Community discovery is an important task for revealing structures in large networks. The massive size of contemporary social networks poses a tremendous challenge to the scalability of traditional graph clustering algorithms and the evaluation of discovered communities.We propose a divide-and-conquer strategy to discover hierarchical community structure, nonoverlapping within each level. Our algorithm is based on the highly efficient rank-2 symmetric nonnegative matrix factorization. We solve several implementation challenges to boost its efficiency on modern computer architectures, specifically for very sparse adjacency matrices that represent a wide range of social networks.Empirical results have shown that our algorithm has competitive overall efficiency and leading performance in minimizing the average normalized cut, and that the nonoverlapping communities found by our algorithm recover the ground-truth communities better than state-of-the-art algorithms for overlapping community detection. In addition, we present a new dataset of the DBLP computer science bibliography network with richer meta-data and verifiable ground-truth knowledge, which can foster future research in community finding and interpretation of communities in large networks. However, it has been shown that linkage-based communities usually have strong correlation with functional communities [Community detection is a key task in the study of large networks, which has recently become a very important area of research –3. Althomunities . The purEarlier work in this area mainly focused on finding linkage-based communities on small networks. Various measures have been defined and optimized for the difference between intraconnections and interconnections among network nodes, such as normalized cut, conductance, and others, using a variety of methods . Manual This paper introduces a scalable algorithm based on rank-2 symmetric nonnegative matrix factorization (rank-2 SymNMF) for large-scale hierarchical community detection. After summarizing some related works, we discuss the problem domain for our new results and our solutions for particular problems within that domain. Then, some highlights and speedup in our implementations are discussed, after which we present comprehensive experimental results to evaluate our new algorithm.The study of network community detection dates back to the well-known Kernighan–Lin algorithm from the early 1970s . At thatk-means objective [A spectral clustering method that minimizes normalized cut was proposed as an image-segmentation algorithm , and it bjective  and applbjective , 14. ThiGirvan and Newman  producedorder statistics local optimization method (OSLOM), which was based on a measurement similar to modularity but was able to handle overlapping communities. Yang and Leskovec studied the properties of large-scale overlapping communities [The early overlapping community detection algorithms , 22 weremunities  and propmunities . They prmunities , 24. Whascalable community detection (SCD). Network communities of good quality should have stronger intraconnections than interconnections. Previous algorithms measure such strength of connectivity only through the number of edges. The uniqueness of SCD is that it is based on a triangular structure of edges. The goal is to identify communities where each node forms more triangles with nodes inside the community than those formed with nodes outside the community.Recently,  proposedOn the other hand, nonnegative matrix factorization (NMF)-based methods exhibit superior interpretability in many application areas such as text mining , 26 and As mentioned above, there is no universally accepted definition for network communities. Rather than defining community detection as optimizing some specific measurement criteria, we believe it would be more effective and flexible to understand the problem by clearly defining what makes a community detection result good.F1 score, which assumes the existence of ground truth. Finally, we would like to know whether the result reveals some new and useful information about the network (Q3). This is mostly relevant for the study of small networks [In this paper, we focus on link-based community detection, and use functional communities (if known) as ground truth. In general, to evaluate link-based community detection results, we may ask several questions. The first is whether the result is coherent from the point of view of network links (Q1). There are many measurement scores defined based on the presumption that a community should have more intraconnections than interconnections. These measures include normalized cut, ratio cut, conductance, etc. The second question is whether the result agrees with prior knowledge, especially the ground truth if it is known (Q2). There have been largely two approaches. One is to manually analyze the results with some known meta-information (such as the entity each node represents), which is not scalable to large networks. Another approach is to compare the community detection result using some measures such as networks . For larGround-truth communities of large networks were not available to researchers until Yang and Leskovec  defined Based on the above observations, we conclude that link-based community detection algorithms have the ability to partially recover functional communities, but such ability is inherently limited by the essential differences between functional communities and link-based communities. This should be kept in mind when comparing link-based community detection results against ground-truth communities.Real-world network communities are usually overlapping. For example, it is common that one user joins a variety of groups in a social network. However, our current focus is on nonoverlapping community detection, since nonoverlapping community detection is also very useful for revealing the network structure, and our algorithm is designed to detect nonoverlapping communities efficiently. The results of a good-quality nonoverlapping community detection algorithm can be used as an effective starting point for overlapping community detection , 3.We present an algorithm called HierSymNMF2 for hierarchical community detection. HierSymNMF2 uses a fast SymNMF algorithm  with ranX, and each column in the matrix H can be seen as a low rank representation of the data item. Nonnegativity constraints allow such a low rank representation to be more interpretable than other low rank approximations such as SVD. This formulation can be applied to areas such as document clustering [m and n [k reaches a value on the order of thousands, NMF algorithms become slow. To solve this issue, [k to linear [Given the vast amounts of nonnegative data available for extracting critical information, the NMF has found a wealth of applications in such domains as image, text, and chemical data processing. It can be shown that applying algorithms to such data without constraining the solution can result in uninterpretable results such as negative chemical concentrations and possibly false negative and/or false positive detections, which could lead to meaningless results . For texustering  and can [m and n . Howevers issue,  developeo linear .S for SymNMF are adjacency matrix i. When S is the adjacency matrix, .The algorithm we introduce in this paper uses the framework shown in Algorithm 1, where a cluster is a community, and the task of splitting a community is performed by our rank-2 version of SymNMF. The decision to choose the next node to split is based on a criterion discussed in the next section. In the following sections, we denote H naturally induces a binary split of the community: suppose ith graph node.Splitting a community is achieved by rank-2 SymNMF of alternating nonnegative least squares (ANLS) algorithm for SymNMF from [A formal formulation of rank-2 SymNMF is the following optimization problem:lgorithm , 14. HowNMF from , we can W and W and H. Formulation  normalized cut determines whether a split is structurally effective since it measures the difference between intraconnections and interconnections among network nodes; and (2) for SymNMF, when S is the normalized adjacency matrix, the SymNMF objective function is equivalent to (a relaxation of) minimizing the normalized cut, which is the preferred choice in graph clustering [The “best” community to split further is chosen by computing and comparing ustering .u, v) is w. Note that for an unweighted graph, k pairwise disjoint subsets of V, where Suppose we have a graph For example, the graph shown in Fig. sparse–dense matrix multiplication (SpMM) was the main computational bottleneck for computing the solution. However, this is not the case with rank-2 SymNMF or HierSymNMF2 for community detection problems on typical large-scale networks. Suppose we have an z nonzeros as an input to rank-2 SymNMF. In Algorithm 2, i.e., Nonnegative Least Squares (NLS) with two unknowns, SpMM costs 2z floating-point operations (flops), while searching for the optimal active set (abbreviated as opt-act) costs 12n flops. Of the 12n flops for opt-act, 8n flops are required for solving n linear systems each of size n flops are incurred by lines 4–5. Note that comparison operations and the memory I/O required by opt-act are ignored.In the previous work on rank-2 NMF  that takThe above rough estimation of computational complexity reveals that if mentclass2pt{minimFigure SpMM is a required routine in lines 1, 4, and 5 of Algorithm 2. The problem can be written assparse matrix–vector multiplication). For the Intel Math Kernel LibraryA depends on the sparsity pattern of A.Most open-source and commercial software packages for sparse matrix manipulation have a single-threaded implementation for SpMM, for example, MatlabA that represents an undirected network. We exploit two important facts in order to reach high performance:A is not assumed to have any special sparsity pattern. Thus, we can store the matrix A in the commonly used generic storage, the compressed sparse column (CSC) format, as is practiced in the built-in sparse matrix type in Matlab. As a result, nonzeros of A are stored column-by-column.Since the nodes of the network are arranged in an arbitrary order, the matrix A is symmetric. This property enables us to build an SpMM routine for AX.The matrix The second fact above is particularly important: When A is stored in the CSC format, computing AX with