text:
Toward Participatory Democracy
It's presidential election season in America. If there is a time when more Americans pay attention to politics, it is now. Now is thus perhaps the best time to challenge how Americans think about democracy. More often than not, the word "democracy" conjures electing representatives to govern. As important as that is, there is more to democracy, and that "more" is well worth pondering.
The influential 20th century American philosopher, John Dewey was one of democracy's most ardent proponents. But his view of democracy was broad and participatory, not limited to electing politicians to govern (Dewey 1927, Morris & Shapiro 1993). As historian James Kloppenberg explains, summarizing the work of one of Dewey's intellectual chroniclers, "First, Dewey believed that democracy is an ethical ideal rather than merely a political arrangement. Second, he considered participation, not representation, the essence of democracy. Third, he insisted on the harmony between democracy and the scientific method: ever-expanding and self-critical communities of inquiry, operating on pragmatic principles and constantly revising their beliefs in light of new evidence, provided Dewey with a model for democratic decision making…Finally, Dewey called for extending democracy, conceived as an ethical project, from politics to industry and society" (Kloppenburg 1992).
To Dewey then, democracy is an ongoing way of being that involves participation in learning and decision-making in most if not all domains of life, including family, work, associations and government. Dewey is, of course, not alone in advocating a more "participatory" version of democracy (e.g., see Pateman 1970, Mansbridge 1983, Barber 2004, Fishkin 2011), but this version is not what prevails in the United States and other modern democracies.
What prevails political observers commonly call representative democracy, or a republic. In contemporary republics, democracy is like a gladiator's contest, as the political scientist Lester Milbrath (1965) aptly described: at any given time, about 5-7% of citizens are the gladiators who run for office and lead political campaigns and organizations. The spectators, who comprise 55-65% of the public, pay attention, express support and vote. The rest, whom Milbrath called "the apathetics," comprising 30-40% of citizens, don't bother to come to the show — they don't pay attention, let alone act politically, and thus know little about politics. This republican democracy is associated with greater inequalities in participation and power as well as greater apathy and partisanship.
Some political scientists claim that republican democracy is inevitable, that one cannot realistically expect citizens to be engaged in the same way all the time, and that more engagement may only lead to more conflict and crisis from competing citizen demands. These claims though generally assume that democracy should be limited to government, and that active citizens are partisans rather than deliberators.
Dewey, like other participatory democrats, contends that all organizations – governmental, business and nonprofit – engage in decision-making, and that decision-making can be made more democratic, involving more people rather than habitually delegating to executives or representatives. Further, whether citizens become rigid, self-interested partisans or flexible, public-interested deliberators depends in no small part on the rules of engagement organizations establish, including the ideal citizens organizations uphold.
Clearly, as numerous notable political observers (e.g., more recently, Haidt 2012, Fukuyama 2014) remark, drawing on evolutionary science, humans are inclined to be self-interested, but we are also routinely cooperative when it suits us, and for better or worse, even self-abnegating when moved by another individual or group to whom we feel committed. As social scientists know well, how we act depends a lot on our social circumstances, and the organizations in which we live and work every day play a large role in defining those circumstances.
The implications are that (a) more participatory democracy is possible, and (b) democratic organizations can make it happen. Indeed, there are plenty of contemporary examples, including organizations like Voice of the People, Healthy Democracy and the Center for Deliberative Democracy. Groups like these get diverse citizens involved in local to international dialogues that nurture more equitable, tolerant, informed and public-spirited decision-making among citizens and policymakers alike. Yet to make democracy a routine practice, as Dewey envisioned, rather than a periodic choice, calls for imagining, discussing and enacting models that democratize not only government, but also society.
Paul Lachelier, Ph.D.
Founder, Learning Life
Barber, Benjamin. 2004. Strong Democracy: Participatory Politics for a New Age. Berkeley: University of California Press.
Dewey, John. 1927. The Public and its Problems. New York: Holt.
Fishkin, James. 2011. When the People Speak. Oxford: Oxford University Press.
Fukuyama, Francis. 2014. Political Order and Political Decay: From the Industrial Revolution to the Globalization of Democracy. New York: Farrar, Straus & Giroux.
Haidt, Jonathan. 2012. The Righteous Mind: Why Good People Are Divided by Politics and Religion. New York: Pantheon Books.
Kloppenberg, James T. 1992. Book review of John Dewey and American Democracy, by Robert B. Westbrook in American Historical Review, p.919-920.
Mansbridge, Jane. 1983. Beyond Adversary Democracy. Chicago: University of Chicago Press.
Milbrath, Lester. 1965. Political Participation: How and Why Do People Get Involved in Politics? Chicago: Rand McNally & Co.
Morris, Debra, and Ian Shapiro, eds. 1993. John Dewey: The Political Writings. Indianapolis: Hackett.
Pateman, Carole. 1970. Participation and Democratic Theory. London: Cambridge University Press.
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\section{Introduction}

Memristors are non-linear resistors that possess a memory~\cite{14}. They were first predicted to exist in 1971~\cite{14} and were formally discovered in device form in 2008~\cite{15} although other memristor devices had been fabricated before (for a recent review see~\cite{RevMemReRAM}). After the first paper to relate memristor theory to a real world device~\cite{15}, excitement abounded over their use for computer memory (alongside ReRAM) and neuromorphic computing, as well as suggestions that they might be more energy efficient and resilient. Neurons are believed to be both the seat of memory and the processor of the brain. Memristors, by combining memory with processing, offer a tantalising glimpse of devices which could do the same.

Neuromorphic computing is the concept of using computer components to mimic biological neural architectures, primarily the mammalian brain. The relation between memristors and neuromorphic computing dates back to 1976 when Chua and Kang expanded the idea of the memristor to a memristive system (which has two state variables rather than the one the memristor possesses) and suggested that the Hodgkin-Huxley model of the nerve axon could be improved by incorporating memristors in place of the non-linear time dependent resistors~\cite{84}: an idea that wasn't theoretically demonstrated until 2012~\cite{247,248}.

In general, the memristor community has concentrated on synapses rather than axons: using memristors as synapses~\cite{41,217}, combining memristors and spiking neurons in simulations (see for example~\cite{278}) and even whether synapses are memristive~\cite{239} (not as unlikely as it seems now that biological materials such as flowing blood~\cite{147}, sweat ducts~\cite{276} and slime mould~\cite{277} have been shown to be memristive). Simulations have shown that memristive connections could be used to reproduce spike-time dependent plasticity~\cite{239} (the process by which synapses adjust their connection weight to implement Hebbian learning~\cite{85}). Experiments have modelled neural action with memristors~\cite{45} and investigated the interactions between living neural cells and memristors~\cite{Ne0}.

As has been shown elsewhere, our memristors~\cite{243,SpcJ} and other memristive devices~\cite{123,macro1,261} present a `transient' current spike in response to a d.c. voltage (which we have also referred to as the `short-term memory of the memristor'). In this paper, we demonstrate that these spikes interact within small memristor networks and give rise to emergent brain-like dynamics.

When assembling multi-memristor systems in the laboratory, it is sensible to first ask which circuits are being designed by theorists and tested by simulationists for use with memristors, and overwhelmingly they investigate the Chua circuit. The original Chua circuit~\cite{256} was created to demonstrate that chaos was a real phenomena (not merely due to rounding errors in the computer simulations) and it has been suggested~\cite{248} that neurons are poised at the edge of chaos, so, it is worth investigating chaotic dynamics (and the related field of complexity) if we are interested in making circuits for neuromorphic computing.

There have been a plethora of different versions of and alterations to the Chua circuit, as summarized in~\cite{250}, but the simplest version built~\cite{257} consists of one inductor, one resistor, two capacitors and a component called Chua's diode: a non-linear circuit element usually fabricated from several other circuit components including op amps. Itoh and Chua were the first to replace Chua's diode with a memristor~\cite{251}; they worked with the concept of an active memristor (a memristor is a passive device, but a circuit of a negative resistance and memristor can be viewed as an active memristor -- a concept which fits well with biological memristors and has been used to model them~\cite{277}). There have been many papers since detailing the rich behaviour and chaotic properties of Chua circuits containing memristors (eg. ~\cite{82},~\cite{61},~\cite{70} and ~\cite{232}). These contain simulations which use Chua's equations~\cite{14} for the perfect theoretical memristor and electronic
experiments which replace the memristor with a circuit equivalent, presumably due to the difficulty in obtaining an actual memristor to use.

An important step forward in the direction of real world functionality was Buscarino's paper~\cite{252} where Chua's diode in Chua's circuit was modelled using Strukov et al's phenomenological model~\cite{15} which is based on real world measurables and relates to a real memristor. The resulting simulation demonstrated chaotic behaviour~\cite{252}. This paper used a pair of Strukov memristors~\cite{15} connected in anti-parallel to give a symmetrical $I-V$ curve as a replacement for Chua's diode. They then used a voltage frequency that took the memristor to its limits (i.e. maximum and minimum resistance) to introduce asymmetry and richer behaviour. However, from this data it is not known whether the chaotic behaviour they observed in their simulations arose from the memristors themselves or from the interactions of the errors in the model, which (even with windowing functions) is weakest at the edges of the memristor.

A recent experimental result of a possible neuromorphic building block was the Hewlett-Packard (HP) `neuristor': a circuit consisting of two memristors and two capacitors (and a load resistor) which gives `brainwave'-like dynamics from a constant voltage source~\cite{272}. This circuit also positioned the memristors in anti-parallel.

Another area of interest is how few components a chaotic circuit can be made with. A recent paper~\cite{40} suggested that the simplest circuit capable of producing chaos could be made with three components: a capacitor, an inductor and a memristor. Thus, circuits involving memristors, capacitors and inductors look likely to product interesting dynamics.

Furthermore, according to Chua~\cite{14} the linear combination of memristors in a circuit with only one input and one output to that circuit is indistinguishable from a memristor with a memristance value calculable by standard series and parallel resistor adding rules (`A 1-port containing only memristors is equivalent to a memristor'~\cite{14}), i.e. the memristors combine in series and in parallel similarly to resistors, which would suggest that a circuit made up of only memristors would be a trivial circuit with the same behaviour as a single memristor.

However, due to our observation of the memristor spikes, we decided to test whether circuits consisting of only real-world memristors would give rise to rich behaviour, rendering the complications of including capacitors or op amps unnecessary. Therefore we investigated interacting memristors using titanium dioxide sol-gel memristors~\cite{M1}. By using experimental prototype devices we are able to make use of the memristor's actual behaviour, whereas theoretical models of the memristor can be less useful in this regard.

