We demonstrate that CD44-expressing MPCs are present at the periphery of the IPF fibroblastic focus, placing them in regions of active fibrogenesis. In a humanized mouse xenograft model, CD44hi IPF MPCs are more fibrogenic than CD44lo IPF MPCs, and knockdown of CD44 diminishes their fibrogenicity. CD44hi IPF MPCs display increased expression of pluripotency markers and enhanced self-renewal compared with CD44lo IPF MPCs, properties potentiated by IL-8. The mechanism involves the accumulation of CD44 within the nucleus, where it associates with the chromatin modulator protein Brahma-related gene 1 (Brg1) and the zinc finger E-box binding homeobox 1 (Zeb1) transcription factor. This CD44/Brg1/Zeb1 nuclear protein complex targets the Sox2 gene, promoting its upregulation and self-renewal. Our data implicate CD44 interaction with the epigenetic modulator protein Brg1 in conveying IPF MPCs with cell-autonomous fibrogenicity.Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic lung disease. We previously identified fibrogenic mesenchymal progenitor cells (MPCs) in the lungs of patients with IPF who serve as drivers of progressive fibrosis. Recent single-cell RNA sequencing work revealed that IPF MPCs with the highest transcriptomic network entropy differ the most from control MPCs and that increased CD44 was a marker of these IPF MPCs. We hypothesize that IPF MPCs with high CD44 (CD44 Idiopathic pulmonary fibrosis (IPF) is the most prevalent and deadly interstitial lung disease, causing up to 40,000 deaths each year in the United States , 2. Desphi expression . Here we show that CD44-expressing MPCs are present at the periphery of IPF fibroblastic foci in human IPF lung tissue, indicating that they are located in regions with active fibrogenesis. Utilizing our humanized mouse xenograft model   to form a nuclear protein complex that interacts with the Zeb1 transcription factor to induce Sox2 gene expression. Taken together, these data indicate that a CD44 interaction with the epigenetic modulator protein Brg1 conveys IPF MPCs with cell-autonomous fibrogenicity.To test this, we used FACS to physically separate IPF MPCs with CD44ft model , we demohttps://doi.org/10.1172/jci.insight.144652DS1). These findings are consistent with our previous published results demonstrating SSEA4 + MPCs concentrated on the perimeter of the fibroblastic focus in human IPF lung tissue and are in contrast to our findings that in control lung tissue, SSEA4-expressing cells, while being observed in small and large airways and vascular structures, are absent in alveolar walls (Our single cell RNA sequencing (scRNA-Seq) data indicate that CD44 is a marker of highly entropic IPF MPCs . Based oar walls , consisthi or CD44lo IPF MPCs were isolated by flow cytometry selecting for SSEA4hi/CD44hi and SSEA4hi/CD44lo cells (hi IPF MPCs was performed by quantitative PCR (qPCR) and Western blot analysis   . H & E aIPF MPCs  compared (hi IPF MPCs transduced with scrambled shRNA (hi IPF MPCs transduced with scrambled shRNA contained numerous human cells expressing procollagen in fibrotic regions (hi IPF MPCs display a robust fibrogenic phenotype and that CD44 is involved in the mechanism.Although our data indicate that CD44ed shRNA . Transduft lung) . Consisted shRNA . In conted shRNA . IHC ana regions , whereas regions . In cont regions . While s regions , very fe regions  or in th regions . Togethehi and CD44lo IPF MPCs for expression of pluripotency markers and their ability to self-renew in a colony-forming assay. Consistent with their high entropy, CD44hi IPF MPCs displayed higher protein levels of pluripotency transcription factors Oct3/4 (23% higher), Nanog (13% higher), and Sox2 (61% higher)   as well  higher)  comparedand Sox2  and decrand Sox2 , indicathi IPF MPCs display greater self-renewal capacity compared with CD44lo IPF MPCs, we next examined CD44hi and CD44lo IPF MPCs for CXCR1 and CXCR2 expression as well as IL-8 expression and secretion. CD44hi IPF MPCs displayed an approximately 3-fold–increased protein expression of CXCR1 compared with CD44lo IPF MPCs , Nanog (45% increase), and Oct3/4 (15% increase) in CD44hi IPF MPCs with the greatest effect on Sox2 expression. IL-8 had no appreciable effect on Nanog expression and modest effects on Sox2 and Oct3/4 expression in CD44lo IPF MPCs   .292RRRCGQKKK300 had been mutated. Prior studies have shown that when the mutant CD44 NLS construct (292AAACGQAAA300) becomes engaged, it is internalized into the cytoplasm, but fails to enter the nucleus   nucleus . Consistith IL-8 . Howeverith IL-8 . We theronstruct . Howevery vector . Of notehi IPF MPCs using CD44 shRNA. Knockdown of CD44 markedly decreased Sox2 expression (73% decrease) in response to IL-8 compared with cells transduced with scrambled shRNA  within the nucleus, a finding confirmed by immunoprecipitation studies. We further demonstrate that IL-8 increases the formation of CD44/Brg1 nuclear complexes. Brg1 is an ATPase subunit within the SWI/SNF chromatin-remodeling complex  and Sp1 , 48, 49. by IL-8 .In summary, this work identifies a CD44-initiated transcriptional program involving Brg1 and Zeb1 that conveys IPF MPCs with cell-autonomous fibrogenicity and heightened sensitivity to IL-8 in IPF.Six primary lung mesenchymal cell lines were established from 6 individual patients fulfilling diagnostic criteria for IPF including a pathological diagnosis of usual interstitial pneumonia . Cell lihiandlo IPF MPCs, primary IPF mesenchymal cells were labeled with mouse anti-human SSEA4 antibody conjugated to Alexa Fluor 647  and mouse anti-human CD44 conjugated to FITC . Cells were sorted on a FACS Aria Cell Sorter (BD Biosciences). Cells that were SSEA4+ and CD44+   were collected as we previously described  and compared with an external calibration standard curve.hiandlo IPF MPCs were incorporated into methylcellulose gels (Stemcell Technologies) and maintained in MSC SFM CTS  . Enumeration of colonies was performed microscopically and the colony size was quantified by ImageJ. In some self-renewal assays the cells were treated with the indicated concentrations of recombinant IL-8 (R&D Systems).Single-cell suspensions of CD44hiandlo IPF MPC conditioned media by ELISA as previously described (IL-8 secretion was quantified in CD44escribed .Nuclear fractions of IPF MPCs were isolated by NE-PER Nuclear and Cytoplasmic Extraction reagents (Thermo Fisher Scientific). CD44 was immunoprecipitated from the resulting nuclear lysates using a CD44 antibody . Control consisted of immunoprecipitation using an isotype control antibody. The immunoprecipitates were subjected to SDS-PAGE followed by silver staining . Bands were excised, destained, and subjected to in-gel trypsin digestion. Peptide samples were resuspended in 98:2 water/acetonitrile, 0.1% formic acid and run on an Orbitrap Velos (Thermo Fisher Scientific) mass spectrometer. The Thermo RAW files were analyzed with PeaksStudio 7.0 build 20140912 (Bioinformatics Solutions) software package for interpretation of tandem mass spectrometry and protein inference.292RRCGQKKK300 was changed to 292AAACGQAAA300. The mutant constructs were verified by DNA-Seq. Cells were transduced using a lentiviral vector containing mutant, WT, or empty vector constructs.For loss of function, CD44, Brg1, and Zeb1 were knocked down using shRNA . Scrambled shRNA served as control. The CD44 nuclear localization sequence mutant was constructed by site-directed mutagenesis using WT CD44S isoform cDNA as a template as previously described . The putg for 15 minutes at 4°C, and the lysates were precleared for 1 hour at 4°C with protein A/G beads and immunoprecipitated for 2 hours at 4°C with the appropriate primary antibody.Western blots were performed as previously described –6. For i6 cells were treated with 5 ng of IL-8 or vehicle control. The experiment was conducted following the manufacturer’s instruction. ChIP-PCR was performed as delineated above.ChIP Assay kit  was used. Six × 10IHC was performed on 4 μm paraffin-embedded serial-sectioned IPF lung tissue and mounted on polylysine-coated slides. The sections were deparaffinized in xylene, rehydrated through a graded Methanol series, quenched with 0.3% Hydrogen Peroxide in Methanol, and immersed in a 98°C water bath for 30 minutes in Citrate Buffer (pH 6·0) for antigen retrieval. Sections were placed in 5% Normal Horse Serum (Jackson ImmunoResearch) to block nonspecific binding of secondary antibodies. A multiplex IHC kit was used for antigen detection according to the manufacturer’s instructions . The tissue specimens were incubated overnight  with the following primary antibodies: anti-rabbit CD44 monoclonal antibody (1:800) ; and anti-human SSEA4 antibody (1:100) . Specimens were cover-slipped with a Prolong Antifade Kit  and stored overnight at room temperature without light before image analysis.hi or CD44lo IPF MPCs to drive fibrotic progression in vivo, we utilized our IPF MPC-based mouse model of fibrotic progression (hi or CD44lo IPF MPCs were suspended in PBS (106 cells/100 μL) and injected via the tail vein following our published protocol (To assess the ability of CD44gression . NOD/SCIprotocol . Alternaprotocol . Histolot test and ordinary 1-way ANOVA as indicated in the figure legend. Data are expressed as mean ± SEM. P < 0.05 was considered significant.Comparisons of data among experiments were performed using the 2-tailed Student’s De-identified patient samples were obtained by our tissue procurement service (Bionet) under a waiver of informed consent from the University of Minnesota Institutional Review Board (University of Minnesota IRB ID: 1504M68341). Animal protocols were approved and conducted in accordance with the University of Minnesota Institutional Animal Care and Use Committee regulations .Single-cell RNA-Seq data for IPF and control MPCs deposited in: BioProject repository accession PRJNA641647.LY and CAH conceived, designed, and directed the studies with input from PBB and DB. LY, CAH, and PBB wrote the manuscript with assistance from all the authors. LY established the primary human mesenchymal cell lines, cultured MPCs, performed flow cytometry for isolation of MPCs, performed qPCR, performed Western blot analysis, and performed gain- and loss-of-function experiments, mouse studies, and IHC. AG and HX assisted with isolation and culture of MPCs, as well as assisted with mouse studies. HX also assisted with IHC analysis. JK performed IHC analysis. KS designed and constructed expression constructs and performed flow cytometry for isolation of MPCs.