multiple threads would incur atomic operations or mutex locks to avoid race conditions between different threads. Implementing multithreaded X on one thread. Our customized SpMM implementation is described in Algorithm 3.In this paper, we present a simple yet effective implementation to compute SpMM for a matrix In addition, the original adjacency matrix often has the value “1” as every nonzero entry, that is, all the edges in the network carry the same weight. Thus, multiplication operations are no longer needed in SpMM with such a sparse matrix. Therefore, we have developed a specialized routine for the case where the original adjacency matrix is provided as input to HierSymNMF2.advanced vector extensions) intrinsics.The search for the optimal active set, opt-act, is the most time-consuming step in the algorithm for NLS with two columns . For example, a 256-bit AVX register can process four double-precision floating point numbers (64-bit each) in one CPU cycle, which amounts to four times speed-up over a sequential program. AVX intrinsics are external libraries for exploiting AVX vector registers in native C code. These libraries are not part of the ANSI C standard but retain mostly the same interface on various operating systems . However, to obtain the best performance from vector registers, functions in the AVX libraries often require the operands having aligned memory addresses . The function calls for aligned memory allocation, which is completely platform dependent for native C code, means that our software would not be easily portable across various platforms if aligned memory allocation were managed in the C code. Therefore, in order to strike the right balance between computational efficiency and software portability, our strategy is to allocate memory within Matlab for the vectors involved in opt-act, since Matlab arrays are memory aligned in a cross-platform fashion.It turns out that a C/Matlab hybrid implementation is the preferred choice for achieving high performance with native C code. Intel CPUs are equipped with AVX vector registers, since the Sandy Bridge architecture and these vector registers are essential for applying the same instructions to multiple data entries . This large discrepancy in time cost would be substantial for vector–scalar operations. Therefore, we replace vector–scalar division, in the form of k is the number of communities to find. Theoretically, this will generate k communities. However, we remove fully disconnected communities, as outliers since they are often far from being significant because of their unusually small size and they correspond to all-zero submatrices in the graph adjacency matrix, which does not have a meaningful rank-2 representation. Therefore, the final number of communities are usually slightly smaller than k, as will be shown in “h2-n(g)(d)-a(x): These labels represent several versions of our algorithm. Here h2 stands for HierSymNMF2, n for the ncut_local criterion, ng for the ncut_global criterion, and ngd for the ncut_global_diff criterion (see previous sections for the definitions of these criteria); ‘a’ means that we compute the real normalized cut using the original adjacency matrix; and ‘x’ indicates that an approximated normalized cut is computed using the normalized adjacency matrix, which usually results in faster computations. We stop our algorithm after SCD: SCD algorithm .BigClam: BigClam algorithm .Graclus: Graclus algorithm .NISE: An improved version of NISE that is published in 2016 .We compare our algorithm with some recent algorithms mentioned in the “k communities Normalized cut  is a meaductance , which ik communities Alternatively, we can measure the qualities of detected communities by comparing them with ground truth. Suppose mentclass2pt{minimThe data used for the experimental results of this paper are mostly from SNAP datasets , 34. In DBLP06 is the only dataset with a complete raw dataset openly available to the public. The other five datasets  were obtained by crawling the web, and they are far from being complete. Crawling large complex graphs is challenging by itself that may need extensive and specialized research efforts. We do not aim to solve this issue in this paper. The Orkut and Youtube datasets can be acquired from [Orkut and Youtube datasets are not complete. Such incompleteness in crawled datasets is expected due to intrinsic restrictions of web crawling such as rate limit and privacy protection. The Friendster data were crawled by the ArchiveTeam, and the LiveJournal data come from [Amazon data were crawled by the SNAP group [Although all of these datasets can be conveniently accessed on the SNAP website as a graph with ground-truth communities, red from . Detaileome from . The AmaAP group . HoweverYoutube, LiveJournal, Friendster, and Orkut), it is common that a user does not join any user groups; (4) SNAP used the dataset from [DBLP06 dataset, which was published in 2006. At that time, the DBLP database was not as mature and complete as it is today. Another issue of the above datasets is that all nodes are anonymized, which ensures protection of user privacy, but limits our ability to interpret community detection results.Possible reasons that many nodes in these datasets do not belong to any communities are (1) SNAP removed communities with less than three nodes, which caused some nodes to “lose” their memberships; (2) the well-known incompleteness of crawled datasets; (3) for social networks . Although the other datasets which we currently cannot improve are also valuable, our goal is to obtain new information from comparison of community detection results and ground-truth communities, rather than simply recovering the ground-truth communities.http://dblp.dagstuhl.de/xml/release/dblp-2015-06-02.xml.gz. The structure of this XML file is illustrated in Fig. Level 1 elements and the children of Level 1 elements Level 2 elements, and so on. Level 1 elements represent the individual data records [DBLP is an online reference for bibliographic information on major computer science publications . As of J records , such asOur goal is to obtain a coauthorship network and ground-truth information (venue–author relation) from the XML file. Although the XML file is highly structured, such a task is still not straightforward due to the ambiguity of entities, such as conflicts or changes of author names, various abbreviations, or even journal name change. DBLP resolves the author ambiguity issue by means of a unique number for each author. However, the venue ambiguity is still an issue in DBLP: there are no unique identifiers for venues. Fortunately, each record in DBLP has a unique key, and most paper keys contain the venue information as follows:Now unique identifiers for both authors and venues make extracting the network and community information very reasonable. The next step is to create a node for each author, and create a link between two authors if they have ever coauthored in the same publication. For community information, each venue is a community, and an author belongs to a community if he/she has published in the corresponding venue.A few authors do not have any coauthor in the DBLP database, and become isolated nodes in the generated network. Thus, we remove these authors. However, after removing those authors, some venues/communities become empty because all of their authors are removed. Hence, we remove those empty communities. After this cleaning, we obtained 1,509,944 authors in 5147 communities (venues).This cleaned network has 51,328 (weakly) connected components, where the largest connected component contains 1,357,781 nodes, which makes 89.9% of all nodes. The remaining 51,327 connected components are all small, the largest of which has only 37 nodes. We take the largest connected component as the network to study. By extracting the largest connected component, we obtain a network with 1,357,781 nodes, 6,369,212 edges and 5146 ground-truth communities. The ground-truth communities were divided into connected components, obtaining 93,824 communities. The divided ground-truth communities were used for comparison with detected communities.DBLP15 dataset is available at https://github.com/smallk/smallk_data/tree/master/dblp_ground_truth.The new We run our experiments on a server with two Intel E5-2620 processors, each having six cores, and 377-GB memory. The results are listed in Tables In the “internal measures” table, “coverage” measures the percentage of nodes which are assigned to at least one community; “algorithm time” and “total time” provide the runtime information. We list two measures of runtime since our algorithm  implemented in MATLAB directly uses a processed matrix in memory as its input. Other algorithms must first read the graph stored as an edge list or an adjacency list and convert the graph to the appropriate internal representation. Therefore, we use “algorithm time” to measure the algorithm runtime without the time needed for reading and converting the graph, which is reported by the algorithms themselves. The “total time” is the wall clock time for running the algorithm, including the time for reading and converting the graph, which is measured with an external timer. BigClam reports its algorithm time as the sum of time used in each core, and therefore, the results are not comparable. For completeness, we added the data-loading and data-preprocessing times, which are measured separately, to obtain a “total time” for the MATLAB algorithms .The “number of clusters” in the “internal measures” table are different across different methods due to the following reasons. The SCD algorithm does not provide an interface for specifying the number of communities to detect, and instead detects the number of communities automatically. For other algorithms, we specify the number of communities to detect as 5000. The actual number of communities generated by HierSymNMF2 is usually less than 5000, as discussed in “In the “external measures” table, the “reverse precision” and “reverse recall” refer to the scores computed as if the ground-truth communities are treated as detected communities and the detected communities are treated as the ground truth, respectively. Note that the number of clusters in “external measures” is less than the one in “internal measures” due to the removal of nodes that do not appear in the ground truth.Amazon dataset. On the Amazon dataset, HierSymNMF2 