Lung cancer is responsible for a significant number of cancer-related deaths worldwide. While various factors can lead to lung cancer, such as tobacco smoking, this article focuses on the relationship between sarcoidosis, a multisystem granulomatous disorder, and lung neoplasm. To investigate this association, the authors conducted a literature search using relevant keywords. The analysis of these reports concluded that while Sarcoidosis and lung cancer together is rare, it is possible. The presenting symptoms, age, gender, and diagnostic procedures of each case should be evaluated, and appropriate diagnostic procedures should be carried out to determine the appropriate treatment for each patient. Clinicians need to be aware of the possibility of these two diseases co-occurring, as they can impact the management of the patient's condition, whether it is curative or palliative. It is essential to rule out metastatic cancer in individuals with sarcoidosis-like clinical and radiographic features. Lung cancer is a severe concern today, as it has become the leading cause of cancer-related deaths for both men and women worldwide. Its insidious onset, early metastasis, and poor prognosis make it all the more concerning . It is eSarcoidosis affects multiple body areas, including the lungs, lymph nodes, liver, eyes, and skin. The disease is systemic and characterized by the formation of multiple granulomas of unknown origin . HoweverIndividuals with sarcoidosis must receive proper medical care and monitoring to manage potential complications and improve their overall health outcomes. Respiratory failure, pulmonary fibrosis, central nervous system involvement, and cardiac damage are severe risks associated with sarcoidosis, which can be life-threatening if not adequately addressed . It is eProper diagnosis is crucial to ensure that individuals with sarcoidosis receive the appropriate medical care and monitoring to manage potential complications and improve their overall health outcomes. Table Based on available data, the outlook for individuals with pulmonary sarcoidosis is positive. Up to 80% of patients with stage I disease have been observed to experience spontaneous regression of radiographic abnormalities. Additionally, the rate of development of chronic respiratory impairment is relatively low, affecting less than 5% of patients over 10 years . It is iCorticosteroid medications are a commonly prescribed treatment for individuals with severe symptoms and advancing pulmonary disease or significant extrapulmonary illness caused by sarcoidosis. In some cases, immunosuppressive treatment may also be necessary .This study focuses on rare cases reported and research articles published globally in the last 10 years to examine the relationship between sarcoidosis and lung cancer and determine whether there is a relationship between the two. It also offers guidance to doctors on how to manage such uncommon encounters.Even though there have been numerous documented associations, the relationship between lung cancer and sarcoidosis is still unclear. The authors break down the discussion session into theories, the diagnostic challenges, and the inflammatory responses related to sarcoidosis that may lead to neoplasm.TheoriesCancer seems more common in sarcoidosis patients, including malignancies of the liver, stomach, small intestine, lung, and skin, as well as non-Hodgkin's lymphoma and leukemia. Chronic inflammation is associated with a high risk of malignant lymphoma or cancer in the affected tissue . TheoretEpidemiological studies show that at least 25% of patients with sarcoidosis and neoplasm may have an etiological relationship . NumerouIn a study conducted in Japan from 1980 to 2007, three cases of sarcoidosis and lung cancer were noted. In one case, the patient developed lung cancer 16 years after being diagnosed with sarcoidosis. In another case, the patient had two different lung cancers, occurring 10 and 18 years after the diagnosis of sarcoidosis. In the third case, both illnesses were detected simultaneously. However, It can be challenging to ascertain whether the coexistence of non-caseating epithelioid cell granulomas with lung cancer in simultaneously detected cases represents a lung cancer reaction or genuine systemic sarcoidosis. These three cases were shown to have smoking indexes of 300,720 and 70, respectively [Ungprasert et al. discovered a higher incidence of malignancy among patients with sarcoidosis than those without sarcoidosis. However, it is essential to note that this relative risk was insignificant once detection bias was accounted for . In anotDiagnostic challenges and differential diagnosisA non-caseating granuloma can help confirm a diagnosis of sarcoidosis, especially in optimal clinical settings . In addiSarcoid-like reactions are autoimmune responses that resemble the sarcoidosis symptoms. Different etiologies, including infections most frequently, might cause this reaction. Additionally linked to sarcoid-like reactions are cancer, medicines, and other autoimmune responses . These tCases with concurrent sarcoidosis and malignant disease may help illuminate the causal relationship between them, even though the distinction between sarcoidosis and sarcoid responses in their mechanisms of development is still unclear . These gA popular diagnostic tool for detecting increased metabolic activity, fludeoxyglucose-positron emission tomography (FDG-PET), has a 96% sensitivity and a 79% specificity for lung cancer. The standardized uptake value (SUV), used to quantify a tumor's metabolic activity, is highly predictive of malignancy when it is more significant than 2.5. This test, however, may yield false-positive results in granulomatous diseases like sarcoidosis . The lymEven though the likelihood of sarcoidosis and lung cancer coexisting is extremely low (less than 1%) of the time, it is more frequently seen in squamous cell lung cancers . When diAccording to Koda et al., some textural traits have been shown to be able to distinguish SCLC from sarcoidosis in patients with mediastinal lymphadenopathy on CT. The combination of EBUS elastographic fat-to-lesion strain ratio and textural feature grey-level run length matrix with high gray-level run emphasis yielded nearly excellent diagnostic capability for discriminating between these two diseases. Therefore, enlarged mediastinal lymph nodes with CT-based texture analysis may help distinguish between sarcoidosis and other diseases that cause mediastinal lymphadenopathy, such as malignant lymphoma and metastatic lymph nodes associated with NSCLC .Pulmonary sarcoidosis has frequently been misdiagnosed as lung cancer, according to reports. In a rare instance, pulmonary sarcoidosis was mistakenly diagnosed as progressing lung cancer, delaying the detection of the disease . The misMedication safety margin in immunotherapy in alternative theories contends that lung cancer chemotherapy, tyrosine kinase inhibitors, or immunological checkpoint inhibitors like ipilimumab and nivolumab that trigger local sarcoid responses may be the cause of sarcoidosis . In justInflammatory response to sarcoidosisThere are conflicting views on the link between sarcoidosis and lung cancer. Some studies suggest that people with sarcoidosis may be at a higher risk of developing lung cancer and experiencing mortality. In contrast, others indicate that the incidence is not higher than expected. Nevertheless, the persistent inflammation and scarring that come with sarcoidosis and abnormalities in cell-mediated immunity could potentially be a factor in triggering cancer development .Active sarcoidosis is characterized by an enhanced local expression of T helper 1 (Th1) and T helper 17 (Th17) chemokines and cytokines like IFN-γ, TNF-α, IL-17A, and IL-22. In various chronic, autoimmune, inflammatory diseases, such as sarcoidosis, the percentage of IL-17A+/IFN-γ+ double-producing Th-cells increases in peripheral blood and is related to high disease activity -26. FurtInterestingly, during sarcoidosis, the expression of cytotoxic T-lymphocyte antigen 4 (CTLA-4) is decreased, while the expression of programmed death-1 (PD-1) is increased in Th17-cells in the mediastinal lymph nodes . Th-cellFurthermore, S100A8/A9 (belongs to the Ca2 + binding S100 protein family) levels in serum are higher in sarcoidosis patients, and monocytes and multinuclear giant cells express S100A8/A9 more cytoplasmically in granulomas. We might surmise that the inflammation associated with sarcoidosis, which is characterized by the expression of IFN, IL6, IL23, and S100A8/A9, shares a milieu with malignancies, which are distinguished by an elevated accumulation and immunosuppressive role of myeloid-derived suppressor cells (MDSC). As a result, sarcoidosis can encourage metastasis by causing cell subsets that promote tumors and regulate the immune system . More reLimitationsFirstly, many of the studies are retrospective, which may introduce bias and limit the ability to establish causality. Additionally, diagnosing sarcoidosis may be complex and subject to inter-observer variability, leading to misclassification and underestimation of the true prevalence of sarcoidosis in lung cancer patients. Furthermore, the heterogeneity of sarcoidosis in terms of clinical presentation and disease course may make it challenging to identify specific subtypes more strongly associated with lung cancer. Finally, the studies included in this review varied widely in terms of sample size, study design, and patient population, which may limit the generalizability of the findings. These studies cited in the article have not demonstrated a clear control adjustment for other variables, especially the ones that cause lung cancer including smoking, environmental exposure, etc. Overall, while the association between lung cancer and sarcoidosis is an essential area of research, further studies are needed to understand better the underlying mechanisms and potential clinical implications of this relationship.When dealing with a potential case of sarcoidosis, it is crucial to remember that it can mimic various other diseases, including infectious, neoplastic, and granulomatous diseases. Furthermore, individuals with sarcoidosis may be more susceptible to lung cancer. This is consistent with the elevated risk of cancer reported in other inflammatory respiratory diseases, and rather than sarcoidosis itself, the risk may be mediated by the accompanying cell regeneration. Therefore, conducting a thorough assessment and maintaining a diverse differential diagnosis is vital. Even though sarcoidosis and lung cancer seldom coexist, doctors should investigate the possibility of lung cancer in sarcoidosis patients who do not respond to early corticosteroid treatment and obtain a biopsy to establish pathological findings from any suspected lung lesion.While there is no evidence of a link between sarcoidosis and lung adenocarcinoma, physicians should ensure no lesion is overlooked in individuals without a biopsy. This structured approach can improve the patient's outcome and prognosis while detecting malignancy earlier. Given the diagnostic conundrum of not being able to differentiate sarcoidosis and lung cancer, it is better to treat the nodal involvement as SCLC metastasis because failure to do so might result in relapse and jeopardize survival. Lastly, more cohort studies are required to enhance the body of literature already in existence. It may lead to the discovery of answers to the link between lung cancer and sarcoidosis.