With the rapid development of information technology and social network, the large-scale group decision-making (LSGDM) has become more and more popular due to the fact that large numbers of stakeholders are involved in different decision problems. To support the large-scale consensus-reaching process (LCRP), this paper proposes a LCRP framework based on a clustering method with the historical preference data of all decision makers (DMs). There are three parts in the proposed framework: the clustering process, the consensus process and the selection process. In the clustering process, we make use of an extended k-means clustering technique to divide the DMs into several clusters based on their historical preferences data. Next, the consensus process consists of the consensus measure and the feedback adjustment. The consensus measure aims to calculate the consensus level among DMs based on the obtained clusters. If the consensus level fails to reach the pre-defined consensus threshold, it is necessary to make the feedback adjustment to modify DMs' preferences. At last, the selection process is carried out to obtain a collective ranking of all alternatives. An illustrative example and detailed simulation experiments are demonstrated to show the validity of the proposed framework against the traditional LCRP models which just consider the preference information of DMs at only one stage for clustering. Group decision-making (GDM) refers to a decision process where a group of decision makers (DMs) express their preferences regarding multiple alternatives and aim to obtain a ranking of the alternatives or select the best one(s) –3. In GDWith the rapid development of information technology and social network, the increasing demands of organizations and the strengthening of the citizens' sense of democracy, more and more stakeholders are involved in different decision problems, which has led to the large-scale group decision-making (LSGDM) , 18. UnlTo our knowledge, the clustering is a widely used tool in LCRP models, and the obtained clusters are usually determined by the preference information of DMs. The existing studies just consider the preference information of DMs at only one stage for clustering. In fact, the DMs' preference information is changing in all decision rounds, thus forming the historical preference information. By comparison, the historical preference information is more comprehensive, and may better guide the clustering process. For example, some DMs may tend to make their preferences more similar to other DMs' preferences when a large gap exists between the DM's own preference and other preferences. Then, the preferences of these DMs are getting closer and closer to the average level during the LCRP. From this point, the preference information of DMs at only one stage fails to fully reflect the change of DMs' preferences. Hence, the historical preference information of DMs in all decision rounds needs to be taken into consideration.With the clustering method, the DMs are separated into a finite number of subgroups based on their preferences –37. In mAlthough large numbers of LCRP models were proposed in the various studies, there are still some challenges which need to be pointed out.To overcome the above challenges and shortcomings, this paper aims to propose a novel LCRP framework based on the clustering method using the historical preferences (CMHP) data of DMs. This framework is called CMHP-LCRP framework for simplicity. In the CMHP-LCRP framework, the historical preferences information of DMs in all decision rounds is used for clustering and the clusters are allowed to be variable accordingly in different decision stages. There are three parts in the proposed framework: the clustering process, the consensus process and the selection process. First, we divide the DMs into several clusters based on their historical preferences data. Next, it comes to the consensus process, which consists of the consensus measure and the feedback adjustment. The consensus level among DMs is measured based on the clustering results. If the consensus level fails to reach the pre-defined consensus threshold, the feedback adjustment strategy is employed to increase the consensus level among DMs. Otherwise, once the pre-defined consensus threshold is reached, the selection process is carried out to obtain a collective ranking of all alternatives. Finally, detailed simulation experiments and comparison analysis are demonstrated to show the validity of the CMHP-LCRP framework against the traditional LCRP models which just consider the preference information of DMs at only one stage for clustering.This paper is organized as follows. Section k-means clustering technique, which can lay the foundation for the rest of this study.In this section, we introduce the basic knowledge of GDM problem and GDM with preference relationsWe introduce the basic knowledge of GDM problem from the perspectives of preference relations, consensus process and selection process.In a GDM problem, there exist two basic elements: a finite set of DMs unctions , prefereunctions , multiplunctions , additivunctions –45 and lunctions . Additivunctions –49. In tAdditive preference relations. Let 2.Consensus processNotably, the evaluation In the existing literature, two processes are frequently used to solve GDM problems: consensus process and selection process.The consensus process aims to raise the mutual agreement among DMs. There are different ways to interpret the consensus, such as the total agreement or some more flexible approaches. The consensus with a total agreement means the solution to the GDM problem is satisfactory for all DMs. In this situation, the cost might be unacceptable or the goal is sometimes hard to achieve in real life, which led to the development of soft consensus. In some studies, the flexible notion of consensus was proposed to soften the full and unanimous agreement , 50. In 3.Selection processIt is a dynamic and iterative discussion process to reach a certain degree of consensus in the CRP . In geneThe selection process aims to obtain a ranking of the alternatives or select the best alternative(s). The selection process usually consists of two phases: the aggregation phase and the exploitation phase.The aggregation phase aims to derive a collective preference through aggregating all the individuals' preference relations. Let operator , 52 is eThe exploitation phase aims to obtain a ranking of all alternatives, which is implemented by deriving the collective preference over alternatives The larger the value of  the Eq. , the ranK-means clustering technique is one of the most popular clustering methods, and it also proves to be robust to determine the clustering result. Algorithm 1 shows the basic steps of the k-means clustering technique.In the LCRP events, there are usually large numbers of DMs with diverse backgrounds and different preferences. For sake of analysis, these DMs are usually divided into several subgroups , 38, 53.Algorithm 1. K-means clustering technique.Input: The preference relations of all DMs Output: Clusters Step 1: Initialize Step 2: For the preference relation Step 3: For each cluster Step 4: Compute the Euclidean distance between the cluster If the criterion is not met, iterate Step 2 and Step 3 until convergence, which means the cluster centers are invariable. Otherwise, output the clusters In this section, we present a flexible LCRP framework using the clustering method with the historical preferences data of DMs.As mentioned before, the clustering is a widely used tool to divide DMs into different clusters based on their preference relations in the LCRP. In the existing studies, the clustering process just considers the preference information of DMs at only one stage, which may fail to fully reflect the change of DMs' preferences. Therefore, it is of significance to take the historical preference information of DMs in all decision rounds into consideration and deal with it using proper theoretical models.Recall that In the decision process, the DMs are adjusting their preferences in all decision rounds, thus forming the historical preference information, which can better reflect the change of DMs' preferences. Therefore, how to design a LCRP framework based on a clustering method with the historical preference data of DMs is of significance.The clustering processFigure k-means algorithm is used for clustering purpose. In previous literatures, the clusters were obtained just based on the preference information of DMs in the latest decision round, rather than the total historical preference data in all decision rounds. As the total historical preference information can reflect the characteristics of DMs' preferences more comprehensively, in this paper the historical preference data of DMs in all decision rounds are employed for clustering, which is quite different from previous studies.2.The consensus processClustering is a popular tool to divide DMs into several clusters and it has been widely used to deal with LSGDM problems. According to the obtained clusters, the analysis becomes much easier. There are a lot of clustering techniques, and k-means algorithm is one of the most popular techniques because of its high efficiency, simplicity and fast convergence . In this3.The selection processFor LCRP events, certain degree of consensus is essential. The consensus process aims to achieve the mutual agreement among DMs and it consists of the consensus measure and the feedback adjustment. The clustering process lays the foundation for the consensus measure. When the clusters are obtained, the consensus level among DMs can be calculated . If the If the pre-defined consensus threshold is reached or it comes to the maximum number of rounds allowed, the selection process is carried out to obtain a collective ranking of all alternatives.In this section, we first present an extended k-means clustering method with the historical preference data. Next, the two main parts of LCRP, the consensus measure for obtained clusters and the corresponding feedback adjustment strategy are provided. Further, a detailed algorithm for the proposed CMHP-LCRP model to rank the alternatives is presented. At last, we use an example to illustrate the CMHP-LCRP model.The generation of historical preference dataK-means clustering technique proves to be robust to divide the DMs into several clusters and be widely used in the LSGDM problems. In this paper, an extended k-means clustering technique is used for clustering based on the DMs' historical preference data. Different from existing LCRP studies where clusters are usually obtained based on the preference information of DMs at only one stage, in this paper the historical preference data of DMs in all decision rounds are used for clustering. The detailed process of obtaining the historical preference data is described as follows.Let Based on Eq. , the dimFor the DM The dimension of 2.k-means clustering technique based on the historical preference dataThe extended Based on k-means clustering method is a widely used tool and it proves to be robust to determine the clustering result, therefore we use it to divide the DMs into several clusters according to the total historical preference data Since Input: All the historical preference relations Output: Clusters Step 1: Initialize Step 2: For the historical preference relation Step 3: For each cluster Step 4: Compute the Euclidean distance between the cluster If the criterion is not met, iterate Step 2 and Step 3 until convergence. Otherwise, output the clusters To achieve a certain degree of consensus in the CMHP-LCRP framework, there are usually two essential processes: consensus measure for clusters and local feedback adjustment strategy. At last, a detailed algorithm for the proposed CMHP-LCRP model to obtain the ranking of alternatives is presented.Weight measure for clustersThe consensus measure aims to calculate the degree of agreement among all DMs based on the obtained clusters. The steps to measure the consensus level of DMs are as follows.Using the extended k-means clustering technique, the DMs are divided into several clusters, which are denoted as If the weights of all DMs are the same, then it follows that2.Preference measure for clustersNext, normalize the weight of each DM in cluster 3.Similarity measure among clustersThe preference relation for cluster 4.Consensus measureFor each pair of clusters Consensus degree for each pair of alternativesThere are three different kinds of consensus degrees , 56, nam(b)Consensus degree for each alternativeFor the pair of alternatives For each alternative (iii)Consensus degree for the whole preference relationsThe consensus degree for the whole preference relations is denoted According to the consensus measure, we can calculate the global consensus level, which reflects the degree of agreement among all DMs. In general, there is a pre-defined consensus threshold. If the actual consensus level reaches the consensus threshold, which means the agreement among DMs is satisfactory, the consensus process ends and the selection process starts. Otherwise, DMs are urged to adjust the preferences in the next round so as to reach a higher consensus level. Overall, there are various models to adjust the preference of DMs. In this paper, a supervised mode, which requires the DMs to follow the following rules, is used.The identification process.The feedback adjustment strategy consists of two parts: the identification process and the adjustments process. The identification process aims to identify the preferences relations that need to be adjusted. Following this, the adjustments process gives the adjustment suggestions. The procedures are as follows.The identification process consists of four identification rules, which help to precisely determine the alternatives, the pairs of alternatives, the clusters, and the DMs where the preferences need to be adjusted in the next round.