achieves much lower average normalized cut than SCD and BigClam, and the variant h2-ngd-a obtained comparable average normalized cut (0.1491) versus Graclus (0.1450), which is not as good as NISE (0.1118). (2) HierSymNMF2 runs slower than most other algorithms on DBLP06 and DBLP15. On the Youtube dataset, HierSymNMF2 runs faster than BigClam, Graclus and NISE. On the Amazon dataset, HierSymNMF2 runs faster than NISE, but slower than other methods. (3) HierSymNMF2 achieves better F1 score than BigClam and NISE on all the datasets we used. Graclus has better F1 score than HierSymNMF2 on DBLP06, Amazon, and Youtube datasets but obtained an unusually low F1 score on the DBLP15 dataset. SCD achieves higher F1 scores than HierSymNMF2. However, SCD often discovers a significantly larger number of (nonoverlapping) communities than expected and has very unbalanced precision and recall scores compared with other algorithms. The SCD algorithm finds the number of communities as it finds the communities and the number of communities cannot be given to SCD as an input. The SCD algorithm starts by assigning an initial partitioning of the graph heuristically. In short, in the initial partitioning, each node and all its neighbors form a community, and special care is taken to ensure that no node belongs to more than one community. As a result, this initial step often creates many more communities than the optimal number, although later refining procedures may reduce the number of communities. As can be seen from the experimental results, when compared with BigClam, Graclus, NISE, and our proposed algorithms that take the number of communities as an input, a much larger number of communities that the SCD generates does not necessarily translate to a better overall community detection result in terms of either normalized cut or F1 scores.We have the following observations from the experimental results: (1) Our HierSymNMF2 algorithm has significant advantages over other methods in average normalized cut on most datasets except the Overall, HierSymNMF2 is an effective community detection method that optimizes the average normalized cut very well, although it is not the fastest method.To address the quality issue of the ground-truth networks, we constructed a more complete and recent version of the DBLP dataset, where most nodes have at least one community membership and also the size of the data is significantly larger.F1 score because of the incompleteness of ground-truth communities and the difference between linkage-based communities (as detected) and functional communities (as in the ground truth). Therefore, it is important to use an internal measure such as the average normalized cut to evaluate an algorithm. In addition, we believe the current evaluation methods for large-scale community detection have certain limitations. These methods are mainly based on some quality scores, e.g., the F1 score. Such quality scores can be used to compare various algorithms. However, they do not provide much more information regarding the quality of results than the average performance. We assert that to better understand a community detection result, it is necessary to develop more comprehensive evaluation methods.We partially answered the three questions we raised in “For Q3, we think that the large scale of the network and the large number of communities make the community detection results hard to interpret. One way to understand the community structure better would be to develop better methods for community visualization.As a result of the growing popularity and utility of social media communications and other channels of communications between people and groups of people, there are vast amounts of data that contain latent community information. The amount of information is overwhelming and very demanding of our current technological capabilities, which may adversley impact the ability of stakeholders to make critical and timely decisions that are important in many domains such as natural disasters, local conflicts, healthcare, and law enforcement, to name a few. These domains typically involve groups of individuals with often hidden links. Thus, it is incumbent on the research community to develop fast and effective methods to first discover the communities formed by these links and then formulate useful summaries of the information provided by the algorithms and measures in order for decision makers to initiate appropriate actions as required.


We show that all these intriguing phenomena can be interpreted based on the unique band structures of graphene quantum dots and graphene as well as resonant quantum tunneling.The occurrence of zero effective mass of electrons at the vicinity of the Dirac point is expected to create new paradigms for scientific research and technological applications, but the related discoveries are rather limited. Here, we demonstrate that a simple architecture composed of graphene quantum dots sandwiched by graphene layers can exhibit several intriguing features, including the Dirac point induced ultralow-threshold laser, giant peak-to-valley ratio (PVR) with ultra-narrow spectra of negative differential resistance and quantum oscillations of current as well as light emission intensity. In particular, the threshold of only 12.4 nA cm In graphene, electrons possess zero effective mass in proximity to the Dirac point, an unusual feature that could trigger the development of novel photonic devices. Here, the authors combine graphene quantum dots with two graphene layers and observe laser action with ultralow threshold. A single layer graphene possesses linear energy momentum dispersion relation at the edges of hexagonal Brillouin zone, which leads the electrons and holes to behave as a massless Dirac Fermion with exceptionally high mobility6. It has triggered the experimental realization of several fundamental physical phenomena and offered a fascinating material for electronic and optoelectronic applications1. Over last few years, several applications have been demonstrated using various intriguing properties of graphene including ultrahigh carrier mobility, excellent tensile strength and high transparency10. Notably, linear dispersion relation makes the graphene Fermi energy (EF) extremely sensitive to its neighboring perturbations11. Thus, it has become a challenging issue to achieve the exceptionally high mobility of graphene film in a real device. Up until now, practical applications of massless Fermions around the Dirac point are not yet realized. On the other hand, graphene quantum dots (GQDs) reveal a new promise of bandgap opening in mesoscopic grapheme13. Several studies suggest that the energy band in GQDs can be highly influenced by the functional groups attached on the GQD matrices as well as the quantum confinement of the carriers14, which makes explicit explanation of the bandgap and related luminescence difficult.Graphene has been studied extensively during last decade owing to its unique physical, chemical and mechanical propertiesHerein, with a simple all-graphene-based architecture consisting of GQDs sandwiched between two graphene layers, we demonstrate the Dirac point induced ultralow-threshold laser action due to the rapid formation of population inversion by fast transport of massless charge carriers. In addition, we discover a giant peak-to-valley ratio (PVR) and ultra-narrow spectra of negative differential resistance (NDR). Furthermore, we observe quantum oscillations in both conductance and light emission spectra. These intriguing features may pave the way to the development of new optoelectronic and nanoelectronic devices.2 substrate  to the bottom-graphene (GrB) as shown in Fig. −1 in the band structure of GQDs20. This result is consistent with the X-ray photoelectron spectroscopy data as shown in Supplementary Fig. EF of the subsequent graphene changes monotonically. The EF close to a certain energy state of GQDs thus causes a resonant tunneling of electrons24as shown in Fig. 25. Notably, the tunneling electrons from the graphene electrode trapped in the energy levels of GQDs will block the tunneling of electrons with energy below the Fermi level of the graphene layer. We therefore can observe a very pronounced peak and valley characteristics. In addition, the injected charge carriers in GQDs can be used to generate electroluminescence and laser action as shown below. These distinct characteristics are very different from the graphene/hexagonal boron nitride (h-BN)/graphene heterostructure as reported previously26. Because h-BN is a wide bandgap material, it hardly carries any energy states in between its conduction and valance bands. Hence, the tunneling can occur from the density of states between the chemical potential of the two graphene electrodes, which leads to a quite different behavior. The average PVR of the current in the lower bias region is found to be >20. The two adjacent current peaks are found to be separated by ~0.30 V, which corresponds to an energy separation of ~ 135 cm−1 based on the estimated EF shift due to the change of external bias27 -grown graphene on SiOI–V curve in Fig. 2 in the curve, the tunneling current is dramatically enhanced owing to the existence of a very low effective mass of electrons4 in yellow region, as shown in the inset of Fig. 3, while the Fermi energy just crosses the neutrality point but still belongs to its vicinity, as the green region shown in the inset of Fig. 29. This abnormal behavior of current has not been realized in late studies on graphene tunnel transistors30. The observed nature of the measured current can be distinguished from previously reported conduction in graphene4 and graphene-based tunneling junctions26 with the help of existing theoretical models31. The energy states of graphene under scanning tunneling microscope (STM) measurement forms a quasi-planar junction with STM tip due to the existence of a large amount of neighboring bulky atoms  around the Dirac point4 as shown in Fig. EF of graphene approaches the Dirac point, as discussed above, the tunneling current arising from massless Dirac carriers is enhanced abruptly, which causes a sudden appearance of massive number of electrons in the conduction band of GQDs as illustrated in Fig. 35. With the application of an external bias, when the Fermi energy approaches the Dirac point of single-layer graphene (SLG), electrons with ultrahigh speed will