We investigate, both theoretically and numerically, a graphene-coated nano-cylinder illuminated by a plane electromagnetic wave in the far-infrared range of frequencies. We have derived an analytical formula that enables fast evaluation of the spectral window with a substantial reduction in scattering efficiency for a sufficiently thin cylinder. This polarization-dependent effect leads to tunable resonant invisibility that can be achieved via modification of graphene chemical potential monitored by the gate voltage. A multi-frequency cloaking mechanism based on dimer coated nanowires is also discussed in detail. In fact, a remote observer will not be able to detect the presence of the objects that are shielded and protected by optical cloaking. The materials needed for practical applications of these techniques, either artificial or natural, require extremal values of their permittivity and/or permeability. However, coatings can be designed such as metal-dielectric multilayer or metasurfaces to drastically reduce the scattered signal from subwavelength particles and make the assembly nearly undetectable7. Of course, light interacts with the nanoparticles, but the destructive interference at the different elements that constitute the complex nanostructure practically cancels out the scattered radiation.Recent progress in design and manufacturing of new photonic materials has enabled novel models and multifunctional devices to achieve unprecedented control of light. In particular, by using various techniques based on transformation optics, the possibilities have appeared to electromagnetically isolate a space region for certain frequencies or range of frequencies. It means that an object located inside such a space region will practically stop interacting with the illuminating light9. High conductivity within atomically thin layer of graphene, and significant tuning ability via the applied bias voltage, enables the cancellation of scattering effects. Drastically reduced overall visibility of the scattering object that was conducted via graphene monolayer wrapping the cylinder, for the first time, was extended to the far-infrared range of frequencies by Chen and Alu10. In that case, the local polarizability of a nanowire with moderately wide diameter and the graphene coating sheet with the opposite signs can be mutually cancelled under suitable layout. Different schemes have been proposed with plasmonic compounds, using a dipole moment of opposite phase to attain a scattering cancellation13. If the incident radiation is p-polarized, however, the spectral response of the scattering cross section seems to be totally dissimilar14. Then, a set formed by a peak, associated with the scattering via a narrow Mie mode, and a valley offers the prospect to obtain invisibility with unprecedented control over the wire resonance spectral location by changing the chemical potential.Graphene represents a groundbreaking and exciting material that combines suitable characteristics for the use in optoelectronic devices in far-infrared and THz region of frequencies due to the intrinsic ability to mold the surface current with low-loss ratesp-polarized incident plane wave that illuminates a graphene coated dielectric nanowire in order to get near the fundamental localized surface plasmon polariton. Subsequently we optimize the scattering cancellation of the nanocavity. The proposed theoretical model is critically discussed with a conclusion that a characteristic lineshape appears comparable to the shape of Fano resonance15. The check of validity and estimation of our results is performed by using the full-wave Lorenz-Mie method16.In the present paper we extend the previous concept by employing a R and relative permittivity εd over-coated by an atomically thin graphene shell, and oriented along the z axis, as illustrated in Fig. ε and illuminated by a plane electromagnetic wave that propagates along the x axis with the electric field vector Ein in the xy plane. The graphene conductivity σ within the coating shell is described in the local random phase approximation by using Kubo formula17 as a sum of intraband σintra and interband σinter contribution, whereLet us consider a cylindrical dielectric nanowire of radius e is the charge of an electron, kB is Boltzmann’s constant, T is the temperature, Γ is the charge carriers scattering rate, μ is the chemical potential and ω is the photon energy. In Fig. T = 300 K with μ = 0.5 eV and Γ = 0.1 meV. Also, the time harmonic dependence of the field is adopted to be of the form exp(−iωt).Here, −20. Details on this well-established technique can be found in our Methods section. The scattering coefficients an, given by Eq.  of scattering coefficient(s). Now, we would like to compare the scattering efficiency of: (i) a bare dielectric cylinder, (ii) a graphene nanotube, and (iii) a uniform-graphene coated dielectric nanocylinder. We assume the dielectric core made of SiO2 with the relative permittivity εd = 3.9 and radius R = 0.5 μm; the graphene monolayer with the chemical potential μ = 0.5 eV; ambient temperature T = 300 K and carrier scattering rate Γ = 0.1 meV. The last two parameters are kept fixed in all numerical calculations that follow. The scattering efficiency spectrum Qsca(λ), where λ is the wavelength of the TEz-polarized illuminating radiation, for each of the three objects is presented in Fig. λ = 36.4 μm and a minimum of scattering efficiency at a wavelength of 28.1 μm. Note that the ratio R/λ = 1.78 × 10−2 when scattering is effectively canceled, confirming that the size of the object to become electromagnetically undetectable is small compared to the operative wavelength. The resonance wavelength and the invisibility wavelength can be found in the vicinities of the resonance peak for the free-standing graphene nanotube, found at λ = 23.4 μm. Without the presence of the graphene coating, the dielectric cylinder cannot exhibit any significant feature of the scattering efficiency in such spectral range. One might presume that the set of peak and valley patterned in the scattering efficiency follows the characteristic lineshape of the Fano resonance, where the emission of electromagnetic waves by the coated nanowire creates the interference between the nonresonant scattering from the dielectric core and scattering by narrow Mie modes in the graphene nanotube21. The graphene-coated nanowire exhibits a scattering efficiency dominated by the graphene nanotube (surrounded by air) at long wavelengths, where the graphene coating isolates the central dielectric cylinder. On the other hand, the bare (uncoated) nanowire dominates the scattering properties for shorter wavelengths, due to its stronger response far from the Rayleigh scattering regime. Note that in Fig. 23.Typically, the terms with low order an and bn26. In this respect, the finite width of the graphene layer (tg) has to be introduced, that leads to the corresponding graphene permittivity εg = iσ/ε0ωtg. In particular, tg = 0.5 nm has been employed, where the calculated scattering efficiency for multilayered nanotubes (not shown here) is in excellent agreement with the results depicted in Fig. It is necessary to point out that evaluation of the electromagnetic field components, given by (14–17), as well as the scattering efficiency (3), can be alternatively achieved by using a matrix method to derive the scattering coefficients 27. Scattering efficiency for a graphene-coated dielectric nanowire as a function of radius R is presented in Fig. R increases.The presence of a peak resonance for a graphene nanotube might be behind the Fano-like resonance in the complex graphene-dielectric nanowire. Under such perspective, tuning the resonance peak of the graphene nanotube will enable to shift the invisibility wavelength on demand. This procedure can be carried out by simply modifying the radius of the graphene nanotubeσintra that dominates the contribution to graphene conductivity for moderate and low frequencies  > 0 in the spectral range of interest. Note that the peak resonance is associated to a localized surface plasmon in the graphene-coated cylinder, and its dispersion relation can be interpreted such that this bound mode cyclically propagates along the cylinder perimeter exactly one effective wavelength corresponding to the flat graphene sheet27. More interesting in our study, a drastic reduction of the scattering efficiency will be experienced under the constrain σ) < Im(σ) occurs at λ > 1.53 μm in Fig. εd > ε, evidencing that our invisibility procedure is only valid in the far infrared for practical uses. The frequency for invisibility will appear atAssuming that μ/RΔε, where Δε stands for the difference of dielectric constants between the core and the environment media. In order to meet either the invisibility or the scattering resonance conditions, for given monochromatic field, the nanowire radius R has to be adjusted. This is illustrated in Fig. As a consequence, the invisibility window depends on the parameter Qsca approaches the well known Fano resonance lineshape15:εd ≫ ε, as discussed below. In the case of anisotropic plasmonic nanotubes such behaviour has been highlighted in ref.28.Interestingly, when the regions of invisibility and resonance start to overlap, i.e. when Ed, and the field in the enviroment medium, Ein + Esca, normalized to the electric field of the incident plane wave, E0, at different wavelengths. Out of the spectral band where the invisibility and resonance exist, as illustrated for (a) λ = 10 μm, and (d) λ = 50 μm, a moderately intense signal that corresponds to a dipolar scattered field is evident. In this case, Rayleigh scattering is governing, which is characterized by a dominant coefficient λ = 28.1 μm, where a drastic reduction of the scattering wave field is apparent. Here the first-order scattering coefficient can be straightforwardly approximated by λ = 36.4 μm, where an enhanced scattered field is emitted by the coated cylinder.In Fig. z polarized, that is the magnetic field lies on the xy plane, thin coated nanowires will scatter upon a dominant monopolar emission (see Appendix); this happens since the electric field is oriented along the axis of the conducting cylinder. For convenience here we appoint the continuity of the electric field on the boundaries of the particle, which will undergo no variation inside under the electrostatic approximation. This heuristic approach enables us to estimate an effective permittivity of the scatterer including the core and its graphene coating as fd and fg are the volume filling factor of the dielectric core and the graphene coating, respectively. Taking into account the lower dimensionality of the graphene layer, we might approach fd = 1. On the other hand the volume of the graphene layer can be set asL represents the length of the cylinder. Since z polarized wave fields, except for the fact that the graphene conductivity is doubled. The latter is verified by using the Lorenz-Mie method and subsequently considering the limit We point out that the invisibility frequency is highly dependent upon the state of polarization of the incident wave field. Provided that the impinging plane wave is TMd by Eq. .L of the graphene-coated cylinder might give rise to unsuitable effects that disturb the demanded reduction of the scattering efficiency. This case has been previously considered in the literature for both metallic and dielectric particles, even including invisible cloaks33. It has been observed that the drastic minimization of the scattered electromagnetic signal persists for structures where L > λ. In addition, one expects that the incident wave interacts with the longitudinal boundaries to create guided modes within the structure (graphene plasmons in the coating monolayer and photonic modes in the dielectric core) that propagate along the cylinder axis. Furthermore, Fabry-Perot resonance conditions can be established in case that the length L of the cylinder matches a multiple half-wave of the characteristic