(a) Identification of alternatives. The first step is to identify the alternatives where the preferences need to be adjusted. Such alternatives are denoted as According to Eq. , \docume(b) Identification of the pairs of alternatives (positions). Based on the Eq. , the nexAccording to Eq. , \docume(c) Identification of the best and worst clusters for each identified position. Based on Eq. , the nexThere are just one best cluster and one worst cluster. To accelerate the LCRP, the DMs in all the other clusters except the best cluster (d) Identification of DMs. According Eqs.  and 26)26), the Based on Eq. , a new p2.The adjustments processAccording to Eq. , the DMsBased on Eq. , the DMsAccording to Eqs.  and 30)30), for Based on the above, the details of the proposed CMHP-LCRP model are described in Algorithm 2.Algorithm 2. CMHP-LCRP model.Input: The preference relations of all DMs Output: The ranking of alternatives.Step 1: Let entclass1pt{minimaStep 2: Gather the individuals' preference relations in each round  to Eqs.  and 9)..Step 2: Step 3: Based on the historical preference relations Step 4: Calculate the global consensus level Step 5: Identify the alternatives, the pairs of alternatives, the clusters and the DMs where the preferences need to be adjusted using the identification rules described in Sect. Step 6: Derive the ranking of alternatives from the evaluation values g to Eq. . Output To demonstrate the applicability of the CMHP-LCRP framework, we further present an illustrative example. In this example, a set of twenty DMs In the following, the proposed CMHP-LCRP framework is employed to show the LCRP.In this example, three clusters are set for analysis. Based on individuals' preference relations, use the k-means clustering technique to classify DMs into the following clusters.Based on Eq. , we can Based on Eq. , the simFrom Eqs. –20), we, we20), Therefore, according to Eq.  the globAs g to Eq. , we concg to Eq. , we furt on Eqs.  and 26)\documentIn the example rom Eqs. –31) the the\docuBased on the historical preference relations, use the k-means clustering technique to classify DMs into the following clusters.Based on Eq. , we can Based on Eq. , the simFrom Eqs. –20), we, we20), Therefore, according to Eq.  the globAs g to Eq. , we concg to Eq. , we furt to Eqs.  and 26)\documentFrom Eqs. –31), th, th31), k-means clustering technique to classify DMs into the following clusters.Based on the historical preference relations, use the Based on Eq. , we can Based on Eq. , the simFrom Eqs. –20), we, we20), Therefore, according to Eq.  the globSince entclass1pt{minimaThen, based on Eq.  the collIn this section, the simulation experiments and comparison analysis are designed to show the validity of the CMHP-LCRP framework.In the simulation experiment, at first the initial preference relations of DMs are randomly generated and further transformed into the historical preference format. Based on these historical preference data, we use the k-means clustering technique to classify DMs into several clusters. Further, the consensus level is computed. If the consensus level fails to reach the consensus threshold, then it comes to the local feedback adjustment stage. According to Eqs. , 23), ,  and 28)28), the The detailed simulation method for the CMHP-LCRP framework ..Step 2: Step 3: Use the k-means clustering technique to classify DMs into Step 4: Compute the global consensus level among DMs g to Eq. . If \docStep 5: According to the identification rules described in Sect. Step 6: Feedback adjustment. For the other DMs except those in Step 7: Output. If Note. In the simulation method, (1) Let mentclass2pt{minimIn general, it takes an average of 2–3 rounds to reach the consensus threshold, and it has high success ratios of achieving a consensus (close to 1) for most cases in the CMHP-LCRP framework.With increasing With increasing From Table As for LSGDM problem, clustering is a widely used tool. In this paper we propose a CMHP-LCRP framework in which all the historical preferences of DMs are employed for clustering. However, the traditional LCRP models are based on a clustering method using just the latest round of preference information of DMs, which may fail to fully reflect the change of DMs' preferences. In order to show the validity of the CMHP-LCRP framework, we aim to compare the CMHP-LCRP framework with the traditional LCRP framework.The simulation method for the traditional LCRP framework  different input parameters For the first comparison analysis, let entclass1pt{minimaFor the second comparison analysis, let For the third comparison analysis, let From Figs. No matter how the input parameters change, the values When setting different input parameters, the values of Clustering is a widely used tool to deal with LSGDM problems. In traditional LCRP framework, just the DMs' preference information in the latest decision round is used for clustering. This paper explores a consensus model using a novel clustering method that takes the historical preference information of DMs in all decision rounds into consideration. By comparison, the historical data can more fully reflect the change of DMs' preferences, thus better guiding the clustering process. To show the validity of the proposed CMHP-LCRP framework, we further compare the CMHP-LCRP framework with the traditional LCRP framework under different input parameters. Compared with the traditional LCRP framework, it is faster to reach the consensus threshold and the success ratio of achieving a consensus is higher for our CMHP-LCRP framework no matter how the input parameters change. The result shows that the CMHP-LCRP framework outperforms the traditional LCRP framework.Meanwhile, three research directions are interesting for future studies. (1) In this paper, we apply the additive preference relations for analysis. However, there are some other preference representation structures, such as multiplicative preference relations and linguistic preference relations. It is of significance to explore other preference representation structures with historical data to further support the proposed model in this paper. (2) Some DMs may adopt non-cooperative behaviors in the LCRP to achieve their own goal or interests. For instance, some DMs may provide dishonest opinions or refuse to change their evaluations for the sake of their own interests , 29. The


Mycobacterium tuberculosis (Mtb) lineage 1 (L1) contributes considerably to the disease morbidity. While whole genome sequencing (WGS) is increasingly used for studying Mtb, our understanding of genetic diversity of L1 remains limited. Using phylogenetic analysis of WGS data from endemic range in Asia and Africa, we provide an improved genotyping scheme for L1. Mapping deletion patterns of the 68 direct variable repeats (DVRs) in the CRISPR region of the genome onto the phylogeny provided supporting evidence that the CRISPR region evolves primarily by deletion, and hinted at a possible Southeast Asian origin of L1. Both phylogeny and DVR patterns clarified some relationships between different spoligotypes, and highlighted the limited resolution of spoligotyping. We identified a diverse repertoire of drug resistance mutations. Altogether, this study demonstrates the usefulness of WGS data for understanding the genetic diversity of L1, with implications for public health surveillance and TB control. It also highlights the need for more WGS studies in high-burden but underexplored regions. Mycobacterium tuberculosis (Mtb) is classified into 9 lineages3, with lineages 1–4 (L1–L4) being widely distributed around the world. Mtb L1, or the India-Oceanic lineage, is the only TbD1-positive Mtb lineage with high incidences in many high-burden countries around the Indian Ocean4. Although whole genome sequencing (WGS) is increasingly used for studying Mtb, our understanding of L1 genetic diversity and epidemiology in high-burden settings remains limited.3 and was subsequently expanded5. Recently, the scheme has been refined6 to consist of three main groups, L1.1-L1.3 and seven third-level sublineages, L1.1.1-L1.1.3 identical to the previous scheme, a novel L1.2.1, L1.2.2 equivalent to previous L1.2.1 and finally L1.3, equivalent to previous L1.2.2 and classified into L1.3.1 and L1.3.2.Mtb L1 was first classified into two main groups based on whole-genome SNPs, denoted L1.1 and L1.2, with five sublineages, denoted L1.1.1, L1.1.2, L1.1.3, L1.2.1 and L1.2.27, L1.1.3 is common in Myanmar8, Bangladesh9 and East Africa10 while L1.1.2.2 and L1.2.2 (previous L1.2.1) are associated with India and Southeast Asia, respectively5. Interestingly, around 80% of Mtb isolates in the Philippines, a high TB burden country, belonged to the last sublineage11. Thus, existing SNP-based classification schemes may not provide sufficient resolution for characterizing major sublineages in high burden countries such as the Philippines and India, each of which has several hundred thousand L1-infected patients each year4.Associations between each of these sublineages with geography have been studied. For example, L1.1.1.1 is predominant in Vietnam12. This scheme classifies Mtb strains with deletions at DVR39–42, 44 and 48 as the East African Indian (EAI) genotype, which is equivalent to L1. Each spoligotype pattern is represented by a series of 0 s and 1 s indicating the absence or presence of each of the 43 DVRs, and is usually abbreviated into a 15-digit octal code with each number representing 3 DVRs sequentially for 14 digits with the last digit as either 0 or 1. Each spoligotype is given an identification number for the ease of use, called the spoligotype international type (SIT). For example, most of the L1.2.2 strains have SIT19 spoligotype or similar, collectively referred to as EAI2. Spoligotypes classify EAI2 into two groups, EAI2_MNL (Manila), associated with the Philippines, and EAI2_NTB (Nonthaburi), associated with Thailand.Spoligotyping is another widely used genotyping method, with data available from most countries. It evaluates the presence of 43 out of 68 genomic segments called direct variable repeats (DVRs) located in the CRISPR region, also called the Direct Repeat (DR) region5. It was not known whether the same homoplastic spoligotype in different countries belonged to the same sublineage or not.Although spoligotyping is cheap and fast, its discriminating power is limited, and is confounded by the presence of common homoplastic spoligotypes such as SIT48 and SIT236, meaning that they could belong to one of several sublineages6. We adopt this three-group scheme here, but kept further nomenclature as backward compatible as possible. We compared the SNP phylogeny to the conventional spoligotype and the underappreciated 68-DVR patterns to gain deeper insights into the geographic distribution and dispersal history of L1 sublineages. We also examined genetic clusters and drug resistance mutations. The information is useful for evaluating TB burden and for understanding of the evolutionary history and pathogenesis, with implications for TB control.This study aims to refine the WGS SNP-based genotyping scheme of L1 to better understand its genetic diversity and population structure in high-burden