It was thought that the memristors would spike with the change of voltage and this would cause a change in resistance within a single memristor, which, with this circuit set-up would lead to a voltage change across the other memristors and thus further spikes (this idea was tested in a music-composing memristor network simulation~\cite{Mu0}).

In this paper we investigate how binary and tertiary combinations of memristors interact and test the assertion that memristor circuits addressed only by their joint one port entry (ie there is one wire coming out and going in to that part of the circuit) are indistinguishable from a single memristor.

\subsection{Methodology}

\subsubsection{Memristor Circuits Tested}

The tested circuits are presented in table~1. From the literature and our own intuition, we expected that two memristors in anti-parallel configuration would be the most likely 2-memristor circuit to exhibit rich dynamics, see circuit no. 4 in table~1. A dynamical system can exhibit chaotic behaviour if it has at least three state variables. For this reason we decided to create circuits with three memristors, which gives us the following three separate state variables: the current through the circuit, and the voltage across two of the memristors (the third being determined by the other two in a system kept at a constant voltage). In order to maximise the anti-parallel interactions of the circuit, the memristors were wired up, two in anti-series, with one in parallel to the two in series as shown in figure~1 circuit 7. For this reason, we decided to count the number of anti-parallel interactions in the 3-memristor circuits and also take note of memristors wired up with opposite polarity in a series circuit (
anti-series), which is circuit 2 in table~1. We expect that circuit number 7 one anti-parallel and one anti-series will give the richest behaviour as it has the most anti-polarity interactions and they are of different types introducing another type of variation into the system.

\begin{table}
 \begin{tabular}{|c|c|c|}
\hline
Experiment		& Wiring Diagram	& Number and Type \\
 Number			& 			& of anti-polarity interactions	\\
\hline
 & & \\
  1	& \includegraphics[width=0.15\textwidth]{SingleMem.png} & 0\\
 & & \\
\hline
 & & \\
  2	& \includegraphics[width=0.30\textwidth]{TwoMemsInAntiSeries.png}	& 1s	\\
 & & \\
\hline
 & & \\
  3	& \includegraphics[width=0.30\textwidth]{TwoMemsinSeries.png}	& 0	\\
 & & \\
\hline
 & & \\
  4 	& \includegraphics[width=0.20\textwidth]{TwoMemsinantiParallel.png}	& 1p	\\
 & & \\
\hline
 & & \\
  5 	& \includegraphics[width=0.20\textwidth]{TwoMemsinParallel.png}	& 0	\\
 & & \\
\hline
 & & \\
  6	& \includegraphics[width=0.30\textwidth]{ThreeMemsInSame.png}		& 0	\\
 & & \\
\hline
  & & \\
  7 	& \includegraphics[width=0.30\textwidth]{ThreeMemsIn2Anti6.png}	& 1s, 1p\\
 & & \\
\hline
 & & \\
  8	& \includegraphics[width=0.30\textwidth]{ThreeMemsCircuit8.png}	& 2p	\\
 & & \\
\hline
 \end{tabular}
\label{tab:TestCircuits}
\caption{Constructed experiments. Anti-parallel memristor interactions are represented by `p', anti-polarity series memristor interactions by `s'.}
\end{table}

\clearpage

\subsubsection{Memristor Types: Curved and Triangular Switching Behaviours}

Our memristors show two characteristic behaviours: A: `curved' pinched hysteresis curves and B: `triangular' pinched hysteresis curves, as previously observed~\cite{M0} and shown in figure~\ref{graphs}. The type A memristors can be modelled by the standard memory-conservation theory~\cite{F0c} and are thus close to Chua's theoretical memristor~\cite{14}. Type B memristors have an ohmic low resistance state as evidenced by a straight-line on the $I$-$V$ graph. We suspect that the `triangular' type B memristors switch via the formation and breaking of conducting filaments and the addition of a conducting filament to the memory-conservation theory of memristance~\cite{254} models this situation well. The memristors used in this work were classified into A or B types based on observed $I$-$V$ curves. All further results presented in this paper are experimental, not simulated.

\begin{figure}[!tbp]
\centering
\subfigure[]{\includegraphics[width=0.49\textwidth]{NiceVersionForFilamentConfPaper-A.jpg}}
\subfigure[]{\includegraphics[width=0.49\textwidth]{NiceVersionForFilamentConfPaper-B.jpg}}
\subfigure[]{\includegraphics[width=0.49\textwidth]{ReRAMSimulation2.png}}
\subfigure[]{\includegraphics[width=0.49\textwidth]{ReRAMSimulation.png}}
\caption{Examples of the two different types of memristors: a. experimental curved memristor, b. experimental filamentary memristor, c. theoretical curved type memristor, d. theoretical filamentary memristor.}
\label{graphs}
\end{figure}

\subsubsection{Experimental Details}

The circuits were wired up according to the table and connected to a Keithley 2400 Sourcemeter in current-sensing, voltage sourcing mode. To get the $I$-$t$ curves, the memristor circuits were taken to +0.4V for 1000 timesteps (1 timestep = 1.6s) the voltage source was then switched to 0V and data was gathered for a further 100 timesteps. 22 different experiments were analysed. To investigate whether a slow changing voltage had an effect, a sinusoidal voltage of 1600 timesteps of 2s was used. In all experiments voltages were kept very low to avoid the creation of filaments via Joule heating which would lead to filamentary memristors switching into lower resistance states.

The $I$-$t$ time-series plots were analysed using MatLab. The periodograms were calculated using discrete Fourier transforms using a fast-Fourier transform algorithm~\cite{DFT}: all periodograms were calculated with the same sample frequency of 0.942Hz. Time-series auto-correlation function (ACF) plots were plotted to test for significant persistence as measured by the number of timesteps (lag) required to predict the next step. Partial auto-correlation function (PACF) gives a measure of the signals `complexity' by measuring the minimum order of auto-regression (AR) function required to fit it, where an AR(0) is a memoryless system, AR(1) requires 1 preceding step and so on. Auto-correlation was performed using MatLab's `autocorr' function~\cite{Box,BoxBook} and the partial auto-correlation using MatLab's `parcorr' function~\cite{BoxBook}. Output $I$-$t$ plots were tested for a departure from randomness based on the ACF data using the Ljung-Box-Pierce Q-test~\cite{BoxBook}, implemented in MatLab as `lbqtest',
 using a threshold p-value of 0.05 (below which we reject the null hypothesis of the data being random). The test was performed lags from 1 to 50 based on the observation that bursting spikes did not tend to persist for more than 100s.

\section{Results}

\subsection{Classification of Dynamical Response}

The 22 experiments are summarised in table~\ref{tab:2} and examples of the behaviour are shown in figures~\ref{fig:181012MTD7p2t}, ~\ref{fig:181012MTD6p1}, ~\ref{fig:241012MTR5p2}, ~\ref{fig:101012MT1p1}, ~\ref{fig:151012MT1p5} and ~\ref{fig:221012MTR6p3}. Six types of behaviour were observed and will be described in rough order of increased `complexity', where we use the term to mean a qualitative measure of the visual complexity observable in the graphs.

\subsubsection{`Ideal' Dynamics}
The simplest dynamics were those which were single memristor-like, and showed a current transient decaying with time, see figure~\ref{fig:181012MTD7p2t} and compare to the single memristor response in figure~\ref{fig:single_mem_new}. This type of response is given the classification `1' in table 2, and accounted for 2/22 experiments. Single memristor-like $I$-$t$ curves with a few single spikes was designated `1s' (4/22), and an example is shown in figure~\ref{fig:Curved2}a. Graphs which looked like a single memristor switching state were designated `w' (2/22), see figure~\ref{fig:181012MTD6p1}. `1', `1s' and `w' were all variations on the single memristor $I$-$t$ in that they have a switching spike when the voltage was turned on or off and decay with time either without spikes (`1'), with a few spikes (`1s') or with several switching spikes (`w').

\subsubsection{Emergent Spiking Dynamics}
The more complex behaviour is the spiking behaviour and this is broken down into two types: `SpO' which is oscillations with a few single spikes overlaid (5/22) and `Sp' which is oscillations with bursting spikes (6/22). Examples of `SpO' responses are shown in figures~\ref{fig:151012MT1p5} and~\ref{fig:241012MTR5p2}. An example of `Sp' type dynamics are shown in~\ref{fig:101012MT1p1}. Neither the `Sp' or `SpO' circuits showed switching spikes when the voltage was turned on or off, which we think is because that spike is the impulse to the circuit and the energy emerges at a later time in the observed spikes.

The remaining type is designated as `fi' as shown in figure~\ref{fig:Triangular2}, where the current is several orders of magnitude higher and the response more linear, both facts suggest that a conducting filament in one (or more) of the memristors has nearly bridged the electrodes (the current is linear when it finally connects). These circuits are not interesting from the dynamical point of view, however the memristors in this state are excellently suited for the task of holding a state and are useful for resistive Random Access Memory (ReRAM).

\subsection{Two Memristor Circuits}

\subsubsection{Type A (curved) Memristors in 2-Memristor Circuits}

Using type A memristors in series, as in circuit 3, gave an $I$-$V$ profile similar to that for one memristor (compare with figure~\ref{fig:single_mem_new}) other than an unexpected spike near the end. However, putting two memristors in `anti-series' as in circuit 2 gave increased noise and several spiking events, as shown in figure~\ref{fig:Curved2}b. The anti-polarity series interactions causes this richer behaviour. Similarly, the 2-memristor parallel interactions, as shown circuits 4 and 5 show more noise and spiking events, see figure~\ref{fig:Curved2}c and d. Note that only the two memristors in series show the expected spike at the start and when the voltage is switched off (as was seen for a single memristor), therefore only circuit 3 can be considered equivalent to a single memristor.

\begin{figure}[!tbp]
\centering
\subfigure[]{\includegraphics[width=0.49\textwidth]{161012MTD2p1.png}}
\subfigure[]{\includegraphics[width=0.49\textwidth]{181012MTD3p2.png}}
\subfigure[]{\includegraphics[width=0.49\textwidth]{181012MTD4p1.png}}
\subfigure[]{\includegraphics[width=0.49\textwidth]{181012MTD5p2.png}}
\caption{Results for type A memristors: a. Two A memristors in series, opposite direction, circuit 2 in table~1, b. Two type A memristors in series, same direction, circuit 3 in table~1, c. Two type A memristors in anti-parallel, circuit 4 in table~1 and d. two type A memristors in parallel, circuit 5 in table~1}
\label{fig:Curved2}
\end{figure}