modal wavelength, causing numerous peaks in the lineshape of the scattering efficiency spectrum by means of wave leakage at the ends. Such Fabry-Perot resonances could be more pronounced using plane wave illumination at oblique incidence, which may even lead to Fano resonances caused by the interplay of these longitudinal guided modes with broadband Mie resonances34. Finally, this rich phenomenology might also be further exploited for invisibility applications.In our study we have considered cylindrical scatterers with an infinite length. In practical cases, the finite length Qsca for a varying chemical potential μ is shown in Fig. R = 0.5 μm and permittivity εd = 3.9. We observe that the peak-to-valley sinuosity governing the scattering spectrum is blueshifted for an increasing value of μ. As long as the chemical potential increases, the graphene conductivity also grows producing a shielding effect in the nanowire, which can be compensated by shifting the working wavelength to the near infrared. For example, the invisibility spectral band is 42.25 μm wide when changing the chemical potential from 0.1 eV for λinv = 62.25 μm to 1.0 eV for λinv = 20.00 μm. One should notice, however, that the spectral width of the invisibility window, tuned by changing the graphene-shell chemical potential, depends on the dielectric core radius R and its permittivity, as well as ε. These parameters are responsible for cloaking activation at other frequencies. Therefore, the results presented in Fig. R = 0.5 μm, and εd = 3.9.A possibility of tuning the invisibility window, as well as the window of the resonance peak, by modifying the chemical potential of the graphene coating that can be monitored by gate voltage seems to be very important for applications. The spectrum of Qsca for a graphene coated dielectric nanowire as a function of the core permittivity εd, when immersed in air (ε = 1), while keeping a fixed R = 0.5 μm, and μ = 0.5 eV. Negative values of the permittivity εd can be realized via semiconductor doping, e.g. n-InGaAs. In the spectral range of interest, dispersion of that semiconductor εd(ω) can be very well described by the Drude model with the plasmon wavelength located at λp = 5.41 μm35. The minimum of the scattering efficiency and the peak of the resonance both shift to longer wavelengths with an increase of the core permittivity. For the core permittivity εd only slightly higher than ε, the invisibility window significantly widens at the rate μm to 16.62 μm in the interval where εd changes from 1.5 to 2.0, whereas the variation of the resonant peak appears in the range of wavelengths from 26.09 μm to 28.53 μm. Moreover, when |εd| < ε, the conducting graphene sheet can excite a resonant peak but the invisibility window vanishes. On the other hand, the maximum and the minimum of the curve virtually coincide for an epsilon-near-zero surrounding material. We point out that a non-negligible value of the imaginary part of ε imposes a correction of Eqs ,,Qsca foεd < ε, even including conducting cylinders, by means of engineering its homogenized surface conductivity. For instance, an atomically thin metasurface composed of a periodic array of subwavelength graphene patches apparently exhibits a reconfigurable conductivity, whose imaginary part can reach arbitrary values from positive (at high frequencies) to negative (at low frequencies) in the THz spectrum by simply altering its geometric arrangement and Fermi energy of the graphene-based components36. When the patterned graphene metasurface is set over a subwavelength cylindrical scatterer, independently from its dielectric or conducting nature, a scattering cancellation is theoretically demonstrated with promising versatility in comparison to the homogeneous graphene monolayer38.Note that nanostructured graphene metasurfaces potentially enable to accomplish a drastic reduction of the scattering efficiency in the case that z polarized fields, our analysis is restricted to such case. In Fig. z-direction. In the first case, a set of a peak resonance and a scattering minimum, found at 39. For the case of two directly adjacent coated nanowires illuminated by light polarized along the interparticle y axis, two minima are identified at wavelengths Next, we analyze the scattering efficiency of clusters of two invisible graphene-coated nanocylinders in close proximity to each other. Since plasmonic coupling of the pair of particles is exclusively expected for incident TE40. The plasmonic response is altered due to a supplementary contribution to the total energy by a dipole-dipole coupled interaction41, together with higher-order multipolar contributions involved at the nanoscale proximity, with the result that a dramatic field enhancement occurs in the interparticle junction. And precisely such significant multipolar inputs are responsible of the collective resonant behavior of particle dimers. Importantly in our discussion, the near-field coupling of neighboring graphene-coated nanowires also induces a multiband reduction of the scattering cross section. In fact, such mode coupling with multiple resonance peaks and valleys has been both theoretically and experimentally reported for metallic particle dimers with a plethora of body shapes like spheres42, circular and ellipsoidal cylinders43, disks44, shells45, rods46, rings47, and others. The strong coupling between the graphene plasmons of each individual coated nanowire can be well described by a hybridization picture relying on the analogy between plasmons and the wave functions of simple quantum systems49.The physical interpretation for the appearance of a couple of peaks relies on the strong coupling of the localized surface plasmon resonances of the individual particlesg = 0 to g = 400 nm as graphically analyzed; meanwhile the main resonance peak is red-shifted as long as g decreases, a fact that also occurs in metallic dimers and discussed in detail in50. It is evident that gaps additionally enhance secondary resonances that in general increases the scattering cross section of the structure and thus partially suppress the invisibility effect. For instance, a small peak appears at g = 200 nm which, on the other hand, has a limited impact on the scattering reduction observed in the vicinities of g = 20 nm where the secondary resonance peak lies near g = 5 nm, g = 2 nm, and g = 1 nm, respectively. In other words, a sharp decrease of the SCS is found within the spectral interval ranged from g and is certainly advantageous for invisibility purposes. Remarkably, a sharp and abrupt change of the electromagnetic signature for a pair of graphene coated nanowires is evident as a conductive contact (that is g = 0) is formed.Figure 51. Here, the intrinsically two-dimensional structure of atomically-thin graphene requires a fundamentally different mechanism of fine-tune the spectral bands of resonance (and invisibility). Modification of the chemical potential Cylindrical plasmonic nanoshells with a dielectric core are supposed to have tunable resonance positions and a complex resonance structure, where decreasing shell thickness leads to a distinct redshiftfrom Eq. . Now we 52, and due to the low absorption of graphene in the spectral range of interest, we can distinguish several peak positions caused by localized surface plasmons excitations of the graphene shell and the outer surface of the 51.The existence of a multifrequency regime is also strongly determined by the permittivity of the nanowire core. Figure R simultaneously for both particles. By increasing the value of R) spectrum is essentially maintained but shifted to longer wavelengths. Therefore, both invisibility windows centered at Now we analyze the influence of the cylinder radius to the scattering response of the dimer nanowires. Figure see Fig. . However53.Finally, it is worth noting that scattering cancellation can be expected to appear at more different frequencies when dealing with clusters formed by a higher number of graphene-coated nanowires. A previously proposed alternate multi-frequency cloaking mechanism is based on several graphene layers implementation at different gate charges54. Finally, we would like to emphasize that the proposed scheme seems to be tunable enough to be implemented in ultra-thin reconfigurable cloaking devices. Within the far-infrared range of frequencies and applied to subwavelength scatterers, this undetectable cylindrical structure can be appropriate for sensing with dynamic operation bandwidth because the receiving field can efficiently reach its interior and inherently stimulate its electromagnetic response despite the strong suppression of its scattering efficiency. The occurrence of additional spectral minima by the excitation of coupled modes in nanowire dimers can cause a considerable increase of the gap field, capturing energy without radiate to the far field, that offers opportunities for different applications such as sensitive spectroscopy and novel nonlinear miniaturized systems.We have demonstrated that the scattering behavior of a sufficiently thin graphene-coated nano-cylinder reminiscences a solid nanowire with a bulk conductivity equal to the ratio of graphene surface conductivity and the wire radius. In that case, some analytical expressions (see equations –8)) hav) hav8)) 20.To estimate analytically the scattering efficiency of the coated nanowire, we followed the Lorenz-Mie scattering method given in detail for instance in refsz-polarized plane wave propagating along the r and ϕ are the radial and azimuthal cylindrical coordinates, respectively, H0 is a constant amplitude, α. In Eq. . The circular active domain has a radius of The problem of multiple scattering by an arbitrary configuration of nonoverlapping parallel cylinders is essentially a boundary-value problem which has been considered by many authors


We propose a method to assess the statistical significance of the overlap between heterogeneously diversified portfolios, which we use to build a validated network of financial institutions where links indicate potential contagion channels. The method is implemented on a historical database of institutional holdings ranging from 1999 to the end of 2013, but can be applied to any bipartite network. We find that the proportion of validated links (i.e. of significant overlaps) increased steadily before the 2007–2008 financial crisis and reached a maximum when the crisis occurred. We argue that the nature of this measure implies that systemic risk from fire sales liquidation was maximal at that time. After a sharp drop in 2008, systemic risk resumed its growth in 2009, with a notable acceleration in 2013. We finally show that market trends tend to be amplified in the portfolios identified by the algorithm, such that it is possible to have an informative signal about institutions that are about to suffer (enjoy) the most significant losses (gains).Common asset holding by financial institutions ( The |I(t)| × |S(t)| ownership matrix W(t) describes portfolios composition: its generic element Wis(t) denotes the number of shares of security s ∈ S(t) held by institution i ∈ I(t). The matrix W(t) can be mapped into a binary ownership matrix Ais(t) = 1 if Wis(t) > 0 and 0 otherwise, which allows to define the degree di(t) = ∑sAis(t) of an institution i as the number of securities it owns at time t, and the degree ds(t) = ∑iAis(t) of a security s is the number of investors holding it at time t. The number of securities held by both institutions i and j, namely the overlap of their portfolios, is instead given by oij(t) = ∑sAis(t)Ajs(t) (with i ≠ j), which is the generic element of the |I(t)| × |I(t)| portfolio overlap matrix projected monopartite network of institutions obtained as a contraction of the binary ownership matrix validated projection of the original bipartite network containing only the most significant overlaps that cannot be explained by a proper null network model. In this way we can drastically reduce the original amount of links and obtain a