Allergy. The final part of the survey involved asking ChatGPT the same statistical question three times. Results: Out of the 40 general questions asked that related to broad statistical analysis, ChatGPT did not fully answer half of them. Assumptions necessary for the application of specific statistical tests were not included. ChatGPT also gave completely divergent answers to one question about which test should be used. Conclusion: The answers provided by ChatGPT to various statistical questions may give rise to the use of inappropriate statistical tests and, consequently, the subsequent misinterpretation of the research results obtained. Questions asked in this regard need to be framed more precisely.Background: Content generated by artificial intelligence is sometimes not truthful. To date, there have been a number of medical studies related to the validity of ChatGPT’s responses; however, there is a lack of studies addressing various aspects of statistical analysis. The aim of this study was to assess the validity of the answers provided by ChatGPT in relation to statistical analysis, as well as to identify recommendations to be implemented in the future in connection with the results obtained. Methods: The study was divided into four parts and was based on the exemplary medical field of allergology. The first part consisted of asking ChatGPT 30 different questions related to statistical analysis. The next five questions included a request for ChatGPT to perform the relevant statistical analyses, and another five requested ChatGPT to indicate which statistical test should be applied to articles accepted for publication in  Allergy pointed out that ChatGPT can generate reliable but incorrect and meaningless responses. For this reason, it is essential to keep expert supervision in mind when publishing this type of material [PLOS Medicine, Professor John P. A. Ioannidis argued that the results of the majority of scientific publications are fraudulent [PLOS ONE show that between 1998 and 2020, there was unfortunately no significant change in the frequency with which medical journal editors used statistical reviewers [PLOS Biology point to the need for education through critical analysis of accepted manuscripts [Allergy is the official journal of the European Academy of Allergy and Clinical Immunology, the first aim of this research was to determine the validity of the statistical analyses used by the authors of the articles published in this journal. The second objective was to analyse the validity of the statistical analyses carried out by ChatGPT on the datasets created, the subjects of which were related to allergology. The third objective of this research was to investigate the correctness of ChatGPT’s answers to questions on various statistical issues, using the medical field of allergology as an example. The fourth objective of the manuscript was, using one statistical question asked of ChatGPT as an example, to indicate the variety of answers given. The final objective was to provide statistical recommendations to bear in mind when considering the use of ChatGPT for conducting statistical analyses.At the end of July 2023, guidance related to the use of artificial intelligence, language models and chatbots was published by JAMA Network. The authors indicated that non-human artificial intelligence tools should not be credited as co-authors. The use of such tools should be transparently reported when writing manuscripts and other content . A similmaterial . Anothermaterial . An intematerial . The proaudulent . Other deviewers . In addiAllergy. ChatGPT provided answers to questions about what kind of statistical test it would apply to specific studies conducted by these authors, the results of which were subsequently published in Allergy. The articles were selected based on the various statistical tests applied. In order to show the variety of answers given, one question related to the conduct of a statistical analysis was selected and then asked several times of ChatGPT. Based on the answers given by ChatGPT to the 40 questions, the statistical recommendations mentioned in the introduction were created. The answers given were scored using a 0–1 system, where 0 was the wrong answer and 1 was the correct answer. Incorrect answers were related to the wrong choice of statistical tests, the tests’ assumptions, and other aspects related to conducting a statistical analysis, such as the correct description of the obtained test results, removal of outliers, etc. In an additional supplement file . A 2(1) = 1.2; p = 0.27. However, the responses associated with the need to use non-parametric equivalents of statistical tests were noteworthy. This refers to ChatGPT’s lack of consideration of the scale of the analysed variables, for example, related to ordinal nature, low sample size and other assumptions necessary for the application of specific tests. The remaining wrong answers given were to do with the wrong choice of statistical test, the wrong measure of effect size and the wrong descriptive statistics to be included in the description of the results obtained . Of the answers given, 12 were incorrect. The difference in the number of correct and incorrect answers given was not statistically significant, χobtained . For thiIn terms of the correctness of ChatGPT’s five different statistical analyses, as was observed for the 30 questions in Allergy. Once again, the incorrect responses were related to the fact that ChatGPT did not consider aspects such as sample size and the equality of the groups of people being compared, and therefore did not select the appropriate effect size measure (ChatGPT gave correct answers to the questions related to three of the five selected articles published in  measure  [9,10,11When asking ChatGPT questions related to different aspects of the statistical analyses being conducted, it was noted that there were discrepancies in the answers given to the same questions. One question was selected from the 30 questions presented in the first table and then presented to ChatGPT three times to show the discrepancies in the answers given. Of the 30 questions presented in Allergy, a study was carried out to analyse the accuracy of ChatGPT’s answers to questions related to statistical analysis in a broad sense. Unfortunately, the results obtained indicate that out of 40 possible questions, only half were correctly answered, which had to do with the lack of specification of questions in which assumptions such as sample size, measurement scale of analysed variables, etc., had to be taken into account. A review article published in Health and Technology pointed out that the use of artificial intelligence tools can help to facilitate comprehensive statistical analysis, including troubleshooting missing data [Schizophrenia with the following title: “ChatGPT: these are not hallucinations—they’re fabrications and falsifications”. Emsley R. pointed out that questions on general methodology produced general and down-to-earth answers. Difficulties were encountered in obtaining slightly more detailed answers related to a statistical analysis plan [PLOS Biology, it is now generally recommended to provide practical instruction in performing statistical analyses, e.g., using examples of published articles in which specific statistical tests have been applied [Nature pointed out researchers’ concerns about students writing homework with the help of artificial intelligence [The present research is, to the best of my knowledge, the first on ChatGPT’s statistical skills. Using the medical field of allergology as an example, including articles published in ing data . ChatGPTing data . A studying data . An intesis plan . This cosis plan . One recsis plan . An incrsis plan ,21,22. Tsis plan ,23. The sis plan ,25. Unfo applied . One pub applied . An artilligence . The nonlligence . ChatGPTlligence . Asking lligence . A commalligence . The prolligence . This maBased on the research conducted, statistical recommendations related to the use of ChatGPT are indicated below. An initial recommendation for authors wishing to use ChatGPT’s indicated answers to questions related to statistical analysis in the broadest sense concerns the initial analysis of the assumptions necessary to apply specific tests. Before the authors decide, for example, to apply a statistical test indicated by ChatGPT, they should first examine the individual assumptions. Assumptions such as normality of distribution, group size, equality of groups, the scale on which the variable being analysed is measured, etc., must be taken into account. ChatGPT should not be asked general questions related to what kind of analysis should be applied, and the highlighted assumptions should be considered. In other words, it is advisable to ask more precise questions about whether 16 samples are enough to apply an analysis, or, for example, whether the measurement scale on which a variable is measured allows the application of a particular test. Without a specific question, the percentage of statistical advice provided by ChatGPT that is appropriate may be low. The second recommendation is to ask the same ChatGPT question several times because, as indicated in this manuscript, there is the possibility that it will provide quite divergent answers and thus subsequently suggest the wrong advice. Once different answers to a single statistical question have been obtained, authors should analyse these in depth, choose the most optimal answer to the best of their knowledge and then, if there are further doubts, relate the chosen answer to the relevant references. One option is also to refer to published statistical recommendations in medical journals such as those published by this journal, among others . This isAt a time when there is a lower level of statistical analysis being conducted, we should have limited confidence in the advice provided by ChatGPT related to conducting statistical analyses.