regions. We performed phylogenetic analysis of 1,764 L1 samples, representing all major sublineages and endemic areas in Asia, Oceania and Africa. While this work was on-going, the revised nomenclature with three major sublineages had been proposed. tuberculosis (Mtb) L1 isolates from Asia, Oceania and Africa were obtained until November 2020. Most isolates were from Thailand (36%), Vietnam (26%), India (13%) and the Philippines (9%). The remaining were from other Asian countries (5%), Africa  and Oceania   of L1.1.1 isolates were from Thailand or Vietnam. L1.1.1.1 was the most common sublineage of L1.1.1 (51%) and accounted for 85% of the samples from Vietnam but only 3% of Thai samples. L1.1.1.4 was also mainly found in Vietnam while most of the remaining sublineages were predominantly found in Thailand. We designated two new clades in L1.1.1: L1.1.1.10 from Vietnam and Thailand and L1.1.1.11 from West Africa10. About 3% of L1.1.1 isolates were left as unclassified , and is most common in India (80%) , followed by the Philippines, Malaysia (Sabah) and Thailand (~ 10–15% each). L1.2.2.2 was mostly restricted to Thailand (89%). L1.2.2.3 was the largest sublineage (40% of L1.2). L1.2.2.4 was a small sister clade of L1.2.2.2. Both L1.2.2.3 and L1.2.2.4 were most common in the Philippines and nearby East Malaysia. L1.2.2.5 was the smallest sublineage, with most samples from Vietnam. The separation of L1.2.2 into five sublineages were supported by their comparable intragroup average pairwise SNP distances, which are less than the intergroup average pairwise SNP distances, and the fixation indices  was widespread in countries surrounding the Indian Ocean. It comprises L1.3.1 (18%) and L1.3.2 (82%) sublineages, with one basal isolate from Thailand. Almost all L1.3.1 isolates (88%) were from Eastern or Southern Africa, with a few basal isolates from India. In contrast, L1.3.2 was most common in India (36%), followed by Thailand (20%)  of L1.1.1 isolates in many sublineages had only those six deletions , L1.1.1.6 (DVR57–60) and L1.1.1.8 (DVR53) Table . Neverthn = 349, 84%) belonged to a clade with shared DVR36–37 deletions. The rest (16%) had intact DVR36-37 and were more basal to this clade, with 16 isolates having only six DVR deletions similar to L1-MRCA  or DVR29–63 (n = 35).Several DVR deletions appeared to occur sequentially along the phylogeny. For example, most of L1.1.1.1  did not possess any resistance conferring mutations and, therefore, were presumably pan-sensitive. Isoniazid resistance was most common , followed by ethionamide , streptomycin , rifampicin , ethambutol , pyrazinamide  and fluoroquinolones  (Supplementary Table n = 224) of the isolates were resistant to one first-line drug (mono-DR) while 7% (n = 123) were MDR. About 1% of the isolates were pre-XDR. Only one isolate was XDR. Most rifampicin-resistant isolates were also resistant to isoniazid .We used TBprofiler to predict drug resistance based on known genetic markers11 although the MDR incidence among new TB cases there was only 2% in 201217. We caution that the prevalence of drug resistance may not be representative due to different sampling designs used by source studies.The prevalence of drug resistance varied across sublineages  had mutations in katG or fabG1/inhA involving 36 alleles, with katG Ser315Thr being most common (52%), followed by the -15C > T mutation in the fabG1 promoter (38%), with only 9 isolates having both variants  possessed mutations in gyrA or gyrB conferring resistance to fluoroquinolones, with Ala90Val and Asp94(Gly/Ala/Asn) in gyrA being the most common (n = 26). Among those isolates, 18 were qualified as pre-XDR and mostly belonged to L1.1.1 or L1.3.2. The only isolate identified as XDR belonged to L1.1.1.9 from Thailand. In addition to pre-XDR mutations, it also had an insertion in Rv0678 at position 192 that made it likely to resist bedaquiline18.A small proportion of isolates , which are equivalent to EAI6_BGD1 and EAI2 respectively. In contrast, spoligotypes defined by a single deletion event are more likely to be homoplastic. The most notable example is SIT48, typically defined by the deletion of DVR62, which occurred several times independently in the evolutionary history of L1. SIT48 can be a member of L1.1.1 or L1.3. Fortunately, since L1.1.1 is largely restricted to MSEA, SIT48 in other regions, such as India or Africa, most likely corresponds to L1.3 whereas SIT48 in MSEA may belong to either L1.1.1 or L1.3 as shown in Fig. We also clarify several genetic relationships between spoligotypes that could facilitate the development of better genetic groupings of spoligotypes. This is important as spoligotyping is more affordable in high-burden countries and provides data from much larger sample sizes with a higher geographic coverage than WGS.19, where none of the SIT236 isolates were present in our dataset. We note that their inferences were dominated by L1.1.2.2 and L1.3, and may suffer from small sample sizes (~ 300 or lower) and non-representative sampling.The geography of isolates with most intact DVRs, similar to L1-MRCA, could be indicative of the region where L1 originated. Such isolates have the SIT236 spoligotype and are found exclusively in certain sublineages of L1.1.1, except for two L1.3.2 isolates Fig. . Since aOne major limitation of this study is that it was based on WGS data collected for various purposes. Epidemiological findings such as the prevalence of drug resistance and genetic clusters were therefore only suggestive and require further investigation. A few source studies were intended for drug resistant bacteria which biased the estimates of drug resistance frequencies.20.Our analysis revealed a vast diversity of resistance-conferring mutation combinations. The results confirmed the presence of the mutations in the rifampin-resistance determining regions (RRDR) in the vast majority (95%) of rifampin resistance isolates, which would allow the GeneXpert system to adequately detect rifampin resistance. Although available commercial line probe assays do not specifically identify mutations in about 37% of rifampicin-, 13% of isoniazid- and 55% of ethambutol-resistant isolates in this dataset, they should be able to diagnose the resistance by the absence of hybridization of wild-type probes. The line probe assay specifically detects most fluoroquinolone resistance mutations21.This study refines a genotyping scheme of L1, particularly of the L1.2 sublineages. Many basal isolates of L1.2 were recognized in ISEA samples, suggesting a possible study site to further elucidate the evolutionary history of the clade. Mapping spoligotypes onto the SNP phylogeny clarifies the genotypic identity of some homoplastic spoligotypes. Analysis of drug resistance mutations revealed a vast diversity of mutated alleles, particularly among the first-line drugs. This has implications for the utility of genetic tests for diagnosis of drug-resistant TB. Finally, genetic clusters in L1 are rare and small in size, suggesting a more limited transmission potential of L1, especially in comparison with L2. tuberculosis L1 isolates from countries in Asia, Oceania and Africa. This is the main dataset. Most of the data were publicly available while a small proportion was from unpublished studies. Supplementary Table 14. This set was analyzed together with the 410 L1.2 isolates from the main dataset, referred to as the L1.2 dataset (n = 364 + 410 = 774) (Supplementary Table We compiled a collection of whole-genome sequencing (WGS) data of 1,764 M22. The processed short reads were mapped to the M. tuberculosis H37Rv reference genome (NC_000962.3) using bwa mem23. Duplicate reads were masked using Picard’s MarkDuplicates, and per-sample SNPs were called using GATK HaplotypeCaller v4.1.6.0 24.Short reads data were quality-trimmed using trimmomatic v0.39 As additional sample quality control, we excluded samples if (i) the median read depth below 10 or the median breadth of coverage (depth at least 10) below 10%, or (ii) it was redundant as determined by the BioSample accession number and sample metadata if provided by the original study, or (iii) the processed reads had unusual %GC content, or (iv) SNPs specific to different sublineages were present in substantial proportions, indicative of mixed strains. The total of 1,764 isolates passed this sample quality control.24. For variant quality control, we dropped SNPs within a region annotated as repeat region, IS6110, PE, PPE, mobile element, phage or 13E12 repeat family. SNPs within known drug-resistant genes were excluded as in Ajawatanawong et al25. Finally, SNPs with quality by depth (QD) < 2 or root mean square of mapping quality (MQ) < 40 were also excluded. This resulted in 153,244 SNPs across 1,764 genomes. The additional L1.2 isolates were processed similarly.Joint SNP calling of all 1,764 isolates was performed using GATK GenotypeGVCFs v4.1.6.0https://github.com/CENMIG/snpplet). Point deletions and missing calls were converted into gaps in the alignment. The H37Rv strain (L4) was used as an outgroup. A maximum likelihood (ML) phylogeny was inferred using IQ-TREE v2.1.126. The best-fit substitution model was determined to be GTR + G4 by ModelFinder27. The bootstrap branch supports were based on 1,000 replicates. We also inferred an ML phylogeny correcting for ascertainment bias as shown in Supplementary Fig. 28.SNP calls passing the quality filters were converted into a multiple sequence alignment using a custom script based on GATK VariantsToTable (available at 29. These included genome-wide averaged nucleotide diversity (π) for each sublineage, pairwise SNP difference and pairwise FST (using Hudson estimator30) for within sublineage and between sublineages, and principal component analysis (PCA) of genome-wide SNPs.Summary statistics of genetic diversity of each sublineage and genetic divergence between sublineages were calculated from the filtered SNPs using scikit-allele v1.3.1To determine deletion pattern of the 68 known DVRs, we mapped the short-read data to a custom-made reference sequence that contained all the DVRs. This reference was constructed based on an L1 isolate (accession: ERR752247) with 62 DVRs, augmented with DVRs 39–42 from the H37Rv strain (accession: NC_000962.3) and DVRs 44 and 48 from an L2.1 isolate (accession: ERR718276).31. The code for DVR calling is available in GitHub at https://github.com/ythaworn/dvrcaller.Each DVR segment was identified as a deletion if one of the following criteria was satisfied: (i) the median read depth was below a cutoff and the proportion of sites with at least one read mapped to was < 50%; (ii) the 25% quantile of read depths was < 3; (iii) the 5% quantile of read depths was 0; (iv) if the overall median depth  was > 50, the median read depth was smaller than a half of the median read depth. For (i), we used the cutoff value of 5, or half of the baseline median read depth calculated from all DVR regions, whichever was lower. This choice as well as the criteria (ii) and (iii) accounted for low-depth samples. The criterion (iv) accounted for samples with high sequencing depths by adjusting for overall read depth across all DVR regions. Given a DVR pattern, we extracted a relevant set of 43 DVRs to determine the spoligotype. SIT and spoligotype group for each spoligotype was obtained from the SITVIT2 database5. The above criteria yielded ~ 6% difference for the spoligotype and 16% difference for the DVR pattern. We also compared our predicted spoligotypes with two other methods, SpoTyping32 and Galru33 . These identified sublineage-specific SNPs serve as markers for genotyping those sublineages. We annotated variants within an ORF using four states of essentiality: essential (ES), growth defect (GD), nonessential (NE) and growth advantage (GA)34. Supplementary Table Once genotypes have been defined, we identified a list of SNPs specific to each genotype using a custom script  recommendations as follows. mono-resistance (mono-DR): one first-line drug ; polydrug resistance (poly-DR): more than one first-line drug other than isoniazid and rifampicin together; multidrug resistance (MDR): both isoniazid and rifampicin together; pre-XDR: MDR and any fluoroquinolone; extensive drug resistance (XDR): pre-XDR and at least one of bedaquiline or linezolid36.We used TBProfiler38.We identified genetic clusters as clades in the phylogeny containing isolates that can be linked via pairwise SNP distances of at most 5, 12 or 20 SNPsSupplementary Information 1.Supplementary Figure 1.Supplementary Figure 3.Supplementary Figure 8.Supplementary Tables.