be injected into the GQDs and result in a robust enhancement of the emitted photons in the GQD matrix. The emitted light from the GQDs suffers multiple scatterings in the GQD matrix while travelling through it, which provides an excellent platform for optical feedback in the active material producing multiple coherent feedback loops. Because the optical feedback matrix in our device is randomly distributed, the observed lasing behavior is therefore named as random action. Under a particular external bias, when the Fermi level of SLG is close to the Dirac point, the resonance tunneling occurs, and the injected electrons can only populate a particular set of vibrational energies. Thus, even though the emitted lights from the GQD matrix produce a broad spectrum, only the emission corresponding to the energy of resonant tunneling states close to the Dirac point can overcome the energy loss due to the massive number of injected electrons. Consequently, the appearance of only three lasing peaks in the emission spectra under the forward bias is due to the recombination of the population inversion in the vibrational energy levels of the GQDs caused by resonant tunneling near the Dirac point and the formation of coherent loops arising from multiple scatterings. For the reverse bias, because the graphene attached to PMMA contains a different initial Fermi energy compared with the graphene on SiO2, it produces slightly different energy band diagram which is not identical to the forward bias. In this case, the tunneling current and optical feedback that cause the random laser action will be different from that of applying forward bias. The enhancement of the background emission corresponding to the higher pumping voltage can be inferred to the enhanced number of injected electrons when the Fermi level approaches resonance tunneling condition. To further consolidate the random lasing mechanism, we have measured the angular dependence of the emission spectra as shown in Fig. 38. Note that the lasing threshold voltage depends on the polarity of external bias, which is due to the difference between the zero-bias Fermi energy of the top- and bottom-graphene layers. The average output power is found to be ~1 nW cm−2 at the applied voltage above lasing threshold.The most fascinating achievement in our study is the observation of electrically pumped ultralow-threshold laser action assisted by the ultrahigh mobility of carriers  of Dirac electrons4, the conductivity is quite high, which enables to greatly reduce the power consumption. Thirdly, the formation of coherent optical feedback loops due to multiple scatterings in the GQD particles is suspended, which enables to overcome the reflection energy loss due to the absorption of substrate. Finally, unlike conventional semiconductor laser devices, which were made with a complicate structure consisting of highly resistive layers, the simple structure of our device only contains a thin layer of GQDs sandwiched by graphene, which can result in ultralow dissipation of input power.Quite interestingly, the Dirac point induced laser action shows a unique feature of ultralow-threshold injection current density. It was observed that the device lases at an applied voltage of ~6.8 V and ~4.5 V under positive and negative bias, respectively, with an injected threshold current density of ~12.4 to 18 nA cm44. With increasing radiation density, the lifetime of excited states decreases still further. For our case, in the vicinity of the Dirac point, the electrons behave as massless Fermions. Thus, when the Fermi energy is tuned to the Dirac point of graphene, the resonance energy tunneling of the Dirac electrons from graphene induces a rapid formation of population inversion in GQDs and generates stimulated emission, which in turn shortens the carrier lifetime.To further confirm the nature of the laser action, we have measured the carrier lifetime of the device under the excitation of a 375 nm pulse laser while applying different bias voltage to the device as shown in Supplementary Fig. I–V curve is observed without any indication of oscillation  as shown in Supplementary Note 2 layer as shown in Supplementary Note 2 layer in between graphene layers does not exhibit any resonant behavior. A more detailed discussion is included in Supplementary Note I-V curve is not observed due to the defect-induced change in the electronic properties of the graphene layers45.To consolidate the results as described above, we have fabricated the device in which the GQD layer was replaced by an inactive SiO48 and is very costive54. Thanks to the unique band structure of graphene integrated with semiconducting materials, all these difficulties can be easily overcome as shown in this study. It is believed that our approach can be extended to several other material systems to achieve laser action covering a wide range of spectrum. For example, using lanthanide composite nanoparticles, indeed, we are able to successfully demonstrate an infra-red laser , the PMMA was spin coated on graphene at 2,000 r.p.m. for 25 s, and annealed at 100 °C for 2 min. We then used FeClAzadirachtaindica) leaf extract was obtained by grinding the Neem leaves using a commercial grinder. It was then boiled at 80 °C for 1 h in 20 ml of ultrapure deionized (DI) water, which was then centrifuged at a relative centrifuge force (RCF) of 12,000 g for 10 min in order to remove the remaining solid residue. The collected mixture was then filtered through a 22 µm membrane to further remove the trace solid residue. The supernatant was then added in a 10 ml glass bottle and stirred and sonicated at 24 °C for 30 min. It was then hydrothermally treated at 300 °C for 8 h in an autoclave, where it produces a brown transparent suspension and black precipitate. After the solution was cooled down to room temperature, the precipitate was carefully discarded. The suspension was then further centrifuged at higher RCF of 25,000 g for 20 min. The precipitate was discarded and the suspension was collected and washed twice in 10 ml ultrapure water. Then, the supernatant was dialyzed for 3 h using a filter of cutoff 3.5 kDa and subsequently dried overnight to get pure GQDs. The yield is found to be ~25.2%, and for every 250 mg of Neem leaf extract, ~75 mg of GQDs were produced. Next, 13.5 mg of obtained GQD was mixed in 60 ml of ultrapure DI water to produce a suspension of GQD which was used to design the device. The estimated cost of the built of materials was found to be 1 USD per 1 g of pure GQD. A detailed analysis of the property of graphene and GQD is provided in the Supplementary Notes To fabricate the GQDs, 250 mg of Neem  which costs 1$ per g. The reported costs for high-quality single-layer graphene is reported to be 115$ per m2. Commercial high-quality silicon wafer is available in 2$ per inch2, which makes the estimated costs of our device ~3$ per device.A schematic diagram of device fabrication is provided in Supplementary Fig. −2. Simultaneously, we applied different bias voltage to obtain the bias voltage-dependent carrier lifetime. The emitted photon intensity was collected by a TRIAX 320 monochromator of Horiba Jobin Yuvon. The obtained lifetime data contain ±0.2 ns errors induced by TRIAX 320.The electronic characterizations of the device were employed using Keithley 2400 electrometer and standard probe system. The optical measurements were performed by iHR 550 spectrometer of Horiba Jobin Yvon. The lasing phenomena is obtained by using IHR 550 spectrometer of Horiba Jobin Yuvon and Keithley 2400 electrometer. A constant bias voltage is applied to the device using Keithley 2400 electrometer and the emission spectrum was subsequently collected by IHR 550 spectrometer and Synapse CCD detector. The carrier lifetime of a particular emission wavelength was obtained by recording the transient intensity of emitted photons corresponding to the emission under 374 nm pulsed laser of pulse width 55 ps, frequency 40 MHz, and energy density of 5 µJ mDt, Db and Dvib are the carrier density of states in top graphene, bottom graphene and the vibrational-level GQDs, respectively26. The ft and fb are the Fermi distribution function of the top and bottom graphene; ɛf = ɛf0 is the initial Fermi energy of the graphene and ɛfi is the Fermi energy of the graphene close to ‘i’th vibrational energy level; 0.0155 eV is the period of the –OH out of plane vibrational energy levels20. T represents the tunneling coefficient, which can be expressed as equation  = [Φt(d−z) + (Φb−eV)z]/d, where the work functions Φt = Φb = 4.6 eV for both of the graphene layers.The current in our device can be expressed by equation , where, The data that support the findings of this study are available from the corresponding author upon request.Supplementary Information


This study aims to use high throughput 16SrRNA gene sequencing to examine the bacterial profile of lymph node biopsy samples of patients with sarcoidosis and to further verify the association between Propionibacterium acnes (P. acnes) and sarcoidosis.A total of 36 mediastinal lymph node biopsy specimens were collected from 17 cases of sarcoidosis, 8 tuberculosis (TB group), and 11 non-infectious lung diseases (control group). The V4 region of the bacterial 16SrRNA gene in the specimens was amplified and sequenced using the high throughput sequencing platform MiSeq, and bacterial profile was established. The data analysis software QIIME and Metastats were used to compare bacterial relative abundance in the three patient groups.P < 0.01). P. acnes 16SrRNA was exclusively found in all the 17 samples of the sarcoidosis group, whereas was not detected in the TB and control groups. The relative abundance of P. acnes in the sarcoidosis group (0.16% ± 0. 11%) was significantly higher than that in the TB  and control groups . The relative abundance of P. granulosum was only 0.0022% ± 0. 