Knowledge graphs have gained increasing popularity in the last decade in science and technology. However, knowledge graphs are currently relatively simple to moderate semantic structures that are mainly a collection of factual statements. Question answering (QA) benchmarks and systems were so far mainly geared towards encyclopedic knowledge graphs such as DBpedia and Wikidata. We present SciQA a scientific QA benchmark for scholarly knowledge. The benchmark leverages the Open Research Knowledge Graph (ORKG) which includes almost 170,000 resources describing research contributions of almost 15,000 scholarly articles from 709 research fields. Following a bottom-up methodology, we first manually developed a set of 100 complex questions that can be answered using this knowledge graph. Furthermore, we devised eight question templates with which we automatically generated further 2465 questions, that can also be answered with the ORKG. The questions cover a range of research fields and question types and are translated into corresponding SPARQL queries over the ORKG. Based on two preliminary evaluations, we show that the resulting SciQA benchmark represents a challenging task for next-generation QA systems. This task is part of the open competitions at the 22nd International Semantic Web Conference 2023 as the Scholarly Question Answering over Linked Data (QALD) Challenge. They enable a versatile and evolving semantic representation of knowledge at the crossroads of various 1. Although they vary in content, size, coverage, and overlap, they all primarily represent a collection of factual statements arranged in entity descriptions, possibly enriched by class hierarchies and corresponding property definitions. Question answering (QA) benchmarks and systems were so far geared primarily towards encyclopedic knowledge graphs such as DBpedia and Wikidata3. Currently, a new type of knowledge graph, called research knowledge graphs, is emerging whose contents are bibliographic metadata and scientific elements, such as ideas, theories, approaches, and claims as they are conveyed in scholarly contributions5 or OMICS data structures for personalized medicine6. These novel research knowledge graphs increasingly intertwine three previously largely isolated aspects: semantic representations (semantic intelligence), machine learning (machine intelligence), and crowd and expert sourcing (human intelligence). In particular, scholarly communication is a more challenging application domain for QA due to: The heterogeneity of knowledge representation;The concept drift and knowledge evolution along with the scientific discourse;The different knowledge granularity used to describe research contributions;The novel knowledge structures that go beyond simple entity descriptions.However, most publicly available knowledge graphs, such as DBpedia or Wikidata, are relatively simple to moderate semantic structures7 (https://orkg.org) currently comprising almost 170,000 resources describing research contributions of almost 15,000 scholarly articles from 709 research fields. These research contributions contain, among other things, details about the research process, methods and materials used, and specific results. Figure 8 described in the ORKG9. This paper reports on four mechanical processes for manufacturing hybrid solid components. In Fig. We present SciQA a Scientific QA benchmark for scholarly knowledge. The benchmark leverages the Open Research Knowledge Graph (ORKG)Following a bottom-up methodology, we first manually developed a set of 100 questions that can be answered with the ORKG. Subsequently, we devised eight question templates with which we automatically generated further 2465 questions that also can be answered with ORKG. The handcrafted and autogenerated SciQA questions cover several research fields ranging from Computer Science, Engineering, Chemistry, and Geology in science and technology, over Immunology and Genetics in the life sciences to Economics and Urban Studies in the social sciences. The questions cover numerous question types, including non-factoid and factoid questions, and have corresponding SPARQL queries of different query shapes and triple pattern sizes. We translated the questions into SPARQL queries over the ORKG and provide a comprehensive set of related ground-truth query results.10. JarvisQA operates exclusively with tables or tabular views of knowledge graphs, and the autogenerated part of SciQA is not based on tables or tabular views. For this reason, the evaluation is performed only for the handcrafted part of SciQA. However, the JarvisQA system is only able to retrieve correct answers for a subset of handcrafted questions due to the more diverse data and question types in SciQA compared to the data JarvisQA was built on10. Second, we present initial insights into using the large language model (LLM) ChatGPT11 for answering the handcrafted questions. This evaluation aims to understand how well one of the current most famous LLMs is able to answer complex queries on scholarly knowledge . In this evaluation, we also focused on the 100 hand-crafted questions to compare the results of JarvisQA with those of ChatGPT. In both preliminary evaluations, we found that the systems perform rather low. In the best-performing configuration , and superlatives. The benchmark ComplexWebQuestions20  has similar complexity: it contains composition questions, superlatives and comparatives. It was generated from WebQuestionsSP by sampling question-query pairs and automatically creating more complex SPARQL queries. From these queries, a set of questions was generated automatically by using 687 templates, and, then, reformulated by Amazon Mechanical Turk workers.The LC-QuAD datasethttp://qald.aksw.org/) are also of high complexity. The QALD- 10 benchmark, generated for testing in the latest challenge , contains 394 manually created Wikidata-based questions of varied complexity and each is annotated with a manually specified SPARQL query and its output. Each question may contain counts, superlatives, comparatives, and temporal aggregators. The questions are available in 4 different languages, i.a., English, German, Chinese, and Russian. The questions in English were collected from speakers with at least a C1-level language proficiency in accordance with the Common European Framework of Reference for Languages and, according to the participants, express real-world information needs. Native speakers translated the questions into other languages.The benchmarks generated for the QALD challenges  for the production, curation, and (re)use of this data. The structured knowledge in the ORKG is contributed, i.e., crowdsourced, by researchers and partially also automatically extracted from the literature or integrated from other resources comprising structured research contribution descriptions21. Figure 8 described in the ORKG9.The ORKGcontributions presented in the form of research papers. A contribution is typically linked to a research field and problem, and its description includes several properties that are specific to the research field or problem. There is no predefined, fixed set of properties to describe research contributions, but these properties can be defined on the fly by the curators. On the one hand, this openness and extensibility allow selecting and defining a very appropriate knowledge representation for the research paper at hand. On the other hand, it brings significant challenges for potential QA applications. Based on described papers and their contributions, contributions dealing with a specific research problem in scholarly literature can be compared in so-called comparisons. Comparisons are tabular representations of the properties of all compared contributions22. Such comparisons provide an overview of key information about a research problem across dozens or hundreds of contributions. In this way, comparisons are a valuable tool, for example, to determine the leading sorting algorithm or to find out how dangerous a virus is compared to other viruses.The core entity in the ORKG is 1. However, we anticipate that these novel structures of scholarly knowledge in the ORKG already pose a challenging task for QA systems.Table 20. To create the SciQA benchmark, we followed a detailed methodology that tackles the following key aspects: Objectives, Data Structure, and Data Collection. Objectives The goal of the benchmark is not only to create a dataset but also to provide a variety of knowledge base scenarios, i.e., what questions can be asked on the knowledge graph or what type of use cases can utilize the data.Data Structure The ORKG4 represents information as papers and a collection of contributions. Figure Data Collection We performed the collection of the data in two main steps: (1) Manual creation and (2) Automated generation. Figure Workflow for the 100 handcrafted questions24 in these fields to limit the scope of the data being queried. Subsequently, we defined several natural language questions according to different types, e.g., single comparison questions, True/False questions, aggregation questions , etc. For each natural language question, we created one SPARQL query in two variations (human-readable and machine-readable). As the questions and queries were created, we collected associated metadata for them, e.g., type, query shape, etc. Finally, the natural language questions, the structured SPARQL queries, and the collected metadata were peer-reviewed multiple times for syntactic and semantic correctness.We started with the first workflow by selecting research fields and corresponding lists of ORKG comparisons26 to create the relevant, realistic and useful questions.All 13 authors from five countries and three continents participated in this workflow to accommodate different perspectives. In addition, these researchers consulted domain experts from their personal networks, when available, to ensure that the questions created were relevant and important to the respective research fields and researchers. Furthermore, we leveraged the expertise of the 21 domain experts participating in the ORKG curation grantsWorkflow for the 2465 autogenerated questions27). The objective of the autogenerated questions is to target specific parts of the ORKG by creating queries with placeholders that can be populated with various entities, thereby facilitating the generation of numerous natural language questions.We performed this second workflow to enrich the SciQA benchmark as, even though the 100 handcrafted questions were purposefully created, this number of questions is rather small for a benchmark. For this purpose, we expanded the SciQA benchmark through the integration with a set of automatically generated questions, which have been created using a structured approach that involves a combination of handcrafted questions and queries, and the utilization of a LLM (in this case GPT-328 that is available in ORKG. Although this dataset belongs to only one research field (Computer Science), it is extensive, with 2236 papers  that are homogeneously described. This homogeneity is important as it facilitates the automatic generation of questions and queries. Second, we decided to focus on the questions and queries with the shape of a tree, the class which-what, and the type factoid to further narrow down the scope of the automatic generation. This shape, class, and type are the most common in the handcrafted questions and also match the nature of the selected papers-with-code data.To create the autogenerated questions, we followed a structured process. When creating the handcrafted questions, we observed that the data in the ORKG is very heterogeneous, which complicates the automatic generation of questions and queries. For this reason, we decided to set certain restrictions on the generation of the questions and queries. First, we decided to focus on a specific dataset from papers-with-code27 with careful manual validation. Next, we collected all possible entities for the placeholders in the queries from the ORKG. We then filled the queries’ placeholders with all possible entities, selecting one question randomly for each query. Finally, we collected the results of the created queries and extracted metadata for the final set of questions.Initially, we crafted a set of eight queries and 32 questions. For each query, we created one question manually and three variations using GPT-3The addition of the autogenerated questions expands the SciQA dataset to a total of 2565 questions and queries, providing a larger corpus for training machine-learning-based question-answering systems. This approach can be particularly useful compared to relying solely on handcrafted questions, which are often limited in number and may not capture the full scope of the underlying data. By contrast, the use of machine-generated questions provides a more diverse and extensive set of questions that can help improve the accuracy and robustness of machine-learning models in answering questions on large knowledge graphs.In this section, we describe our approach to creating the SciQA benchmark. Creating a QA benchmark requires a collection of questions covering different aspects and scenarios on which a knowledge graph contains data. In particular, our approach consists of two main steps: (1) Manual creation of 100 handcrafted questions and (2) Automated generation of 2465 autogenerated questions. This approach is inspired by the procedures of related works that also combined the use of manual question creation and automated question generationIn this section, we provide an overview of the SciQA questions and their corresponding SPARQL queries. We first explain how we classified the questions to extract the metadata, before presenting some examples of handcrafted and autogenerated questions in more detail.An appropriate question typology helps to satisfy two main goals of QA benchmark development. Namely, (1) an extensive coverage of the different topics in various subject areas that appear in the knowledge graph, and (2) the validation of the patterns