much sparser validated network with a clearer meaning.In this contribution we propose a new statistical method to quantitatively assess the significance of the overlap between a pair of portfolios, with the aim of identifying those overlaps bearing the highest riskiness for fire sales liquidation. Since we apply the method to institutional portfolios we will use interchangeably the terms institution and portfolio throughout the paper. In practical terms, the problem consists in using assets ownership data by financial institutions to establish links between portfolios having strikingly similar pattern of holdings. Market ownership data at a given time All methods to build validated projections proposed in the literature involve the use of a threshold to determine which links are retained in the monopartite network, but vary in how the threshold is chosen28303235Bipartite Configuration Model (BiCM)Configuration Modelt, in order to distinguish the true signal of overlapping portfolios from the underlying random noise, every link of the projected network has to be independently validated against the BiCM null hypothesis. Thus, for each pair of institutions  having overlap oij(t), we compute the probability distribution π of the expected overlap under the BiCM (see section Methods). The statistical significance of oij(t) is then quantified through a p-value:The method we propose here overcomes all the limitations of its predecessors by building on a null hypothesis described by the π, namely the probability to have an overlap larger or equal than the observed one under the null hypothesis. If such a p-value is smaller than a threshold P*(t) corrected for multiple hypothesis testing (see section Methods), we validate the link between i and j and place it on the monopartite validated network of institutions. Otherwise, the link is discarded. In other words, the comparison is deemed statistically significant if the observed overlap would be an unlikely realization of the null hypothesis according to the significance level P*(t). This procedure is repeated for all pairs of institutions, resulting in the validated projection Vij(t) = 1 if P[oij(t)] < P*(t), and 0 otherwise.where the right-hand size of i.e., those securities making up the validated overlaps) represent a larger average share of institutional portfolios, a configuration which would exacerbate the effect of fire sales. Additionally, we show that the presence of a validated link between two institutions is a good indicator of portfolio losses for these institutions in times of bearish markets, and of portfolio growth in times of bullish markets: validated links should indeed represent self-reinforcing channels for the propagation of financial distress or euphoria. More in general, we find that market trends tend to be amplified in the portfolios identified by the algorithm. Finally, we apply the validation procedure to the overlapping ownerships of securities to identify contagion channels between securities themselves, and observe a stable growth of validated securities over the considered time span. This signals an ongoing, deep structural change of the financial market and, more importantly, that there are more and more stocks that can be involved in a potential fire sale. The presence of local maxima within this trend correspond to all periods of financial turmoil covered by the database: the dot-com bubble of 2001, the 2007–2008 global financial crisis and the 2010–2011 European sovereign debt crisis.When applied to a historical database of SEC 13-F filings . Validated pairs of portfolios indeed correspond to overlaps which constitute a considerable fraction of the total portfolio value of the pair. In short, pairs are validated when neither the diversification of the investments nor the degree of the securities are sufficient to explain the observed overlap. As we shall see later, the same mechanism is at play when we project the bipartite network on the securities side. In this case, since the degree of a security is a good approximation of its capitalization and of the dilution of its ownership42In order to properly understand the results of our validation method for overlapping portfolios, it is useful to provide a specific example. After these preliminary observations, we move to the temporal analysis of the structural properties of the whole validated network of institutions. In i.e., which fraction of the outstanding shares a given institution is holding) nor the relative importance of different securities in a portfolio . These are clearly important types of information, since one would expect a mild price impact following the liquidation of the asset by an institution if the latter owns only a small fraction of that security’s outstanding shares. Conversely, if the asset represents a considerable fraction of the portfolio market value, a price drop will have a stronger impact on the balance sheet of the institution. However, despite validating weighted overlaps t) in the validation procedure, as in this case it would be impossible to build an analytical null model—which would make the validation procedure extremely involving. Thus, we are forced to rely on binary overlaps. However, the dataset at our disposal allows us to check a posteriori the features of the portfolio positions which contributed to the formation of validated links.A seemingly major shortcoming of using a binary holding matrix ps(t) and outstanding shares σs(t) of different securities s at time t, we compute the fraction of the total market value of portfolio i represented by security s, namely wis(t) = ps(t)Wis(t)/∑xpx(t)Wix(t), and the fraction of outstanding shares of s held by institution i, namely cis(t) = Wis(t)/σs(t). We apply this procedure to each position Wis(t) of the bipartite ownership network in order to characterize the features of the positions belonging to validated overlaps. i.e., securities making up the validated overlaps) represent a larger share of the validated portfolio, namely 6% more than the average share given by the inverse of the degree.To this end, using the information about the price s belongs by construction to ds(t)[ds(t) − 1]/2 pairs of overlapping portfolios, and we can compute which fraction fs(t) of such pairs that are validated by the algorithm. We then compute for each security the total capitalization (as a proxy for the liquidity of the security) as well as the average ownership fraction per institution 〈ci(t)〉 = ∑icis(t)/ds(t) as a function of fs(t). In i.e., with the average fraction of outstanding shares detained by an institution) and decreases as a function of the degree of the security. The relation is stronger for securities with higher degree, because of the larger number of available data points.In order to study the concentration of ownership of different securities we use the following procedure. Each security As a final test of the effectiveness of the validation procedure, we study the ability of the algorithm to retrieve (pairs of) institutions which are about to suffer significant losses. The dataset at our disposal indeed covers periods of financial distress  and it is in such periods that one would expect some institutions to incur fire sales. Then, if the algorithm does filter information in a useful way, the presence of a validated link between two institutions should represent a channel for the propagation of losses. Note that we do not attempt here to design a test for detecting self-reinforcing fire sales. Rather, we check if the presence of a validated link ultimately contains information on the occurrence of losses.t the set n institutions experiencing the highest drop in portfolio value between t and t + dt (which we refer to as “distressed” institutions). We first consider drops in absolute terms  which we believe is of macroeconomic significance and check the relation with portfolio returns later on. We use here n = 300  and omit in the following the n subscript. We then compute the fraction l(t) of distressed institutions with respect to the total number of institutions I(t) and compare it with the fraction of distressed institution GI(t) = 1 the algorithm is not doing anything better than putting distressed institutions at random in the validated network, whereas, if GI(t) > 1 we effectively gain information by knowing that a institution belongs to i.e., all pairs whose portfolios having at least one security in common) are considered. The ratio between these two quantities, namely GI(t) and RI(t) to an index that encompasses many securities. r(t) between t and t + dt as measured by the Russell 2000 index. Indeed, both ratios are correlated with the total loss, and are significantly larger than 1 when r(t) ≪ 0 . Notably, both ratio are close to 1 when the market loss is close to 0, and decline afterwards. This could be interpreted as the fact that, in times of market euphoria, overlapping portfolios turn into self-reinforcing bubbles.To this end, we construct for each date i.e., we use portfolio returns to label institutions as distressed) we do not obtain meaningful results. This is however due to the fact that abnormal returns are in general observed for small portfolios for which we have few data points. Given the statistical nature of our method we cannot hope to correctly identify such situations for which a different (probably case by case) methodology is clearly needed. We can however take a simpler point of view and take for each time t all portfolios whose return is smaller (in absolute term) than a threshold rmax that we use as a parameter. We then use this subset to compute the average return of validated portfolios rmax, we then have a scatter plot of these two quantities (one point for each date t) which is well approximated by a straight line (see P*(t) used one has roughly half of the institutions in each set  than those of their not validated counterpart.When we repeat the same procedure for portfolio returns  bipartite network, corresponding respectively to the positions acquired (or sold) by each institution between t − dt and t: Wis(t) − Wis(t − dt) > 0 and 0 otherwise; Wis(t) − Wis(t − dt) < 0 and 0 otherwise. Validation of these bipartite networks then highlights the institutions that have updated their portfolios in a strikingly similar way. As a case study we consider the Hedge Funds (HF) buy/sell networks, meaning that we only consider the positions bought or sold by HF , and apply the validation procedure to these subnetworks. The focus on this particular subset of funds is motivated by the Great Quant Meltdown of August 2007, during which quantitative HF, in particular those with market neutral strategies, suffered great losses for a few days, before a remarkable  reversal  = ∑sAis(t)Ajs(t)). The presence of a validated link between two securities then reflects the fact that they share a significantly similar set of owners, which again translates into a potential contagion channel through fire sales. dot-com bubble of 2001, the global financial crisis of 2007–2008 and the European sovereign debt crisis of 2010–2011. As for the case of institutions, the similarity pattern of securities ownerships is maximal at the end of the considered time span.In this section we finally use our method to detect statistically significant common ownerships of securities, in order to identify contagion channels between securities themselves. Thus, we apply the validation procedure to the security ownership overlap The fact that the average degree of the validated network of securities keeps growing boils down to the fact that institutions choose securities, not the opposite. While the number of institutions in our dataset has increased over the years, the number of securities has been roughly constant. If a new institution selects at random which assets to invest in, then the average degree of the securities network would stay constant. This is not the case, if only because of liquidity constraints. Therefore, on average, the portfolio of a new