0044% in the sarcoidosis group. P. granulosum 16SrRNA was not detected in the other two groups.Overall, 545 genera were identified; 38 showed significantly lower and 29 had significantly higher relative abundance in the sarcoidosis group than in the TB and control groups  contains supplementary material, which is available to authorized users. Sarcoidosis is a systemic granulomatous disease and often involves the respiratory system. The disease frequently affects middle-aged adults, in particular women aged 40–50 years. The histopathology of sarcoidosis is characterized by non-caseous necrotizing granulomas, and the clinical presentation of the disease is very diverse . The exaPrevious reports studying the association of infection and sarcoidosis mainly focus on Mycobacterium tuberculosis (MTB) and propionibacteria, and show controversial results. Eishi et al.  used reaMost of the previous studies investigated only a few bacterial species. A full bacterial profile of the tissue specimens of patients with sarcoidosis is still lacking. The current study is filling this knowledge gap. In the current study, we aim to conduct high throughput 16SrRNA gene sequencing to investigate the bacterial profile of mediastinal lymph node specimens. The 16SrRNA gene is present in the genome of bacteria, chlamydia, rickettsia, mycoplasma, spirochaeta, and actinomycetes, but absent in eukaryotic organisms, viruses, and fungus. The sequence of V4 region of the bacterial 16SrRNA gene, which varies greatly in different bacterial species, is often considered as a unique signature for a bacterial species and thus commonly used for bacterial taxonomic classification –12. The The protocol for collecting and handling patients’ biopsy specimens has been approved by the Institutional Review Board of Shanghai Pulmonary Hospital and Tongji University School of Medicine . Biopsy specimens from the mediastinal lymph node of 36 patients, who were treated in Shanghai Pulmonary Hospital between August 2014 and August 2015, were collected and analyzed. All the 36 patients, including 17 cases of sarcoidosis, 8 cases of TB, and 11 cases of control, were inpatients of Shanghai Pulmonary Hospital. The 8 patients with TB were newly diagnosed based on positive MTB in their sputum. The 11 controls included 6 cases of nonspecific lymphadenitis and 5 cases of mediastinal lymph node metastasis of lung cancer. The clinical data of the patients are displayed in Table The diagnostic criteria for sarcoidosis followed the 1999 guidelines of ATS/ERS/WASOG  and the Patients with TB were diagnosed according to the 2013 tuberculosis diagnosis and treatment guidelines recommended by the China Medical Association Tuberculosis Society . The diahttp://ccb.jhu.edu/software/FLASH/)[http://drive5.com/uchime/uchime_download.html) using the UCHIME Algorithm  [Because the sequence of V4 region of the bacterial 16SrRNA gene is a unique signature of a bacterial species, the sequence can be used to identify the specific bacterial taxonomic position . The V4 go.html)  to identgo.html) . The reshttp://drive5.com/uparse/) [http://greengenes.lbl.gov/cgi-bin/nph-index.cgi) [http://sourceforge.net/projects/rdp-classifier/) [The identified 16SrRNA sequences (the effective sequencing tags) were annotated using the Uparse software package v7.0.1001 (uparse/) . The anndex.cgi)  and the sifier/) . The seqTo estimate if the identified 16SrRNA sequences could include all the bacteria in the samples, we conducted alpha diversity analysis. The alpha diversity was analyzed using the Quantitative Insights into Microbial Ecology (QIIME) software (Version 1.7.0) . To evalhttp://drive5.com/uparse/) were used to analyze bacterial abundance. The Metastats software (http://metastats.cbcb.umd.edu/) was used to compare the difference in bacterial abundance between different groups. The Metastats software is specifically designed to compare bacterial abundance by quantitatively analyzing the 16SrRNA gene sequences [Bacterial relative abundance was determined to identify the most predominant bacterial species, which represent the bacterial profile in the samples. The Quantitative Insights into Microbial Ecology (QIIME) software (Version 1.7.0) and Uparse software package v7.0.1001  > 0 represented inter-group difference > intra-group difference; R < 0 represented inter-group difference < intra-group difference; P < 0.01 was considered statistically significant. A P value was first calculated for inter-group difference in relative abundance. The P value was then adjusted to q value using an algorithm established by Storey and Tibshirani [P and q values, Taxonomic units showing significantly different relative abundance in the three groups were identified. When both P < 0.01 and q < 0.01, the differences were considered statistically significant. The software Graphpad prism 5 was used to prepare the curves of bacterial relative abundance. The software SPSS 20.0 was used for chi-square analysis of the inter-group difference in bacterial positive rate; P < 0.01 was considered statistically significant.Intra-group comparison was performed to evaluate the differences in the bacterial profile of samples in the same patient group. Inter-group comparison was performed to estimate the difference in the bacterial profile of the sarcoidosis, control, and TB patient groups. Because bacterial profile is expected to vary considerably in different individuals, evaluation of inter- and intra-group difference may reveal the unique characteristics of bacterial profile in sarcoidosis, control, and TB patient groups. For inter-and intra-group comparison of bacterial profile, R software (version 3.1.3) was used for nonparametric multi-response permutations procedure (MRPP)  and the bshirani . Based on = 10) with sarcoidosis were at chest X-ray stage I. The percentages of specific T cell populations, including CD3, CD4, and CD8, were similar in the sarcoidosis and TB groups. So were the levels of IgG, IgA, IgM, C3, and C4  and the 16SrRNA sequence database, GreenGene database (http://greengenes.lbl.gov/cgi-bin/nph-index.cgi). The annotations of all the 8748 are presented in Additional file Sequencing data are presented in Additional file At the taxonomic genus level, the OTUs were assigned to 545 genera based on the Greengenes database . Among the 67 genera, 38 showed significantly lower relative abundance in the sarcoidosis group than in the TB and control groups  and 11 control patients . In contrast, the other 28 bacterial genera were detected in all the patients in the three groups, suggesting that these bacterial genera may not be specific for patients with sarcoidosis. The relative abundance of the Propionibacterium Genus in the sarcoidosis group was 0.16% ± 0. 11%, which was significantly higher than that in the control  and TB groups .Among the 29 bacterial genera with significantly higher relative abundance in the sarcoidosis group, The Propionibacterium Genus was found in all the 17 patients with sarcoidosis (100% positive rate) but was completely absent in all the 8 patients with TB  and control  groups, the differences was not statistically significant. The relative abundance of mycoplasma was also very low in the three groups: 0.0067% ± 0.0090% in the sarcoidosis group, 0.0026% ± 0.0034% in the control group , and 0.0020% ± 0.0023% in the TB group . The differences were not statistically significant. Neither Borrelia nor and chlamydia was detected in the samples.In addition to propionibacteria, mycobacteria , 27, 28,P = 0.0010, q = 0.0038) and TB  groups  and TB  groups  and Propionibacterium granulosum (P. granulosum). P. acnes was detected in all the 17 patients with sarcoidosis, whereas P. granulosum was detected in only 5 of the 17 patients. Both Propionibacterium species were not found in the control and TB groups. The relative abundance of P. acnes in the sarcoidosis group (0.16% ± 0. 11%) was significantly higher than that in the control  from patients with non-sarcoidosis diseases showed positive P. acnes culture . Oswald To identify potential pathogenic bacteria of sarcoidosis, we previously used quantitative PCR to quantify the copy number of 16SrRNA gene of P. acnes and P. granulosum in the lymph node specimens and found that propionibacteria existed in 80% of sarcoidosis specimens but only in 4.5% of TB specimens . To furtThe possible pathogenic mechanism of P. acnes in sarcoidosis development has been tested in animal models. Kouji Iio et al. used heat-inactivated P. acnes and Freund's complete adjuvant to induce pulmonary granuloma in mice, and found that the total number of lymphocytes and the ratio of CD4+ to CD8+ T cells in the bronchoalveolar lavage fluid of the mice with pulmonary granuloma were higher than those of the control mice . Their sCompared with quantitative PCR approach, high throughput16SrRNA gene sequencing is a more effective approach to investigate bacterial profile of biopsy specimens and identify the predominant bacterial species in the specimens. The sequencing approach also allows us to compare the diversity and relative abundance of bacteria in specimens. Although quantitative PCR approach may be sensitive, low-cost, and fast, the sequencing approach can produce data for comprehensive analyses, such as bacterial taxonomy, bacterial diversity, and the correlation of bacterial profile evolution and sarcoidosis progression. The current study first used high throughput sequencing approach to investigate the bacterial profile in lymph node specimens of patients with sarcoidosis and to identify that propionibacteria were exclusively present in the sarcoidosis specimens. The advantages of the sequencing method include high throughput, high reproducibility, and high accuracy. The limitations of the sequencing method include technically complex and requiring special instrument and software for the analysis. In addition, the roles of those bacteria with significantly higher or lower abundance in the sarcoidosis group compared with the control and TB groups in the development of sarcoidosis remain unclear. Furthermore, the control groups in the current study included lymph node specimens of patients with nonspecific lymphadenitis and of patients with mediastinal lymph node metastasis of lung cancer, which may contribute to the highly various bacterial profiles in the control group. Another limitation of the current study is that the number of tissue specimens in the current study was relatively small. We are currently collecting more samples to further investigate the dynamic evolution of bacterial profiles in the sarcoidosis lesions at different disease stages.The current study used high throughput 16SrRNA gene sequencing to demonstrate that P. acnes exclusively existed in the mediastinal lymph node specimens of patients with sarcoidosis but was absence in the specimens of patients with TB or other non-infectious lung diseases. These findings indicate that P. acnes may correlate with sarcoidosis etiology.