\subsubsection{Type B (triangular) Memristors in 2-Memristor Circuits}

Figure~\ref{fig:Triangular2} shows the results from the constructions of circuits 2,3,4 and 5 with filamentary memristors. These circuits show a richer behaviour with the emergence of oscillatory type behaviour in circuits 2, 3 and 5. Filamentary memristors in series (circuit 3) do not act like single memristors. Also, in some cases the filaments partially connect, as seen in figure~\ref{fig:Triangular2}c.

\begin{figure}[!tbp]
\centering
\subfigure[]{\includegraphics[width=0.49\textwidth]{151012MT2p1.png}}
\subfigure[]{\includegraphics[width=0.49\textwidth]{151012MT3p1.png}}
\subfigure[]{\includegraphics[width=0.49\textwidth]{241012MTR4p1.png}}
\subfigure[]{\includegraphics[width=0.49\textwidth]{241012MTR5p2.png}}
\caption{Results for type B (triangular) memristors: a. Two B memristors in anti-series, circuit 2 in table~1, b. Two B memristors in series, circuit 3 in table~1, c. Two B memristors in anti-parallel, circuit 4 in table~1 and d. two B memristors in parallel, circuit 5 in table~1}
\label{fig:Triangular2}
\end{figure}

\subsection{Three Memristor Circuits}

\subsubsection{Type B (triangular) 3-memristor circuits}

Typical results for this circuit 7 are given in figure~\ref{fig:101012MT1p1}a. Comparing this with the expected curve in figure~\ref{fig:single_mem_new}a for one memristor shown in figure~\ref{fig:single_mem_new}a, we can see differences. The large spike at the start has vanished, as has the one at the end. We see oscillations in the baseline interrupted by spontaneous spiking. Figure~\ref{fig:MT1p4} shows a later run where we see sections of oscillations of different frequencies. The experiment was repeated to see if there was a repetition in the spiking pattern and thus if the circuits were following long-term periodic dynamics, this was not observed.

\begin{figure}[htbp!]
 \centering
 \includegraphics[bb=0 0 576 432,scale=0.5,keepaspectratio=true]{101012MT1p4.png}

 \caption{Another typical $I-t$ profile for circuit 6 in table ~\ref{tab:TestCircuits}.}
 \label{fig:MT1p4}
\end{figure}

We attempted to effect this oscillation by driving it with a very slow a.c. voltage, the results of which are shown in figure~\ref{fig:MT2} (the whole data is for one period). This does not show the expected response for a single memristor (a lagged and distorted sinusoidal current), or any change in the `baseline' as a result of the changing voltage, however the auto-correlation data looks more harmonic than for the constant voltage (figure not included).

\begin{figure}[htbp!]
 \centering
 \includegraphics[bb=0 0 576 432, scale=0.5]{101012MT1p2.png}

 \caption{An I-t curve for a very slow sinusoidally varying voltage.}
 \label{fig:MT2}
\end{figure}

\clearpage

\subsection{Type A 3-memristor Circuits}

Do these results mean that multi-memristor circuits do not combine as expected? Not necessarily. We decided to repeat the tests with three type A memristors which are closer to theoretical Chua memristors (type A from paper~\cite{260}). We specifically chose three memristors that had similar looking $I-V$ curves that operated over a similar current range (i.e. were in a similar starting state) to try and decrease the compositional complexity of the circuit.

Figure~\ref{fig:181012MTD7p2t}a shows the same memristors wired up in circuit 7, and the output current looks like a single memristor. For the type A memristors, we found that three memristors in a circuit wired up with the same polarity (i.e. as in circuit 6) behave qualitatively just like one memristor. For circuit 6, we also sometimes see occasional switching events with a decay, see figure~\ref{fig:181012MTD6p1}b which are designated as `w'. The shapes of these individual curves are similar to the overall spike curve seen when the voltage switches, which strongly suggests that the more event-rich behaviour seen in the other memristor systems are to do with interacting switching spikes.

\begin{figure}[!tbp]
\centering
\subfigure[]{\includegraphics[width=0.49\textwidth]{181012MTD7p2.png}}
\subfigure[]{\includegraphics[width=0.49\textwidth]{181012MTD6p1.png}}
\caption{Results for type A memristors: a. three memristors in circuit 7, b. three memristors in circuit 6}
\label{fig:Curved3}
\end{figure}

\section{Statistical Analysis}

\subsection{Ideal Networks}

The periodograms for these data show increasing power with period because the data lacks short time oscillations (other than noise) and has a maximum power at the longer periods because the data is not oscillatory (the long time oscillations are an artifact of the finite experiment length), see figure~\ref{fig:181012MTD7p2t}b. The auto-correlation plots, see for example~\ref{fig:181012MTD7p2t}c, show an exponential drop off far from zero, this is strong auto-correlation indicating that there is a trend present in the data, which is identified as the underlying decay of the current transient. The PACF plots show the minimum persistence of the data (i.e. the order of auto-regression (AR) function required to fit the data), which tails off quickly because this is a simple trend, see figure~\ref{fig:181012MTD7p2t}d which only needs an auto-regression function of order 4 or 5 to encapsulate its dynamics. The presence of spikes in the data causes longer-time persistence in the PACF, the average AR order for `1'
dynamics was 4.5 compared to 27.6 for `1s' dynamics. Figure~\ref{fig:181012MTD6p1} shows that the presence of  switching effects in ~\ref{fig:181012MTD6p1}a leads to a combined series of exponentials in the ACF (figure~\ref{fig:181012MTD6p1}c) instead of a smooth single exponential, indicating that there are several half-lives in the data due to the switches.

\subsection{Filaments}

When the filaments in the memristors connected, as in figure~\ref{fig:Triangular2}c, the ACF was a straight line with a very low PACF (of AR(1) for the data in figure~\ref{fig:Triangular2}c) indicating that the data can be modelled as an ohmic filament. This is the case when the memristors connected or almost connected, rendering the circuit essentially a resistor.

\subsection{Spiking Networks}

The periodograms show a different distribution compared to the ideal networks, they have a large number of low period frequencies  and the power envelope does not increase with increasing period, see for example~\ref{fig:101012MT1p1}b,~\ref{fig:151012MT1p5}b and \ref{fig:241012MTR5p2}b. In some cases the oscillation frequencies can be picked out, but because the system changes over the course of the experiment, the periodograms are not straightforward. The ACF plots help us to understand the dynamics. If there is a periodicity in the ACF (likely one of several interacting frequencies, some examples are figures~\ref{fig:221012MTR6p3}c and \ref{fig:241012MTR5p2}c) then we have sinusoidal waveforms in the data. The decay envelope of these waveforms is a measure of the noise in the system as a proportion of the oscillations and are most clearly visible in the simpler `SpO' results. The ACF patterns are different for these data compared to the single memristor-like data, making the ACF technique a useful
diagnostic tool. The PACF tends to have higher minimum frequencies for the spiking data, indicating that these data are more complex than the single memristor-like data (as they seemed to be by eye). On average, the spiking networks have a higher AR function order than the ideal networks (29.5 and 30.33 for `Sp' and `SpO'), due to spike correlations introducing a larger persistence in the data. The number of significant ACF lags is smaller for the spiking networks (14 and 5.17 for `Sp' and `SpO' respectively) than for the ideal networks (which are over 50) because there is no background trend in the spiking network data.

\subsection{Is the Spiking Caused by Random Processes?}

For all the data presented in table 2 the Ljung-Box Q-test found that data was non-random, with the exceptions of the experiments in figure~\ref{fig:101012MT1p1} (circuit 7), figure~\ref{fig:241012MTR5p2}  (circuit 5) and circuit 8 (graph not shown), which had respectively 15/50, 18/50 and 25/50 auto-correlation lags might have been the result of a random process. Inspection of the $I$-$t$ curves at these points showed that it was the low power oscillations which were sufficiently noisy that we can not say with 95\% certainty that they are not the result of random processes (and, of course, we expect noise in our experimental system). This does show that although we cannot entirely rule out noisy processes as the cause of our observations we can be reasonably certain that most of the observed oscillations and spikes are not noise. Thus, the high minimum order AR requirement suggests the increased persistence discussed above is due to spike correlation and not a random effect.

\subsection{Circuit (Compositional) `Complexity'}

From the data in table 2 we can draw some conclusions about the circuits. As discussed in the previous section, type A memristors are closer to the Chua memristor and thus act more like an ideal system, in that combinations of them are more likely to behave like a single memristor. All the observed ideal network behaviour came from A type memristors, and accounted for 6/10 A type circuits. Similarly, all the `fi' dynamics (those which are thought to be the result of filament connecting) arose in circuits made with B type memristors (accounting for 3/12 B type circuits) which is expected as these are filamentary memristors.

The number of significant ACF lags increases in both A and B type circuits with an increasing number of memristors. For 2 memristors, A circuits have an mean of 18.5, B circuits 14.75, overall 16.62. For 3 memristors, A circuits have a mean of over 50, B circuits have 22.875, which is because 2 memristors are overwhelmingly likely to be `1s' type dynamics. Concentrating on just the spiking networks, we have an increase in mean significant number of lags from 3 for the 2-memristor networks to 14 for the 3 memristor networks, suggesting that 3 memristor circuits have a significantly higher persistence. The order of AR function is 22.8 for 2-memristor circuits and 33.4 for 3-memristor circuits (due to the fact that `Sp' dynamics are more complex and observed more often in 3-memristor circuits), quantifying the amount that 3-memristor circuit dynamics are more complex (which accords with what was observed). This demonstrates that the ACF and PACF data does seem to identify and quantify the concept of `complexity'
 in the circuit dynamics.

We can perhaps take this complexity analysis further. Table 1 includes a count of the anti-series and anti-parallel interactions as an attempt to roughly quantify the compositional `complexity' of the circuit (where we are taking complexity to mean the likelihood of rich dynamics and not a measure of the time taken to solve a problem). For the 12 spiking data sets, the mean PACF for the circuits can be used to order them based on the complexity of the dynamics. The three circuits with 0 interactions (i.e. all the memristors are wired up with the same polarity), numbers 3 and 5 have mean minimum AR functions of order 28.5 and 21.5 respectively. Circuit 6 with 3 interacting memristors of the same polarity do not generate spikes, perhaps due to the 3 memristor networks stabilising any arising fluctuations the way that anti-polarity networks propagate them. The circuit with 1 anti-series interaction requires an AR of order 14, and the circuit with 1 anti-parallel interaction requires an AR of order 45, agreeing
with our expectation that anti-parallel circuits would give richer behaviour than anti-series. In the three-memristor circuits, as we suspected, having 2 different types of anti-polarity interaction (i.e. anti-series and anti-parallel) gives richer dynamics requiring an AR of order 40.33 to fit, whereas a circuit with 2 anti-parallel (circuit 8) required 29.6 (however, this was a smaller set so this result has a larger standard error).