institution is correlated with the ones of pre-existing institutions.internal a validated link connecting two securities with the same BICS label, and we compute the internal degree as the degree of a security restricted to internal links. As In order to detect if the observed patterns concern peculiar classes of securities, we perform an analysis of the validated network distinguishing securities according to the Bloomberg Industry Classification Systems (BICS)e.g., membership, physical attributes, cultural and taste affinities, biological functions, to name a few) and where the presence of (unlikely) similar sets of neighbors is of interest.In this work, we have proposed an exact method to infer statistically robust links between the portfolios of financial institutions based on similar patterns of investment. The method solves the problem of evaluating the probability that the overlap of two portfolios of very different size and diversification is due to random allocation of assets of very different market capitalization and number of owners. The use of an appropriate null hypothesis provided by the bipartite configuration modelThe present study then points to the conclusion that, just before financial crises or bubble bursts, the similarity of institutions holdings increases markedly. Perhaps worryingly for equity markets, the proposed proxy of fire sale risk, having reached a peak in 2008 and subsequently much decreased, has been increasing again from 2009 to the end of our dataset (2013) up to levels not seen since 2007. Despite our method relies on binary ownership information, we also found that on average overlapping securities correspond to larger shares of validated portfolios, potentially exacerbating fire sales losses. In addition, the proposed validation method can effectively retrieve the institutions which are about to suffer significant losses in times of market turmoil , as well as those with the highest growth in times of market euphoria (when overlapping portfolios turn into self-reinforcing bubbles). Finally we show that the number of securities that can be involved in a potential fire sale is steadily growing in time, with an even stronger proliferation of contagion channels.in fine) equivalent strategies, which implies portfolio overlap and subsequent increased risk of fire sales, which triggers further leverage adjustment, as pointed by refs In this work we have only investigated patterns of portfolio overlap, not the probability that they lead to fire sales. This is a more complicated problem for which other datasets and econometric techniques are needed. However, even if we cannot draw any strong implication from our findings, all the analysis we performed confirm the coherence of our method and suggest that overlapping portfolios do play a role in financial turmoils. Furthermore, the relationship between holdings and future portfolio changes must be better characterized. Indeed, even if two institutions with different strategies converge to a similar portfolio, this does not imply that they will update the latter in the same way and at the same time. However, it is likely that part of the institutions follow (https://www.sec.gov/) from the Factset Ownership database from 1999Q1 to 2013Q4, covering institutions valued more than 100 million dollars in qualifying assets which must report their long positions to the SEC at the end of each trimester. As the 13-F dataset contains only positions greater than 10000 shares or $200000, very small positions are already filtered out. The dataset is composed of a set I(t) of approximately 1500 ÷ 3500 institutions, holding positions from a set S(t) of securities, whose size fluctuates around 12500 (see We extracted 13-F SEC filings (2500 see . Note thP*(t) to be used in the validation procedure, we have to account for the multiple hypothesis tested (corresponding to the number npairs(t) of possible pairs of institutions having a nonzero overlap). Here we use the rather strict Bonferroni correctionP*(t) = ε/npairs(t). Note that the choice of the significance level still leaves some arbitrariness. While results presented in the paper are obtained with ε = 10−3, we have tested our method with various values of ε, and employed also the less-strict false discovery rate (FDR) criterionIn order to choose an appropriate threshold (the significance level) npairs(t) scales as P*(t) is too small to validate even the smallest non-zero p-value in most of the subnetworks. As a result, the validated network becomes almost empty by construction. Overall, while the method proposed in ref. As stated in the Introduction, the approach proposed in ref. a priori to compare a portfolio with very few assets and one with very many assets. However, the bipartite configuration model (BiCM)Determining the probability distributions used in Ais〉Ω, i.e., the ensemble probability Qis of connection between nodes i and s, is given by:In a nutshell, the BiCM prescribes to build the null model simply as the ensemble Ω of bipartite networks that are maximally random, under the constraints that their degree sequences of institutions and security is, on average, equal to the one of the original network. This is achieved through maximization of the Shannon entropy of the network subject to these constraints, that are imposed through a set of Lagrange multipliers Qis over all the possible I × S pairs of nodes. In other words, links are treated as independent random variables, by defining a probability measure where links correlations are discarded. The key feature of the BiCM model is that the probabilities {Qis} can be used to directly sample the ensemble of bipartite graphs and to compute the quantities of interest analytically. We can thus use the matrix i and j as:and the probability of occurrence π of the expected overlap under the null hypothesis of random connections in the bipartite network—which, according to the BiCM prescription, only depends on the degrees of institutions i and j. Indeed, π is actually the distribution of the sum of S independent Bernoulli trials, each with probability QisQjs. This distribution can be computed analytically using a Normal approximation of the Poisson-Binomial distributionπ. Indeed, the computational complexity of the numerics can be substantially reduced by recalling, again, that Qis ≡ Qis′ if ds ≡ ds′ ∀i: connection probabilities only depends on nodes degree values. This is an important observation, which translates into the following statement: the expected overlap between any two institutions i and j restricted to the set of securities with a given degree follows a binomial distributionwith probability QisQjs (where s is one of these securities) and number of trials equal to the cardinality of such set. More formally, if different degrees within securities, h is any security having degree i and j restricted to securities having degree or to compute the probability distribution π can now be more easily obtained as the sum of (much fewer than S) binomial random variablesπh≤ is the distribution of the overlap restricted to securities with degree smaller or equal than h, we haveThe overall distribution and π is obtained, the p-value P(oij) can be associate to the overlap oij using P(oij) ≤ P*. Note that since this computation is made on the whole network, i.e., considering all the securities, we have a fairly large spectrum of possible p-values. Thus, also if we still use a threshold depending on the number of hypothesis tested , we have a much higher resolution than in ref. Once the distribution How to cite this article: Gualdi, S. et al. Statistically validated network of portfolio overlaps and systemic risk. Sci. Rep.6, 39467; doi: 10.1038/srep39467 (2016).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Direct growth of graphene integrated into electronic devices is highly desirable but difficult due to the nominal ~1000 °C chemical vapor deposition (CVD) temperature, which can seriously deteriorate the substrates. Here we report a great reduction of graphene CVD temperature, down to 50 °C on sapphire and 100 °C on polycarbonate, by using dilute methane as the source and molten gallium (Ga) as catalysts. The very low temperature graphene synthesis is made possible by carbon attachment to the island edges of pre-existing graphene nuclei islands, and causes no damages to the substrates. A key benefit of using molten Ga catalyst is the enhanced methane absorption in Ga at lower temperatures; this leads to a surprisingly low apparent reaction barrier of ~0.16 eV below 300 °C. The faster growth kinetics due to a low reaction barrier and a demonstrated low-temperature graphene nuclei transfer protocol can facilitate practical direct graphene synthesis on many kinds of substrates down to 50–100 °C. Our results represent a significant progress in reducing graphene synthesis temperature and understanding its mechanism. For example, the upper temperature limit to integrate graphene and Si-based devices should not exceed ~400 °C. It is further lowered to ~100 °C for plastic-based semiconducting electronic devices with organic molecules6. A method to directly grow good quality continuous graphene on versatile substrates without a graphene transfer step is also important. Established graphene transfer methods using PMMA with substrate removal etching and electrochemical delamination9 are cumbersome and costly for scaled-up production; contaminants  incorporated and morphological irregularities  introduced during transfer can deteriorate device performance. To overcome the above two major challenges, we present a new approach to grow continuous good quality graphene directly on versatile substrates down to 50 to 100 °C using diluted methane source and molten gallium catalyst. This represents an important step toward graphene synthesis and its integration into electronic devices.The reduction of graphene synthesis temperature remains a critical challenge for its application in electronic devices14 produce good-quality graphene with a high processing temperature of 1000 °C or more, and the use of graphene requires a transfer method. There have been attempts to reduce the graphene synthesis temperature. Such growth attempts require the production and migration of the atomic carbon species at a lower temperature. For example, low pressure chemical vapor deposition (LPCVD) with a unique choice of the carbon source such as benzene15, toluene16, methanol, ethanol, and propanol precursors17 was developed. Microwave and/or plasma assisted CVD is also a possible way to activate the source decomposition at 400~600 °C19. Introduction of nuclei seeds such as coronene20 and polycyclic aromatic hydrocarbon21 seems to reduce the growth temperature blow 300 °C. However, the quality of such low-temperature synthesized graphene generally need further improvement. In particular, the reduction of graphene synthesis temperature down to 100 °C and below would be crucial for integrated processing of graphene into future plastic and bio-electric devices, and yet has not been realized.The conventional chemical vapor deposition (CVD) methods on copper or nickel32. For example, Ga vapor is an effective catalyst in graphene nanoribbon synthesis from an amyloid fibrils template, and the width of nanoribbon is controlled only by the width of pristine template26. Large-area graphene can be synthesized with methane CVD combining Ga vapor29. Monolayer graphene can also be readily produced on the Ga surface since the solubility of carbon in Ga is negligible31. More importantly, Ga is a liquid at room temperature. This fluidity of Ga presents several unique advantages as a graphene catalyst. First, molten Ga enhances the transport of carbon atoms and improves graphene growth kinetics; this helps reduce the graphene synthesis temperature. Second, molten Ga forms a conformal fluid-like interface with a substrate. This alleviates substrate lattice mismatch issue32. Moreover, a liquid catalyst could potentially enable low-temperature graphene