For future on-chip communication schemes, it is essential to integrate nanoscale materials with an ultrafast optoelectronic functionality into high-frequency circuits. The atomically thin graphene has been widely demonstrated to be suitable for photovoltaic and optoelectronic devices because of its broadband optical absorption and its high electron mobility. Moreover, the ultrafast relaxation of photogenerated charge carriers has been verified in graphene. Here, we show that dual-gated graphene junctions can be functional parts of THz-circuits. As the underlying optoelectronic process, we exploit ultrafast photo-thermoelectric currents. We describe an immediate photo-thermoelectric current of the unbiased device following a femtosecond laser excitation. For a picosecond time-scale after the optical excitation, an additional photo-thermoelectric contribution shows up, which exhibits the fingerprint of a spatially inverted temperature profile. The latter can be understood by the different time-constants and thermal coupling mechanisms of the electron and phonon baths within graphene to the substrate and the metal contacts. The interplay of the processes gives rise to ultrafast electromagnetic transients in high-frequency circuits, and it is equally important for a fundamental understanding of graphene-based ultrafast photodetectors and switches. The graphene is electrically contacted by metal strips made from Ti/Au (10 nm/250 nm), which allow to apply a bias voltage Vsd. First, two adjacent 10 nm thick Pt gates are lithographically defined on a sapphire substrate. Then, a 30 nm thick, ALD-grown (atomic layer deposition) Al2O3-layer isolates the gates from the graphene. Subsequently, CVD-grown  graphene is mechanically transferred to the sampleIsampling7Iphoto is measured with the help of a current-voltage amplifier while the sample is scanned laterally by the help of piezoelectric positioners. Iphoto at Vsd = Vg1 = Vg2 = 0V. For these experimental parameters, Iphoto shows opposite signs on both sides of the junction, as it will be discussed below.For the excitation of the graphene, we use a laser system that emits laser pulses with a 30 fs pulse duration at a repetition rate of 80 MHz in the spectral range from 900 to 1500 nm cf. . The lasIsampling across the Auston-switch samples the electromagnetic transients on the striplines as a function of the time-delay Δt between the two laser pulses, and it is directly proportional to the ultrafast photocurrents in the graphene. Hereby, the photocurrents in the graphene can be measured with a picosecond time-resolution7For measuring the ultrafast optoelectronic dynamics of the graphene-based junction, we use an on-chip THz-time domain photocurrent spectroscopy of the stripline circuits 7212223, 721222122Rtotal is measured with a current-voltage amplifier to gain insights into the electronic properties of the dual-gated graphene junction. Rtotal, when both voltages of gate1 and gate2 are varied simultaneously. We find the charge neutrality point of the graphene for negative gate voltages (Vd < 0V), which implies that it is intrinsically n-doped. Rtotal, when Vg1 and Vg2 are varied independently. We apply the following modelThe two-terminal resistance n0 is the residual charge carrier density (i.e. the minimum sheet carrier concentration), nind the gate-induced charge carrier density, n the total charge carrier density. The term Δ is defined as C* the specific capacitance εr = 6.0 the relative permittivity and d = 30 nm the thickness of the Al2O3 layer). The data in Here, Rcontact accounts for the constant resistance of the metal striplines and the ungated graphene region. Rgi represents the gate voltage dependent resistance of the two  gated graphene sections, respectively. This resistance is related to the conductivity by l = 4 μm and w = 25 μm are the length and width of the gated graphene sections, respectively. The conductivity σi(Vgi) can be tuned by the gate voltage Vgi and is calculated from Rcontact = 2.57 kΩ and mobilities of μ1 = 157 cm2V−1s−1 and μ2 = 278 cm2V−1s−1. The charge neutrality point is reached at Vd1 = −1.92 V and Vd2 = −2.77 V. The fit further reveals Δ1 = 3.20 V and Δ2 = 2.62 V. The expression VTE can be written asThe photo-thermoelectric effect910113467Ti the change in electron temperature for a segment with constant thermopower Si. We define the x-axis as in T and for constant thermopowers Sg1 and Sg2 for the two areas on top of gate1 and gate2. We note that a symmetric heat profile is introduced by the laser pulse with the given FWHM of ≈5.5 μm  from the fit of Rtotal = Rtotal in Vg1 and Vg2: for a given gate voltage Vg2 and Seebeck coefficients Sg2  for different gate voltages Vg1 and Vg2. The voltage settings are defined by the indices in t between pump and probe pulse is zero. For a longer time-delay, we observe a rising and decaying behavior of Isampling (gray area). We label the latter response as ‘decay current’. For each gate voltage setting, we fit Isampling(Δt) with a constant offset, a Lorentzian (green area), and an exponentially rising and decaying function τdecay and τriseVg2 than on Vg1 for this laser position. We further note that the peak current and the decay current can have different polarities at certain gate voltages  and the difference in the thermopowers on both sides of the graphene give rise to a thermoelectric voltage VTE = (Sg1−Sg2) ΔT, that drives a photocurrent as long as the temperature profile is present. We can further infer that the so-called photovoltaic effect plays only a negligible role. In particular, the photovoltaic effect would only depend on the difference of both Fermi levels of dual-gated graphene junctions. In other words, its sign should change along the diagonals of 44In order to explain the measured ultrafast peak and the decay currents in 1316193233343513171837394142Δt < 13.3 ps as a function of both gate voltages for three different excitation positions of the laser spot. The excitation position is varied in steps of 2 μm from an excitation position closer to gate1 (Vg1 (Vg2). In other words, the decay current predominantly depends on the voltage of the gate at which the laser is focused. The numerical model .


