color manipulations (red, gold, plain), each lasting 15 days. Participants were blinded to the fact that all packs contained their usual cigarettes. Mixed-effects models examined between- and within-subject differences on risk beliefs, product perceptions, and smoking behavior.ResultsWarning type and package color did not impact cigarette consumption or subjective ratings. However, use increased in all conditions (2.59–3.59 cigarettes per day) relative to baseline. While smokers largely held correct risk beliefs at baseline (Mean = 6.02, SE = 0.17, Range:0–8), the cumulative number of incorrect or uncertain cigarette risk beliefs increased from baseline in all pack color manipulations in the text (IRR range = 1.70–2.16) and graphic (IRR range = 1.31–1.70) warning conditions. Across all pack color periods, those in the graphic (vs. text) warning condition had reduced odds of reporting their study cigarettes as ‘safer’ than regular cigarettes (OR range = 0.22–0.32).ConclusionsPack color modification may increase uncertainty about several key cigarette risk beliefs, though graphic warnings may attenuate these effects. Regulatory agencies could consider supporting policy changes with information campaigns to maximize public knowledge.Trial registrationNovember 25, 2014; Registration number: NCT02301351.

Glycan/Protein-Stable Isotope Labeling in Cell Culture for Enabling Concurrent Quantitative Glycomics/Proteomics/Glycoproteomics.

The complexity and heterogeneity of protein glycosylation present an analytical challenge to the studies of characterization and quantitation. Various LC-MS-based quantitation strategies have emerged in recent decades. Metabolic stable isotope labeling has been developed to enhance the accurate LC/MS-based quantitation between different cell lines. Stable isotope labeling by amino acids in a cell culture (SILAC) is the most widely used metabolic labeling method in proteomic analysis. However, it can only label the peptide backbone and is thus limited in glycomic studies. Here, we present a metabolic isotope labeling strategy, named GlyProSILC (Glycan Protein Stable Isotope Labeling in Cell Culture), that can label both the glycan motif and peptide backbone from the same batch of cells. It was performed by feeding cells with a heavy medium containing amide-15N-glutamine, 13C6-arginine (Arg6), and 13C6-15N2-lysine (Lys8). No significant change of cell line metabolism after GlyProSILC labeling was observed based on transcriptomic, glycomic, and proteomic data. The labeling conditions, labeling efficiency, and quantitation accuracy were investigated. After quantitation correction, we simultaneously quantified 62 N-glycans, 574 proteins, and 344 glycopeptides using the same batch of mixed 231BR/231 cell lines. So far, GlyProSILC provides an accurate and effective quantitation approach for glycomics, proteomics, and glycoproteomics in a cell culture system.