It is interesting to compare this work to our ongoing work with neural cell cultures connected to single memristors~\cite{Ne0}. In those experiments, the single memristor underwent `1', `w' and `1s' type behaviour, demonstrating that this behaviour can arise from single memristor circuits. These experiments also showed noisy spiking that is believed to be from the spiking of the neural cells due to its different character (oscillations and bursting spikes were not observed in the joint system). A more-thorough analysis of those $I$-$t$ curves using the techniques presented here will be forthcoming.

\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
Experiment 	& Circuit 	& No. 		& Type 		& No. of 	& PACF 		& classi-\\
No.	 	& No. 		& of 		& of 		& significant	& minimum	& fication\\
		& 		& memristors	& memristors	& ACF lags	& AR order	&  \\
\hline
-- 		& 1		& 1		& A		& 43		& 5  		& 1\\
\hline
\hline
1 		& 2		& 2		& A		& $>$50		& 42 		& 1s\\
2 		&2		& 2		& B		& 5		& 14 		& SpO\\
\hline
3 		& 3		& 2		& A		& 3		& 46 		& SpO\\
4 		& 3		& 2		& B		& 2		& 11 		& Sp\\
\hline
5 		& 4		& 2		& A		& 16		& 45 		& SpO\\
6 		& 4		& 2		& B		& $>$50		& 2  		& fi\\
\hline
7 		& 5		& 2		& A		& 4		& 36 		& SpO\\
8 		& 5		& 2		& B		& 1		& 7	  	& SpO\\
\hline
\hline
9 		& 6		& 3		& A		& $>$50		& 37 		& 1s \\
10 		& 6		& 3		& A		& $>$50		& 5  		& w \\
11 		& 6		& 3		& A		& $>$50		& 4  		& 1s \\
12 		& 6		& 3		& B		& $>$50		& 1		& fi\\
13 		& 6		& 3		& B		& $>$50		& 50 		& fi\\
\hline
14 		& 7		& 3		& A		& 50		& 3		& w\\
15 		& 7		& 3		& A		& $>$50		& 5		& 1\\
16 		& 7		& 3		& A		& $>$50		& 4		& 1\\
17 		& 7		& 3		& B		& 6		& 31		& Sp\\
18 		& 7		& 3		& B		& 18		& 47		& Sp\\
19 		& 7		& 3		& B		& 13		& 33		& Sp\\
\hline
20 		& 8		& 3		& B		& 12		& 47 		& Sp\\
21 		& 8		& 3		& B		& 2		& 34 		& SpO\\
22 		& 8		& 3		& B		& 33		& 8 		& Sp\\
\hline
\end{tabular}
\caption{Results from repeats. Key :- `Sp': bursting spikes and oscillations; `SpO': Oscillations with single spikes; `fi': filament connecting; `1': single memristor-like; `1s' single memristor like with single spikes; `w': switching single memristor.}
\label{tab:2}
\end{table}

\section{Conclusion}

We have introduced the concept of compositional complexity as a way of comparing circuits' compositions -- to our knowledge there is not an existing framework to do this (although several rules of thumb are known). This work shows that the intuitive idea about which circuits are more complex does seem to work for predicting increased complexity in the circuit dynamics, and that partial autocorrelation function and autocorrelation function analyses are a useful tool in quantifying this complexity. Specifically, compositional complexity is a measure of the number of memristors and the interactions between them; for 2 memristors the most complex configuration is anti-parallel and in series the least complex.

The idea of compositional complexity was invented to find a metric to compare circuits with the same function -- beyond counting the number of components used or measuring the area of silicon covered, which are not fair comparisons for memristors because they can do more than other components~\cite{UCNC,Spc2} and one of their advantages is that they can be made much smaller. We would like to separate the gains caused by clever circuit design from those caused by technological progress shrinking of circuit size. Putting this idea on a more quantitative standing is an area of current research.

We have shown that anti-parallel interactions give rise to more complex behaviour, specifically an increased likelihood of spiking and more complex spiking dynamics. This result demonstrates that the chaotic dynamics seen in simulations~\cite{272} is likely due to the sub-circuit of two anti-parallel memristors rather than numerical effects. Our results could also suggest that the repeating spiking oscillations seen in the neuristor~\cite{252} may arise from the anti-parallel sub-circuit rather than the interaction between a memristor and a capacitor. We know from the data presented here that the spikes are not random, but further work will be required to demonstrate if they are chaotic in nature -- the fact that 3-memristor networks possess a richer, more persistent and quantitatively different dynamics to 2-memristor networks is suggestive (because chaotic systems need at least 3 state variables).

We have classified our results into two types: The first are ideal networks which show voltage-switch-related spikes followed by smooth exponential decay. The second are switching networks, which lack the voltage-switch-related spikes, have a flat baseline and possess both oscillatory dynamics and bursting spikes. The ideal networks combine like resistors, in that a network of only memristors addressed by their joint 1-port entry is indistinguishable from a single memristor. The spiking networks add up differently and emergent dynamics would make it easy to tell that there was more than one memristor present if we were given a `black-box' circuit and asked to identify whether it contained a single memristor or a network (this is the sort of thought experiment the phrase `addressed by their joint 1-port entry' describes~\cite{Chua1969}).

This work demonstrates some methods for controlling which behaviour is observed in a memristor network. To build an ideal network we require A-type or ideal memristors (i.e. described by the base memory-conservation theory~\cite{F0c} and Chua's constitutive relation~\cite{14}), a low degree of compositional complexity and a smaller network. To build a spiking network we can use either ideal or filamentary memristors (those which require the extension of a ohmic filament~\cite{254}, equivalently those which are memristive systems~\cite{84} with the state of filament as a second state variable), a network above 2 memristors (for the most stable dynamics) and a high degree of compositional complexity.

What is the underlying cause of the spikes? These spikes are widely observed in memristors and referred to as current transients -- which is a description of the dynamics rather than a cause -- the cause is often ascribed to capacitance in the circuit. These networks were tested without a capacitor in the system, but there is of course parasitic capacitance in the circuit and all real devices include some measure of capacitance, resistance and inductance. Furthermore our devices have aluminium electrodes which are known to act as sources and sinks of oxygen ions~\cite{178,175,177,189}, which can stabilise switching~\cite{229,159}, and can be thought of as a slow, ionic capacitor.

However, the spikes can be fitted by memristor theories (both ours~\cite{F0c} and others~\cite{15}, see~\cite{SpcJ}), which suggests that they may arise from the memristance in the devices. When it first appeared, the memristor was thought of as an a.c. device and the question of the d.c. response was not addressed. But obviously, the ions within the memristor will drift to voltage regardless of whether it is varying or constant (with the proviso that if the voltage varies too fast the ions won't have time to drift one way significantly before changing direction; this is covered by the part of the memristor definition which described the hysteresis as the frequency tends to infinity, see~\cite{14,279}). Thus, we expect there to be a time-varying response to a d.c. current, and the observed current spike is a compelling candidate. We have experimentally demonstrated that this spike shape is related to frequency-related hysteresis changes~\cite{hystc}, a fingerprint of the memristor. This fact is suggests that
the spikes are memristance, but the question is not conclusively answered yet.

We shall now take the position that the spikes are mainly due to memristance rather than capacitance. A question of interest is then why does the current equilibrate (which we know it does, see~\cite{SpcJ}). This is something we are investigating and early results are presented in~\cite{Spc2}. Due to this behaviour, we have called the spikes the short-term memory of the memristor and have shown that single memristor-like spikes can interact within this time window and can be used to implement logic gates and perform addition~\cite{UCNC,Spc2}. If the memristors in the circuit possess a short-term memory it could explain the cause of the emergent behaviour. As these tests were done across the whole circuit, we do not know how the state of the 3 memristors vary individually. If there is a time delay in a voltage induced current spike propagating across the network, then the response of a memristor to it would change the voltage across another memristor by $\Delta V$. If these changes are not synchronised then
these small $\Delta V$'s can move around the network causing the individual memristors to spike and propagate a different $\Delta V$ (remember the entire network is subject to a constant voltage so any small change in resistance on one memristor will affect the voltage across the others): this situation is called the `roving $\Delta$V in~\cite{Mu0}. This idea explains the loss of the voltage spikes seen in ideal networks, the dampening of the slow a.c. voltage in figure~\ref{fig:MT2} and the sudden emergence of bursting spikes from an almost flat baseline that has been observed (see the control in~\cite{Ne0}). Thus, the oscillations can then be explained as the `ringing' of the network that results from constructive and destructive interactions of spikes as a $\Delta V$ is passed around and the bursting spikes would occur when the spikes constructively interact. If the bursting spikes were caused by this sort of system, it would explain the correlation between spikes that has been observed. Finally, the $\
Delta V$ would be more likely to become unsynchronised if the network contained more sources of delay and difference, such as those which would be caused by introducing more memristors, more  types of memristors, mismatched polarity, increased numbers of junctions, and so on. These sources of delay match the conditions under which spiking networks were observed as reported in this paper. This is an explanation, however it is not the only one we have entertained. Another explanation is to think of the boundary between on and off resistance material as continually oscillating slightly around equilibrium as ions diffuse around: it could be the interaction of these individual oscillators that causes the oscillations in the network (as is seen in other systems our group studies such as \textit{Physarum}~\cite{AndysBook} and BZ reactions~\cite{336,337}) and the interaction of oscillations could cause the bursting spikes.

Are these spiking networks brainlike? In this paper we have shown unexpected, emergent properties arise from simple memristor-only networks, which has not been observed before. The pattern of bursting spikes seen is qualitatively similar to those observed in neural bursting spikes. Brainwaves are oscillatory behaviour that are observed and known to be correlated to neural action, but their exact cause is not known. They are thought to be related to the neural voltage spikes (which can encode and process information) and perhaps arise from interaction of these spikes in the network, but there are unanswered questions: which spike patterns give rise to which frequencies; which aspects of the neural network causes which aspects of the dynamics and whether they are an observable side-effect of information processing that arises from the material or actually transmitting useful information. Memristors are similar to braincells because they also involve ionic conduction, have a short-term memory and possess
spiking dynamics; they differ in that (our) memristors give current spikes in response to voltage whereas the brain operates on (ionic) current-controlled voltage spikes and memristors are simpler in operation than the components of the brain. Thus, memristor networks can act as a simplified hardware model for the brain and we expect that constructing memristor networks can help us understand where electro-physiological phenomena such as brainwaves come from and what they might mean.

