Common Label Research Articles

Certified reference materials for GMO analysis-more than 25 years of GMO CRM production at EC JRC.

Certified reference materials (CRMs) play a crucial role in ensuring the quality of analytical measurements. Particularly, the EU labelling legislation on genetically modified organisms (GMOs) in food and feed products explicitly requires CRMs for its implementation. The CRMs are used to calibrate and control the quantitative real-time polymerase chain reaction (qPCR) method and support official control laboratories, such as National Reference Laboratories (NRLs), in maintaining their ISO/IEC 17025 accreditation. The Joint Research Centre of the European Commission (EC JRC) is specialized in the production of reference materials and has been producing GMO CRMs since 1998. Together with a validated event-specific qPCR method, these GMO CRMs form the EU reference system for the quantification of EU-authorized GMO events in food and feed products and ensure a common GMO labelling threshold. This article gives a retrospective view on the more than 25 years of GMO CRM production at JRC. It describes requirements for GMO CRMs in view of an EU market authorization under (EC) No. 1829/2003. The evolution and major improvements of the production of GMO CRMs at JRC are summarized as well as the current understanding of theEU's GMO reference system for GMO quantification and its impact on commutability. It provides insights into GMO CRM sales and their worldwide distribution. This information may be useful for policymakers and researchers in understanding the current EU GMO measurement landscape and to anticipate possible future demands related to GMO events based on new genomic techniques (NGTs).

Grappling with Refusal, Self-representation, and Visual Sovereignty at the Knoflokskraal Khoisan “Reclaim”

ABSTRACT In 2020, a group Khoisan activists began occupying state-owned land near Grabouw, South Africa. Knoflokskraal has since attracted thousands of residents against the backdrop of widespread disappointment with land reform, heritage policies, and various forms of socio-economic marginalisation. The common labelling of Knoflokskraal as a “land invasion” overlooks the unique features of this self-styled “reclaim”, not least the agency that its residents embody in asserting a sense of indigenous visual sovereignty. Drawing on ethnographic fieldwork conducted in 2022 among residents and community representatives, we highlight instances of interlocutors refusing to go along with mainstream research practices and conforming to widely held expectations surrounding Khoisan representation, but instead imprinting their presence on the landscape in unique ways. Knoflokskraal offers a rare glimpse into self-representation through land reform beyond the purview of the government. Read through the lens of refusal, this case study also prompts researchers to grapple with broader issues relating to research practices, indigenous agency, and visual sovereignty.

Systematic literature review on Calcium Pyrophosphate Deposition (CPPD) nomenclature: condition elements and clinical states— A Gout, Hyperuricaemia and Crystal-Associated Disease Network (G-CAN) consensus project

ObjectivesThe Gout, Hyperuricaemia and Crystal-Associated Disease Network (G-CAN) has developed a calcium pyrophosphate deposition (CPPD) nomenclature project. This systematic literature review constituted its first step and aimed to provide a...

From “isolation” to “me-time”: linguistic shifts enhance solitary experiences

ABSTRACT Spending time alone is a virtually inevitable part of daily life that can promote or undermine well-being. Here, we explore how the language used to describe time alone – such as “me-time”, “solitude”, or “isolation” – influences how it is perceived and experienced. In Study 1 (N = 500 U.S adults), participants evaluated five common labels for time alone. Descriptive and narrative evidence revealed robust interindividual variability and significant mean differences in how these labels were evaluated. Overall, “me-time” was rated most positively, and “isolation” was rated least positively (but not negatively). In Study 2, we experimentally manipulated the linguistic framing of time alone, describing it as either “me-time” or “isolation”. Participants (N = 176 U.S undergraduates) then spent 30 min physically alone without in-person or digital interaction. Notably, positive affect increased for “me-time” participants but decreased for “isolation” participants. Negative affect decreased in both conditions, but the magnitude of the decrease was greater after “me-time”. People’s beliefs about being alone improved after “me-time” but not after “isolation”. Further, we explored participants’ behaviours and thoughts while alone. These findings demonstrate meaningful variation in how people perceive different time alone labels and provide preliminary evidence that simple linguistic shifts may enhance subjective experiences of time alone.