The parameters considered included the propagation velocity, frequency, and attenuation. Mono-, bi-, tri-, tetra-, and penta-layer graphene samples were prepared by transferring individual graphene layers onto LiNbO3 crystal surfaces at room temperature. Intra-layer lattice deformation was observed in all five samples. Further inter-layer lattice deformation was confirmed in samples with odd numbers of layers. The inter-layer lattice deformation caused stick–slip friction at the graphene/LiNbO3 interface near the temperature at which the layers were stacked. The thermal expansion coefficient of the deformed few-layer graphene transitioned from positive to negative as the number of layers increased. To explain the experimental results, we proposed a few-layer graphene even–odd layer number stacking order effect. A stable pair-graphene structure formed preferentially in the few-layer graphene. In even-layer graphene, the pair-graphene structure formed directly on the LiNbO3 substrate. Contrasting phenomena were noted with odd-layer graphene. Single-layer graphene was bound to the substrate after the stable pair-graphene structure was formed. The pair-graphene structure affected the stacking order and inter-layer lattice deformation of few-layer graphene substantially.The thermodynamic properties of few-layer graphene arbitrarily stacked on LiNbO This negative TEC results in many challenges in the area of electronic devices because 2D carbon materials generally require a 3D material as a substrate and most 3D materials such as Si, SiO2, and SiC have positive TECs near room temperature. These diametrically opposite temperature coefficients often lead to residual thermal stress at the 2D/3D material interface, which greatly affects the electrical properties of the 2D carbon material and leads to instability regarding the characteristics of the resulting electronic devices.Few-layer graphene, a two-dimensional (2D) carbon material with atomic thickness, has a negative in-plane thermal expansion coefficient (TEC) due to both graphene sheet rippling9. However, graphene layers must be grown on a catalyst substrate such as copper or nickel foil. It is also necessary to transfer the resulting graphene layer to a substrate in order to fabricate electronic devices. Several types of crystal defects such as point defects10, one-dimensional dislocations11 grain boundaries12, and wrinkles15 formed during growth and ripple defects from the transfer process can be introduced into the graphene layer. Defects can form during in growth due to both crystal imperfections of the catalyst substrate and large temperature differences during cooling from the growth temperature to room temperature. On the other hand, defects introduced during the transfer process, such as ripples, form at room temperature via deformation of the graphene layer18 and introduction of stress20. For above reasons, perfect or defect-free graphene layers have not yet been obtained. Therefore, it is important to elucidate the effects of defects on the TEC of a graphene layer.Graphene is a promising 2D material for electronic devices for which extensive applied research is available22. Other experimental results have showed that negative graphene TECs are related to in-plane contractions of ripples and wrinkles in the graphene layer24 Recently, molecular dynamics (MD) simulations showed that graphene origami structures obtained via pattern-based surface functionalization provide TECs that are tunable from large negative values such as − 465 × 10−6 to large positive values such as + 33 × 10−6 K−1 between 250 and 350 K25. These simulations showed that the mechanisms that give rise to this property are exclusive to graphene origami structures, as they emerge from a combination of surface functionalization, large out-of-plane thermal fluctuations, and the 3D geometries of the origami structures.It has been reported that the Young’s modulus of graphene increases as the atomic vacancy density decreases26. When a suspended sheet is pressed on using an AFM tip, its TEC can be obtained by measuring the temperature dependences of various stresses. Also, experimental estimation of the graphene TEC has been performed by analyzing the temperature-dependent shift of the Raman G band of the graphene layer on SiO2 while carefully excluding substrate effects4. However, few-layer graphene is energetically unstable and defects and ripples are introduced, especially when the layer is stacked on a 3D substrate. Many challenges remain regarding evaluation of the thermodynamic properties of such deformed few-layer graphene.Measurement of graphene TECs is challenging because conventional experimental techniques designed for bulk materials cannot be applied to such thin samples. For this reason, few new graphene TEC measurement methods have been reported. Using atomic force microscopy (AFM), it is possible to accurately measure the indentation and stress of a suspended graphene sheet28, graphene30, 1D carbon nanotubes32, and zero D quantum dots34. In this study, we investigated the thermodynamic properties of deformed few-layer graphene by analyzing the propagation velocities, frequencies and attenuation characteristics of SAWs that passed through the graphene layers. We found that the SAWs were sensitive to changes in the number of graphene layers. Deformation of the graphene layers resulted in a large TEC change from negative to positive as the number of graphene layers increased from one to five. A stable pair-graphene structure formed preferentially in few-layer graphene. The pair-graphene structure played an important role in thermodynamic properties such as the TEC and interfacial friction of few-layer graphene.Surface acoustic wave (SAW) propagation is a powerful method of investigating the mechanical and electrical properties of low-dimensional materials such as 2D electron gases3 crystals at room temperature under atmospheric conditions. The graphene layers have an incommensurately stacked structure and there is no crystalline correlation between the layers. In order to confirm their stacking structures, the graphene/LiNbO3 structures were analyzed using X-ray diffraction. The X-ray measurements were performed at 45.0 kV and 200 mA, with a wavelength of 0.15406 nm and with 2θ between 5° and 85°. The wavelength of the X-ray used in this study is almost the same as the distance between adjacent carbon atoms in the graphene sheet, 0.142 nm. Thus, large X-ray reflectance occurs during the diffraction measurement.In this study, the graphene layers were transferred layer-by-layer onto the surfaces of LiNbO3 structures in the 2θ range of 5°–85° are shown in Fig. 3 crystal. An enlarged view of the X-ray diffraction patterns of mono-, bi-, tri-, tetra-, and penta-layer graphene/LiNbO3 structures in the 2θ range of 5°–55° are shown in Fig. 3 crystal or aluminum IDT metal electrodes. It is interesting that the diffraction intensity varies with the number of layers near 23 crystal. These are 0.345 nm for the tri-layer graphene and 0.335 nm for the tetra- and penta-layer samples.X-ray diffraction patterns of mono-, bi-, tri-, tetra-, and penta-layer graphene/LiNbOmentclass2pt{minimmentclass2pt{minimBased on the above X-ray diffraction measurement results, the following facts are strongly suggested. First, the spacings of the tetra- and penta-layers are close to that of graphite, i.e., 0.335 nm. Second, the spacing of the tri-graphene layer is 0.345 nm, which is slightly larger than that of graphite. Third, the spacing of the bi-layer may be much larger than that of the tri-layer. In other words, the few-layer graphene spacing decreases as the number of layers increases. The spacing approaches that of the graphite basal plane when the number of layers is four or more. Based on the size of the graphene layer  layer graphene exhibits N bilayer-like bands, and 2 N + 1 layer graphene additionally exhibits a monolayer-like band. Such structural effects result in a unique stacking order in the few-layer graphene. Therefore, we can propose the following stacking order model: I/for mono-, II/for bi-, II/I/for tri-, II/II/ or tetra-, and II/II/I/for penta-layer graphene/LiNbO3, where I/ represents a single layer and II/ represents paired layers. In samples with odd numbers of layers, the single layer binds to the substrate and paired-layers form on the single layer because such a stacking order is energetically stable. Since the single layer has a linear dispersion relation and a bandgap of zero, charge transfer to the substrate is easier. In contrast, charge transfer is hindered by a non-zero bandgap, resulting in weaker interactions between graphene layers with even numbers of layers and the substrate. It has been reported that the bandgap of two-layer graphene varies with charge transfer and that the range of variation is on the order of several hundred meV52.The above results confirm that, in addition to van der Waals interactions, phonon interactions occur between graphene layers in few-layer graphene with odd layer counts. In fact, a pair-graphene structure with a more stable energy state has been identified in few-layer graphene54. Therefore, once a single graphene layer is transferred to the LiNbO3 substrate, the electronic and electrical influences of the substrate can be nearly blocked. After the first layer, the even–odd layer number effect dominates stacking order formation in few-layer graphene.Also, because of its high carrier mobility, the screening length of graphene is reported to be comparable to the thickness of a single graphene layer57. However, energy bandgap variation can be ignored when the electric field strength is less than 57. In this study, the maximum value of the piezoelectric potential electric field components in the parallel and vertical directions on the LiNbO3 surface is In addition to the effects from charge transfer and substrate defects, external electric fields also affect the energy bandgap of few-layer graphene3 structures are formed by transferring graphene layers to the LiNbO3 substrate, there is no thermal stress at the interface at the transfer temperature. Static friction occurs at the interface when the temperature subsequently changes. If the temperature change is sufficiently large, sliding friction appears and stick–slip behavior occurs58. In fact, we reported abnormal behavior regarding parameters of SAWs that pass through the penta-layer graphene/LiNbO3 interface in our previous research30. Discontinuous changes occur in both the SAW intensity and frequency near the transfer temperature. In this study, these discontinuous changes are also observed from the tri-layer graphene/LiNbO3 structure.Because the graphene/LiNbO3 structure at temperatures of 300 K, 302 K, 304 K, 306 K, 308 K, and 310 K are shown in Fig. The strengths of SAW device output signals with the tri-layer graphene/LiNbOMoreover, the fundamental frequencies 3 interface. It acts as a compressive stress as the temperature increases, resulting in an increase in 30. Fourth, temperature-dependent stress occurs because of interfacial stick–slip friction that is dependent on the graphene and LiNbO3 crystal lattice structures. The difference between the graphene and LiNbO3 lattice constants can lead to the appearance of moiré patterns with larger periodic structures than those of graphene and LiNbO360. The stick–slip friction observed in this study may be related to the moiré pattern period and occurs within 61. We also must point out that the single graphene layer bonded to the LiNbO3 surface plays an important role in the stick–slip friction process.The above results indicate that the output signal strength is proportional to the input signal strength, and that 30. The discontinuous changes correspond to thermal contraction and expansion stresses at the LiNbO3 surface for the tri- and penta-layer samples, respectively. The results indicate that changing the number of layers from three to five changes