The future directions for this work have been described above, but what are the future uses for this technology? If memristor spiking networks are operating under similar principles to those in the brain, it is possible that spiking memristor circuitry would be a good choice for brain-machine interfaces for use in neural prosthetics, disease treatment (e.g. a memristor-based circuit breaker for epileptic fits or a spike generator for dealing with the symptoms of Parkinson's disease) and functionalised prosthetics (i.e. artificial limbs that interface directly with the nervous system). We have started to investigate this by combining spiking memristor networks with spiking neural cell culture to see if the two spiking networks can influence each other electrically~\cite{Ne0}. Another area of interest is biomimetic robotics where spiking memristor networks could be used to process sensory inputs and control a robot. Finally, a spiking memristor computer may be capable of a more bio-inspired approach to
computing and could prove better at task at which biology excels, such as pattern recognition, fuzzy processing, learning and so on.

\begin{figure}[!tbp]
    \centering
    \subfigure[]{\includegraphics[width=0.49\textwidth]{single_mem_newITPlot.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{single_mem_newPeriodogram.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{single_mem_newACFPlot.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{single_mem_newPACFPlot.pdf}}
    \caption{Single memristor data: a: $I-t$ curve in response to constant voltage; b: a periodogram; c: autocorrelation function; d: partial autocorrelation function. The ACF shows an exponential trend, the PACF shows this is a simple system.}
    \label{fig:single_mem_new}
\end{figure}

\begin{figure}[!tbp]
    \centering
    \subfigure[]{\includegraphics[width=0.49\textwidth]{181012MTD7p2tITPlot.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{181012MTD7p2tPeriodogramPlot.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{181012MTD7p2tACFPlot.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{181012MTD7p2tPACFPlot.pdf}}
    \caption{Ideal network example of 3 A-type memristors in circuit 6: a: $I-t$ curve in response to constant voltage; b: a periodogram; c: autocorrelation function; d: partial autocorrelation function. This demonstrates that using similar memristors with low circuit complexity gives the expected result.}
    \label{fig:181012MTD7p2t}
\end{figure}

\begin{figure}[!tbp]
    \centering
    \subfigure[]{\includegraphics[width=0.49\textwidth]{181012MTD6p1ITPlot.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{181012MTD6p1PeriodogramPlot.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{181012MTD6p1ACFPlot.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{181012MTD6p1PACFPlot.pdf}}
    \caption{Switching network type (`w') network example using 3 A-type memristors in circuit 7: a: $I-t$ curve in response to constant voltage; b: a periodogram; c: autocorrelation function; d: partial autocorrelation function. This demonstrates that increasing compositional complexity can move an ideal network away from ideality.}
    \label{fig:181012MTD6p1}
\end{figure}

\begin{figure}[!tbp]
    \centering
    \subfigure[]{\includegraphics[width=0.49\textwidth]{151012MT1p5ITPlot.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{151012MT1p5PeriodogramPlot.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{151012MT1p5ACFPlot.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{151012MT1p5PACFPlot.pdf}}
    \caption{Spiking network type `SpO' example from B type memristors in circuit 7: a: $I-t$ curve in response to constant voltage; b: a periodogram; c: autocorrelation function; d: partial autocorrelation function. The ACF clearly shows the interaction of several oscillations and a dampening directly after the single peak, the PACF shows we need an AR of order 7 to fit the data. }
    \label{fig:151012MT1p5}
\end{figure}

\begin{figure}[!tbp]
    \centering
    \subfigure[]{\includegraphics[width=0.49\textwidth]{241012MTR5p2ITPlot.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{241012MTR5p2PeriodogramPlot.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{241012MTR5p2ACFPlot.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{241012MTR5p2PACFPlot.pdf}}
    \caption{Spiking network type `SpO' example for B-type memristors in circuit 5: a: $I-t$ curve in response to constant voltage; b: a periodogram; c: autocorrelation function; d: partial autocorrelation function. The ACF clearly shows the interaction of several oscillations and a dampening directly after the single peak, the PACF shows we need an AR of order 7 to fit the data. }
    \label{fig:241012MTR5p2}
\end{figure}

\begin{figure}[!tbp]
    \centering
    \subfigure[]{\includegraphics[width=0.49\textwidth]{101012MT1p1ITPlot.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{101012MT1p1PeriodogramPlot.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{101012MT1p1ACFPlot.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{101012MT1p1PACFPlot.pdf}}
    \caption{Spiking network type `Sp' example of 3 B type memristors in circuit 7. : a: $I-t$ curve in response to constant voltage; b: a periodogram; c: autocorrelation function; d: partial autocorrelation function. We see groups of bursting spikes and underlying oscillations.}
    \label{fig:101012MT1p1}
\end{figure}

\begin{figure}[!tbp]
    \centering
    \subfigure[]{\includegraphics[width=0.49\textwidth]{221012MTR6p3ITPlot.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{221012MTR6p3PeriodogramPlot.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{221012MTR6p3ACFPlot.pdf}}
    \subfigure[]{\includegraphics[width=0.49\textwidth]{221012MTR6p3PACFPlot.pdf}}
    \caption{An example of an oscillatory spiking `SpO' network: a: $I-t$ curve in response to constant voltage; b: a periodogram; c: autocorrelation function; d: partial autocorrelation function. The ACF shows the type of pattern expected for a small number of interacting sinusoids.}
    \label{fig:221012MTR6p3}
\end{figure}