Identification of Multi-functional Therapeutic Peptides Based on Prototypical Supervised Contrastive Learning.

High-throughput sequencing has exponentially increased peptide sequences, necessitating a computational method to identify multi-functional therapeutic peptides (MFTP) from their sequences. However, existing computational methods are challenged by class imbalance, particularly in learning effective sequence representations. To address this, we propose PSCFA, a prototypical supervised contrastive learning with a feature augmentation method for MFTP prediction. We employ a two-stage training scheme to train the feature extractor and the classifier respectively, underpinned by the principle that better feature representation boosts classification accuracy. In the first stage, we utilize a prototypical supervised contrastive learning strategy to enhance the uniformity of feature space distribution, ensuring that the characteristics of samples within the same category are tightly clustered while those from different categories are more dispersed. In the second stage, a feature augmentation strategy that focuses on infrequent labels (tail labels) is used to refine the learning process of the classifier. We use a prototype-based variational autoencoder to capture semantic links among common labels (head labels) and their prototypes. This knowledge is then transferred to tail labels, generating enhanced features for classifier training. The experiments prove that the PSCFA method significantly outperforms existing methods for MFTP prediction, making a significant advancement in therapeutic peptide identification.

Cellular and molecular changes in a unified and integrated single cell atlas of Alzheimer’s disease across community datasets

AbstractBackgroundNumerous studies have identified AD‐associated molecular and cellular changes to the cortex using single nucleus RNA sequencing (snRNA‐seq) and, to a lesser extent, single nucleus ATAC‐seq (snATAC‐seq), applied to millions of cells across hundreds of donors. It has proven challenging, however, to determine whether changes are consistent because of differences in cohort selection, reported clinical metadata, data pre‐processing, cellular taxonomy construction/mapping, and analytical strategies across studies.MethodWe uniformly re‐processed 10 publicly available datasets (Table 1) that had applied snRNA‐seq to 4.3 million cells from the dorsolateral pre‐frontal cortex (DLPFC) of 780 donors. We integrated each of them with SEA‐AD’s multi‐modal, single cell atlas of AD, which contains 4.5 million cells from the middle temporal gyrus (MTG) and DLPFC that were profiled with snRNA‐seq, snATAC‐seq, snMulitome or spatial transcriptomics (MERFISH). We then harmonized clinical data across donors and mapped all cells to the same 139 fine‐grained cell supertypes in SEA‐AD’s shared MTG and DLPFC cellular taxonomy. With common clinical data and cellular labels, we then compared cohorts and data quality across studies as well as identified cellular changes consistently associated with AD.ResultCohorts from nearly every study sampled across the spectrum of plaque and tangle pathology, even in those that performed case‐control analyses. With rare exception, the fraction of donors in each cohort with an APOE4 allele, clinically diagnosed dementia, and severe co‐morbidities were also similar. Comparing datasets, SEA‐AD was uniquely successful in profiling both many donors and many nuclei per donor, while deeply sequencing libraries to drive high gene detection. Most importantly though, of the 34 supertypes that were significantly changed in the DLPFC in SEA‐AD, 8 were also significantly changed across external studies with large enough cohorts to power discovery. This included 5 selectively vulnerable Sst interneuron supertypes and 1 Microglia supertype that were changed early in AD in SEA‐AD.ConclusionConsistent and early selective loss of Sst interneurons and increase in disease associated Microglia underscore their importance to AD progression. Our integrated dataset provides a valuable common starting point for future comparative analyses from the community and, we hope, will accelerate discovery of novel therapeutic targets.

A novel domain-private-suppress meta-recognition network based universal domain generalization for machinery fault diagnosis

Domain generalization aims to generalize knowledge to target domains not seen during the training phase, even in domain gaps. However, in complex industrial settings, the emergence of new fault types is frequent. Concurrently, the rarity of these faults means that the data collected may not fully capture the entire range of potential fault conditions. As a result, it is challenging to ensure that there is an overlap between the label sets of the multi-source domains and the unseen target domains. This problem requires no prior knowledge of label sets, and it requires a model to learn from multi-source domains and perform well on unknown target domains. In this paper, we propose a Domain-Private-Suppress Meta-Recognition Network (DPSMR). It quantifies channel-level transferability to continuously enhance the robustness of channels to domain shifts, thereby promoting the generalization of a common label set. Using an enhanced meta-recognition calibration algorithm to avoid overconfidence in neural network predictions, we ensure the successful recognition of private samples. By employing dual-consistency loss, we reduce channel instability and facilitate learning domain-invariant features. Experimental results on two multi-domain datasets demonstrate that DPSMR outperforms the state-of-the-art methods.