used for writing questions and queries to ensure a better and more balanced distribution of questions across the possible different types of information requested.29 proposed a conceptual taxonomy with 13 conceptual classes, e.g., causal antecedent, goal orientation, enablement, etc. Li and Roth30 developed a two-layered taxonomy based on the answer type semantics: six coarse classes  and 50 fine classes (subclasses of different coarse classes do not overlap). Singhal et al.31 designed a small set of simple answer types corresponding to question classes, words, and expected answer types: Person, Location, Organization, Date, Quantity, Duration, Linear Measure. For example, if a question starts with who or whom, its type will be Person. The system Quarc32 defines a question categorization based on the use of certain interrogative pronouns, e.g., who, what, when, where, or why. A similar approach was used by the system AskBill33, where eleven question types were defined with question patterns such as type “QTemporalAge” identified with pattern “How old/At (Which/What) age”.There are many existing approaches to defining taxonomies of question types. Wendy Lehnert34 and analysis of characteristic problems associated with the task of mapping natural language to formal queries presented in Cimiano and Minock35. These problems include:Lexical ambiguities arise when one word can be interpreted in different ways, i.e., it can refer to different entities or concepts.Light expressions such as the verbs “to be” and “to have”, and the prepositions “of” and “with” either refer to an ontological property in a highly underspecified way or do not correspond to any property at all.Lexical gap between the user’s vocabulary and that of the ontology.Complex questions that can only be expressed using queries involving aggregation functions, comparisons, superlatives, and temporal reasoning.Research data and their descriptions have a very complex structure and semantics. When developing questions to search for information within this data, it is useful to define the types of expected answers and the focus of the questions. The definition of necessary types of expected answers is based on the results of evaluation campaigns of QALD36 defined the question focus as a word or sequence of words that indicate what information is being asked about in the question. Ferret et al.37 defined the question focus as “a noun phrase that is likely to be present in the answer” consisting of a head noun and a list of its modifiers. For example, the question “What types of nanocarriers do have therapeutic effect?” has the focus on “types of nanocarriers”. According to Mikhailian et al.38 there are two types of question foci: What are the research problems Vernier Effect is related to?”.Asking Point (AP), which is denoted explicitly, e.g., words “research problems” in the question “Who are the authors of the SOSA ontology?”.ExpectedAnswerType (EAT), is an implicit answer that can be inferred from the information provided by the question, e.g., answer type “person” is the EAT for the question “The definition of the focus of a question makes the search for an answer more specific. Moldovan et al.36 by combining the question types, e.g., WHAT, WHO, WHICH, etc., corresponding to classes from the ORKG schema, e.g., Paper, Problem, etc., and the question patterns that define the expected answer . For instance, the question “Who is the author of the most recent paper about insects?” has the pattern WHO-WHAT. We also classified the questions according to the following dimensions:ORKG-content This classification is based on the structure of the ORKG schema.Which papers use DBLP as a dataset?”.Paper-based: Questions on the content of a single or multiple research papers, e.g., “What is the most common knowledge representation method in Semantic Representations of Scholarly Communication?”.Comparison-based: Questions on the content of a comparison, i.e., on the properties that the contributions participating in a comparison share, e.g., “Question content Following the approach of Mikhailian et al.38, we classify the questions into either factoid, i.e., AP, or non-factoid, i.e., EAT. Factoid questions assume an explicit AP mapping to the entities of the ORKG ontology. If the answer to a question requires inference of a sequence of facts, counting, or filtering, we consider such questions non-factoid. We further classify them according to superlatives, e.g., “What is the most common lead compound in Anuran Antimicrobial Peptides Activity and Mechanism Against Different Biological Membranes?”, negation questions, e.g., “What percentage of comparisons lacks a class link?”, questions with counts, e.g., “What is the total number of species examined in Invasion Biology-Enemy release hypothesis?”, ranking questions, i.e., asking for a min/max value, e.g., “What is the maximum female percentage in Brief Psychotherapy for Depression Studies?”, temporal questions, e.g., “How many studies are published after 2019?”, or a combination of various types of content, e.g., “Which was the most popular approach for summarization until 2002?”.For our methodology, we modified the approach of Moldovan et al.Number of triple patterns In contrast to simple questions, the SPARQL query of complex questions consists of more than a single triple pattern18. As presented in Tables Query shape: We identified the shape  of the queries according to Bonifati et al.39. Note that the classification based on the number of triple patterns is incorporated in this classification, as simple questions can be classified as single-edge queries.Query components We have noted the keywords and operators that are used to build each query, for instance, SELECT, ASK, DESCRIBE, COUNT, REGEX, STR, FILTER. These components give an insight into how complicated a query is and what feature should a QA system support to generate such structured queries.Finally, we characterized the questions based on important properties of their respective SPARQL queries:23.The key parts of the SciQA benchmark are natural language questions, which are translated into formal queries in the SPARQL query language and classified along a comprehensive query classification (presented in the “Methods” section). We first give an overview of the 100 SciQA queries, before presenting three exemplary questions and corresponding queries in detail. While Table Handcrafted QuestionWhat is the average energy generation for each energy source considered in 5-year intervals in Greenhouse Gas Reduction Scenarios for Germany?Energy Systems from the domain of Mechanical Engineering. This non-factoid question is based on the comparison Greenhouse Gas Reduction Scenarios for Germany41, which summarizes the results of various studies analyzing a future low-carbon energy system with a focus on electricity generation for Germany. The question of average values for the energy generation for different energy sources in 5-year intervals is typical for this research field. Consulted domain experts confirmed that these average values are needed for trend analysis, for example. The corresponding SPARQL query includes seven triple patterns, uses eight query components, and is shaped as a tree.The first question (ID 55 in SciQA-Handcrafted) belongs to the research field Handcrafted QuestionWhat is the most common knowledge representation method in Semantic Representations of Scholarly Communication?Databases/Information Systems from the domain Computer Science. This non-factoid question is based on the ORKG comparison Semantic Representations of Scholarly Communication22. This comparison provides an overview of publications on semantic representations of scholarly communication by focusing on scholarly communication as a whole and not specific data such as citations. The question is a typical one about the most frequent occurrence of information. Specifically, it is about the most commonly used knowledge representation data model for scientific communication, which in this case is the Resource Description Framework . The SPARQL query includes three triple patterns, uses seven query components, and is shaped as a chain.The second question (ID 3 in SciQA-Handcrafted) belongs to the research field Handcrafted QuestionWhere did the study with maximal geographic scale take place in Genetic Variability (COI Variation) in Studies Large Sampled (>1000 Sequences)?Ecology and Biodiversity of Animals and Ecosystems, Organismic Interactions from the domain of Zoology. This non-factoid question is based on the comparison Genetic Variability (COI Variation) in Studies Large Sampled (>1000 Sequences)42 which compares the genetic variability in studies containing more than 1000 cytochrome c oxidase I (COI) barcoding sequences. The question aims to identify where the study with the maximum geographic scope took place, which in this case is a study conducted in the United States of America, Mexico, and Canada. The SPARQL query has six triple patterns, uses six query components, and is shaped like a tree.The third question (ID 78 in SciQA-Handcrafted) belongs to the research field Autogenerated QuestionCan you provide the highest benchmark result, including the metric and score, for the Sequential MNIST dataset?Computer Science. This non-factoid question is based on the content of the ORKG imported from papers-with-code28. The question is about fetching the top (or the best) evaluation score recorded in the ORKG, the results should be fetched for each distinct evaluation metric used in the evaluation. The related SPARQL query includes ten triple patterns, uses nine query components, and has a shape of a tree.The fourth question (ID 1355 in SciQA-Autogenerated) belongs to the research field Autogenerated QuestionList the title and ID of research papers that contain a benchmark over the SST-2 Binary classification dataset?Computer Science. This factoid question is based on the content of the ORKG imported from papers-with-code28, which describes the evaluation results of machine learning models benchmarked on commonly used datasets in the natural language processing and machine learning communities. The question requests the IDs and titles of papers that have models that benchmarked a particular dataset, in this case, the SST-2 Binary classification dataset. The related SPARQL query includes six triple patterns, uses four query components, and has the shape of a tree.The fifth question (ID 524 in SciQA-Autogenerated) belongs to the research field Below, we present three example handcrafted questions and two example autogenerated questions with their corresponding SPARQL queries for different research fields. Although the ORKG uses alphanumeric identifiers (similar to Wikidata), we present the queries here with human-readable identifiers for properties obtained from the corresponding resource labels. For convenience, SciQA is accompanied by a SPARQL query preprocessor, which converts the human-readable queries back to the ones with alphanumeric identifiers.10. Second, we show initial insights into using ChatGPT11 for answering the handcrafted questions.In this section, we present two preliminary evaluations using the handcrafted part of the SciQA benchmark. First, we show the results of a proof-of-concept implementation of a QA system based on the JarvisQA system10.In a preliminary analysis, we aim to understand how SciQA can be used by a QA system that is focused on scholarly knowledge. For this purpose, we investigate the performance of a proof-of-concept implementation based on JarvisQA43 but works only on tables and tabular views of scholarly knowledge graphs, such as ORKG comparisons. SciQA does not rely only on tables and tabular views (comparisons) but has a broader spectrum of question/answer types. For this reason, we can answer 52 of the handcrafted questions (52%) with JarvisQA as they correspond to its input form. We configured our proof-of-concept implementation of JarvisQA to run on the compatible questions of SciQA, and we use seven distinct experimental setups that JarvisQA provides. Due to the limited coverage of questions that the system can answer, we limit the results to two categories of questions. The evaluation is conducted in terms of precision@k, recall@k, and f1@k metrics.JarvisQA is fundamentally designed to answer questions about scholarly knowledge. The system is based on BERTnormal and overall. While the category normal refers to single-answer questions, the category overall aggregates single-answer questions and all other question types that JarvisQA can answer, such as listing and boolean questions. We note that the performance decreases across all the setups for the overall category because of the complex nature of the SciQA benchmark and the answers it expects, unlike what JarvisQA was trained with and thus can answer10.Table 11. Numerous LLMs adept at solving common natural language tasks have now been released such as ChatGPT11, Galactica44, LaMDA45, Codex46, or Sparrow47. Some of them show better performance on technical knowledge tasks, e.g., Galactica44, some in the medical domain, e.g., PubMedQA48 and MedMCQA49, etc. In this experiment, we do not aim to test all these LLMs on SciQA, but to estimate the baseline performance of LLMs for scholarly questions about various topics. We chose ChatGPT for our experiment as it is one of the most prominent LLMs at the moment, and it is not domain-specific. ChatGPT should be able to answer the questions from SciQA, as the source texts of the papers and the topics of the questions that were used to develop the dataset are mostly openly available on the internet. For this reason, we assume that questions from the SciQA can be potentially processed and answered by LLMs such as ChatGPT. In this way, this evaluation aims to gain initial insights into how well one of the current most famous LLMs, which is not trained specifically on ORKG data, is able to answer complex queries on scholarly knowledge .Besides the use of SciQA with a QA system focused on scholarly knowledge, we performed an additional preliminary evaluation based on all handcrafted questions using ChatGPTThe underlying model of ChatGPT is designed to generate detailed answers to a user’s questions. For this reason, we have added the additional prompt “short:” to each of the 100 handcrafted questions to get shorter answers similar to the answers in SciQA. Although ChatGPT’s responses were shorter, they were still very detailed. In the future, a more refined individual tuning of the prompt is necessary to obtain answers in a more similar format to the answers of our dataset. However, we decided that this way of retrieving the answers is sufficient for our preliminary evaluation of SciQA. After collecting the 100 answers to all questions, the assessment of its correctness was performed with expert opinion.Four experts compared the ChatGPT’s answers with the answer from the SciQA dataset. If correct facts were mentioned in the text returned by ChatGPT, that answer was assessed as “Correct”. Otherwise, the result was assessed as “Incorrect”. Also, if the system returned a response that it could not answer the question, it was assessed as “No answer”. After all the answers were independently assessed by the experts, the experts compared their results and discussed any disagreements in a meeting. During this discussion, situations arose in which the experts could not agree whether the answer was derived from the paper or data source mentioned in the question or whether the answer had been generated from common sense or general knowledge. As a result, the experts assessed these answer as “Uncertain.”