\bibliographystyle{unsrt}

redpajama_set_name: RedPajamaArXiv


text:
Experts: Miami Beach Property Values May Fall As Sea Levels Rise
Energy Efficiency & Sustainability, Industrial, Industry News, Multifamily, Office, Property Management, Retail
Parts of Miami Beach could be inundated with flood waters in as little as 15 years, and property values may slide amid the rising tide, according to nearly two dozen university heads and climate change experts who were on hand to answer questions on the effects of sea-level rise on South Florida during a Miami Beach Chamber of Commerce event at the W Hotel.
Flooding in Miami Beach
The purpose of the recent event, organized by land use and environmental attorney Wayne Pathman, was to warn business owners, developers, and contractors that the effects of sea-level rise will be impacting the property values fairly soon. Already, media around the globe are publicizing the fact that South Florida is "ground zero" for the adverse economic impact of sea-level rise, Pathman argued. Unfortunately, the region is still behind in preparing its infrastructure for the future.
"All eyes are upon us and South Florida isn't ready," said Pathman, co-founder of the Downtown Miami-based law firm of Pathman Lewis LLP and future chairman of the Miami Beach Chamber of Commerce.
Thanks to a slowing gulfstream, warming oceans, and ice flows submerging beneath the ocean from Greenland and Antarctica, the oceans are rising faster than ever, said Keren Bolter, research coordinator for Florida Atlantic University Center for the Environmental Studies. This has caused an increase in flooding events in recent years and it will only get worse. By 2100, the oceans are projected to increase by seven feet, Bolter added. At that level, The Keys, along with large chunks of Miami-Dade and Broward counties, will be inundated with sea water at high tide, destroying fresh water reserves, compromising underground sewage lines and septic tanks, and creating a host of other problems.
But you don't have to wait 84-years to see the adverse effects of sea-level rise. Bolter said that in as little as 15 years, flooding in Belle Isle will grow much worse, especially at Island Terrace, a 16-story condo built in 1967. "It's coming up not just at the sides," she said while showing Lidar maps depicting future sea-level rise at Island Terrace and Belle Isle.
"It comes up from underground. That's partly because the limestone that South Florida land is predominately made of us is extremely porous. Because of this, not even sea walls will stop the flow of water," Bolter said. "By 2060 the oceans are projected to rise by two feet. At that level, "the western half of Miami Beach is under water."
"As the oceans rise, the cost of insurance will skyrocket," Pathman said. "Meanwhile, in an attempt to cope with the new reality, community leaders will raise taxes while property taxes are declining. As for the infrastructure of future residential and commercial projects, Miami Mayor Tomas Regalado recently declared on a radio show that the financial burden will fall on developers. However, at least some of the negative impacts of sea level rise can be mitigated if the business community takes a leadership role now. Many places around the world have already started adapting."
Among the invited guests at the chamber event were Florida International University President Mark Rosenberg, Florida Atlantic University President John Kelly, and University of Miami's Rosenstiel School of Marine and Atmospheric Science Dean Roni Avissar. They argued that their respective colleges are already training scientists and engineers who are not only studying the future effects of climate change, but also figuring out solutions on how communities like South Florida can adapt.
"We are very fortunate that we have a strong university system and a strong system of public education," argued Matthew Welker, principal of MAST Academy at Florida International University's Biscayne Bay campus. "That's a very valuable resource."
Josh Sawislak, global director of resilience for the Los Angeles-based engineering firm AECOM, said Miami could even replace Amsterdam as the true innovator of anti-flooding solutions.
"The brand can be, 'This is a resilient city… Don't go to Amsterdam to see how to prevent from being cut off by the sea, although they've got tasty cheeses. Come to Miami and see how to live with water,'" Sawislak declared.
One innovative idea has already been hatched in Miami. Rather than fight sea level rise, Bolter of FAU pointed out that "one student from the University of Miami" came up with the idea of simply making western Miami Beach "floodable" with the creation of new bays and living shorelines along with new boardwalks and flood-adapted buildings. (The UM student in question who developed that plan is Isaac Stein, who now works for the urban planning and landscape firm West 8.)
Besides speeches from experts, the event included an hour-long breakout session where business leaders sat at tables and asked questions to the assembled experts, some of whom flew in from other parts of the country to be there. The media, however, was ushered away from the session. Upon hearing that reporters were even present at the event, Donald Kipnis, founder and CEO of Brickell-based Development Service Solutions, walked out. Dozens of other chamber members left before the session even ended.
Harold Wanless, chair of the Department of Geological Sciences at the University of Miami, didn't think the breakout session was long enough. Experts barely had 10 minutes to answer business leaders' questions or lay out what needs to be done.
"We need to be planning, that is the bottom line," said Wanless, who has long studied past sea-level rise events in Florida.
Following the breakout session, Jessica Goldman Srebnick, CEO of Goldman Properties, applauded the panel's efforts. She also urged some restraint. Showing slides that show Miami Beach being submerged is what "gets picked up by the news."
"We have to be very… strategic about how we discuss the reality of sea level rise," Goldman said.
Pathman said the purpose of the event was just to "whet everyone's appetite." On September 14, the chamber plans to hold a roundtable discussion with "leading political and civic leaders about current and future strategies for sea level rise in South Florida" at a location to be announced.
April 11, 2016 /by ADMIN
https://ven-americanre.com/wp-content/uploads/2016/04/Rising-Sea-Levels_2.jpg 600 1600 ADMIN https://dev.ven-americanre.com/wp-content/uploads/2021/03/ven-american-logo-header-060805-181-114.png ADMIN2016-04-11 18:47:452016-04-11 18:47:45Experts: Miami Beach Property Values May Fall As Sea Levels Rise
How Climate Change Is Fueling the Miami Real Estate Boom
As a city sitting virtually at sea level, Miami has been called ground zero for the problems posed by climate change, a place where rising sea levels threaten its future existence.
The latest forecast of sea level rise from the Intergovernmental Panel on Climate Change, for example, predicts that by later this century, global sea levels will be two feet higher than they are today, quite possibly higher. Under that scenario, the nuisance flooding in Miami that periodically comes with high tides will be a daily affair, the storm surge impact of hurricanes will be amplified, and lower-lying areas of the city will be uninhabitable. That's actually not the worst of it: Under higher sea levels, the Biscayne Aquifer—where southeast Florida draws its drinking water—will increasingly suffer from saltwater intrusion, a problem for which there is no foreseen solution other than the investment of billions of dollars in water treatment facilities.
As bleak as this future would seem to be, few with real skin in the game in Miami—residents, real estate investors, and companies—are backing away from long-term investment. Exhibit A: Miami has been undergoing a nearly unprecedented surge in real estate construction, with planning discussions centering less on who will leave first and more on how high new projects can be built. Among the projects under way, for example, is an 80-plus-story behemoth in Brickell Center, the city's urban core. If Miami is on the verge of being a modern-day Atlantis, those who would have the most to lose are apparently not buying it.
Why this apparent deafness to the dire warnings? Well, here's a paradox. If one talks to developers and city commissioners in the area, it's hard to find evidence of overt denial of current and future risk; Miami was a city, after all, almost completely destroyed by a hurricane in 1926, and most concede that a recurrence is a matter of when, not whether. Likewise, few deny that the city's unique geography makes it vulnerable to the effects of rising sea levels. It's a long-term problem that the planning commissions of Miami and Miami Beach acknowledge exists and threatens to get worse.
Where locals disagree with outsiders, however, is about how best to deal with the problem. Rather than sounding alarms and cutting back on development, there's an implicit sense that the best approach may be, ironically, to do the opposite. And while a strong case can be made that this behavior has no rational basis, it may represent Miami's best long-term hope for dealing with the threats posed by climate change, one that other cities might be advised to mimic: The best strategy, in fact, may be to foster a collective belief that there's no threat—or at least not one serious enough to lose sleep over.
Before an explanation why, let's first address the two standard explanations for the building boom, explanations that are indeed part of the puzzle. The first is that real estate developers, by their nature, are gamblers with short planning horizons. In the late 2000s, the real estate and equities crash quickly wiped out many builders. One might assume that would have made them skittish. To the contrary, the quick recovery that followed taught most that big risks are worth taking, and are survivable. While developers today may concede that sea levels are rising, it's a risk that lies well beyond their investment horizons, and in any case is dwarfed by the more immediate risk of a returning recession.
The second explanation is that many of the buyers for all the new condo units are cash investors from Latin America, and the risks of Miami real estate—overdevelopment, speculation, environmental unsustainability—remain small relative to similar investments back home. No one is saying that real estate isn't risky in Miami, or that sea level rise is fiction. What they are saying is that all investment carries risk, and development there is a bet they're prepared to take.
But there's another rational reason why even risk-averse residents in South Florida might, paradoxically, hope that buyers and sellers remain collectively naïve, or at least act as if they are, about the risks of sea level rise. South Florida relies almost exclusively on real estate taxes to fund public infrastructure. If the threat (or reality) of sea level rise suppresses property valuations, there will be less public money to address the risk. As an illustration, the head of public works for Miami Beach recently argued that the city would be wise to accelerate its investments in storm water drainage improvements ($100 million now and $400 million planned) simply because the city has the tax base to afford it—something it could not necessarily count on in the future.
Because buyers and sellers in Miami Beach have yet to connect the dots between nuisance flood events and the future consequences of sea level rise, property buyers continue to be drawn to the area, and development projects continue unabated—both of which are essential for a continued healthy tax base. If and when buyers and sellers do connect the dots, everything changes: Doing so could spark a rapid downward wealth spiral that, once initiated, would be difficult to reverse. Lowering property valuations would reduce the city's tax revenue which, in turn, would leave it with less money to shore up the city against sea level rise. The city would then be forced to choose between two losing remedies: increase taxes on those who choose to stay, or decline to make the needed improvements. Both, of course, would only exacerbate the problem. Miami's best move at that point would be to go hat in hand to the state and federal government for a bailout, but that seems unlikely. Quite aside from the "I-told-you-so" reactions that such pleas might evoke, almost all coastal communities would be facing similar problems and asking for commensurate help. Miami Beach as we know it now could cease to exist long before the Atlantic reclaims Collins Avenue.
Given this, South Florida's best shot at coping with the long-term environmental threat may be a strategy that no doubt seems perverse to environmentalists: aggressively foster a collective belief that sea level rise is not something we urgently need to worry about. South Florida is potentially facing a huge adaptation bill down the road, and paying for it will require a healthy tax base. Keeping that tax base flush depends on a cooperative equilibrium where buyers and sellers maintain an optimistic view that it's tomorrow's problem, one that will be easily tackled when the time comes. This keeps the coffers filled and provides the resources needed to pay for the engineering adaptations required to keep the game going.
In this light, Miami's construction cranes aren't monuments to climate change denial. Quite to the contrary—they're the instruments that may, indirectly, allow the city to survive global warming. Controlled ignorance, in some cases, can be a good thing.
Source: Bloomberg Businessweek
October 20, 2014 /by ADMIN
https://ven-americanre.com/wp-content/uploads/2014/10/How-Climate-Change-Is-Fueling-the-Miami-Real-Estate-Boom.jpg 600 1600 ADMIN https://dev.ven-americanre.com/wp-content/uploads/2021/03/ven-american-logo-header-060805-181-114.png ADMIN2014-10-20 21:26:192014-10-20 21:26:19How Climate Change Is Fueling the Miami Real Estate Boom
Property Insurance Rates Fall Up To 20% For Those With Low Catastrophe Risks
Industrial, Industry News, Office, Property Management
Primary commercial property insurance is a buyers' market with rate decreases of up to 20% for many U.S. accounts that renewed at midyear, with the exception being those with high catastrophe exposures.
Limited catastrophe losses and an influx of insurance capacity exerted considerable pressure on prices, brokers and market experts say. "The market is very insurance buyer-friendly right now," said Duncan Ellis, U.S property practice leader at Marsh USA Inc. "Purchasers of property insurance are finding a very favorable environment toward pricing, toward capacity and toward interest in their risks."
Premium decreases should be "on an average basis, probably high single digits to low double-digit percentage decreases," Mr. Ellis said. Some accounts could see rates fall up to 20% "based upon good solid competition in the marketplace."
"For 2014, we are definitely in a rate decrease environment," said David Finnis, Atlanta-based national property practice leader at Willis North America Inc. Willis clients saw property rates fall 7.5% to 12% through June 30, he said.
However, catastrophe-exposed accounts will find a somewhat "less friendly" market than those without catastrophe exposure, Mr. Finnis said. "As one would expect, premiums are still higher in high-catastrophe areas like Florida and California vs. noncatastrophe areas like the Midwest," he said.
Stewart Ellenberg, risk manager for the city of Boulder, Colorado said the city was fortunate to renew with a "slight" rate increase despite a large property insurance claim related to the September 2013 flood in the region. Likewise, Union County, North Carolina renewed its commercial property coverage for a 2% price increase, but deductibles for flooding and earthquake each doubled to $50,000, said Tiffany Allen, the county's risk manager.
Looking ahead, about the only thing that could turn the market would be a major hurricane or other disaster. "If there are no catastrophe events, we predict that you're going to be looking at double-digit decreases for the remainder of the year," said Al Tobin, New York-based managing principal of Aon Risk Solutions' property practice. "Double-digit decreases will continue," approaching 20% for some accounts, he said.
"There's just so much capacity in the property market right now, between incumbent insurers wanting to increase their lines or new underwriters trying to get on to the accounts," Mr. Finnis said.
Mr. Tobin said, "What's driving the market as much as anything is increased appetite among the top 10 catastrophe property carriers."
"The absence of major losses … would be the No. 1 market driver, because that's starting to attract capital," Mr. Ellis said. "When looking at the results for 2013 combined with what we have seen in 2014 thus far, property is looking like a solid bet right now and is thus why we are seeing a lot of money or capital flowing into the property space."
Analysts Agreed
"If you take a step back, it's how financial markets work," said Cliff Gallant, an analyst at Nomura Securities International Inc. in San Francisco. "There's been an area where profits have been pretty good in recent years relatively speaking and so capital is flowing there in different forms."
"I think where there's underwriting success, that attracts capital to those lines," said James Auden, managing director at Fitch Ratings Inc. in Chicago. "So if you have large underwriting gains in a segment, existing players put more capital into those lines."
Alternative capital flowing into the reinsurance space may reduce reinsurance pricing for primary insurers, but it has not significantly affected primary insurance prices. "Reinsurance is just one ingredient in the makeup of (primary insurance) costs," Mr. Tobin said.
Also, there is no broad lingering effect from Superstorm Sandy on property pricing this year, Mr. Tobin said. "Insurance companies are more acutely aware of deductibles and limits, but price has not been affected," he said.
"There is no Sandy hangover on pricing," Mr. Finnis said. "The only lingering result is that individual insurers are no longer providing $100 million in limits in the areas that were affected." Those policy limits now vary by account but usually range from $25 million to $50 million.
What's more, the uncertainty of congressional renewal of the federal terrorism insurance backstop thus far has not caused property pricing movement. The backstop will expire at the end of the year unless Congress renews it. Renewal legislation has been introduced in the House and Senate.
"There has not been any effect on (property) pricing and there is not likely to be because there is more supply," Mr. Tobin said of the federal terrorism program.
Souce: Business Insurance
July 7, 2014 /by ADMIN
https://ven-americanre.com/wp-content/uploads/2014/07/commercial-insurance1.jpg 599 1600 ADMIN https://dev.ven-americanre.com/wp-content/uploads/2021/03/ven-american-logo-header-060805-181-114.png ADMIN2014-07-07 21:33:492014-07-07 21:33:49Property Insurance Rates Fall Up To 20% For Those With Low Catastrophe Risks