Novel Nonlinear Laser Wave-Mixing Spectroscopy Coupled with Capillary Electrophoresis for the Sensitive Detection of Sars-Cov-2 Biomarkers.

We present a highly sensitive detection method for the multivalent SARS-CoV-2 nucleocapsid protein, a crucial RNA-binding protein involved in virus assembly and genome packaging. The nucleocapsid protein's structure facilitates the recognition of antiviral drug targets, making it essential for viral RNA production and antiviral drug development. Traditional nucleic acid amplification assays like PCR, gene sequencing, CRISPR, CT-scan, ELISA, LFA, and LAMP are currently used for COVID-19 identification. However, these techniques have limitations such as false results, template quality issues, cost, limited specificity, and sensitivity. To overcome these challenges, we introduce our patented nonlinear laser wave-mixing technique, offering numerous advantages including label-free native detection, enhanced sensitivity, minimal sample requirements, short optical path length, and high spatial resolution. Our approach utilizes four-wave mixing (FWM), a nonlinear optics phenomenon, to generate coherent signals by interacting two or three wavelengths and producing new wavelengths. Phase- matched four-wave mixing is achieved using collinear incommensurate waves, resulting in efficient signal generation within the analyte. The small probe volume, achieved by overlapping two laser beams, makes our technique compatible with microfluidics and capillary-based electrophoresis systems. Furthermore, wave-mixing, being an absorption-based method, enables the detection of small amino acids and proteins using common labeling methods like fluorophores and chromophores. In our study, we successfully detected the SARS-CoV-2 nucleocapsid protein labeled with the Chromeo P540 tag using a 532 nm solid-state laser as the excitation source. Our technique exhibited high sensitivity, with a detection concentration limit of 0.26 nM, corresponding to a mass detection limit of 19.3 zeptomoles. This innovative detection method holds great promise for advancing COVID-19 diagnostics by providing superior sensitivity, compatibility with various labeling techniques, and the ability to detect infected carriers who may not exhibit clinical symptoms.

NSAID Allergy Labels Associated With Mortality and Cardiovascular Outcomes in Stroke.

Mislabeled drug allergy can restrict future prescriptions and medication use, but its prevalence and impact among patients with stroke remain unknown. This study investigated the prevalence of the most commonly labeled drug allergies, their accuracy, and their impact among patients with stroke. In this combined longitudinal and cross-sectional study, we compared the prevalence of allergy labels among the general population and patients with ischemic stroke between 2008 and 2014 from electronic health care records in Hong Kong. Outcomes between patients with stroke with or without the most prevalent labels (ie, NSAID) were compared. Rate of mislabeled NSAID allergy was confirmed by provocation testing. Compared with the general population (n=702 966), patients with stroke had more labels (n=235) to cardiovascular and hematopoietic system (prevalence, 19.5% versus 9.2%; odds ratio [OR], 2.4 [95% CI, 1.74-3.32]; P<0.001) and radiographic and diagnostic agents (prevalence, 4.2% versus 0.9%; OR, 4.82 [95% CI, 2.56-9.08]; P<0.001). The most common labels were to NSAID (prevalence, 1.8%). Patients with NSAID allergy labels were significantly less likely to be prescribed aspirin after acute stroke (OR, 0.24 [95% CI, 0.09-0.60]; P=0.003) and on follow-up (OR, 0.22 [95% CI, 0.08-0.56]; P=0.002). The median duration of follow-up was 6.7 years (6499±2.49 patient-years). Patients with stroke with NSAID allergy labels also experienced significantly higher mortality (OR, 7.44 [95% CI, 2.44-23.18]; P<0.001), peripheral vascular disease (OR, 9.35 [95% CI, 1.95-44.86]; P=0.005), and major adverse cardiovascular events (OR, 6.09 [95% CI, 2.00-18.58]; P=0.001) in the poststroke period. Patients with NSAID allergy labels (who remained alive and could consent) were referred for allergist assessment and offered drug provocation testing. The majority (80%; 4/5) had negative provocation tests and were delabeled. NSAID allergy labels were significantly more prevalent among patients with stroke, associated with excessive mortality, peripheral vascular disease, and major adverse cardiovascular events. Given the high rate of mislabeled allergies, multidisciplinary neuro-allergy interventions could have the potential to improve patient outcomes.