CVD on not just flat planner substrates but also three-dimensional (3D) objects. Such versatile 3D graphene or composite structures are for example very attractive electrode materials for battery or supercapacitors39 and possibly for future plastic electronics and energy-related applications. Finally, molten Ga catalyst, unlike Cu or Ni, is easily removed by a gas jet after graphene growth. These advantages warrant detailed studies of graphene synthesis using molten Ga. Such studies should address not just how Ga catalyzes graphene growth but also whether Ga enables graphene growth at a lower temperature.Recently, gallium, as a metal catalyst, has shown similar metallurgical characteristics for graphitization on the surface of liquid metals as copperThe graphene synthesis with molten gallium catalyst can remarkably produce continuous good-quality graphene on either sapphire or plastic polycarbonate substrate down to either 50 or 100 °C respectively. The key requirement for such low-temperature growth is to have pre-existing graphene island nuclei on the substrate. The nuclei are either produced by conventional CVD or by a special nuclei transfer technique at low temperature desribed below. Raman spectroscopy analyses indicate that carbon atoms in molten gallium are efficiently transported to pre-existing graphene nuclei, resulting in exclusive graphene growth at the graphene island edges. The remarkable low graphene synthesis temperature is facilitated by a surprisingly low apparent reaction barrier, which derived from Arrhenius plots of graphene growth and methane dehydrogenation rates gives a value of ~0.58 eV above 500 °C but a mere 0.16 eV below 300 °C. The very low barrier is a result of competing pathways, i.e., methane decomposition at the Ga surface and methane absorption in bulk liquid Ga followed by its decomposition in Ga; these two processes are respectively favored at higher and lower temperatures, thus explaining the weak temperature dependence and the unexpected low barrier. The pathway of methane absorption seems unique for molten Ga and is ineffective for other common graphene catalysts such as Cu, Ni.The demonstration of our near-room-temperature graphene growth method and its successful application on, e.g., polycarbonate suggests its potential for future developments and opens up new possibilities for future integration of graphene into various, including plastic, electronic devices.13C-methane and molten Ga for 300 seconds at 1050 °C, followed by quenching of the substrate temperature. Detailed temperature profile for nuclei growth is shown in Fig. 2 gas and replaced by a fresh Ga droplet. 12C-methane was then used for further graphene growth at various selected lower temperatures . Conversely, 12C- and 13C-methane separately used in the nucleation and growth was also tested  mixed with 250 sccm of 6N-Ar. During the CVD growth, the source gas pressure was ~20 Pa. The substrate was C-face sapphire with a miscut angle of 0.2° or polycarbonate. Figure s Figure . Using 1d Figure  to suppoThe presence of pre-existing graphene nuclei is a key requirement for subsequent low-temperature graphene edge growth. In addition to the nuclei preparation with conventional CVD in protocol A, protocol B employs a graphene nuclei transfer method across the solid-liquid phase transition of a graphene-enriched Ga droplet. This facilitates a whole graphene synthesis all at lower temperatures, and is successfully applied to the polycarbonate substrate.13C-methane at high temperature, after further low-temperature growth with 12C-methane, still consist of 13C only. The Raman maps and spectra for graphene nuclei to full coverage graphene film are illustrated in Fig. 28 of ~0.5 to ~1 μm in diameter . The edge growth temperature was 100 °C and the growth duration was 17 hours. At 300 °C, the whole growth duration was 2–3 hours; at 50 °C, 20 hours. The lowest onset temperature of graphene film growth from the nuclei edge was 50 °C.The graphene nuclei prepared with ter Fig. –S4. The 13C-graphene nuclei and 12C-graphene film are separated. Despite the small size (~μm2) and low density (~104/mm2) of graphene nuclei, nuclei are crucial sites that enable subsequent seamless and continual graphene growth around them. Graphene growth from the edge of an individual nucleus is isotropic but the overall morphology of edge-grown graphene can be affected by substrate step direction and appeared anisotropic. In the 60% coverage sample  images in Fig. T2 was conducted directly without pre-existing nuclei. As shown in Fig. T2 is greater than 900 °C. At T2 = 1000 °C28, graphene with good quality is evident from well-separated D- and G-band Raman peaks, a small G-band FWHM (~25 cm−1), and a large I2D/IG ratio of ~2 to 3. The quality of graphene quickly deteriorates if T2 is below 900 °C. No crystalline graphene can be identified at T2 < 600 °C regardless of a long synthesis time up to 24 hours. Therefore, the initial presence of graphene nuclei is essential – it greatly promotes graphene quality and growth speed while significantly reducing the growth temperature. To realize an all-at-low-temperature graphene synthesis, pre-existing graphene nuclei cannot be prepared with conventional CVD. We have devised a method that uses the liquid-solid phase transition of gallium (at ~30 °C) to transfer graphene nuclei between different substrates. After the transfer, it was observed that the graphene film grew only within the region where the Ga droplet was placed, not beyond the region. This phenomenon was also reported in the references 41. A gallium droplet, when solidified, can peel graphene nuclei off a sapphire substrate. The solidified Ga droplet then can be transferred to another substrate . Upon melting, graphene nuclei embedded in the Ga droplet will be released onto the substrate.The growth methods depicted in Fig. 23, despite a low carbon solubility in gallium. At ~1000 °C, carbon atoms diffusing through bulk liquid gallium is also possible42. In contrast to Cu-based graphene CVD, carbon retention and diffusion are therefore unlikely restricted to the Ga surface. If the gallium removal step after nuclei formation was skipped . In Fig. 44. The device and measured gated conductance curve are shown in Fig. p-type characteristics, and the Dirac point at minimal channel conductance shifted to ~1 V gate bias. Hole mobility, estimated from the back gate voltage dependence of graphene conductivity, was ~390 cm2/Vs for graphene grown at 100 °C, ~650 cm2/Vs for graphene grown at 300 °C and ~1350 cm2/Vs for graphene grown at 500 °C. These mobility values rival that of conventional CVD graphene  was usedene Fig. . Moreoveper Fig.  or below2/sec]. The mean growth speed was evaluated by the required time to reach a full graphene coverage of the interfacial area under a gallium droplet (0.8 cm in diameter and 0.5 cm2 in size). As the mean growth speeds at different temperatures were all evaluated when the graphene coverage just became full and the preparation procedure of nuclei was identical, the energy barriers for the rate-limiting steps can be estimated from the Arrhenius plot.The apparent reaction barrier for graphene growth is estimated from the temperature dependence of the mean average graphene growth speed [cmEa of ~0.58 eV and a low-temperature region <300 °C with an activation barrier Ea of ~0.16 eV. The window between 300 °C to 500 °C is a transition range in which processes associated with the two above barrier values are both contributing. The Ea ~0.58 eV barrier is consistent with the reported values from 0.52 eV to 0.77 eV obtained with a Ni catalyst46. However, the observed low Ea ~0.16 eV for the low-temperature graphene edge growth is surprising. It suggests that a simple methane activation process is unlikely valid. Instead, parallel processes could be competing in the molten gallium catalyst, particularly at a lower temperature.The Arrhenius plot shows two temperature windows with distinct energy barriers, i.e., a high-temperature region >500 °C with an activation barrier The effect of parallel processes can be illustrated by replacing fresh gallium droplets with carbon-enriched gallium droplets. Such C-enriched Ga drops can be obtained by sintering molten gallium at 1000 °C for 10 hours in a carbon crucible with diluted methane source gas to feed carbon atoms on the surface or in the Ga bulk. Using C-rich Ga remarkably reduces the required graphene growth time by about 40%, in particular at lower temperatures. The presence of background carbon atoms in C-enriched Ga not only enhances the graphene growth rate but also weakens the temperature dependence of carbon atom supply. The apparent barrier of graphene growth is further reduced to 0.06 eV . Hydrogen production by methane decomposition was then measured by gas chromatography. Figure Eapparent), a value of 0.26 eV is derived from the temperature-dependent initial slopes of the H2 production curves , C(Ga), and H(Ga) stand for absorbed CH4, absorbed carbon atoms, and absorbed hydrogen atoms. Here, absorption of methane in Eq.  of the decomposition of absorbed methane inside Ga is estimated to be 1.22 eV. We evaluated this barrier from hydrogen production rates measured at various temperatures below 350 °C after methane was first intentionally absorbed in Ga at a low temperature of 50 °C ; this gives a surprisingly small 0.16 eV barrier of graphene growth rate and 0.26 eV for hydrogen evolution rate. The fluidity of molten gallium contributes to easier transport and production of atomic carbon. The sequential combinations of methane absorption, methane dehydrogenation, and effective carbon transport through the molten gallium to graphene island edges underlie the driving forces for the near-room-temperature graphene CVD. Although graphene growth is confined to the interface of the Ga droplet and the substrate, the liquid nature of Ga catalyst is likely applicable to the fabrication of conformal graphene over 3D objects. The graphene FET devices show promising high electron mobility comparable to CVD graphene grown on Cu. Finally, we have tested an all-at-low-temperature graphene CVD on polycarbonate by transferring and seeding the essential graphene nuclei across the solid-liquid phase transition of Ga. This results in successful graphene growth on a plastic substrate at 100 °C for the first time, thus representing a significant step toward the integration of graphene in electronic devices, including the challenging flexible plastic electronic devices.In summary, we have firstly demonstrated that graphene can grow at near room temperature with molten gallium as an efficient catalyst under dilute methane atmosphere. Graphene grows at the edge of graphene island nuclei, as confirmed by isotope labeling with 7 for TEM observation.The crystal quality of the synthesized graphene was evaluated using Raman spectroscopy  with a probe laser (λ = 532 nm), scanning electron microscopy , and high-resolution transmission electron microscopy . We used a KOH solution to separate graphene from the sapphire substrate, followed by the transfer process using polymethyl methacrylate (PMMA) onto a porous carbon meshWe used a C-face sapphire substrate (1 × 1 cm square) with a miscut angle of 0.2°. We recrystallized the sapphire surface at 1000 °C for 1 hour in ambient dry air to produce regular steps with ~40-nm-wide terraces. A Ga droplet with a diameter of ~5 to 8 mm was placed on the recrystallized substrate inserted in an one-inch diameter quartz tube reactor. Since graphene forms quickly at >1000 °C, quick temperature control was required for finer growth control and was achieved by using a furnace body-conveying technique. To quickly heat up the specimen from ~850 °C to ~1050 °C (in 3 minutes), the electric furnace heater body kept at 1050 °C was first placed ~10 cm away from the sample in a 250 sccm of Ar flow, and then was slid to the correct specimen position. To prepare graphene nuclei, diluted methane was let in the furnace for ~5 minutes at 1050 °C, and then the furnace body was again slid out to quench the nuclei growth. The specimen temperature was quickly cooled down to below 200 °C within 1 minute.2 fibers for the measurement of methane dehydrogenation efficiency. The test specimen contained ~7.6 mg of gallium nanoparticles, which were dispersed on the fibers by temperature gradient heating between the gallium source and the fiber in a vacuum. The gallium was heated at 1000 °C in 10−4 Pa, and the fiber was ~10 cm away from the furnace center and was at ~400 °C.We prepared gallium nanoparticles supported on SiO2 fibers was then placed in a PYREX glass tube reaction cell with a volume of 57.40 cc. After evacuating the cell, the sample was heated up to a certain temperature . Then, CH4 (760 Torr) was introduced into the cell within 1 min. The production amount of H2 was evaluated immediately as a function of time by measuring 0.5 cc sampled gas species of by TCD gas chromatograph. The hydrogen amount for the y-axis of the Arrhenius plot  supported on SiOlot Fig.  was for Supplementary Information