This review focuses on the role of the lung microbiome in idiopathic pulmonary fibrosis. Although historically considered sterile, bacterial communities have now been well documented in lungs both in healthy and pathological conditions. Studies in idiopathic pulmonary fibrosis (IPF) suggest that increased bacterial burden and/or abundance of potentially pathogenic bacteria may drive disease progression, acute exacerbations, and mortality. More recent work has highlighted the interaction between the lung microbiome and the innate immune system in IPF, strengthening the argument for the role of both host and environment interaction in disease pathogenesis. Existing published data suggesting that the lung microbiome may represent a therapeutic target, via antibiotic administration, immunization against pathogenic organisms, or treatment directed at gastroesophageal reflux. Taken altogether, published literature suggests that the lung microbiome might serve in the future as a prognostic biomarker, a therapeutic target, and/or provide an explanation for disease pathogenesis in IPF. Idiopathic pulmonary fibrosis (IPF) is a rare lung disease of unknown origin which leads rapidly to death ,2. HowevIn this review we are going to approach successively the respiratory microbiome in healthy subjects and the main alterations described in IPF microbiome, during stable disease and during exacerbations, as well as its interaction with the host response. We have performed a systematic search in PubMed by typing the words: (“Microbiota”[Mesh]) and  or “Idiopathic Pulmonary Fibrosis”[Mesh]) and (microbiome) and  or “Idiopathic Pulmonary Fibrosis”[Mesh]), and selecting those thought to be relevant. Publications dates of selected papers range from 2010 to 2017. The epithelial surfaces of the respiratory tract, previously thought to be sterile, have been shown, by using culture-independent techniques, to accommodate dynamic microbial communities. High-throughput bacterial 16s-rRNA sequencing has been described to identify bacterial DNA in 95.7% of bronchoalveolar lavage (BAL) specimens compared with conventional culture techniques, which detected bacteria in 39.1% of BAL samples . Prevotella sp.), Firmicutes (including the genera of Streptococcus sp. and Veillonella sp.) and, to a lesser extent, Proteobacteria and Actinobacteria [The bacterial communities of healthy lungs closely resemble those of the mouth , while bbacteria ,28,29.The exact composition of the lung microbiota results from three main factors. The first one is microbial immigration due to microaspiration of gastric content, direct mucosal dispersion from the oro-nasal cavities, and to the inhalation of air. The second one is the microbial elimination, which results from the mucociliary clearance, cough and immunity. Finally, the third factor is the local microbial growth environment that includes notably nutrient availability, oxygen tension, pH and temperature. As a consequence of those three factors, the lung microbiota represents a steady state between microbial influx, efflux and reproduction rates, the latest being mostly altered in case of pathological processes. In every lung disease studied to date, the lung microbiome is altered compared with that of healthy subjects .Given the sensitivity of the molecular technologies employed, an obvious concern in many studies is contamination of samples from the upper respiratory tract when sampling, providing a false representation of the true microbiome ,19. AlthBeyond studying the microbiota, a small minority of studies have focused on fungi and viruses. Recent studies have shown that commensal fungi not only affect the host immune system, but can also affect bacterial composition and have a particularly important influence during restoration of the bacterial microbiota after antibiotic treatment . Virome Even if the exact pathophysiology of IPF is still incompletely understood, the microbiome is suspected to play a role in the pathology . Indeed,The hypothesis that IPF progression is influenced by microbes is supported by the finding that immunosuppression increased the risk of death and hospitalization . AnotherHaemophilus, Streptococcus, Neisseiria and Veillonella sp. are more abundant in patients with IPF compared to controls [The microbiome of IPF patients is distinct from healthy individuals: their bacterial load is overall higher, and the genera controls . It is acontrols , as eachcontrols .Streptococcus, Neisseria and Actinobacterium sp. genera. Interestingly, bacterial DNA was not detected in five out of eight patients colonized with Pneumocystis jirovecii, suggesting this fungus may impair bacterial colonization of the airways [The first exploratory application of a culture-independent molecular technique in IPF studied the microbiome in BAL from 17 IPF patients . Using 1 airways . A small study then investigated the upper and lower respiratory tract microbiota in a heterogenous group of 18 patients with interstitial lung disease (ILD), including five with idiopathic interstitial pneumonia (IIP), six patients with pneumocystis associated pneumonia and nine healthy controls . The 16sPrevotella, Veillonella and Cronobacter sp. Moreover, the presence of a specific Streptococcus or Staphylococcus OTU  above a certain threshold was associated with a faster-progressing disease, after adjusting for age, sex, smoking status, respiratory function, six-minute-walk test and the presence of gastro-intestinal reflux [Later on, a multicenter cohort study of Correlating Outcomes with biochemical Markers to Estimate Time-progression in idiopathic pulmonary fibrosis (COMET) , retrospl reflux . Howeverp < 0.0001). Secondly, there was a significant association between patients with higher BALF bacterial load and disease progression at six months  by 10%, or death) compared with controls (p = 0.02). Furthermore, it was possible to stratify patients according to bacterial burden in order to predict mortality risk, patients with higher bacterial burden having an increased risk of mortality (hazard ratio: 4.59) compared with patients with low bacterial burden. After logistic regression analysis, the abundance of Veillonella, Neisseria, Streptococcus and Haemophilus sp. remained significantly associated with IPF. Moreover, the study found that patients carrying a minor allele at the MUC5B promoter had a lower bacterial burden, providing a mechanistic link between bacterial burden and a mutation known to be relevant in IPF. A large study published in 2014 investigated 65 well-defined IPF patients and 44 controls which included 27 healthy controls and 17 patients with moderate chronic obstructive pulmonary disease (COPD) . The firToday, it is still unclear whether changes in microbiome seen in IPF are a cause or a consequence of the disease. The PANTHER trial  has idenNlrc4, Pglyrp1, Mmp9, Defa4). Additionally, that team found two genes encoding specific antimicrobial peptides (Slpi and Camp). Several of the identified genes were linked to poor survival and disease progression. These results suggest a host response to alterations of the respiratory microbiome in IPF, suggesting that these microbial changes would possibly trigger a response associated with the damage often observed in IPF patients. As they conclude, “the bacterial communities of the lower airways may act as persistent stimuli for repetitive alveolar injury in IPF” [In a study from Molyneaux et al. , the aut in IPF” .Streptococcus sp. OTU, and that this phenomenon is correlated with poorer PFS. Staphylococcus and Prevotella sp. OTUs are also associated with poorer PFS, with decreased expression of immune response pathways genes and with overexpression of TLR-9 in PBMC. Finally, the increased presence of a specific Veillonella sp. OTU is correlated with increased CpG fibroblast responsiveness. This study demonstrates that host defense, as assessed by immune pathway gene expression, may be modulated by variations in the lower airway microbiome and that bacteria with increased abundance and decreased diversity are associated with decreased immune pathway genes expression and poorer PFS. This study also demonstrates that host-microbiome interaction may influence immune-mediated fibroblast responsiveness.Another independent study  evaluateOn the other hand, Wang et al.  have att2)/inspiratory oxygen fraction (FiO2) ratios, higher C reactive protein (CRP) levels, higher percentages of neutrophils and lower percentages of lymphocytes in BALF compared with survivors. Amongst those factors only CRP was found to be an independent predictor of survival, suggesting that infection  and/or inflammation can be one of the pathogenic mechanisms contributing to acute exacerbations [The progression of IPF is marked with exacerbations, similar to a number of chronic lung diseases. Acute exacerbations are associated with a particularly poor prognosis. Among patients with acute exacerbations, non-survivors had shorter durations of dyspnea, lower arterial oxygen tension (PaOrbations .An exacerbation is currently defined as “an acute, clinically significant deterioration of unidentifiable cause in a patient with underlying IPF” , and it Campylobacter sp. and Stenotrophomonas sp., coupled with a significant decrease in Veillonella sp., and Campylobacter sp., although best known as a gastrointestinal pathogen, was previously identified in the respiratory microbiota of individuals with severe COPD. Its presence in the respiratory microbiota is likely to arise from silent micro-aspiration of gastric contents [Another study on 20 patients with diagnosed acute exacerbations of IPF and 15 matched controls with stable IPF who underwent bronchoscopy and DNA extraction has shown that IPF patients presented an increased bacterial burden during exacerbations, up to four times higher [All of these findings open the possibility of a place for antibiotherapy in IPF patients; in particular, they provide a rationale for clinical trials of long-term antibiotherapy acting as an immunomodulator and an antibioprophylaxis to prevent acute exacerbations. In the future, an improved knowledge of the dynamic alterations of the lung microbiome might help to select appropriate, targeted and more personalized antibiotherapy in the course of the disease, in particular in IPF exacerbations. In this context, more advanced metagenomic analyses are required to elucidate the functional role of individual bacterial genera and communities in IPF progression.As recent studies demonstrated interactions between host immune response and microbial community in IPF, further studies will probably focus on the exploration of therapeutic approaches targeting modulation, not only of the lung microbial community of patients with IPF, but of specific components of the innate immune system.