Engineering and Monitoring the Sustained Release of Extracellular Vesicles from Hydrogels for In Vivo Therapeutic Applications

Extracellular vesicles (EVs) are gaining interest in regenerative medicine and biomaterials have been shown to extend EV bioavailability following delivery. Herein, the labeling of both hydrogels and EVs is reported to better understand hydrogel design for sustained EV release into tissues. Shear‐thinning hydrogels are engineered using guest–host (i.e., adamantane–cyclodextrin) modifications to hyaluronic acid (GH), as well as GH hydrogels with the addition of gelatin crosslinked via transglutaminase (GH+Gel) to temporally control hydrogel properties. When labeled with a near‐IR dye and injected into rat myocardial tissue, the GH+Gel hydrogel is retained (&gt;14 days) longer than the GH hydrogel alone (≈7 days), likely due to the added gelatin network. To overcome challenges associated with common EV labeling methods, a highly versatile metabolic labeling methodology is utilized via the incorporation of N‐azidoacetylmannosamine‐tetraacylated during EV synthesis to introduce azide groups that can then be reacted with DBCO dyes. When injected in saline, EVs are cleared within 24 h in hearts; however, hydrogels enhance EV retention, with levels based on hydrogel degradation behavior, namely, &gt;14 days for GH+Gel hydrogel and ≈7 days for GH hydrogel alone. These findings support the use of hydrogels in EV therapies.

Uncovering conserved networks and global conformational changes in G protein-coupled receptor kinases

G protein-coupled receptor kinases (GRKs) are essential regulators of signaling pathways mediated by G protein-coupled receptors. Recent research suggests that GRK-mediated phosphorylation patterns dictate functional selectivity, leading to biased cellular responses. However, a comprehensive understanding of the structural mechanisms at the single-residue level remains elusive. This study aims to define the general conformational dynamics of GRKs with a particular focus on quantifying the transitions between the closed and open states. Specifically, we examined these transitions, classified based on the ionic lock between the regulatory G protein signaling homology domain and kinase domain. To facilitate a precise structural comparison, we assigned common labels to topologically identical positions across the 47 GRK structures retrieved from the Protein Data Bank. Our analysis identified both general and subfamily-specific dynamic movements within the networks and measured the conformational change scores between the two states. Elucidating these structural dynamics could provide significant insights into the regulatory mechanisms of GRK.

Survival (Time-To-Event) Curve Names and Endpoints

In oncology, “survival curves” frequently appear in journal articles and meeting presentations. The most common labels on survival curves are overall survival, relapse-free survival, progression-free survival, distant metastasis-free survival, and local and/or regional control. Unfortunately, consistency in the definition of an event differs between authors for the same prescribed survival analyses. Furthermore, the quality of survival curves can be greatly impacted by the methodology used for endpoint selection. This paper will briefly explain widely used names and event endpoints for survival analyses in a way that will help radiation oncologists consistently present and interpret experimental findings that influence clinical practice decisions.