Substitutional boron doping of devices based on graphene ribbons gives rise to a unipolar behavior, a mobility gap, and an increase of the  Graphene is a two-dimensional honeycomb lattice of carbon atoms which was first systematically isolated by mechanical exfoliation from bulk graphite in 2004 , and is quation) ,6,7,8. Mquation) ,10,11.From the point of view of electronic devices, the thinness, two-dimensional geometry, and high mobility of graphene have suggested its application as the conduction channel of scaled transistors. However, the absence of an energy gap in two-dimensional monolayer graphene has hampered the fabrication of graphene-based MOS  transistors with a circuits   is boron, an element of the III chemical group  that has a size comparable to carbon and presents a quite low ionization energy if substituted into the graphene lattice. In particular, in Reference  we have eference  are stilThe effect on transport of the boron impurities is strictly related to the details of the (short-range) atomistic potential around the boron dopants. Therefore, an envelope-function approach such as that of References ,34,35,36ion code ,38, whicE is the energy of the charge carriers, I the identity matrix, and H is the nearest-neighbor tight-binding Hamiltonian matrix including only the Regarding the transport side of the simulation, the Schrödinger equation with open boundary conditions is solved within the Green’s function formalism . More ineference  is adopte is the elementary charge, p and n are the electron and hole concentrations, In order to determine the device electrostatics, the Poisson equation is solved  on a tridimensional domain including the device. We consider Neumann conditions (corresponding to constant—and in particular zero—electric field) at the boundaries of the solution domain, and Dirichlet conditions  at the gates. In particular, the Poisson equationThe transport and electrostatic equations are mutually connected. The Schrödinger equation depends on the potential energy h is Planck’s constant, f is the Fermi–Dirac occupation function, The current flowing through the device can be computed from the converged Green’s function through the Landauer equation:S of the ribbon . We considered ribbons with lengths 10, 20, 30, 40, 50, 60, and 70 nm. For each length, we analyzed several different realizations of the boron dopant spatial distribution . In We observe that the increase of the graphene channel length has the positive effect of enhancing the L for the four values of the voltage L. When the device is in its OFF state, the resistance R increases exponentially with the length L, thus suggesting that the device is working in a strong localization transport regime. Instead, when moving towards the ON regime, the resistance dependence on the channel length becomes linear. The transport in the ON state is therefore diffusive. Only when further increasing the channel length, the transport regime in the ON state is expected to become localized, with an exponential suppression of the current on a length scale much larger than that in the OFF state. In further detail, let us assume the behaviors for In order to better analyze our numerical results, This is verified in L should lead to a nearly-exponential enhancement of the Equation  allows uWe numerically analyzed the transport characteristics, as a function of the channel length, of MOS transistors based on graphene ribbons randomly doped with substitutional boron atoms. The study was carried out through self-consistent quantum simulations based on a tight-binding atomistic model, validated through comparison with ab-initio DFT calculations. The numerical complexity of the calculations, increasing with the length of ribbons, required the adoption of enhanced convergence schemes. From the results of our simulations, performed for ribbon lengths ranging from 10 nm to 70 nm, we were able to observe a strongly localized transport in the OFF operating region and a diffusive transport in the ON operating region. This made it possible to extrapolate an analytical, nearly exponential relationship between the


Programmed cell death 1 (PD1) inhibitors have recently shown promising anti-cancer effects in a number of solid tumor types. A predictive biomarker to this class of drugs has not been clearly identified; however, overexpression of the PD1 ligand (PD-L1) has shown particular promise in lung adenocarcinoma. In this study, we explore the staining characteristics, prevalence, and clinico-molecular correlates of PD-L1 overexpression in pancreatic ductal adenocarcinoma (PDAC).A tissue microarray (TMA) was constructed from cases of resected PDAC. PD-L1 immunohistochemistry (IHC) was performed using the SP142 primary antibody. Immunohistochemical assessment for deficient mismatch repair status (MMRd), CD3 and CD8 were performed. All biomarkers were assessed independently by two anatomical pathologists and consensus achieved on all cases. Survival analysis was performed using three thresholds  for tumor cell membrane staining.p = 0.0367). No statistically significant association was identified between PD-L1 status and MMR status or tumor infiltrating lymphocytes.Two-hundred fifty-two cases were included in the TMA and evaluable by IHC. Thirty-one (12%), 17 (7%), 12(5%) cases were positive at percentage cut offs of >0, >5, and >10% respectively. Increased PD-L1 expression was associated with inferior prognosis (This data suggests that there is an inverse relationship between PD-L1 expression and disease specific survival times in resected PDAC. Consequently, this association may represent a phenotype where increased PD-L1 expression has an effect on tumor biology and could therefore identify a subgroup where PD1 blockade could have enhanced effectiveness. Pancreatic ductal adenocarcinoma (PDAC) ranks fourth for overall cancer-related death with over forty-thousand estimated deaths in 2015 in the United States. The five-year survival rate is 26% in resectable disease and drops to 2% if unresectable. Surgical resection is only attempted in 20% of cases [Inhibitors of the programmed cell death 1 (PD1) signalling axis have yielded improved survival benefits for a number of solid tumor types. Large randomized clinical trials have been successful in treating melanoma, non-small cell lung cancer, and renal cell carcinoma , 3. ThrePD-1 is an inhibitory receptor expressed by T cells and other immune cell types. It plays an important role in immune suppression when activated by its ligand (PD-L1). The latter is physiologically expressed by normal tissue and can occasionally be aberrantly expressed by tumor cells as a means for evading immune destruction –8. BlockThe response to PD-1/PD-L1 inhibition has been mixed in various malignancies such as colorectal, prostatic and pancreatic adenocarcinomas and is exemplified in the results of a study by Brahmer et al. which failed to show an objective response to anti-PD-L1 therapy in 14 patients with pancreatic cancer , 10. In In this study, we explore the prevalence of PD-L1 expression in PDAC using IHC and compare this to clinical characteristics, including MMR status and tumor infiltrating lymphocytes and examine if an association with clinical outcome exists.Ethical approval and a waiver of consent for research on this retrospective cohort was obtained from the University of British Columbia Clinical Research Ethics Board (H12–03484).A tissue microarray was constructed using duplicate 0.6 mm cores from the epithelial component of all available, resected, pathologically confirmed pancreatic ductal adenocarcinomas derived from the archives of the Vancouver Coastal Health Region between 1995 and 2014. All patients received primary surgery with a subset receiving adjuvant chemotherapy with a pyrimidine nucleoside analog. Cores for the tissue microarray were obtained from areas of tumor as determined by routine microscopy on hematoxylin and eosin-stained sections. Cases were excluded if they lacked clinical follow-up data or if clinico-pathologic variables were lacking.Immunohistochemistry was performed on 4-μm-thick formalin-fixed paraffin-embedded sections of tissue microarrays. PD-L1 immunohistochemistry was performed at the Deely Research Centre at the British Columbia Cancer Agency using the Intellipath FLX autostainer (Biocare) platform. Mismatch repair, CD3 and CD8 immunohistochemistry was performed in the clinical laboratory of Vancouver General Hospital using the Ventana Discovery XT and the Ventana Benchmark XT automated system .For PD-L1, slides were incubated with the clone SP142  at 1/100 dilution in Da Vinci Green diluent at room temperature for 30 min. Slides were then washed and incubated with Mach2 Rabbit-HRP polymer for 30 min at room temperature and detected with IP DAB chromogen for 5 min. Nuclei were counterstained with a 1/10 dilution of CAT hematoxylin then slides were again washed, air dried and coverslipped with Ecomount. The antibody clone was selected based on its strong concordance to three other PD-L1 clones and RNA in situ hybridization (ISH) in NSCLC .For MMR stains, slides were incubated with MLH1 , MSH2 , MSH6 , and PMS2  for 32 min at room temperature. For the slides to be stained for PMS2 were additionally prepped with the Epitomics DAB prep kit before antibody incubation.Antibodies were detected using the Ventana DABMap kit, counterstained with hematoxylin and treated with a proprietary bluing agent (Ventana). Positive and negative controls are performed as part of the routine clinical quality assurance; in addition to the external quality control program , a provider of proficiency testing for Canadian clinical laboratories).PD-L1 status was assessed independently by two anatomical pathologists (BSS and DG) and consensus achieved on all cases. Positivity was evaluated by H-Score, a combination of staining intensity and percentage of tumor cell staining. Staining intensity was scored as 0 (negative), 1 (weak), 2 (moderate), or 3 (strong) based on membranous localization and each score multiplied by the percentage of cells (0% - 100%) staining. Therefore, H-scores ranged from 0 to 300. To account for potential intra-tumoral heterogeneity, the mean of both cores was used to generate the score for each case.Mismatch repair (MMR) was quantified as per Riazy et al. . BrieflyIndividual tumor infiltrating lymphocytes were counted and typed in the epithelial and stromal compartments using clinically validated IHC stains for CD3 and CD8. Scoring was performed independently by two anatomical pathologists (BSS and MA-K) and consensus achieved on all cases. To account for potential intra-tumoral heterogeneity, the average of both cores were used to generate the final score for CD8+ and CD3+ tumor infiltrating lymphocytes.Standard treatment, clinical and pathologic parameters were collected from the British Columbia Cancer Agency which included: age at surgery, sex, adjuvant