the TEC from positive to negative. The results also suggest that the penta-layer sample II/II/I/ stacking order causes different inter-layer lattice deformation than the tri-layer sample II/I/ stacking order.The fundamental frequency ratios 63 at the graphene/LiNbO3 interface. A vanishing stick–slip friction phenomenon has been observed in few-layer graphenes65. Friction decreases with decreasing the number of layers and finally approaches zero for one and two graphene layers. This phenomenon has been explained quantitatively using a classic theoretical model that considers the lateral stiffness, slip length, and maximum lateral force61.Also, the II/II/ tetra-layer and II/ bi-layer stacking orders (even numbers of layers) do not cause inter-layer lattice deformation and no discontinuous TEC change is detected. Moreover, the aforementioned discontinuous change is not detected in the mono-layer sample. This may be related to superlubricity3 substrate play important roles in both deformation of few-layer graphene and changes in its TEC.From the above results, it is clear that both the number of pair-graphene structures and the presence of a single layer bonded to the LiNbO3 crystal and stacking order of the layers was analyzed via X-ray diffraction and Raman scattering. Moreover, surface acoustic waves were propagated along the graphene/LiNbO3 interface to characterize its thermodynamic properties.Few-layer graphene was transferred onto the surface of LiNbO3 crystal surface in parallel. The tetra- and penta-layer graphene spacings were 0.335 nm, which were similar to that of graphite. The spacing of the tri-layer graphene was 0.345 nm.First, few-layer graphene was arbitrarily stacked on the LiNbO3 structures exhibited negative charge transfer from the graphene layers to LiNbO3 crystal because a red-shift of the Raman scattering G band was observed for all samples.Second, mono-, bi-, tri-, tetra-, and penta-layer graphene/LiNbOThird, the wavenumber mapping measurements of the Raman scattering G band at the basal plane indicate the presence of intra-layer lattice deformation with an average size of hundreds of square micrometers.3 crystal with graphene layers and the delay times of SAWs that passed through the graphene/LiNbO3 interface showed that inter-layer lattice deformation occurs in few-layer graphene with odd numbers of layers.Fourth, measurements of both the frequency thermal coefficient of the LiNbO3 interface and a TEC that transitions from negative to positive as the number of layers increases.Fifth, inter-layer lattice deformation in few-layer graphene with odd numbers of layers results in both stick–slip friction at the graphene/LiNbO3 surface.Finally, inter-layer lattice deformation may be related to the presence of both a stable pair-graphene structure and a single graphene layer bonded to the LiNbObO3 crystal with dimensions of bO3 interface and finally were received as alternating pulse signals through the output IDT electrode.The SAWs were generated and received using interdigital transducer (IDT) electrodes on the surface of 128 degree Y-cut LiN30. The SAW fundamental frequency, attenuation, and delay time were measured to characterize interactions of the SAWs with the few-layer graphene films.The fundamental frequency of the SAW device was 50 MHz and the SAW propagation time from the input electrode to the output electrode, i.e., the delay time associated with passing through the delay line, was 11 cm−2.Graphene films with mono-, bi-, tri-, tetra-, and penta-layers were transferred onto the surfaces of the SAW devices at room temperature. Before the transfer process, the graphene layer was grown via CVD on a copper foil substrate with a thickness of 35 bO3 sample was placed in a vacuum chamber with a residual gas pressure of less than 1.1 × 10−7 Pa. The sample temperature was controlled using a controller  and a cryostat  in the range of 280–320 K in 0.1 K steps at a rate of 0.14 3 interface under various experimental conditions.The graphene/LiN3 crystals were characterized via X-ray diffraction (RIGAKU SmartLab R&D 100) and Raman scattering (JASCO NRS-5500) after the SAW measurements were performed.Graphene films on LiNbO


Culture, while long viewed as exclusively human, has now been demonstrated across diverse taxa and contexts. However, most animal culture data are constrained to well-studied, habituated groups. This is the case for chimpanzees, arguably the most ‘cultural’ non-human species. While much progress has been made charting wild chimpanzees' cultural repertoire, large gaps remain in our knowledge of the majority of the continent's chimpanzees. Furthermore, few studies have compared neighbouring communities, despite such comparisons providing the strongest evidence for culture, and few have studied communities living in anthropogenic habitats although their culture is in imminent danger of disappearing. Here we combine direct, indirect and remote methods, including camera traps, to study, over 2 years, four unhabituated neighbouring chimpanzee communities inhabiting human-impacted habitats in Cantanhez NP, Guinea-Bissau. From traces collected during 1089 km of reconnaissance walks and 4197 videos from 56 camera trap locations, we identified 18 putative cultural traits. These included some noteworthy novel behaviours for these communities, and behaviours possibly new to the species. We created preliminary behavioural profiles for each community, and found inter-community differences spanning tool use, communication, and social behaviour, demonstrating the importance of comparing neighbouring communities and of studying previously neglected communities including those inhabiting anthropogenic landscapes. Since then, evidence of animal culture has been identified in numerous taxa, from fish to meerkats, cetaceans and apes  , and regcophrys) . Since tes e.g. –14), bot, botParuMultiple approaches to studying animal culture have been proposed. Among these, the one most widely used in the wild to date is the ethnographic method  or ‘methPan troglodytes, but not all known communities, engage in army ant (Dorylus spp.) dipping, there is variation in the technology employed even among different communities of the same subspecies [et al. [Coula edulis) nut-cracking in three habituated neighbouring chimpanzee communities in Tai National Park, Ivory Coast. Even though these neighbouring communities inhabit the same forest habitat with minimal ecological variation between their home ranges, the study showed that there was marked inter-community variation in hammer size and raw material preferences [et al. [While large-scale ethnographic studies have given us valuable insights into species-level variation in putatively cultural behaviours ,25, it hbspecies . Some ofbspecies . Therefobspecies ,12,28,29 [et al.  have sho [et al. , but manIn chimpanzees, arguably the most ‘cultural’ among non-human species, much effort has focused on building a comprehensive catalogue of the species' cultural repertoire ,25. Yet,et al. [et al. [Achatina achatina) against wooden anvils and eating them (a behaviour first described in a recent study of Bili-Uéré's (DRC) chimpanzees, though direct evidence is yet to be found [The present study aims to fill these gaps by being the first to explore cultural variation between neighbouring chimpanzee communities inhabiting Cantanhez National Park (CNP), Guinea-Bissau. CNP is thought to be home to approximately 10–12 chimpanzee communities  inhabitiet al.  studied et al. ; Bessa e [et al.  also sugbe found ). Additibe found ,42—has aWe collected behavioural data on four unhabituated neighbouring chimpanzee communities in central CNP. Due to the communities' close proximity to local people, we chose not to habituate chimpanzees, therefore any direct observation was merely opportunistic, and the majority of data collection relied on a combination of indirect methods and camera traps. As such, our aim was also to demonstrate the feasibility of using this combination of methods to achieve our three primary goals: (1) to document new behaviours for the studied communities, expanding our to-date limited knowledge of their behavioural repertoire; (2) to identify behavioural variation among communities; and (3) to contribute to continental-level comparisons in the ever-growing chimpanzee behavioural repertoire. Our indirect data collection methods (see below) were particularly suitable for behaviours that leave trace evidence , while the camera trap data were our principal source of information for behaviours that are more ephemeral/do not leave traces . We structure our results according to these categories.. 2. 2.12 and is a mosaic of settlements, agricultural fields, sub-humid forest, secondary forest, palm groves, mangrove and savannah. Recent work has revealed that a large percentage of forest cover remains across the habitat matrix, but these forest areas are highly fragmented and interspersed with cultivated land. Areas cleared for agriculture often have remnant forest edges that together with fallow land and orchards can work as corridors for some wildlife [2, Madina 19.0 km2, Cambeque 7.1 km2 and Lautchandé 8.4 km2. Additionally, previous work had estimated the minimum community size in Caiquene-Cadique as 49 individuals  and Madina as 48 individuals [CNP is located in the Tombali region of south-west Guinea-Bissau. CNP has an area of 1057 kmwildlife   were coure see ,49). Eve. Evein s. 2.2.2Camera traps (Bushnell Trophy Cam HD Aggressor No-glow) were set up at 56 different locations in CNP  and categorized observations as either species-typical behaviours , or putatively cultural . The categorization was done based on previously published data on chimpanzee culture and behavioural variation . Def. 3During the study period a total of 1089 km were walked over 195 recces, and camera traps were set up in 56 locations, for a combined total of 9695 days, yielding a total of 4197 videos of chimpanzees . During recces, 1747 indirect chimpanzee traces were recorded, including 204 extractive stick tools. For detailed information on study effort and data collected in each community, see Elaeis guineensis, Detarium senegalense and Parinari excelsa) was confirmed. Termites (Macrotermes sp. and Cubitermes sp.) were present at all study sites, as were army ants (Dorylus sp.). At other chimpanzee research sites, these resources are exploited with the use of tools (i.e. nut-cracking and termite and ant fishing/dipping) (see [Achatina achatina) were present in all CNP communities’ home range, nonetheless no conclusive evidence (e.g. direct observation) of the exploitation of snails was found. Honeybees (Apis mellifera) were present at all five study sites and a species of stingless bee (Meliplebeia sp.) was confirmed for all four CNP sites. A second species of stingless bee (Meliponula sp.) was only confirmed at Cambeque and Madina. Tool-use evidence as well as trace evidence of discarded honeycombs with teeth marks confirmed that each CNP community fed on at least one of the different types of honey  (see ). Nonethng) (see . Movableney (see ).A total of 22 behaviours of interest were identified during the study period; . 3.1A total of 204 dipping tools and 16 leaf sponges were found across the CNP study sites. The pattern of tool recovery exhibited some marked variation among the four neighbouring communities. No evidence of extractive dipping tools to access fluid (including honey) was found in Caiquene-Cadique, while the extraction of different honey types with tools appeared to be frequent in Cambeque (100 tools) and Madina (103 tools) . Video e. 3.2Avicennia germinans (Mangrove-eat). No evidence of mangrove use was found at Lautchandé even though mangroves are present within the chimpanzees' known ranging area. Camera trap evidence of Mangrove-use was recorded for Madina and Caiquene-Cadique.The use of mangroves and their resources  was confirmed in three of the CNP communities. Chimpanzees at Caiquene-Cadique and Madina were seen Saltwater-drinking, and they, as well as Cambeque chimpanzees, were confirmed to chew on the salty leaves of black mangrove trees, . 