Knowing the unknowns: Network traffic detection with open-set semi-supervised learning

Network traffic classification plays a crucial role in network security and network quality of service. The new emerging traffic data in the real world is unlabeled and may contain unknown classes, which introduces the problem of identifying unknown-class traffic in unlabeled data. However, existing deep learning-based methods either rely on large amounts of labeled data or assume the labeled and unlabeled data share the common label space. Therefore, they are unable to identify unknown traffic, resulting in decreased detection accuracy. This paper introduces DivinEye, a novel method for unknown network traffic detection based on open-set semi-supervised learning. DivinEye utilizes a small quantity of labeled data and a large amount of unlabeled data to train the model, achieving the ability to identify known classes accurately while also detecting unknown ones. To leverage incoming unlabeled traffic, DivinEye employs open-set semi-supervised techniques to select known-class data from unlabeled data to optimize the known traffic detection model. To detect unknown traffic, DivinEye combines the known traffic classifier and multi-binary classifier to construct an unknown traffic detection model, generating auxiliary targets for all unlabeled data as supervision to train the model. Trace-driven experiments demonstrate that DivinEye outperforms state-of-the-art methods by a large margin.

Carbon-14 Labeling Synthesis of RORγt Inhibitor JNJ-61803534.

Carbon-14 labeling synthesis of RORγt inhibitor JNJ-61803534 (1) was accomplished in four steps with the C14 label located at the thiazole-2-carboxamide carbon. The synthesis featured a highly efficient conversion of nitrile [14C]-12 to ester [14C]-17 under mild conditions via an imidate intermediate, overcoming the unsuccessful direct hydrolysis of nitrile 12 under either acidic or basic conditions. Since carbon-14 labeling via [14C]-nitrile installation and subsequent conversion to [14C]-carboxylic acid derivatives is a common labeling strategy, an efficient conversion of a nitrile to an ester under mild conditions could be of use for the future C14 labeling syntheses.

“I am a survivor!”: Violently Injured Black Men’s Perceptions of Labeling After a Violent Firearm Injury

Self-appraisal after a life-altering event is a critical process for individuals, often comprised by assigned labels that may not align with an individuals’ perceptions of themselves or of their situation. Existing research within this victim-survivor dichotomy largely rests in the interpersonal violence space, with a victim assuming legal recourse and wrongdoing, and a survivor associating with positive personal characteristics like grit and resilience. Much existing literature on self-appraisal after interpersonal injury is heavily concentrated within the sexual violence literature, and this study applies these concepts to a sample of Black men injured by firearms. Ten Black men enrolled in a hospital-based violence intervention program (HVIP) were interviewed to understand how they label their experience of firearm injury, and if their perceptions aligned with common labels seen among other populations and/or in other areas of study (e.g., cancer, domestic violence). Each participant assigned themselves their own label, with three labels emerging: survivor, victim and survivor, and neither victim nor survivor. The results illustrate the nuance of experiences beyond the victim-survivor dichotomy, and how labels and personal identities may shift following injury into new terms and considerations of resilience and trauma processing. More research is warranted to understand the factors that shape self-labeling within this population, including influences of masculine norms, racialized stereotypes, community context, and availability of services. Findings support public awareness campaigns to reframe surviving violence as a strength, and for community partners and practitioners to increase access to culturally competent and trauma-informed mental healthcare.

Examining labelling guidelines for AI-based software as a medical device: A review and analysis of dermatology mobile applications in Australia.

In recent years, there has been a surge in the development of AI-based Software as a Medical Device (SaMD), particularly in visual specialties such as dermatology. In Australia, the Therapeutic Goods Administration (TGA) regulates AI-based SaMD to ensure its safe use. Proper labelling of these devices is crucial to ensure that healthcare professionals and the general public understand how to use them and interpret results accurately. However, guidelines for labelling AI-based SaMD in dermatology are lacking, which may result in products failing to provide essential information about algorithm development and performance metrics. This review examines existing labelling guidelines for AI-based SaMD across visual medical specialties, with a specific focus on dermatology. Common recommendations for labelling are identified and applied to currently available dermatology AI-based SaMD mobile applications to determine usage of these labels. Of the 21 AI-based SaMD mobile applications identified, none fully comply with common labelling recommendations. Results highlight the need for standardized labelling guidelines. Ensuring transparency and accessibility of information is essential for the safe integration of AI into health care and preventing potential risks associated with inaccurate clinical decisions.