. In Table correct, 40 answers are incorrect, and nine answers are uncertain. Although these results are slightly better compared to the results of the best performing configuration of the proof-of-concept implementation of JarvisQA  the higher complexity of scholarly knowledge; (2) the insufficiency of LLMs for dealing with scholarly knowledge, and (3) the advancement of knowledge graphs towards cognitive knowledge graphs for facilitating the integration of semantic and machine learning approaches.We argue, that scholarly knowledge is more complex than common sense knowledge, e.g., encyclopedic knowledge. We illustrated this complexity with some examples, e.g., Fig. 50. Another significant limitation is that LLMs like ChatGPT and BERT do not possess the contextual understanding specific to a knowledge graph, such as the ORKG, which further hinders their performance on the SciQA benchmark. Taking into account all the factors mentioned above, it becomes increasingly clear that there is a pressing need for the research community to rally behind the SciQa benchmark. By collaborating to develop systems that perform well on SciQA, researchers can contribute to improve and expand this QA dataset, as well as make progress in this field of QA for scholarly knowledge. With this goal in mind, we launched the Scholarly Question Answering over Linked Data (QALD) Challenge with a task using SciQA as one of the open competitions at the 22nd International Semantic Web Conference 202352. With this challenge, we hope to generate more baselines and inspire the community to create an array of scholarly-oriented tools and QA systems. Ultimately, this collaborative effort will foster significant advancements in the field, benefiting academia as a whole.In our preliminary evaluations, we observed that even QA systems specifically designed for academic data, including one of the most advanced LLMs available at the time of this article, struggle to excel on the SciQA benchmark. The SciQA dataset encompasses various question types stemming from distinct entities within the knowledge graph, rather than solely relying on tabular views, which is the primary format JarvisQA operates on. Moreover, to accurately respond to SciQA queries, a QA system must comprehend the context of each question, which is embedded in the ORKG graph structure. One contributing factor to this challenge is the deficiency in NLP components tailored to academic data, such as entity linkers and query builders53. We deem, that this is an inherent characteristic of LLMs, which can also not be addressed with further improvements of the models themselves. However, a combination of LLMs with symbolic knowledge representation approaches (such as the ORKG and SciQA) can be a promising avenue for leveraging the potential of AI and also for domains with more unique knowledge production such as science.A reason for this challenge even for LLMs lies in the fact, that these models are very good at recreating common sense knowledge, which can be found in varying forms in several different sources. However, due to their nature of employing probability distribution over sequences of words, they are not good at dealing with knowledge to be found only in a single or very few sources. This issue was also shown recently with the failed LLM Galactica trained on scientific literature, which had to be taken offline after three days when it became clear that the model’s ratio between hallucination and reasonable answers is too unfortunate to be of any use54, common vulnerabilities and exposure descriptions in developer security55 or OMICS data for personalized medicine6. We see these base constituents of cognitive knowledge graphs to be complex fabrics of entity descriptions arranged according to certain patterns, such as graphlets. In network analysis and graph theory, the notions graphlet56 and motif57 were introduced to provide a structuring element between whole graphs and individual nodes and edges. Hence, in order to be able to effectively represent and manage more complex knowledge artifacts, the notion of graphlets can be applied to knowledge graphs (as we did in SciQA with research contributions). Cognitive knowledge graphs can be of particular importance to support the step from correlation to causality—while correlation arises from the detection of statistical relationships and patterns in the data, we plan to use rich contextual knowledge from knowledge graphs as additional signals for causality testing. Such integration of symbolic and sub-symbolic intelligence as HybridAI  for a recent survey of approaches) can help us to systematically anchor transparency, traceability, explainability, trustworthiness, and reliability in data science and AI methods.Scholarly knowledge graphs such as ORKG demonstrate the advance of the knowledge graph concept towards more cognitive knowledge graphs, which enable the trustworthy integration of artificial and human intelligence. In cognitive knowledge graphs, the constituents will be more complex elements, such as ideas, theories, approaches, and claims as they are conveyed, for example, in scholarly contributions, but also in other areas such as industrial product models1. In contrast, the consideration of scientific knowledge combined with knowledge graphs is rather new and challenging due to heterogeneous representations, concept drifts and evolution over time, different levels of granularity, and novel semantic structures.In this section, we draw some conclusions and point out directions for future work. We address the problem of missing QA benchmarks for scholarly knowledge. So far, QA systems and corresponding benchmarks were mainly geared towards encyclopedic knowledge composed of relatively simple to moderate semantic structuresFor these reasons, we developed the SciQA benchmark for scholarly knowledge as a new challenging task for the next-generation QA systems with 13 different researchers using a defined bottom-up methodology. SciQA contains 100 handcrafted natural language questions with paraphrases, corresponding human- and machine-readable SPARQL queries with their results. These questions and queries are analyzed according to several classifications and cover 48 different fine-grained research fields such as Computer Science, Engineering, Chemistry, Geology, Immunology, and Economics  Challenge with a task using SciQA as one of the open competitions at the 22nd International Semantic Web Conference 202352. An extension of this work is to perform QA on federated scholarly knowledge graphs that link ORKG content to metadata about articles, datasets, people, organizations, etc. published by other scholarly infrastructures60. Given the advanced standardization of the persistent identification, description, interlinking, and exchange of metadata about these entities as well as the provision of (programmatic) access to metadata through systems such as the GraphQL-based PID Graph, the federated integration of ORKG content with metadata about contextual entities is straightforward. This will enable QA on scholarly knowledge understood broadly to include both the scientific knowledge published in articles interlinked with contextual knowledge about its production and consumption.This work is the foundation for a longer research and technology development agenda. We envision advancing the concept of knowledge graphs from rather simple, atomic entity descriptions towards richer structured knowledge graphs, comprising fabrics of complex knowledge structures such as knowledge graph cells


In all the numerical results, the temperature variation has been considered from 200 to 350 K. It has been concluded that the graphene–InSb interface provides more temperature assisted tunability to the interfacial surface modes, commonly known as surface waves, as compared to monolayer graphene. Further, the graphene parameters can play a vital role in the dynamical tuning of electromagnetic surface waves in THz to IR frequency range. The numerically computed results have potential applications in designing of thermo-optical waveguides, temperature assisted communication devices, thermo-optical sensors and near field thermal imaging platforms.The thermal agitation plays a vital role in tunability of optoelectronic, structural and chemical characteristics of the temperature sensitive materials. Graphene enables the THz optics, due to its unprecedent controlling characteristics over the traditional materials. The influence of temperature on the monolayer graphene is very negligible due to its low free charge carrier density, to enhance the thermal sensitivity of graphene, the graphene loaded temperature sensitive material interface has been proposed. A theoretical analysis has been carried out on temperature dependent propagation characteristics of electromagnetic surface waves supported by the graphene loaded semi-infinite indium antimonide (InSb). The InSb has been taken as temperature sensitive material. The Drude model has been used for the modeling of InSb in the THz region while the modeling of the graphene has been done by random phase approximation-based Kubo’s formulism. To realize the graphene loaded indium antimonide interface, the impedance boundary conditions (IBCs) have been employed. The numerical analysis has been conducted to analyze the influence of temperature on the characteristics of electromagnetic surface waves i.e., dispersion curve, effective mode index (N The electromagnetic surface waves propagate along the interface of two dissimilar media and decays exponentially as moved away from the interface6. The different types of surface waves can be guided by interface of different partnering materials i.e., the metal–dielectric interface supports the surface plasmon polaritons (SPPs) wave, the dielectric-anisotropic dielectric interface supports the Dyakonov surface waves, the nonlinear and chiral interface guide the nonlinear and chiral surface waves respectively9. The characteristics of the surface waves highly depends upon the nature and characteristics of the partnering material10. The tunability of the surface waves is the major demand for their practical implementation. To overcome this issue, the different passive and active techniques have been developed to tune the surface waves11. Meanwhile, the different studies on corrugated semiconductors-based structures for plasmonic spoof have been conducted by different researchers to tune the propagation band of the SPP waves from visible range to THz range15.Electromagnetic surface waves have exciting applications in surface communication, optical sensing, chemical sensing, spectroscopy and near-field imaging16. Similarly, an interface of partnering material can guide the surface wave at different temperature if the constitutive parameters of these materials are sensitive towards temperature. In literature, indium antimonide (Insb) and vanadium dioxide (VO2) have been extensively studied as temperature sensitive semiconductor materials due to their temperature dependent metal–insulator transition characteristics18. The temperature dependent electromagnetic surface waves supported by the temperature sensitive materials (TSMs) have been reported by many researchers i.e., Mackay and Lakhtakia solved the canonical boundary value problem based upon the uniaxial material and isotropic indium antimonide (InSb) material and reported the temperature dependent transition of Dyakonov waves to SPP waves at 0.6 THz frequency19. Further they extended their work and carried out the theoretical investigations on the temperature dependent hyperbolic materials composed of Insb and reported the propagation of surface waves with negative phase velocity at 2 THz frequency range20. In parallel, Fedorin numerically analyzed the influence of temperature on the surface waves supported by the planar interface between the porous nanocomposite material and hypercrystal composed of n-type InSb semiconductor layers and reported that the effective mode index, penetration depth and propagation frequency range are sensitive to the temperature. Moreover, a detailed analysis has been presented on the dissipation factors which affects the propagation of surface plasmon modes as well as hybrid additional modes and concluded that the under appropriate temperature range the propagation frequency band and surface wave parameters can be enhanced21. Recently, the theoretical investigation on the temperature dependent propagation of SPP waves supported by the silver/vanadium-dioxide