3.3Treculia africana during drumming displays.Leaf-clipping, a type of non-extractive tool use, was observed in all four CNP communities. All Leaf-clipping was confirmed through camera trapping and performed in association with Buttress-drumming. Leaf-pulling in association with drumming was also recorded in all CNP communities. Raspberry vocalization was captured on camera in three of the CNP communities, but not at Lautchandé. Additionally, in Caiquene-Cadique male chimpanzees were caught on camera throwing the giant fruit of . 3.4T. africana fruits was recorded on camera in Caiquene-Cadique and in Madina, between a mother and her dependent offspring as well as between adults. Rain-dance display was only confirmed at Caiquene-Cadique.Fruit-sharing of large . 4in situ or remote observation. It is also evident that the longer a study runs the more researchers can learn about a population's behavioural repertoire, not simply because data accumulate over time but also because these studies may end up habituating or semi-habituating the individuals to researchers. Nonetheless, despite the constraints of working with unhabituated populations, the present study was able to compile, through a combination of direct and indirect methods, and remote monitoring, a list of behaviours and potential behavioural variations in four neighbouring chimpanzee communities in Guinea-Bissau. Furthermore, we were able to identify some noteworthy novel behaviours for these communities, as well as behaviours that are possibly new to the species as a whole, thus filling gaps in our knowledge of the chimpanzee behavioural repertoire at a continental scale.An essential component of identifying cultural variation in non-human species is documenting behavioural variation across populations. However, studying behaviour and its variation in groups of unhabituated wild animals is a notoriously difficult and long process (e.g. ). Verifyabsence of most of the studied behaviours, but considering the encounter rates of certain behaviours some putative variation seems likely. Perhaps the most striking case in point is Honey-dipping. While we confirmed the presence of at least one type of stingless bee (Meliplebeia sp.) in all four CNP communities’ ranges and the presence of honeybee (Apis mellifera) at all four study sites, evidence of dipping for honey was only found in two of the four CNP communities. Although we cannot conclude with certainty that dipping for honey is absent in Caiquene-Cadique and Lautchandé—and indeed the picture may change as research efforts continue—it is likely that it occurs with at least greater frequency in the communities of Cambeque and Madina . We chosion see . We succsion see . Additiosion see , the behsion see . This besion see .One group of behaviours, which occurred exclusively in the mangrove habitat, is of particular interest. To our knowledge the use of mangroves has only been described in Loango (Gabon) , but seeIt is worth noting that despite the study effort being similar across the four sites , the numet al. [et al. [et al. [et al. [Notwithstanding, in 2 years of research, we were able to compile an extensive list of wild chimpanzee behaviours never before described for CNP, for Guinea-Bissau, or (in the case of some behaviours) for the rest of Africa. We concede that this list is likely to be far from comprehensive since it is clear that longevity of study and close, direct observation of individuals are key factors that influence the size of the behavioural repertoire assembled for each chimpanzee community, as is evident through the increase in the number of confirmed behaviours over time . The recet al.  comparinet al. . This woet al. . However [et al.  and 7 of [et al. . These n [et al. , even coThus, our research shows that studying the behaviour of neighbouring primate communities inhabiting human-impacted areas can be a useful source of information in studies of animal culture, and re-affirms the value of using a combination of direct and indirect methodologies to document the behaviour of unhabituated communities. The fact that over the course of our relatively short study we identified behaviours which do not leave noticeable material evidence behind and that are rare, seasonal, or absent in other known chimpanzee communities, provides further justification for the validity of this approach. Additionally, and contrary to some previous studies, our approach was not constrained to a pre-selected list of behaviours. This allowed us to, for example, explore behaviours specific to unusual habitat types, such as mangroves, and identify previously unknown behaviours that we would have missed otherwise. At the same time, it is important to remember that behavioural repertoires are not static and should not be seen as such: behaviours can disappear , resurfa


The effects of temperature and humidity on AMI hospitalization did not differ by age and sex , but differed by season. However, higher temperatures in spring  and higher humidity in autumn  were risk factors for AMI hospitalization. Increased average temperatures and humidity, 1 day before hospitalization, are associated with a decreased number of AMI hospitalizations.Weather conditions affect the incidence of acute myocardial infarction (AMI). However, little is known on the association of weather temperature and humidity with AMI hospitalizations in a super-aging society. This study sought to examine this association. We included 87,911 consecutive patients with AMI admitted to Japanese acute-care hospitals between April 1, 2012 and March 31, 2015. The primary outcome was the number of AMI hospitalizations per day. Multilevel mixed-effects linear regression models were used to estimate the association of the average temperature and humidity, 1 day before hospital admission, with AMI hospitalizations, after adjusting for weather, hospital, and patient demographics.Lower temperature and humidity were associated with an increased number of AMI hospitalizations (coefficient − 0.500 [− 0.524 to − 0.474] per °C change,  Furthemore, heavy exercise or physical exertion, diet, sexual activity, emotional stress, and environmental conditions have been associated with AMI4. Air pollution, influenza epidemics, and weather changes have been reported as environmental triggers of AMI6. Among the environmental conditions, low temperatures have been reported to be a potentially important risk factor for AMI4, and cardiovascular mortality8. Therefore, meteorological temperature is considered a risk factor for AMI.Acute myocardial infarction (AMI) is recognized as one of the leading causes of death in the elderly worldwide. Several traditional factors, including age, hypertension, diabetes, hyper cholesterol, and smoking, are associated with the onset of AMI12. In recent years, Japan has become a super-aging society with average life expectancies of 81 and 87 years for males and females, respectively. Consequently, 23.8% of the population die from heart and cerebrovascular diseases (https://www.mhlw.go.jp/english/database/db-hh/1-2.html). However, little is known on the association of weather temperature and humidity with AMI hospitalizations in the super-aging society of Japan.Humidity can easily be obtained together with temperature. However, the association between humidity and the risk of AMI is still unclear. This is due to the lack of consensus on the relationship between high and low humidity and the risk of AMITo address these issues, we conducted an observational study using the Japanese registry of all cardiac and vascular diseases (JROAD) database. The JROAD database is the largest database in Japan in the field of cardiovascular diseases. It contains data for patients who require hospitalization for cardiovascular diseases. We hypothesized that temperature and humidity are associated with AMI hospitalizations, and we expected their effect to vary by age, sex, and season. Until now, there has been a few nationwide research data on the effect of temperature and humidity on the risk of AMI. The present study provides important insights on the risk of AMI in the aging Japanese society.The baseline characteristics of the study population are shown in Table p < 0.001 and coefficient − 0.012 [− 0.023 to − 0.001] per %, p = 0.039, respectively) after adjustments for season, hospital, and patient demographics. Association of cumulative number of AMI hospitalization each month with temperature, and with humidity was shown in Supplementry Fig. The multilevel mixed-effects linear regression analysis shown in Table Figure p = 0.004) in Table p < 0.001). When classified into five quantiles of average humidity, the number of AMI hospitalizations in Quantile 1 was significantly lower than that in Quantile 4 . However, there was no difference between the ≤ 64 years(interaction p = 0.150) and the ≥ 90 years (interaction p = 0.702) age groups. The effects of average weather temperature did not differ between male and female patients (interaction p = 0.090). Among the four seasons, the effect of average temperature on AMI admission was most pronounced in autumn  followed by winter . In spring, however, the increased average weather temperature was associated with a greater number of AMI hospitalizations .The association between average weather temperature and specific risk estimates of incident AMI is summarized in Fig. p < 0.001).No difference in the effects of average humidity was found among age categories and between sexes  lower temperatures were associated with an increased number of AMI hospitalizations: (2) lower humidity was associated with an increased number of AMI hospitalizations and Quantile 4 of humidity posed the lowest risk; (3) the effect of temperature and humidity on AMI hospitalization was consistent across age groups and sex, but responses varied by season. Overall, this study strongly suggests that protection against the cold and low humidity is important for the prevention of incident AMI in the super-aging society of Japan. To date, thera have been few reports clarifing the association between humidity and the incidence of AMI. A major strength of this study is the large number of subjects as compared to the previous studies17. These studies support our results. Compared to previous studies, we found that the effect of temperature on AMI hospitalization was consistent across age groups and sex in the super-ageing society of Japan. The mechanisms underlying the cold-induced increased risk of incident AMI are not fully understood. Cold temperatures lead to a rise in catecholamine levels18, resulting in increased heart rate and blood pressure, and subsequent myocardial ischemia and plaque vulnerabilities. Cold temperatures also increase serum cholesterol19, platelets, and fibrinogen activity, leading to elevated blood viscosity20. In particular, rapid exposure to low temperatures is known to cause coronary spasm21.Several epidemiological studies have reported that lower temperatures are a risk factor for the incidence of AMI22. Similarly, our data shows that the 75–89 years age group was most affected by the low temperatures. Cold-induced changes in blood pressure23, plasma cholesterol, and fibrinogen20 have been reported to be more pronounced in the elderly. This may result in a higher risk of incident AMI with a decrease in temperature in the elderly than in younger people. In contrast, our study found that the effect of temperature on people aged over 90 years did not differ from that of younger people. This may have been due to the higher rate of medication intake and less outdoor activity in the ≥ 90 years age group. Alternatively, we considered the possibility that the effect of “age” was greater than the effect of temperature, thereby masking the association with AMI risk.Contrary