chemotherapy agents used, lymphovascular invasion, perineural invasion, pathologic primary tumor (pT) stage, and pathologic regional lymph-node status (pN). The primary outcome measure was defined to be disease-specific survival, where survival time was calculated as the difference between the date of last follow-up and the date of surgery, expressed in years. Patients were censored if they were alive at last follow-up or had died from a cause other than their pancreatic malignancy. Deaths attributable to treatment related toxicities or inter-current diseases were considered censored observations for this analysis.P-value was determined to have the strongest prognostic effect. Parametric survival analysis was used in order to further elucidate the gradient dependent effect of PD-L1 expression on disease specific survival (DSS) for the scoring methodology with the greatest prognostic effect. This procedure modelled the disease specific survival data with 5 different distributions which included: weibull, log-normal, exponential, frechet, and log-logistic. The best distribution to be used for parametric survival analysis was determined by selecting the one with the lowest Bayesian Information Criterion from the model fits. This analysis produces a quantile plot with logSurvival Time plotted against PD-L1 expression which illustrates the gradient dependent relationship between disease specific survival and PD-L1 expression. Based on these findings, a series of three cut-points were created starting with an H-Score or percentage positive cells of 1- as these identify the equivalent cases. Subsequent cut-points were set at increments of 5 which correspond to the increments used for the assessment of percent positive cells. The resultant groups were subjected to univariable survival analysis to quantify differences in disease specific survival using the Kaplan-Meier method. A multivariable approach to disease specific survival, using the Cox Proportional Hazards Model, was used to determine if survival differences between PD-L1 expression categories were independent of adjuvant chemotherapy. Assessment for heterogeneity of clinico-pathologic variables was performed with the following statistical approaches: continuous variables were examined using the Wilcoxon Rank-Sum Test, categorical comparisons were computed using Fisher’s exact test. A P-value of <0.05 was considered as statistically significant for all analyses. All analyses were computed with JMP v13.1 .To determine if H-Score or the percent of positive cells for PD-L1 expression yield differential prognostic ability, each scoring method was subjected to an omnibus assessment utilizing the Cox-Proportional Hazards Model to determine if the expression of PD-L1 was a significant prognostic marker in the context of the clinico-pathologic variables outlined previously with the exception of pT-Stage due the fact that the vast majority of the cases in this cohort are pT3. The proportionality assumption for each variable was assessed through examination of Cox-Snell residuals and continuous variables were assessed for linearity. The PD-L1 scoring methodology with the smallest P = 0.0466 compared to H-Score with a P = 0.10 , >5% , and >10% . Univariable survival analysis using these three cut-points showed no disease specific survival differences at the > = 1 cut-point (p = 0.51) or the >5 cut-point (0.52), but the >10 cut-point yielded statistically significant disease specific survival differences of p = 0.027 . When we sequentially removed statistically insignificant variables from the model . The remaining statistically significant variables included pN-Stage (P < 0.0001), adjuvant chemotherapy (P = 0.0002), and perineurial invasion (P = 0.009) and resection status (P = 0.0263).Based on the prognostic non-inferiority of PD-L1 percent positive, we elected to pursue this scoring method for the remainder of the study. Cut-points were determined according to our criteria outlined in the methods and resulted in: > = 1%  in PD-L1 positive patients has been reported in previous studies . SanmameWe found no significant association between MMR and PD-L1 status. Our results are somewhat different from what was observed by Le et al. (2016) who reported that, in a series of 30 cases, PD-L1 was only expressed in MMR deficient (MMRd) tumors, most of which being colorectal carcinomas . This inThere are several limitations to our study, one being the lack of consensus for PD-L1 IHC expression cut-off and gold standard, which our study has attempted to explore. Our IHC protocol for PD-L1 previously showed fairly strong concordance when compared to three other PD-L1 clones and RNA in situ hybridization (ISH), Sheffield et al., in NSCLC . Our samThe prevalence of PD-L1 positivity in PDAC has been examined in numerous other studies with the percentage of tumor cells staining positive ranging from 4% - 49%. Each of these previous studies utilized different cut-points that varied between 1% - 10% making their results nearly impossible to compare –29. Of pIn summary, this is the first study to systematically investigate the association between clinical outcome and biomarker expression across differing scoring methodologies and cut-points for PD-L1 immunohistochemistry in this disease. We have demonstrated a gradient dependent association between PD-L1 expression and inferior survival that is independent of the prognostic advantage conferred by adjuvant chemotherapy. We postulate that the association presented here may indicate that higher PD-L1 protein expression levels represent a phenotype where PD-1 inhibition may be more effective. However, this hypothesis would have to be tested in the context of a randomized clinical trial. With studies in other diseases also indicating that deficient MMR (MMRd) status has been shown to be a predictive biomarker for immunotherapy, it is entirely plausible that PD-L1 immunohistochemistry is an imperfect biomarker for sensitivity to anti-PD-1 therapy. Interestingly, we found no association between MMRd status and PD-L1 expression in this cohort. More data on the role of PD-1-axis inhibition in PDAC is needed, specifically examining the use of predictive biomarkers in the context of patients treated with immunotherapy. Future studies should endeavor to build predictive models based on multi-marker expression that will serve as tools to triage the PDAC patient population to immunotherapy or other treatment regimens.


However, tumor cell-intrinsic PD-L1 signals also regulate immune-independent tumor cell proliferation and mTOR signals, among other important effects. Tumor-initiating cells (TICs) generate carcinomas, resist treatments and promote relapse. We show here that in murine B16 melanoma and ID8agg ovarian carcinoma cells, TICs express more PD-L1 versus non-TICs. Silencing PD-L1 in B16 and ID8agg cells by shRNA (‘PD-L1lo’) reduced TIC numbers, the canonical TIC genes nanog and pou5f1 (oct4), and functions as assessed by tumorosphere development, immune-dependent and immune-independent tumorigenesis, and serial transplantability in vivo. Strikingly, tumor PD-L1 sensitized TIC to interferon-γ and rapamycin in vitro. Cell-intrinsic PD-L1 similarly drove functional TIC generation, canonical TIC gene expression and sensitivity to interferon-γ and rapamycin in human ES2 ovarian cancer cells. Thus, tumor-intrinsic PD-L1 signals promote TIC generation and virulence, possibly by promoting canonical TIC gene expression, suggesting that PD-L1 has novel signaling effects on cancer pathogenesis and treatment responses.As tumor PD-L1 provides signals to anti-tumor PD-1 Studies of PD-L1 signals in tumor immunopathogenesis and treatment responses have focused on cell-extrinsic effects. We introduce a major shift from the current paradigm by showing novel cell-intrinsic PD-L1 signaling that promotes TIC generation, virulence and treatment responses, greatly augmenting understanding of PD-L1 signals in cancer pathogenesis and treatment responses.2 Immune checkpoint blockade with anti-PD-L1 monoclonal antibodies (αPD-L1) is clinically effective in many cancer models. The αPD-L1 antibody atezolizumab was recently Food and Drug Administration approved to treat certain bladder and lung cancers. Its principal mode of action is thought to be protecting anti-tumor T cells from inhibition by tumor surface-expressed PD-L1.2 We recently reported that PD-L1 also mediates important cell-intrinsic signals that regulate immune-independent tumor growth, mTOR signaling and autophagy in melanoma and ovarian cancer cells.3 Tumor-intrinsic PD-L1 also regulates tumor glucose metabolism regulation, affecting anti-tumor T cells.4 Thus, cell-intrinsic PD-L1 signals merit further studies.PD-L1  is an immune co-signaling molecule in the B7-H (B7 homology) family. It has a key role in maintaining an immunosuppressive tumor environment by negatively regulating anti-tumor responses through fostering apoptosis, anergy or exhaustion of PD-1-expressing T cells, and is thus immunopathogenic in many cancers.6 Despite their importance, mechanisms for TIC generation and virulence are incompletely understood.Tumors are comprised of genetically and functionally heterogeneous cell populations. Among these are tumor-initiating cells (TICs), initially reported in hematologic cancers, but that also give rise to epithelial carcinomas.6 PD-L1 and PD-1 are expressed on TICs,7–10 but definitive evidence for their roles in TIC generation has not been reported to our knowledge. We demonstrate here that tumor cell-intrinsic PD-L1 directly drives the generation and functions of TICs in murine melanoma and ovarian cancer cells, and a human ovarian cancer cell line. Tumor PD-L1 is well known to give negative cell-extrinsic signals to PD-1+ T cells, but we now show that tumor PD-L1 also appears to generate intra-tumor cell signals that augment canonical TIC gene expression, regulate TIC numbers and functions, and sensitize them to rapamycin and interferon-γ. These previously unknown intracellular signaling effects define important new mechanisms for PD-L1 participation in cancer immunopathogenesis and suggests novel treatment strategies.Because TICs are resistant to many therapies, and can give rise to tumor relapse, much attention has been given to targeting them for treatment.scidIl2rgtm1Wj1/SzJ (non-obese diabetic/severe combined immunodeficiency (NOD/SCID)/interleukin-2Rγ KO, NSG) mice were purchased from Jackson Laboratory , maintained under specific pathogen-free conditions and given food and water ad libitum. Eight- to ten-week-old age- and sex-matched mice were used for experiments. All animal studies were approved by our Institutional Animal Care and Use Committee.Male and female wild-type (WT) C57BL/6J (BL6) and NOD.Cg-Prkdc11 We generated an aggressive ID8 line (ID8agg) by serial passage through WT mice.3 The murine melanoma B16-F10 cell line (herein ‘B16’) and the human ovarian cancer cell line ES2 were purchased from the American Type Culture Collection . Cells were not re-authenticated for this work. Mouse cells are on the BL6 background. Cells were used in passages <5 and maintained in 5% fetal bovine serum-containing medium RPMI-1640 plus 1% penicillin/streptomycin, 1% l-glutamate and 1% HEPES buffer (complete medium). B16, ID8agg and human ES2 cells with stable PD-L1 knockdown were generated and described in our recent report.3 In brief, cells were transfected with murine or human control shRNA (‘control’) or shRNA against PD-L1 (‘PD-L1lo’). Monoclonal cells were selected in puromycin and PD-L1 expression status confirmed by flow cytometry and western blotting.The murine ovarian cancer cell line ID8, a gift from George Coukos, University of Pennsylvania, is a well-accepted transplantable mouse model that produces tumors replicating important aspects of human ovarian cancer, including local spread and ascites after intraperitoneal injection into syngeneic BL6 mice.All cells were cultured under identical conditions, including cell density and passage