interface has been carried out for the temperature sensing and crystallographic phase detection applications. The characteristics of SPP wave are found sensitive to the entire range of thermal hysteresis of VO2 i.e., that as the surface waves follows the thermal hysteresis22.The control over the characteristics of partnering materials plays vital role in the tuning and propagation of surface waves. Any physical or chemical sources near to the interface can induced the variations in the characteristics of surface waves, which provides the chemical sensing, optical sensing, defect sensing23. Generally, the indium antimonide detectors are sensitive to the wavelength range 1–5 µm24.Until now, the temperature dependent electromagnetic surface waves supported by the InSb have been investigated for only some values of visible and near IR frequencies, however the extended propagation frequency range is major requirement for the broad applications. To full fill this gap, the graphene coated indium antimonide heterostructure has been proposed for temperature dependent surface waves for THz to IR board propagation band frequency range. The Indium Antimonide (InSb) is a small band gap semiconductor material which have potential applications in designing the thermal imaging cameras, FLIR systems, infrared homing missile guidance systems, and infrared astronomy25. However, as the graphene is the allotrope of carbon, displays one atom-thick hexagonal configuration with low carrier density of free electrons, the efficiency of such devices is not so high as traditional devices. To enhance the optical, electrical and thermal properties of graphene, the different schemes as well as interfaces have been studied i.e., Lan et al., examined the transmission based highly efficient IR modulation assisted by the asymmetric light plasmon coupling on graphene nanoribbons based platforms and reported that the increase of 4% to 41% in the efficiency26. Zhao et al., worked on the electrochemical characteristics of graphene based composite organic structure (PMDA-NiPc-G) for charge transportation characteristic of the lithium ions batteries and reported that the incorporation of graphene improves the charge kinetics of lithium ions in the two dimensional grid structure27. Chen et al., investigated the structural, optical and electronic properties of the graphene–Bi2O2Se heterojunction under Van der Walls interaction in the frame work of density functional theory and reported that the control over the interfacial length between the graphene and Bi2O2Se can tune the photoelectric characteristics of the heterojunction28.Meanwhile, the graphene based plasmonic devices has open up new horizons for the researchers regarding the active control and manipulation of THz to IR band gap which are not possible by traditional liquid crystal and micromirror based devices. The unusual and extraordinary mechanical, electronic, optical and thermal properties of graphene made it suitable candidate for the plays important role in the m29. In such studies, it is very important to understand the effect of temperature near to the interface of graphene and partnering materials and their interfacial plasmonic modes. To address this issue, the graphene loaded indium antimonide (InSb) interface has been studied for the temperature assisted plasmonic modes. The InSb has been chosen as temperature sensitive material because it has wide applications in IR as detectors, optoelectronics, microelectronics and solar cells and photovoltaics. Due to monolayer thickness and low charge density of the free electrons, the thermal response of the single layer graphene is very low and not sufficient for the efficient response to the thermal agitation. It is hypothesized that the thermal response of the graphene layer can be enhanced by the adding the TSM substrate. Keeping in view of these requirements, the theoretical study on the propagation of surface waves supported by the graphene loaded indium antimonide (InSb) interface has been conducted to achieve the subsequences objectives i.e., to enhance of the thermal sensitivity IR sensors/detectors based on the indium antimonide by graphene deposition, to enhance the thermal response of the monolayer graphene, and to get the wide propagation range of plasmon modes range from THz to IR region. For this purpose, the characteristic equations for TM-polarized and TE-polarized surface wave have been calculated analytically. The numerical results for the effective mode index (Neff), penetration depth (δ), propagation length (Lp), phase velocity (Vp), field profile and dispersion curve have been presented. The partition of the work is as follows i.e., “Du et al., achieved the thermal electrons via photoexcitation pumping of the mid IR pulses on the stacked layers of graphene via Bernal sequence and reported the observation of the near IR wavelengths of hot electrons. Moreover, the correlation between the number of staking layers and photocurrents has been determined and concluded that the photo-excited currents depend upon the number of staked layers, which leads to the electron thermalization process and ultrafast mid IR photodetectorsThe analytical formulation based upon the canonical boundary value problem for the propagation of surface waves supported by the graphene-loaded temperature sensitive material i.e., indium antimonide (InSb) has been presented in this section. The geometry of the problem is depicted in Fig. 2(ω) and μ2(ω) have been used to model the TSM media, while the Kubo’s formulation is used in the analytical modeling of graphene. The optical conductivity (σg) of the graphene layer is modeled as the function of operating frequency (ω), electron-photon scattering rate (τ), chemical potential  or with the assistance of a continuous electric field 32. The analytical formulation has been presented for the two states of polarization i.e., Transverse magnetic (TM) and secondly the transverse electric (TE) polarization. The Propagation of surface waves is supposed to be along the x-direction, as showed in Fig. The dispersive constitutive relations i.e., εmentclass2pt{minim21;Field equations for the transverse magnetic (TM) mode of polarization of surface waves, supported by Graphene-loaded temperature sensitive material for each region are as given in Refs.for region z > 0,33For region z < 0By incorporating Eqs. –7) in E in E7) i21;The mode of propagation of surface wave in which the electric field (E) is supposed to be in direction perpendicular to the plane of incidence, is called transverse electric (TE) polarized surface wave. The associated field phasors are given asfor region z > 033The characteristic equation has been derived by after enforcing the impedance boundary conditions (IBCs) on the field phasors given in Eqs. –16) as3 as316) aTo get more insight into understanding the physical significance, the numerical results have been presented for these characteristic equations in the next section.In this section, the numerical results, regarding the electromagnetic surface wave propagating along the graphene-loaded indium antimonide (InSb) interface, have been presented. In the first part, the electromagnetic modeling of InSb and its temperature dependent insulator to metal transition characteristics has been analyzed under different temperature range. In second part, the possible numerical solution for electromagnetic surface waves supported by graphene–InSb interface has been computed. All these numerical computations have been executed in the Wolfram Mathematica software pack.20;The electromagnetic modelling of the indium antimonide (InSb) has been done in the frame work of hybrid Drude model as34. Further, the interface of adjacent materials supports surface waves only when the real part of permittivity of such materials should have opposite in signs22. From the Fig. It is obvious from the Fig. entclass1pt{minimaentclass1pt{minima35. All these numerical calculations are taken at different values of temperature and chemical potential graphene. The electromagnetic surface waves are highly sensitive to the state of polarization. The TE-polarized surface wave is very loosely confined at the graphene layer when compared to the TM-polarized surface wave35. Therefore, in the subsequent results, the dispersion curve, phase velocity (Vp) effective mode index (Neff), penetration depth (δ), propagation length (Lp) and field profiles of TM-polarized surface waves supported by the graphene-loaded indium antimonide InSb structures, have been discussed.The conditions describe above are in favor to propagation of surface wave so we are going to study the existence of surface waves on graphene-coated indium antimonide, this part covers the temperature dependent characteristics of surface waves. The contour plot technique has been implemented in Wolfram Mathematica kernel to find out the possible solutions for the unknown wave propagation constant (β) that satisfy the characteristic equations6.First of all, to study the collective response of THz waves at the interface of graphene–InSb, the dispersion curve analysis has been computed numerically between the angular frequency (ω) and propagation constant Re(β). The dispersion relation has significant role in analyzing the propagation characteristics and nature of wave propagation in media33. For lower temperature range The Fig. entclass1pt{minimaeff) as function of frequency at different value of temperature i.e., 35. The confinement of the surface waves on the interface is estimated by the effective mode index. It is obvious from the figure that the confinement of the surface waves on the graphene–InSb interface is sensitive to the external temperature i.e., with the increase of temperature, the effective mode index is increasing. To further study the impact of effective mode index as function of temperature under variation of operating frequency and chemical potential has been presented in Figs. In Fig. It is clear from the fig that the effective mode index has the explicitly depends upon the temperature. Moreover, the confinement of the surface wave can be dually controlled by temperature as well as operating frequency. The Fig. eff) as a function of temperature has been presented in Fig. eff) decreases, but the all the graphs terminated on the same temperature i.e., 320 K. For the temperature 33. The penetration depth is normalized by wave number of free space i.e., 22 and with the increase of temperature the hysteresis width increases. In Fig. p) as a function of frequency for the propagation of EM surface waves on the Graphene-based InSb has been presented. The fig shows that the propagation length (Lp) follows the thermal hysteresis type trend under different values of temperature i.e., p) is almost same for higher values of frequency but for temperature range The influence of chemical potential p) of the surface waves guided by the graphene–InSb interface. To compute the normalized phase speed in kernel, the mathematical definition is used as p) has been done with the speed of light. It is clear from the graph that phase speed of electromagnetic surface wave follows the hysteresis trend which can be controlled by tuning the temperature as discussed in Ref.21.Figure The field profiles of electromagnetic surface waves supported by Graphene loaded indium antimonide, as a function of transverse distance from interface (z) under different values of temperature i.e., (i)The electromagnetic surface waves guided by the graphene-loaded indium antimonide (InSb) interface are akin to the to the surface plasmons polaritons (SPPs) waves.(ii)The temperature as an external parameter plays an active role in controlling the propagation characteristics of surface waves i.e., dispersion relation, confinement of surface waves and phase speed, penetration depth, propagation length and field profile.(iii)The chemical potential/doping of graphene also provide additional degree of freedom to tune and modify the propagation characteristics of the surface waves.(iv)The numerical results for penetration depth, propagation length and phase speed reveal that they have thermal hysteresis type dependence against temperature and frequency variation.(v)The computed numerical results in this work may have potential applications in thermo-optical sensor designing, THz photoemission temperature assisted communication devices, near field thermal imaging and spectroscopy platforms designs.The thermally tunable electromagnetic surface waves supported by the graphene-loaded InSb has been studied. The propagation characteristics of surface waves, such as dispersion curve, effective mode index, penetration depth, normalized phase velocity, propagation length and field profile for the graphene-loaded InSb, are found sensitivities to external temperature and chemical potential. The following conclusions have been drawn from the numerically computed results:


JAC-Antimicrobial Resistance, Volume 5, Issue 2, April 2023, dlad030, https://doi.org/10.1093/jacamr/dlad030This is a correction to: Hala Kandil, Robert M Gray, Rakan El-Hamad, Madhuri Vidwans, Tejal Vaghela, Omar Naji, Sebastien Van De Velde, Empirical use of temocillin in hospitalized patients: results from a retrospective audit, In the originally published version of this manuscript, the affiliations of authors Rakan El-Hamad and Tejal Vaghela were incorrect.Rakan El-Hamad's affiliation was given as ‘Microbiology Department, West Hertfordshire Teaching Hospitals NHS Trust, Vicarage Road, Watford, Hertfordshire WD18 0HB, UK’ instead of ‘Department of Pathology and Microbiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan’.Tejal Vaghela's affiliations were given as ‘Microbiology Department, West Hertfordshire Teaching Hospitals NHS Trust, Vicarage Road, Watford, Hertfordshire WD18 0HB, UK’ and ‘Department of Pathology and Microbiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan’, instead of just ‘Microbiology Department, West Hertfordshire Teaching Hospitals NHS Trust, Vicarage Road, Watford, Hertfordshire WD18 0HB, UK’.These errors have been corrected.