Double-Layer Hybrid-Label Identification Feature Selection for Multi-View Multi-Label Learning

Multi-view multi-label feature selection aims to select informative features where the data are collected from multiple sources with multiple interdependent class labels. For fully exploiting multi-view information, most prior works mainly focus on the common part in the ideal circumstance. However, the inconsistent part hidden in each view, including noises and specific elements, may affect the quality of mapping between labels and feature representations. Meanwhile, ignoring the specific part might lead to a suboptimal result, as each label is supposed to possess specific characteristics of its own. To deal with the double problems in multi-view multi-label feature selection, we propose a unified loss function which is a totally splitting structure for observed labels as hybrid labels that is, common labels, view-to-all specific labels and noisy labels, and the view-to-all specific labels further splits into several specific labels of each view. The proposed method simultaneously considers the consistency and complementarity of different views. Through exploring the feature weights of hybrid labels, the mapping relationships between labels and features can be established sequentially based on their attributes. Additionally, the interrelatedness among hybrid labels is also investigated and injected into the function. Specific to the specific labels of each view, we construct the novel regularization paradigm incorporating logic operations. Finally, the convergence of the result is proved after applying the multiplicative update rules. Experiments on six datasets demonstrate the effectiveness and superiority of our method compared with the state-of-the-art methods.

Can You Rely on Synthetic Labellers in Preference-Based Reinforcement Learning? It’s Complicated

Preference-based Reinforcement Learning (PbRL) enables non-experts to train Reinforcement Learning models using preference feedback. However, the effort required to collect preference labels from real humans means that PbRL research primarily relies on synthetic labellers. We validate the most common synthetic labelling strategy by comparing against labels collected from a crowd of humans on three Deep Mind Control (DMC) suite tasks: stand, walk, and run. We find that: (1) the synthetic labels are a good proxy for real humans under some circumstances, (2) strong preference label agreement between human and synthetic labels is not necessary for similar policy performance, (3) policy performance is higher at the start of training from human feedback and is higher at the end of training from synthetic feedback, and (4) training on only examples with high levels of inter-annotator agreement does not meaningfully improve policy performance. Our results justify the use of synthetic labellers to develop and ablate PbRL methods, and provide insight into how human labelling changes over the course of policy training.

CONSULT-II: accurate taxonomic identification and profiling using locality-sensitive hashing.

Taxonomic classification of short reads and taxonomic profiling of metagenomic samples are well-studied yet challenging problems. The presence of species belonging to groups without close representation in a reference dataset is particularly challenging. While k-mer-based methods have performed well in terms of running time and accuracy, they tend to have reduced accuracy for such novel species. Thus, there is a growing need for methods that combine the scalability of k-mers with increased sensitivity. Here, we show that using locality-sensitive hashing (LSH) can increase the sensitivity of the k-mer-based search. Our method, which combines LSH with several heuristics techniques including soft lowest common ancestor labeling and voting, is more accurate than alternatives in both taxonomic classification of individual reads and abundance profiling. CONSULT-II is implemented in C++, and the software, together with reference libraries, is publicly available on GitHub https://github.com/bo1929/CONSULT-II.

Self-paced semi-supervised feature selection with application to multi-modal Alzheimer’s disease classification

Semi-supervised multi-modal learning has attracted much attention due to the expense and scarcity of data labels, especially in disease diagnosis field. Most existing methods follow the paradigm by iteratively inferring the pseudo-labels of unlabeled data and add them into training sequence, but they ignore the reliability of those pseudo-labels, where inaccurate and wrong supervision will lead to negative influence on model learning. In this paper, we propose a Self-paced Semi-supervised Multi-modal Feature Selection (SSMFS) method, and apply it to Alzheimer’s disease classification. Specifically, SSMFS projects multi-modal biomedical data into the common label space with discriminative feature selection. Under the guidance of prior multi-modal similarity graphs, a unified graph is adaptively learned and embedded to preserve the neighborhood structures. More importantly, SSMFS dynamically investigates the discriminability and credibility of pseudo-labels, and adaptively assigns a weight to each unlabeled sample via self-paced learning such that the negative influence of wrong supervision can be reduced. Finally, a multi-kernel support vector machine is used to fuse the selected multi-modal features for final disease prediction. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset demonstrate the effectiveness of our method.