number, and studied at ~80% confluency on 100 mm plastic culture plates in complete medium. Indicated cultures included rapamycin  or mouse recombinant interferon-γ  for 48 h (B16 and ID8agg) or 60 h (ES2) at concentrations shown. Cell viability and numbers were determined on a Vi-Cell XR  or hemocytometer and stained as described below. Dimethylsulfoxide (Sigma) or phosphate-buffered saline was used as negative controls (vehicle) for rapamycin or interferon-γ, respectively.12 using BD LSRII and FACSAriaII hardware, and analyzed by FACSDiva  or FlowJo  software. Zombie Yellow Fixable Viability kit, anti-mouse PD-L1 , CD44 (Per-CP-Cy5), CD133 (PE-Cy7), CD24 (PE), anti-human CD44 (Per-CP-Cy5), CD24 (PE), PD-L1  and matched isotype controls were purchased from BioLegend . ALDEFLUOR kits were purchased from STEMCELL Technologies . The ALDEFLUOR assay was done with 1×106 ES2 cells per ml. A volume of 5 μl of the specific aldehyde dehydrogenase (ALDH) inhibitor diethylaminobenzaldehyde was added to control tubes. Tubes were incubated with 5 μl BODIPY aminoacetaldehyde, a fluorescent substrate for ALDH and incubated for 45 min at 37 °C. Following washes, samples were kept at 4  °C for remaining staining. TICs were defined as CD44+CD133+CD24+ (B16)13, CD44+CD24+ (ID8)14 and ALDHhi or CD44+CD24− 16 by flow cytometry.Cells were stained and sorted as previously described,17 or ID8agg cells or corresponding TIC intraperitoneally B16 growth was measured with Vernier calipers and tumor volume calculated as (length×width2)/2. Survival was determined by tumor size ⩾1800 mm3 or animal distress. ID8agg survival was determined by ascites formation or distress.18 For serial re-transplantation, tumors were removed under sterile conditions when they reached 1800 mm3, digested, stained for viability and 104 sorted TICs were injected into naive NSG mice subcutaneously. For ES2 TICs, female NSG mice were challenged with 20 000 sorted ALDHhi TICs intraperitoneally. The survival of these mice was determined by ascites accumulation causing weight ⩾130% of baseline, or distress.WT or NSG mice were injected with indicated numbers of B16 cells or corresponding TICs subcutaneously (s.c.),4 TICs were sorted from cultures and grown in DMEM-F12 medium  with B27 , 20 ng ml−1 epidermal growth factor  and 20 ng ml−1 fibroblast growth factor (PeproTech)14 for 1–2 weeks in 25 cm2 flasks. At least six fields per clone were counted. Spheroid images were taken and analyzed using QCapture Pro 6.0 software . Diameter was calculated using the 100 μm measurement bar.10nanog (Mm02019550_s1), sox2 (Mm03053810_s1), pou5f1  and rptor (Mm01242613_m1) according to the manufacturer’s instructions with β-actin (Mm02619580_g1) as the internal control. ES2 NANOG (Hs04399610_g1), OCT4 (Hs00999632_g1), SOX2 (Hs01053049_s1), RPTOR (Hs00375332_m1) and GAPDH were amplified in a similar manner. Fold changes were calculated by ΔΔCT between TICs and respective total cultures and P-value calculated by unpaired t-test. These fold changes were compared between control and PD-L1lo cells by unpaired t-test.Total RNA was isolated from cells using a Direct-zol RNA miniprep kit . Complementary DNA was synthesized with 1 μg total RNA using the ImPromII Reverse Transcription System  and random primers. Quantitative PCR was conducted using the 7900HT Real-Time PCR System , amplified with Taqman gene expression assays (Applied Biosystems) for mouse t-test was used for comparison between individual means. Kaplan–Meier estimates and the log-rank test were used to analyze survival. P<0.05 was considered significant.Statistical analyses were conducted using Prizm software . Data were plotted as means±s.e.m. For tumor growth, two-way analysis of variance plus Bonferroni post tests were used to compare replicate means. Unpaired 14 . B16 and ID8agg PD-L1lo TICs expressed lower PD-L1 versus their respective control TICs , respectively, lo TICs formed tumors. Confirming tumorigenicity of control TIC, challenge with equal numbers of total cultured B16 cells did not produce detectable tumors in WT mice during this period (not shown). As PD-L1 can inhibit tumor rejection through immune mechanisms,21 we next challenged severely immune-deficient NSG mice with TICs from control or PD-L1lo B16 TICs. Similar to the observations in WT mice, tumor growth was significantly slower , respectively, lo ID8agg TIC in WT mice .22 In RNA-sequencing studies of total cultured ID8agg, these canonical TIC genes produced too few reads for statistics. Quantitative PCR analyses of highly purified, sorted B16 and ID8agg TICs demonstrated that nanog and oct4, but not sox2 message were significantly higher in control or PD-L1lo TICs versus their respective total cultures, and significantly lower in PD-L1lo TICs versus respective control TICs  and lin28a23 and additional TIC genes nes and ck18 were significantly reduced in total ID8agg PD-L1lo versus control cells by RNA sequencing  is a positive mTOR regulator that acts as a scaffold for mTORC1 assembly and predicts mTORC1 activity. We used quantitative PCR of highly sort-purified TICs to show that rptor expression was significantly reduced in B16 PD-L1lo TICs versus respective control TICs , we used ALDHhi expression to identify ES2 TICs in vitro. Control ES2 TICs expressed higher PD-L1 versus control non-TICs  can kill T cells,7 squamous cell carcinoma of the head and neck,9 and colon cancer where PD-L1 was higher than non-stem cells,8 but a mechanistic relationship between PD-L1 and stem cell generation or function was not defined. We show here that TICs express higher PD-L1 levels than non-TICs in mouse melanoma and mouse but not in human ovarian cancer. This cell-intrinsic PD-L1 controlled numbers of TIC generated in vitro in mouse B16 melanoma and ID8agg ovarian cancer cells, and in human ES2 ovarian cancer cells. Thus, this novel capacity of cell-intrinsic PD-L1 to promote TICs is not confined to a single tumor type and is also relevant to human cancer. The significance of higher PD-L1 expression in some TICs versus non-TICs is uncertain, but could relate to additional TIC-specific signaling in differing tumor cell types. In human head and neck cancers, cancer stem cells defined solely by CD44 expression and sorted into PD-L1+ and PD-L1− populations effected comparable tumorigenicity in NSG mice, whereas PD-L1+ stem cells elicited more evidence for T-cell dysfunction in vitro versus PD-L1− stem cells.9 By contrast, we show here that PD-L1lo TICs in melanoma and ovarian cancer had poor tumorigenicity in immune sufficient and immune-deficient mice, thus confirming that TIC-intrinsic PD-L1 has unexpected TIC growth promoting effects in melanoma and ovarian cancer cells, in addition to anticipated immune evasive effects. PD-L1 mediates an immune evasive role in all cancers and TIC tested thus far. The lack of clear PD-L1-mediated immune-independent TIC growth promotion in head and neck cancers that contrasts with our data in melanoma and ovarian cancer cells suggests that cell-intrinsic PD-L1 growth promotion also depends on tumor-specific properties yet to be defined, an area worthy of additional study. The above mentioned study tested fewer mice and assessed one dose of TICs, which could help explain the divergent outcomes.PD-L1 is expressed by cancer stem cells in human glioma,3 We show here that PD-L1 increased the mTORC1 component rptor in B16 and ID8agg TIC, and also in human ES2 TIC, consistent with increased mTORC1 signals. The preferential mTORC1 inhibitor rapamycin reduced control (PD-L1 replete) B16, ID8agg TIC numbers in vitro with significantly diminished effects on PD-L1lo TIC, consistent with the concept that PD-L1-driven mTORC1 promotes TIC generation in these cells. TIC PD-L1 also sensitized human ES2. TICs are considered to be treatment resistant, but we show that TIC PD-L1 sensitizes TICs to the small-molecule mTORC1 inhibitor rapamycin. This observation deserves additional exploration, including assessing other mTORC1 or dual mTORC1/2 inhibitors. Our data predict that efficacy of αPD-L1 in PD-L1+ tumors for which αPD-L1 is most likely to be effective27 could be enhanced in some cases when combined with an appropriate mTOR inhibitor.We recently showed that in total B16 and ID8agg cell cultures, tumor PD-L1 promoted tumor cell proliferation and mTORC1 activation.27 Thus, as expected, interferon-γ augmented TIC PD-L1 expression. However, we show here that PD-L1 also appears to alter interferon-γ signals, as TIC PD-L1 unexpectedly sensitized murine B16 and ID8agg TICs, and human ES2 TICs, to interferon-γ-mediated growth suppression in vitro. These data demonstrate the first TIC immune-sensitizing mechanism to our knowledge. In adaptive immune resistance, the incoming immune response, including interferon-γ, makes the tumor resist further immune attack.28 TIC PD-L1 might suppress anti-tumor T-cell responses as seen in studies of total tumors21 and head and neck cancer TIC,9 yet TIC PD-L1 could render them more vulnerable to immune-mediated clearance. This complexity requires much additional study to understand in which TIC their PD-L1 alters immune effects on tumor and to optimize immunotherapy treatment strategies. For example, our work suggests that αPD-L1 plus interferon-γ, T cells producing interferon-γ (such as TIC-targeting CAR T cells) or perhaps interferon-α are strategies for further testing in TICs in which PD-L1 sensitizes them to interferon-γ-mediated growth inhibition.Interferon-γ-induced PD-L1 regulation is well known.oct4 and nanog, but not sox2. By contrast, PD-L1 promoted SOX2 but not OCT4 or NANOG in human ES2 ovarian cancer cells. These data are consistent with the notion that PD-L1-driven stemness gene expression is a mechanism for its capacity to generate TIC. Our data further suggest that the specific stemness genes driving TIC depend on the tumor, likely reflecting differing mutational landscapes in ovarian cancers versus melanomas,30 and also helping understand the many cell-specific outcomes, we define here in regards also to rapamycin and interferon-γ sensitivity. Mechanisms for how PD-L1 alters gene expression requires further study. Effects are transcriptional, as evidenced by reduced canonical stem cell gene message in PD-L1lo TICs we show here and in our recent work on non-TICs;3 however, how PD-L1 effects transcriptional control remains to be defined. Regulation could also be through mTOR-related translational effects.31 Understanding these specific mechanisms could define novel means to reduce TIC generation.In assessing mechanisms for TIC generation, we found that PD-L1 signals promoted canonical stemness gene expression. In mouse B16 melanoma and ID8agg ovarian cancer cells, PD-L1 augmented expression of 10 B7-H3, such as PD-1 and PD-L1, is an immunoglobulin superfamily member. These three molecules all exert tumor cell-intrinsic effects, including regulating tumor mTOR, its regulators or its targets.32 As mTOR could have a critical role in TIC generation,33 the concept that tumor cell-intrinsic immunoglobulin superfamily members exert related, important signaling effects in cancer immunopathogenesis or treatment responses merits much additional attention.PD-1, a major PD-L1 receptor, was recently shown to regulate cell-intrinsic and immune-independent melanoma growth in mouse and human melanoma cells.+ anti-tumor T-cell activities and suggest many additional avenues for investigations of tumor immunopathogenesis, treatment approaches and potentially treatment responses. Given the virulence and treatment resistance of TIC, such studies could also help develop successful new treatment approaches that specifically target TICs. Finally, immune checkpoint molecules in the immunoglobulin superfamily could have common tumor cell-intrinsic properties that will expand our understanding of their contributions to cancer pathogenesis and provide insights into effective cancer treatments.In summary, we show that tumor PD-L1 effects are considerably broader than simply blunting PD-1