redpajama_set_name: RedPajamaCommonCrawl


Milan and Barcelona's quarter-final is continuing to be contested away from the pitch. Rob Paton asks if that is a good or bad sign for the Rossoneri.
"To have 17 attempts at goal on a pitch like this carries much value. San Siro is very complicated, it doesn't breathe, there's no sun, it is always very bad, but that depends on those in charge. I imagine that Inter and Milan don't play on a field like that," offered Pep Guardiola in reaction to seeing his side fail to score for the first time in the Champions League for 28 months.
UEFA's director of competition assures Barcelona had no reason to lodge a complaint about Milan's San Siro pitch.
Milan have posted a brief statement defending the quality of San Siro's playing surface, after Barcelona launched an official protest.
Gokhan Inler has promised that Napoli will do everything in their power to secure third place this season, starting against Juventus.
The Azzurri travel north to Turin to take on the title-chasing Bianconeri three points adrift of Lazio in the third and final Champions League qualification position.
Inler, who helped Udinese to qualify for the competition last season before playing in it with Napoli this term, is keen that the Partenopei take something from Week 30's match, so as to maintain momentum.
Edinson Cavani is desperate to make an immediate return to the Champions League but insists it won't determine his future with Napoli.
Napoli currently sit one place and three points behind the final Champions League qualification spot in the League table, with nine games to go.
Milan will need to be perfect at the Camp Nou if they are to dump Barcelona out of the Champions League, says Stephan El Shaarawy.
The last eight tie is currently still at 0-0 after Wednesday night's draw at San Siro between the two European giants.
Milan showed they could close down Barcelona with a great defensive performance that needn't necessarily be 'defensive,' argues Susy Campanale.
When you are playing against the greatest team in the world, are missing your midfield filter, two quarters of your defence and the third quarter has a limp, you could be forgiven for expecting a thrashing. Instead, Milan's performance against Barcelona was an impressive display in how to defend without resorting to catenaccio.
The 0-0 result in this Champions League quarter-final means the Rossoneri can go through with a score draw at the Camp Nou next Tuesday.
The 0-0 first leg draw leaves qualification from the Champions League quarter-final wide open going into the Camp Nou next Tuesday.
Milan Vice-President Adriano Galliani feels "Barcelona are afraid of Milan," but reveals plans to revamp the San Siro turf with an artificial base.


text:
Q: Repeat DoubleAnimation I want to apply the following animation to my Window:
var ani = new DoubleAnimation(610, TimeSpan.FromSeconds(0.7));
BeginAnimation(Window.WidthProperty, ani);

The problem is that this animation works only the first time, the other times it has no effect.
Why? How can I fix this problem?

A: You have to specify the fromValue when you create the DoubleAnimation (as first argument).
var ani = new DoubleAnimation(ActualWidth, 610, TimeSpan.FromSeconds(0.7));

A: You can add the following code:
ani.RepeatBehavior = RepeatBehavior.Forever;

The animation will repeat itself once it is finished with RepeatBehavior set to Forever

redpajama_set_name: RedPajamaStackExchange


Solidarity With Immigrants Under Attack: Demonstrate!
the process of achieving permanent resident status.
Europe during the World War I era "Red Scare." Chinese were imported and then "excluded" by KKK-influenced immigration laws. Japanese Americans were illegally interned during World War II.
coming for the immigrants first. Just as the people of Denmark, under Nazi occupation, responded to a call from their king to put on yellow Jewish "Stars of David" when the Nazis ordered Danish Jews to where such yellow stars, people in the U.S. need to step up against this repression.
On January 10, let a thousand voices proclaim "I am Spartacus" to the demands of the new Empire, as the rebel slaves did to the demands of the Roman Empire.


text:
Q: One-To-Many-Relationship: How to sort data based on latest payments and based on their latest order ids I have an Online Store project written in Laravel 5.8 and I can properly get the latest orders like this:
$orders = Order::query()->latest('orders.created_at');

And at the Blade:
@forelse($orders as $order)
    <tr>
    ...
    </tr>
@empty
@endforelse

But now I need to sort data based on the latest payments.
I have also define One To Many relationship between these two Models:
Payment.php:
public function order()
    {
        return $this->belongsTo(Order::class, 'pay_ord_id', 'pay_id');
    }

Order.php:
public function payments()
    {
        return $this->hasMany(Payment::class, 'pay_ord_id', 'ord_id');
    }

Basically every payment has a record named pay_ord_id which is the Order Id.
And I need to sort data with these conditions:

1- Get latest orders
2- Go to the payments table and search data with the retrieved order ids
3- Sort data based on latest payments data

So how can I properly get the latest payments based on the latest order ids in this situation?

UPDATE:
$orders = Order::query()->latest('orders.created_at')->with([
            'payments' => function($query){
                $query->orderBy('created_at', 'DESC');
            }
        ])->get();

I just tried this but still shows latest orders!

A: forgive me that I don't know Laravelle but the software principles are the same everywhere. Wouldn't you be better getting the correct order in the query in the first place. I saw on this page that (like most other DB frameworks) Laravelle supports an 'order by' as part of the query.?

A: Did you try to use with() function to get all order payments
$orders = Order::orderBy('created_at', 'DESC')->with([
    'payments' => function($query){
        $query->orderBy('created_at', 'DESC');
    }
])->get();

A: You need to join the payments table first if you want to sort the orders by the latest payment:
$orders = Order::leftJoin('payments', function($join){
        $join->on('orders.ord_id', '=', 'payments.pay_ord_id')
            ->on('payments.pay_id', '=', \DB::raw("(SELECT pay_id from payments WHERE orders.ord_id = payments.pay_ord_id ORDER BY created_at DESC limit 1)"));
    })
    ->with([
        'payments' => function($query){
            $query->orderBy('created_at', 'DESC');
        }
    ])
    ->orderBy('payments.created_at', 'DESC')
    ->orderBy('orders.created_at', 'DESC')
    ->get();