Sarcoidosis is an inflammatory disease that involves the eyes in 10–55% of cases, sometimes without systemic involvement. All eye structures can be affected, but uveitis is the most common ocular manifestation and causes vision loss. The typical ophthalmological appearance of these uveitis is granulomatous (in cases with anterior involvement), which are usually bilateral and with synechiae. Posterior involvement includes vitritis, vasculitis and choroidal lesions. Tuberculosis is a classic differential diagnosis to be wary of, especially in people who have spent time in endemic areas. The diagnosis is based on histology with the presence of non-caseating epithelioid granulomas. However, due to the technical difficulty and yield of biopsies, the diagnosis of ocular sarcoidosis is often based on clinico-radiological features. The international criteria for the diagnosis of ocular sarcoidosis have recently been revised. Corticosteroids remain the first-line treatment for sarcoidosis, but up to 30% of patients require high doses, justifying the use of corticosteroid-sparing treatments. In these cases, immunosuppressive treatments such as methotrexate may be introduced. More recent biotherapies such as anti-TNF are also very effective (as they are in other non-infectious uveitis etiologies). Cutibacterium acnes, on a predisposing genetic terrain ..74].The visual prognosis for ocular sarcoid uveitis is generally good . Fewer tThe level of evidence in the literature on the management of sarcoid uveitis is low and is mainly based on small retrospective studies . The recLocal treatment is the first-line treatment for anterior uveitis . TopicalThe decision to treat intermediate and posterior uveitis depends not only on symptomatology but also on visual acuity and anatomical involvement such as macular edema, severity of retinal vasculitis or active choroiditis . Topical®, Abbvie, North Chicago, IL, USA) are currently taking over from subconjunctival injections with a duration of action of 4–6 months compared with 3 weeks [® Alimera Sciences Inc., Alpharetta, GA, USA) cover a period of 3 years and could decrease the frequency of non-infectious uveitis [In practice, dexamethasone intravitreal implants  or because of the side effects of systemic corticosteroid therapy ,49,55. TThe reference biological treatments are anti-TNF . ParadoxThe use of other biotherapies is more exceptional in sarcoid uveitis . TocilizSarcoidosis is one of the most common causes of uveitis. Uveitis is the most common ophthalmological involvement in sarcoidosis, hence the requirement for knowledge on ophthalmological semiology in the management of sarcoidosis. The ophthalmic symptomatology should be known not only by ophthalmologists but also by all specialists dealing with patients with sarcoidosis. A close collaboration between the different specialists is necessary for both diagnosis and therapeutic management. Larger prospective studies are still needed to improve the level of evidence for the recommendations on the management of sarcoid uveitis.


Staphylococcus aureus in migratory birds (such as white storks) is necessary to understand their relevance in the “One Health” ecosystems. This study determined the nasotracheal carriage rates of S. aureus from white storks in Southern Spain and genetically characterized the within-host diversity. A collection of 67 S. aureus strains, previously obtained from 87 white stork nestlings  fed by their parents with food foraged in natural and landfill habitats, were tested for their antimicrobial resistance (AMR) phenotypes. Moreover, the AMR genotypes, immune evasion cluster (IEC), virulence genes and the detection of CC398 lineage were studied by PCR. The spa types and multilocus-sequencing-typing (MLST) were also determined by PCR and sequencing. Staphylococcus aureus carriage was found in 31% of storks . All isolates were methicillin-susceptible (MSSA) and 8.8% of them were also susceptible to all tested antibiotics. The AMR phenotype/percentage/genes detected were as follows: penicillin/79.1%/blaZ; erythromycin-clindamycin-inducible/19.1%/ermA, ermT; tetracycline/11.9%/tetK; clindamycin/4.5%/lnuA and ciprofloxacin/4.5%. Twenty-one different spa types, including 2 new ones (t7778-ST15-CC15 and t18009-ST26-CC25), were detected and ascribed to 11 clonal complexes (CCs). MSSA-CC398 (8.2%), MSSA-CC15 (7.1%) and MSSA-ST291 (5.9%) were the most prevalent lineages in storks. Moreover, tst-positive (MSSA-CC22-t223 and MSSA-CC30-t1654), eta-positive (MSSA-CC9-t209) and etb-positive strains (MSSA-CC45-t015) were detected in four storks. The 18.5% of storks harboured distinct MSSA strains (with different lineages and/or AMR genes). Nestlings of storks foraging in landfills (10 CCs) had more diverse S. aureus strains than those of parents foraging in natural habitats (3 CCs). Low level of AMR was demonstrated among S. aureus strains. The predominance of MSSA-CC398 (an emergent clade) and toxigenic MSSA strains in stork nestlings highlight the need for continuous surveillance of S. aureus in wild birds.The molecular ecology of The online version contains supplementary material available at 10.1007/s00248-023-02208-8. Humans are dependent on healthy ecosystems. The “One Health” approach, under which the World Health Organisation (WHO) has based the global strategy to tackle the problem of antimicrobial resistance (AMR), is based on the close link between human, animal and environmental health . The recCiconia ciconia) among other wild birds  has attracted interest in the study of bacterial ecology and epidemiology as they have been shown to forage in human settlements, farmlands and dumpsites  [Recently, white stork ( wastes) –5. Also, wastes) , 6.Staphylococcus genus is one of the common colonizers of the skin, nostrils and trachea of many wild animals [S. aureus represents the most clinically important staphylococcal species in human and animal health [S. aureus carriage rate (34.8%) in white stork nestlings exposed to human residues [S. aureus among magpies and cinereous vultures [S. aureus [The  animals –9. Especl health . Previouresidues . Subsequvultures . Both stvultures , 11. Suc. aureus .S. aureus strains have shown economic importance in livestock production and this fact is mainly represented by the emergence and spread of certain antimicrobial resistances and clones (livestock associated) that drastically reduce animal yield [S. aureus (MRSA) of clonal complex 398 (CC398) was first described in pigs and humans in contact with them in 2005 and was considered a livestock-associated (LA)-MRSA lineage [S. aureus-CC398 (MSSA-CC398) is being detected as an emerging clone that has largely been implicated in human invasive infections in some countries, including Spain [Certain al yield . Methici lineage , 14; in  lineage . Hence,  lineage . On the ng Spain . HoweverS. aureus isolates obtained in that study, determining genetic lineage diversities, antimicrobial resistance mechanisms, immune evasion cluster (IEC) types and virulence factors of these isolates. Consequently, we analysed and compared S. aureus isolates obtained from nasal and tracheal samples of white stork nestlings from nests associated to either natural habitat and landfills and that are thus fed by their parents with differentially landfill or natural habitat foraged food, in Southern Spain.In a previous study, the nasotracheal microbiota of white stork nestlings of parents foraging in different habitats was analysed, obtaining over 800 bacteria isolates of different genera and species, including different staphylococcal species . The preS. aureus [S. aureus colonies (up to 6 per sample) were randomly selected based on colour, size and/or hemolysis. The strains were identified by matrix-assisted laser desorption/ionisation time-of-flight  using the standard extraction protocol recommended by Bruker Daltonics as previously described [S. aureus (n = 67) were obtained from 29 of the 137 samples (21.2%) of 27 of the 87 storks (31%) analysed in a previous study [S. aureus carriage was higher in the nasal than in the tracheal cavities , as indicated in the previous study [S. aureus carriage was found in stork nestlings whose parents foraged in natural habitats and landfills, respectively. Moreover, 7.1% and 16.3% tracheal carriage was found in stork nestlings fed natural habitat and landfill foraged food, respectively  and 52 nasal (N). Of these animals, both nasal and tracheal samples were collected from 49 of them. The storks corresponded to four different colonies of regions of southern Spain with different foraging strategies. Storks in colonies 1 and 2 were located in natural habitats in open woodland devoted to extensive livestock grazing where storks forage, while those in colonies 3 and 4 are located on or close to landfills and the adults forage in landfills. These samples were processed for the recovery of the bacterial community diversities, which included . aureus . Potentiescribed . The strus study . Specifius study . In relaFor DNA extraction, the strains were streaked on BHI agar and incubated for 24 h at 37 °C. An isolated colony was suspended in 45 μL of sterile MiliQ water and later 5 μL of lysostaphin was added (1 mg/mL) (Sigma®). The mixture was vortexed and incubated for 10 min at 37 °C. Forty-five microliters of sterile MiliQ water, 150 μL of Tris–HCl  and 5 μL of proteinase K (2 mg/mL) (Sigma®) were added. This was vortexed and incubated for 10 min at 60 °C. Finally, it was boiled for 5 min at 100 °C and centrifuged at 12,000 revolutions per minute for 3 min. The DNA was stored at − 20 °C.S. aureus strains following the recommendations and breakpoints of the European Committee on Antimicrobial Susceptibility Testing [Susceptibility testing for 13 antimicrobial agents was performed by the disc diffusion method with all  Testing . The antblaZ, mecA and mecC), erythromycin and clindamycin  and tetracycline . Primers and conditions of PCRs for the AMR genes tested are included in Supplementary Table The presence of the following resistance genes was tested by single PCRs, selected according to the antimicrobial resistance phenotype: beta-lactams  were tested by PCR in every strain. Also, the