to our expectations, we found that the response to AMI risk factors was consistent across age groups and sexes. Other studies from Hong Kong and Taiwan reported that the association of low temperature with AMI risk was stronger for the 65–74 and > 75 years age groups compared with the ≤ 64 age group9. They found a negative correlation between hospital admission for AMI and relative humidity, which supports our results. In contrast, Panagiotakos et al., in a study of admissions for acute coronary syndromes in the Athens area (n = 5458), found relative humidity to be positively correlated with hospital admission12. Swartz et al. estimated the effects of humidity on hospital admissions for heart disease and myocardial infarction in individuals aged > 65 years in 12 U.S. cities, and showed that there was no evidence of a humidity effect11. The inconsistent results may be due to differences in research locations and races, because studies14 conducted in East Asia, including the present study, showed a tendency toward a negative association.We found that lower humidity was associated with an increased number of AMI hospitalizations. Lee et al. used the Korean AMI registry (n = 2136) to investigate the association between meteorological parameters and hospital admission for AMIUnlike most previous studies, however, our study demonstrated that humidity Q4 posed the lowest risk of hospitalization for AMI. Mohammad et al. conducted a prospective, large scale, population-based, nationwide observational study  and reported a U-shaped association between humidity and AMI incidence. In their report, the lowest risk of AMI was at 70–80% humidity, which supports our results. Therefore, it may be important to avoid extremely high and low humidity.The exact mechanism of the increased incidence of AMI at very high and low humidity still remains unknown. It is hypothesized that very high humidity may interfere with the processes of perspiration and body temperature homeostasis, resulting in respiratory fatigue and increased heart rate. On the other hand, very low humidity and low temperatures can interact with each other to increase the incidence of respiratory diseases and influenza, which in turn can increase AMI incidences.4. A SWEDEHEART nationwide observational study reported a negative correlation between minimum temperatures and the incidence of AMI from April to December but not from January to March10. Yamaji et al., using a nationwide database, showed that an increased mean temperature was independently associated with lower AMI hospitalization numbers in winter but not in summer13. In summary, except for the study by Yamaji et al.13, the effects of low temperatures on increased AMI admissions have been reported to be greater in seasons other than winter. The reason behind higher temperatures in spring increasing AMI hospitalization is unknown. In spring, the sudden rise in temperatures from the cold winter may be related to the onset of AMI. The magnitude of the effect of the temperature change may be as equally important as the absolute value of the temperature.We found that the most pronounced effects of lower and higher temperatures were observed in autumn and spring, respectively. A study in Belgium showed pronounced effects of temperature decreases in summer; however, the effects were not detected in winter24, heart rate variability25, weight change26, cholesterol levels19, and endothelial dysdunction27. Further studies are needed to confirm the effects of seasonal variations in temperature and humidity.Additionally, higher humidity in the autumn is associated with AMI onset, but there is no data to explain the differences in seasonal responses. Potential mechanisms include seasonal differences in blood pressure reactivityHospitalization for AMI is a growing public health concern in the super-aging society of Japan. Therefore, the results of the present study have important implications for reducing the risk of AMI. The findings may guide public health interventions to control and prevent the cardiovascular effects of exposure to cold temperatures and low humidity, particularly for the very elderly who are at a high risk of hospitalization due to coronary artery disease. In addition to keeping warm, keeping humid is also important, especially during winter. Maintaining the main rooms at the right temperatures and humidity can prevent factors that trigger incident AMI.There are several limitations to this study that should be acknowledged. First, our study included only Diagnosis Procedure Combination (DPC) hospitals with cardiovascular beds that meet the requirements of the Japanese Circulation Society (JCS). Although these institutions account for 29% of all hospital beds in Japan, the applicability of our findings to non-certified or non-specialist hospitals is unclear. However, the JROAD is the largest cross-sectional study of nationwide cardiac health outcomes in Japan and constitutes a comprehensive database of epidemiological data for population-based studies. Second, cross-sectional studies do not determine cause and effect; however, the large sample size is a noteworthy strength of this study. Future longitudinal studies of the mechanism of the temperature/humidity effects on AMI events and their outcomes are warranted. Determinants of the populations that are most vulnerable to weather changes should be further explored. Third, the present study could not include some important confounding factors such as medication before hospital admission and type of AMI (ST-segment elevation or non-ST-segment elevation). The diagnosis of atrial fibrillation was defined from the JROAD-DPC database and not from the electrocardiogram. Therefore, this study would underestimate the comorbidities. Finally, Japan is long in east–west and north–south directions. The regional differences such as Okinawa and Hokkaido were adjusted for east–west as covariate factors, but regional effects need to be examined in the future.Lower weather temperatures and humidity are both associated with an increased incidence of AMI hospitalizations in the aging society of Japan. The risk of AMI hospitalization did not differ with age or sex, but did differ by season. Higher temperatures in spring and higher humidity in autumn tended to increase the risk of AMI hospitalizations. The findings of this study can provide guidance to healthcare providers on advising patients to be watchful of weather temperature and humidity in a country with an increasingly aging society.28. Briefly, the JCS developed the JROAD database, which includes the demographics of each hospital since 2004. The JCS also developed the JROAD-DPC nationwide database, which includes data from the Japanese diagnosis procedure combination/per diem payment system (DPC/PDPS) since 2014. The DPC database is a mixed-case classification system linked with a lump-sum payment system, which was launched in 2002 by the Japanese Ministry of Health, Labour and Welfare29. Compared with other registry databases, the Japanese DPC database enables researchers to conduct nationwide studies of both descriptive and/or analytical epidemiology in a real-world clinical setting. The DPC database includes data on the following elements: demographics for each patient ; principal diagnoses ; comorbidities at admission (ICD-10 coded); complications after admission (ICD-10 coded); procedures, including surgery, medications, and devices used during hospitalization; length of stay; discharge status; and medical expenses31. Institutions using the DPC system encompass a wide variety of centers, including academic, urban, and rural hospitals32. All data included in this study were from hospitalized patients with clinically apparent cardiovascular disease. We collated and used a dataset of weather variables in Japan from the National Institute for Environmental Studies (http://www.nies.go.jp/db/index-e.html). The weather variables included hourly weather temperatures and humidity. Localized weather data were obtained by identifying the weather station closest to each hospital. We searched the sites of weather stations by the municipality code which the Ministry of Internal Affairs and Communications in Japan assigns to each municipality specifically and we merged the sites of the hospitals and the monitoring stations closest to them. The weather variables were merged with the DPC database using the acute-hospitalization day and the municipal code provided by the Japanese Ministry of Internal Affairs and Communications (http://www.soumu.go.jp/denshijiti/code.html).The JROAD database is a nationwide prospective registry. The database was designed to assess the clinical activity of each Japanese institution regarding cardiovascular care, and to provide adequate feedback to teaching hospitals for improving patient care. A detailed description of the database design and methods was previously publishedThis study included 911 hospitals, where 2,369,165 consecutive patients were admitted during the study period. We collected the data of patients with AMI requiring hospitalization among these data. The exclusion criteria were as follows: planned hospitalization; no AMI; missing temperature or humidity data; and missing patient-characteristic data. Finally, 87,911 patients were included in the analysis  code I21, which is recorded in the “main diagnosis,” “admission precipitating diagnosis,” “most resource-consuming diagnosis,” or “second most resource-consuming diagnosis.”This study did not investigate data on blood tests or electrocardiography because we used only DPC data. AMI is assigned International Statistical Classification of Diseases and Related Health Problems 10th Revision Seasons were separated into spring (March–May), summer (June–August), autumn (September–November), and winter (December–February). The average temporaries were defined as the average of each hourly temperature and humidity within a day. The averages of weather variables on a certain day were assigned to the day before an emergency hospitalization due to AMI. This is because hospitalization times were not available in the database.We conducted a cross-sectional study using data from the JROAD and JROAD-DPC, and weather variables between April 1, 2012 and March 31, 2015.The primary outcome was the number of AMI cases requiring hospitalization per day.The average weather temperature and humidity, as continuous variables, were adjusted for season, hospital demographics , and patient demographics .33. The interaction was examined for age, gender, and season. All analyses were carried out using STATA statistical software, version 14 . Statistical significance was defined as p < 0.05.Patient characteristics are expressed as medians and interquartile ranges for continuous variables. Categorical variables are presented as frequencies (%). We used multilevel mixed random effects and population-averaged linear models to evaluate the association between the number of AMI hospitalizations and weather variables. The multilevel mixed-effect models were used to evaluate the random effects of hospital variations  determined by the JROAD study. The covariates were considered clinically important factors by experienced cardiologists. The average weather temperature and humidity, as continuous variables, were adjusted for the covariates. Linearity was checked for continuous and categorical variables using STATA's multivariable regression splines (MVRS) command and the 5th percentile, respectively. MVRS selects the regression spline model that best predicts the outcome variable. MVRS indicated a linear spline relationship with humidity. The cuff-off value of humidity was determined based on the knots calculated by MVRS and was tested using “mkspline” command in STATA. The mkspline creates variables containing a linear spline. For subgroup analysis, we classified the patients’ age into 4 groups according to the Japan Geriatrics Society guidelinesSupplementary Figure 1.