KS tumors consist of latently infected endothelial cells; however, lytic phase gene products are important for KS onset. Early KS lesion progression is driven by proinflammatory cytokines supplied by immune cell infiltrates including T cells and monocytes. KSHV-infected monocytes may supply the lytic viral products and the inflammatory milieu conducive to KS tumor progression. To establish successful infection, KSHV extensively modulates the host immune system. KSHV antigens activate both innate and adaptive immune responses including KSHV-specific T cells, but lifelong infection is still established. Programmed death ligand 1 (PD-L1) is a prosurvival cell surface protein that suppresses T-cell-mediated killing. PD-L1 is variably present on various tumor cells and is a targetable marker for cancer treatment. We show that KSHV infection of human monocytes increases PD-L1 expression and transcription in a dose-dependent manner. We also saw evidence of lytic gene expression in the KSHV-infected monocytes. Intact KSHV is needed for full PD-L1 response in human monocytes. KSHV induces a general proinflammatory cytokine milieu including interleukins 1α, 1β, and 6, which have been implicated in early KS lesion progression. KSHV-mediated PD-L1 increase may represent a novel mechanism of KSHV-mediated immune modulation to allow for virus survival and eventually malignant progression.Kaposi’s sarcoma-associated herpesvirus (KSHV) is associated with the human malignancy Kaposi’s sarcoma and the lymphoproliferative disorders primary effusion lymphoma and multicentric Castleman’s disease. KSHV establishes lytic infection of monocytes  KSHV is the etiologic agent of Kaposi’s sarcoma and the lymphoproliferative disorders primary effusion lymphoma and multicentric Castleman’s disease. Programmed death ligand 1 (PD-L1) is an immunosuppressive cell surface marker that inhibits T cell activation. We report that KSHV infection of primary human monocytes upregulates PD-L1 transcription and protein expression. Analysis of the cytokine and chemokine milieu following KSHV infection of monocytes revealed that KSHV induces interleukins 1α, 1β, and 6, all of which have been implicated in KS development. Our work has identified another potential immune evasion strategy for KSHV and a potential target for immunotherapy of KSHV-derived disease. KS re–gression . Therefogression –16. T ceression –, 17–20. sion –17–, 20. Chrsion –17––23. A spion –17––, 25. In ion –17–––30. Impoon –17––––34.in vivo and in vitro , IL-1β, and IL-6 are found within the KS tumor microenvironment -like receptors, to adaptive immune mechanisms, such as downregulation of major histocompatibility complex I (MHC-I) antigen presentation and expression of immunosuppressive viral cell surface homologues . Increasde novo infection of primary monocytes with KSHV induces increased PD-L1 levels through increased PD-L1 gene transcription. We also found evidence of lytic gene expression in the KSHV-infected monocytes. UV-inactivated KSHV failed to induce similar PD-L1 increases, suggesting that infection with KSHV is driving PD-L1 increase. Infection with increasing doses of KSHV correlate with increasing PD-L1 surface expression and transcription. KSHV-infected monocytes produce a general proinflammatory response similar to the KS tumor microenvironment cytokines with enrichment of IL-1α, IL-1β, and IL-6 compared to uninfected control cells. Overall, these data suggest that KSHV infection of monocytes may promote an immune tolerant environment and a cytokine milieu potentially allowing for both viral evasion and oncogenic progression.We report that As KSHV has evolved multiple strategies for evasion of the immune response, we hypothesized that KSHV may modulate the PD-1–PD-L1 pathway. Monocytes are permissive for KSHV infection  and exprn = 14), 48 hpi (n = 9), and 72 hpi (n = 8). These data suggest that infection with KSHV increases PD-L1 transcription at early time points and that while the protein level  to assess PD-L1 transcript levels. All PD-L1 transcript levels were normalized to β-actin message. PD-L1 transcription peaks at 24 h postinfection and then tapers off at 48 and 72 h postinfection . The genin level  remains To ensure that the increase in PD-L1 message and level of expression is due to primary KSHV infection, the level of KSHV RNA present inside the infected monocytes was assessed. RNA was extracted from monocytes at 48 h after infection with either intact KSHV or UV-inactivated KSHV. The levels of viral interleukin-6 (vIL-6) were quantified via real-time PCR for message levels at 48 h postinfection . KSHV vISimilar to other herpesviruses, KSHV infection displays either a latent or lytic life cycle . We infe48–We investigated the ability of UV-inactivated KSHV virions to increase PD-L1 levels at 48 h postinfection . PD-L1 s8 genomes/well) and a high dose of KSHV (2.1 × 109 genomes/well). At both 24 and 72 h postinfection, the cells that were infected with the higher dose of virus expressed higher levels of PD-L1 on the cell surface than the cells infected with the low dose of virus , and alpha tumor necrosis factor (TNF-α) changed significantly. Raw concentrations for significantly changed cytokines are shown in PD-L1 expression can be driven by cytokine signaling pathways . FurtherInterestingly, the levels of nearly all of the 35 analytes tested were increased in response to KSHV infection in monocytes compared to mock-infected cells . MonocytIn this report, we show that KSHV infection of human primary monocytes results in an increase in PD-L1 expression. This increase was at the transcriptional level, and although transcript levels decreased over time, protein levels remain increased at least until 72 h after primary infection with KSHV. We show that at 48 h postinfection of monocytes, KSHV expresses multiple lytic transcripts and the lytic protein ORF45, suggesting establishment of an overall lytic phase of infection. Cells that were exposed to increasing amounts of virus exhibited increased KSHV vIL-6 transcription as well as increased PD-L1 transcription and protein expression. In addition to an increase in PD-L1 expression, infected cells also increased production of a variety of generally proinflammatory cytokines and chemokines. Together these data suggest that KSHV induces an immune response in cells and one of the potential mechanisms that KSHV may utilize to decrease immune responses is through upregulation of the immunomodulatory protein PD-L1.KSHV-mediated expression of PD-L1 may be an additional mechanism among many already identified for KSHV evasion of host immune responses. It is critical that KSHV avoid detection in order to maintain lifelong infection within the host. To this end, mechanisms for blockade of many major immunological pathways have already been identified including the host complement system, type I interferons, Toll-like receptors, Nod-like receptors, and cytosolic DNA sensors . For manin vitro  using the monocyte isolation kit II (Miltenyi Biotec) according to the manufacturer’s instructions. The CD14-positive monocytes were assayed for purity, and in all experiments, only a ≥85% pure monocyte population was used. The resulting monocytes were plated into six-well dishes at 5 × 10Kaposi’s sarcoma-associated herpesvirus (KSHV) was isolated from iSLK.219 cells harboring latent recombinant KSHV.219 (rKSHV.219) . iSLK.217 to 7.8 × 109 genomes/well and 10 μg/ml Polybrene in serum-free RPMI medium at 2,500 rpm for 90 min at 30°C as previously described  and PD-L1  prior to analysis on a MACSQuant VYB cytometer (Miltenyi Biotec) and FlowJo software (Tree Star). Programmed death ligand 1 (PD-L1) levels were ascertained after gating on CD14.6 monocytes, cells were resuspended in 40 microliters of a solution of 50 ml NP-40 buffer  and subsequently frozen and thawed once to complete lysis. For samples more than 5 × 106 monocytes, cells were resuspended in 2× Laemmli sample buffer and simultaneously boiled and vortexed at 1,200 rpm in a ThermoMixer (Eppendorf) until the pellet was no longer visible (10 to 20 min). Equal amounts of protein were loaded per lane, resolved by SDS-PAGE, and then transferred to a nitrocellulose membrane. The antibodies used were PD-L1 , ORF45 , and actin .Cells were harvested, washed once with PBS, and then lysed. Lysis was mediated through two separate methods depending on cell number harvested. For samples less than or equal to 5 × 10CT) values similar to those of the aforementioned primers. The forward and reverse primers for cytokines, chemokines, and other targets follow: for viral IL-6 (vIL-6), forward, 5′-CGGTTCACTGCTGGTATCT, and reverse, 5′-CAGTATCGTTGATGGCTGGT (RNA was isolated using RNeasy Micro kit (Qiagen) and reverse transcribed with SuperScript III reverse transcriptase (Invitrogen) and oligo(dT) (Invitrogen). Real-time PCR was performed on a QuantStudio 6 Flex (Applied Biosystems) with PowerUp SYBR green PCR master mix (Applied Biosystems). The PCR was carried out with 1 cycle of 50°C for 2 min and 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min. All fold activations were normalized to β-actin. The primer sequences used for β-actin and PD-L1 were as follows: for β-actin, forward, 5′-TCATGAAGTGTGACGTGGACATC, and reverse, 5′-CAGGAGGAGCAATGATCTTGATCT ; for PD-TGGCTGGT ; for opeTGGCTGGT .Supernatants were precleared and tested with a human cytokine magnetic 35-plex panel (Life Technologies) per the manufacturer’s instructions and analyzed with a MAGPIX instrument (Luminex).All calculations were conducted using the R software package version 3.3.3 “Another Canoe” (released 6 March 2017) with the specific statistical tests indicated. Code is available upon request.