immune evasion cluster (IEC) genes  were analysed and classified accordingly into seven different IEC types (A–G), based on the combination of the positive genes. The scn gene  was used as a marker of the IEC system. Primers and conditions for all PCRs for virulence and IEC genes are included in Supplementary Table The presence of the virulence genes S. aureus strains were characterized for spa genotyping by PCR/Sanger sequencing. New repeat combinations were submitted to the Ridom spa Server (https://spa.ridom.de/submission.shtml). CC398 clone was determined by a specific PCR protocol for the sau1-hsdS1 variant developed by Stegger et al. [spa types.All r et al. . PrimersS. aureus . The 7 housekeeping genes  of S. aureus strains were amplified as previously described (Supplementary Table http://pubmlst.org/).Multilocus sequence typing (MLST) was performed in selected Positive controls from the collection of the Universidad de La Rioja were included in all the PCR assays in this study.S. aureus and the foraging habit of the colonized and non-colonized storks. These tests were carried out as two-tailed and outcomes with a probability value less than 0.05 were considered statistically significant.Statistical Package for Social Sciences (SPSS) Version 26  was used for data analysis. Associations between categorical variables  were compared using the chi-square test. Data were subjected to univariate logistic regression to compute odds ratio (OR) at a 95% confidence interval (95% CI) between the prevalence rate of S. aureus strains recovered of nasal and tracheal samples of white stork nestlings were MSSA, and 7 of them (8.8%) were susceptible to all the antibiotics tested   with blaZ detected in 90.6% of PENR strains. Other AMR-phenotypes/percentage/genes detected were as follows: erythromycin + clindamycin-inducible/19.1%/ermA, ermT; tetracycline/11.9%/tetK; clindamycin/4.5%/lnuA and ciprofloxacin/4.5%. Multidrug resistance (resistance to at least 3 different families of antibiotics) was identified in one strain (1.5%) , and they were ascribed to 11 clonal complexes (CCs): CC1, CC5, CC9, CC15, CC22, CC25, CC30, CC45, CC97, CC130 and CC398 (Table spa types t571 (n = 5), t1451 (n = 3) and t1456 (n = 4); all of them were of the IEC-type C. Six of these CC398 strains were penicillin-resistant and presented the erythromycin-clindamycin inducible resistance phenotype with the corresponding genes blaZ and ermT. Moreover, two of the MSSA-CC398 strains were ciprofloxacin-resistant (Table spa-type t6220) strains were identified, and they were susceptible to all antimicrobial agents. Other genetic lineages detected in high number were the following ones (number of strains): (a) MSSA-CC5-t227/IEC-B (n = 6) and CC5-t1094/IEC negative (n = 1); (b) MSSA-CC15-t774/IEC type-E (n = 5); (c) MSSA-CC15-t085/t335/IEC-C (n = 5); (d) MSSA-CC9-t209/IEC negative (n = 4) and (e) MSSA-CC45-t015/IEC-B (n = 3). All genetic lineages and their corresponding AMR phenotypes are presented in Table S. aureus strain showed a multidrug-resistant (MDR) phenotype (PEN-ERY-CLII-CIP) and was typed as MSSA-CC398-t571. Interestingly, two of the storks carried tst-positive S. aureus strains of different lineages . Also, eta-positive (MSSA-CC9-t209-scn negative) and etb-positive strains (MSSA-CC45-t015-IEC-B) were detected in two additional storks. However, all strains were luk-F/S-PV-negative  than those of parents foraging in natural habitats (only 3 CCs and lineage ST291)  harboured S. aureus strains with diverse spa-types or AMR genotypes in the same animal. Between 2 to 5 genetically distinct S. aureus strains were detected in these animals (Table S. aureus carriages: (1) MSSA-ST291-t2313-PENR-blaZ and MSSA-CC130-t6620-PENS and (2) two MSSA-CC15-t774 and one MSSA-t7778 strain (Table spa types (t571 and t1451): one of the MSSA-CC398 strains was completely susceptible to antibiotics, whereas the other carried the ermT gene  than in traquel samples (11.9%) of the nestlings [S. aureus carriage rate in wild birds with varios frequencies (4.6–34.8%) [estlings . Other s samples .9% of th6–34.8%) , 23. S. aureus carriage rate than those with parents that forage in natural habitats [S. aureus contamination than those foraging in landfills [S. aureus carriage could be attributed to the stork nestling’s exposure to human residues. Thus, the exploitation of human household residues as a major food source seems to be a risk factor for the acquisition of S. aureus by wild birds [In relation to the foraging habit of the birds, nestling storks of parents that forage in landfills had a relatively higher habitats . This coandfills . In the andfills , the higld birds . Especiald birds .spa t571 as predominant in the human infections [For more than a decade, MRSA-CC398 has consistently been detected in humans who had contact with livestock (mainly pigs and piggery environments). However, lately, the MSSA-CC398 strains have also attracted interest for epidemiological and evolutionary purposes because MSSA-CC398 strains could be implicated in emergent invasive human infections ; many offections . NeverthS. aureus clones (such as the MSSA-CC398). This could be one of the reasons why certain wild birds often carry MSSA-CC398 clones mainly adapted to humans [ermT gene was identified in > 90% of our MSSA-CC398 strains. Thus, the ermT gene and IEC type C seem to be clear genetic markers of the MSSA-CC398 clone. However, some strains without these genes have previously been reported [In this study, it appears that storks, especially those fed food foraged in landfills, had a higher MSSA-CC398 carriage rate. Potentially, some of the nestlings in this study were fed parts of animal carcasses and discarded household residue from refuse plants that could be colonized by certain human-associated o humans . In our o humans . Resistareported .S) among invasive MSSA human strains has been observed, opening therapeutic opportunities for these infections; this phenotype has frequently been found among scn-negative or CC398 strains, which suggests a potential animal-associated link [S. aureus strains were PENS. Aside from the MSSA-CC398-PENS clone with a high potential for invasive infections, other MSSA-PENS clones, such as the CC5, were often reported to cause bloodstream infections (BSIs) in a large-scale study in Spain [Recently, an increased detection of penicillin susceptibility phenotype  and it has been proposed that this clonal lineage evolved from humans to ruminants . None ofspa-type t2313 has been responsible for invasive infections with low-level AMR (often carrying only blaZ gene). However, it has previously been detected in healthy people in Germany [Besides the MSSA-CC398 lineage, MSSA-CC15 and MSSA-ST291 were detected in high frequency from both nasal and tracheal samples. The MSSA-ST291 lineage has been reported by most previous studies, and is globally distributed –32. It itst-carrying MSSA strains of the genetic lineage MSSA-CC22 has been found in storks. Nevertheless, tst-carrying MSSA-CC30-t012 strains were previously reported by Gomez et al. [tst-positive MSSA-CC522 strains were previously reported in other wild animals (such as wild boar) in Spain as well in healthy ewe in Tunisia [tst-carrying MSSA-CC22 strains (of spa-type t790) have been associated to wound infections in Iran [tst gene, the detection of MSSA-eta- and etb-positive strains in the two storks from our study are also relevant and can pose clinical and public health implications. We are not aware of any previous report of etb-positive MSSA in wild birds.The pathogenicity of MSSA is largely determined by the presence of certain important virulence factors . To our knowledge, this is the first time that z et al.  in stork Tunisia , 34. It  in Iran . A possispa types. One of them, t7778 belonged to the ST15/CC15 while the other t18009 belonged to the ST26/CC25 lineage. Concerning the MSSA-CC15, this lineage has previously been described with diverse spa types to be circulating in many European countries and Asia [blaZ-positive strains from human oral samples in Japan [In this study, we report two new and Asia –39. Concin Japan . Hence, The abundance and diversity of genetic lineages and antimicrobial resistance genes (ARG), within and between samples  of healthy stork nestlings are of public health concern. In all, there are significantly more distinct CCs and ARGs in nasal than tracheal cavities, and stork nestlings fed food foraged in landfills compared to those fed food foraged in natural habitats. There are several potential reasons for these findings. Notably, many sites in the nasal cavity host highly complex and robust microbial biofilm structures. It has been posited that the compact structure of microbes within nasotracheal epithelial provides a favourable environment for the acquisition of ARGs , 42.S. aureus strains were highly diverse as landfills are often “dumping sites” of all sorts of solid wastes that act as complex microbiological niche favouring and giving rise to an abundant microbial community with potentially greater numbers of distinct strains and ARGs [For the stork nestlings whose parents forage in landfills, it is not surprising that the and ARGs , 43. ThiS. aureus carriers in the nasal and/or tracheal cavity, being S. aureus more frequently found in nasal samples. All S. aureus recovered were MSSA, showing in most cases low AMR levels. About 8% of the storks carried MSSA-CC398 strains, harbouring mainly ermT gene and the IEC-type-C. Remarkably, intra-sample and intra-animal variations of genetic lineages and AMR patterns were identified. Moreover, MSSA-CC398 clade found in these migratory birds suggests they may have a major role in transmission across various sources and the environment. The detection of tst-, eta- and etb-carrying S. aureus strains is of great public health concern. Considering the genetic diversity of the S. aureus strains, more effective control measures are needed to control the dissemination of S. aureus across the “One Health” ecosystems.One-third of the nestling storks of this study were Supplementary file1 (DOCX 21 KB)Supplementary file2 (DOCX 24 KB)Below is the link to the electronic supplementary material.