text:
Presubmission Inquiries
PLOS Medicine publishes research and commentary of general interest with clear implications for patient care, public policy or clinical research agendas.
Why Publish with PLOS Medicine?
Perspective Perspectives are commissioned from an expert and discuss the clinical practice or public health implications of a published study. The original publication must be freely available online.
See all article types »
Why Do Cancer Cells Become "Addicted" to Oncogenic Epidermal Growth Factor Receptor?
Ingo Mellinghoff
https://doi.org/10.1371/journal.pmed.0040321
Citation: Mellinghoff I (2007) Why Do Cancer Cells Become "Addicted" to Oncogenic Epidermal Growth Factor Receptor? PLoS Med 4(10): e321. https://doi.org/10.1371/journal.pmed.0040321
Copyright: © 2007 Ingo Mellinghoff. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This article was supported in part by a Department of Defense Physician Scientist Research Award and the Brain Tumor Funders' Collaborative. The funder played no role in the preparation of the article.
Competing interests: The author has received a research grant from Johnson and Johnson.
Abbreviations: BIM, BCL2-interacting mediator of cell death; EGF, epidermal growth factor; EGFR, epidermal growth factor receptor; NSCLC, non-small cell lung cancer; TKI, tyrosine kinase inhibitor
Targeting the Epidermal Growth Factor Receptor Kinase in Cancer
During the last five years, kinase inhibitors have emerged as a promising new class of cancer therapeutics [1]. These drugs target enzymes that are often ubiquitously expressed within the human body, control a wide range of cellular responses, and are tightly regulated under physiological conditions [2]. Cancer cells can escape these normal restraints on kinase activity through mutations in kinase-encoding genes or genes regulating their function [3]. Even though tumorigenesis is a multistep process requiring multiple genetic aberrations, cancer cells can become so dependent on, or "addicted" to, a deregulated signaling pathway that blocking this signal results in cell death [4]. Oncogene addiction was first documented in genetically engineered mouse models of cancer [5–7] and subsequently proven in the clinic by the dramatic response of BCR-ABL–positive leukemias to the small molecule ABL-kinase inhibitor imatinib [8,9].
Among kinase candidates to be targeted in epithelial cancers, the epidermal growth factor receptor (EGFR) was one of the first choices [10] based on the evidence in human tumor samples for oncogenic EGFR activation through EGFR gene amplification, gain-of-function deletions in the EGFR extracellular domain, and coexpression of EGFR and its ligands [11]. EGFR-targeted therapeutics have been explored in a large number of human malignancies and have shown clinical activity in subsets of patients with non-small cell lung cancer (NSCLC), glioblastoma, squamous cell carcinomas of the head and neck, colorectal carcinoma, and certain other malignancies [12]. The identification of EGFR kinase domain mutations in patients with NSCLC, and the association of these mutations with clinical responses to EGFR tyrosine kinase inhibitors (TKI), constituted a landmark discovery for our understanding of EGFR-mediated oncogenesis [13–15].
Linked Research Articles
This Perspective discusses the following new studies published in PLoS Medicine:
▪ Gong Y, Somwar R, Politi K, Balak M, Chmielecki J, et al. (2007) Induction of BIM is essential for apoptosis triggered by EGFR kinase inhibitors in mutant EGFR-dependent lung adenocarcinomas. PLoS Med 4(10): e294. doi:10.1371/journal.pmed.0040294
Using a panel of human drug-sensitive EGFR mutant lung cancer cells, William Pao and colleagues show that induction of BIM, a member of the BCL2 family, is essential for apoptosis triggered by EGFR kinase inhibitors.
▪ Costa DB, Halmos B, Kumar A, Schumer ST, Huberman MS, et al. (2007) BIM mediates EGFR tyrosine kinase inhibitor-induced apoptosis in lung cancers with oncogenic EGFR mutations. PLoS Med 4(10): e315. doi:10.1371/journal.pmed.0040315
Susumo Kobayashi and colleagues provide evidence that the polypeptide BIM is involved in tyrosine kinase inhibitor (TKI)-induced apoptosis in sensitive EGFR-mutant cells and suggest that induction of BIM may have a role in the treatment of TKI-resistant tumors.
▪ Cragg MS, Kuroda J, Puthalakath H, Huang DCS, Strasser A (2007) Gefitinib-induced killing of NSCLC cell lines expressing mutant EGFR requires BIM and can be enhanced by BH3 mimetics. PLoS Med 4(10): e316. doi:10.1371/journal.pmed.0040316
Andreas Strasser and colleagues demonstrate that activation of the proapoptotic BH3-only protein BIM is essential for tumor cell killing and that shutdown of the EGFR–MEK–ERK signaling cascade is critical for BIM activation.
Exactly how inhibition of EGFR signaling results in the often dramatic tumor responses of EGFR-mutant lung tumors has remained an enigma. Three highly complementary studies published in this issue of PLoS Medicine address this important question and identify the proapoptotic molecule BIM (BCL2-interacting mediator of cell death, also called BCL2-like 11) as critical mediator of EGFR TKI-induced cell death in EGFR-driven cancer [16–18].
What the Three New Studies Show
To study the mechanisms of EGFR TKI-induced cell death, all three research teams took advantage of the large number of NSCLC cell lines that have been characterized in terms of their EGFR mutational status and cytotoxic response to the EGFR TKIs gefitinib and erlotinib: H3255, PC-9, and HCC827 cell lines showed the most dramatic apoptotic responses; H1975, A549, and H460 cells were resistant; and H1650 cells showed an intermediate response. Cell death in response to EGFR kinase inhibition featured cytochrome c release and activation of BAX and could be rescued by overexpression of BCL-xL, all consistent with activation of the mitochondrial "intrinsic" pathway of apoptosis.
Since activation of the intrinsic cell death pathway is governed by the balance between proapoptotic and antiapoptic BCL2 family members [19], the studies next looked for changes in the expression of BCL2 proteins that were most consistently correlated with the phenotype of EGFR TKI-induced apoptosis. Rapid dephosphorylation and increasing levels of the proapoptotic family member BIM, and in particular its splice variant BIMEL, was observed in all cell lines with a cytotoxic response. This correlation between BIM induction and EGFR TKI induced cell death was not limited to the in vitro environment as shown by Yixuang Gong and colleagues using two distinct transgenic mouse models of EGFR-driven lung cancer [16]. In contrast to the findings with BIM, changes in the expression of other "BH3-only proteins" (BAD, PUMA, and BMF), BAX family members (BAX and BAK), or antiapoptotic BCL2 family members (BCL2, BCL-xL, BCL-w, and MCL1) were not consistently associated with apoptosis.
Daniel Costa and colleagues further explored the relationship between BIM induction and EGFR TKI response in isogenic cell lines and showed that stable overexpression of a gefitinib-resistant EGFR allele (delE746-A750/T790M) in HCC827 cells markedly attenuated BIM induction and apoptosis in response to gefitinib [17]. BIM induction and apoptosis were restored in these cells when gefitinib was switched to the irreversible EGFR kinase inhibitor CL-387,785 which does inhibit the delE746-A750/T790M EGFR allele. Costa and colleagues also described a new EGFR mutation (L747S) in a tumor with acquired EGFR kinase inhibitor resistance and showed that IL3-independent Ba/F3 cells overexpressing this allele showed an intermediate degree of BIM1 induction and intermediate levels of apoptosis in response to gefitinib (i.e., relative to the hypersensitive L858R mutant and the resistant L858R/T790M mutant).
To prove the biological significance of BIM induction for EGFR TKI-induced cell death in EGFR-driven cells, all three groups demonstrated that RNA interference-mediated knockdown of BIM was sufficient to block or at least markedly attenuate the apoptotic response to EGFR kinase inhibition.
Using BH3 profiling [28] of mitochondria from EGFR mutant cells, Jing Deng et al. similarly identified BIM as critical effector of EGFR TKI induced cell death [29].
Using RNAi technology and cells from gene-targeted mice, Junya Kuroda et al. recently demonstrated that BIM (and to lesser degree BAD) play a critical role in imatinib-induced apoptosis of BCR-ABL–positive leukemia cells [20]. The current studies demonstrate that activation of the intrinsic cell death pathway through induction of BIM is also required to elicit an apoptotic response to EGFR TKIs in NSCLC cells harboring mutant EGFR. While this observation remains to be formally proven in patients, it should be emphasized that the preclinical models used in these studies have a strong track record of predicting drug response and drug resistance in patients with NSCLC and EGFR mutations [13,21–24]. The current studies thus raise the question whether mutations in the intrinsic cell death pathway provide a mechanism of resistance to EGFR TKIs and perhaps other kinase inhibitors.
The level of BIM induction in EGFR mutant preclinical models appears to correlate with the extent of apoptosis and is influenced by drug concentration, EGFR genotype, and other unknown cellular factors. Since BIM functions by inhibiting BCL2 and its antiapoptotic homologs [25], it is perhaps not surprising that a BH3-mimetic compound (ABT-737), which binds to BCL2, BCL-xL, and BCL-w, can synergize with low-dose EGFR kinase inhibitor therapy to induce apoptosis in EGFR-driven cells as shown by the Gong and Cragg groups [16,18]. This latter finding suggests that combining EGFR TKIs with a BH3-mimetic peptide might provide additional benefit over EGFR TKI monotherapy in tumors with questionable drug penetration or certain EGFR genotypes.
BCR-ABL: Fusion of the BCR (breakpoint cluster region) gene on Chromosome 22 to the ABL (Ableson leukemia virus) gene on Chromosome 9 is found in 95% of patients with chronic myeloid leukemia. The resultant fusion protein BCR-ABL is a constitutively active cytoplasmic tyrosine kinase and the molecular target of the small molecule kinase inhibitor imatinib (Gleevec).
The BCL2 protein network: The family of BCL2 (B cell lymphoma 2)-related proteins in mammals includes at least 20 members that compete with each other to promote or block cell death in response to various forms of stress. BCL2-related proteins are usually grouped into three distinct categories: the antiapoptotic proteins (BCL2, BCL-xL, MCL1, BCL-w, and A1/Bfl-1), the proapoptotic proteins (BAX and BAK), and the "BH3-only proteins" (BIM, BAD, PUMA, NOXA, BIK, and BID), which share a single motif (a BCL2-homology 3 [BH3] domain) with other BCL2 proteins. BH3-only proteins promote cell death by inhibiting antiapoptotic BCL2 family members, which themselves restrain BAX and BAK activity. BAX and BAK activation result in permeabilization of the mitochondrial membrane, cytochrome c release, and the activation of caspases, which execute the cell-suicide program. The BIM mRNA undergoes alternative splicing to give rise to the short, long, and extra-long protein variants (BIMS, BIML, and BIMEL).
Ba/F3 cells: Murine Ba/F3 pro-B lymphocytes provide a unique model system with which to examine kinase inhibitor sensitivity because stable expression of oncogenic kinases can relieve these cells from their intrinsic dependence on interleukin 3 (IL3) for survival. The Ba/F3 cells do not express endogenous EGFR and thus provide an opportunity to compare ectopically expressed EGFR mutants within the same cellular background.
The Gong and Cragg groups show that inhibitors of the EGFR effector kinase MEK1/2 phenocopy the effects of EGFR TKIs on BIM, but fail to trigger apoptosis [16,18]. These data suggest that BIM-independent EGFR signals are required to cause cell death in EGFR-mutant NSCLC cells. Additional studies are needed to identify these signals and determine how specific oncoproteins modulate BIM and other BCL2 family members at the transcriptional and post-translational level [26,27]. By identifying critical effectors of kinase inhibitor–induced cell death, the recent studies have brought us one big step closer to unraveling the mysteries of oncogene addiction.
Note Added in Proof
References 28 and 29 are cited out of order because they were added when the article was in proof.
1. Sawyers C (2004) Targeted cancer therapy. Nature 432: 294–297.
2. Schlessinger J (2003) Signal transduction. Autoinhibition control. Science 300: 750–752.
3. Blume-Jensen P, Hunter T (2001) Oncogenic kinase signalling. Nature 411: 355–365.
4. Evan GI (2006) Can't kick that oncogene habit. Cancer Cell 10: 345–347.
5. Chin L, Tam A, Pomerantz J, Wong M, Holash J, et al. (1999) Essential role for oncogenic Ras in tumour maintenance. Nature 400: 468–472.
6. Pelengaris S, Littlewood T, Khan M, Elia G, Evan G (1999) Reversible activation of c-Myc in skin: induction of a complex neoplastic phenotype by a single oncogenic lesion. Mol Cell 3: 565–577.
7. Felsher DW, Bishop JM (1999) Reversible tumorigenesis by MYC in hematopoietic lineages. Mol Cell 4: 199–207.
8. Druker BJ, Talpaz M, Resta DJ, Peng B, Buchdunger E, et al. (2001) Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia. N Engl J Med 344: 1031–1037.
9. Druker BJ, Sawyers CL, Kantarjian H, Resta DJ, Reese SF, et al. (2001) Activity of a specific inhibitor of the BCR-ABL tyrosine kinase in the blast crisis of chronic myeloid leukemia and acute lymphoblastic leukemia with the Philadelphia chromosome. N Engl J Med 344: 1038–1042.
10. Mendelsohn J (2002) Targeting the epidermal growth factor receptor for cancer therapy. J Clin Oncol 20: 1S–13S.
11. Hynes NE, Lane HA (2005) ERBB receptors and cancer: The complexity of targeted inhibitors. Nat Rev Cancer 5: 341–354.
12. Baselga J, Arteaga CL (2005) Critical update and emerging trends in epidermal growth factor receptor targeting in cancer. J Clin Oncol 23: 2445–2459.
13. Paez JG, Janne PA, Lee JC, Tracy S, Greulich H, et al. (2004) EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304: 1497–1500.
14. Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, et al. (2004) Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med 350: 2129–2139.
15. Pao W, Miller V, Zakowski M, Doherty J, Politi K, et al. (2004) EGF receptor gene mutations are common in lung cancers from "never smokers" and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc Natl Acad Sci U S A 101: 13306–13311.
16. Gong Y, Somwar R, Politi K, Balak M, Chmielecki J, et al. (2007) Induction of BIM is essential for apoptosis triggered by EGFR kinase inhibitors in mutant EGFR-dependent lung adenocarcinomas. PLoS Med 4(10): e294.
17. Costa DB, Halmos B, Kumar A, Schumer ST, Huberman MS, et al. (2007) BIM mediates EGFR tyrosine kinase inhibitor-induced apoptosis in lung cancers with oncogenic EGFR mutations. PLoS Med 4(10): e315.
18. Cragg MS, Kuroda J, Puthalakath H, Huang DCS, Strasser A (2007) Gefitinib-induced killing of NSCLC cell lines expressing mutant EGFR requires BIM and can be enhanced by BH3 mimetics. PLoS Med 4(10): e316.
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