NEWARK – Attorney General Christopher S. Porrino and the Division of Consumer Affairs today announced that the State is seeking to revoke the license of a Warren County doctor who allegedly accepted more than $136,000 from a drug maker while indiscriminately prescribing the powerful fentanyl painkiller "Subsys" to patients who did not meet the federal criteria for receiving the highly addictive cancer pain medication.
Dr. Kenneth P. Sun, a pain management practitioner in Phillipsburg, was paid up to $12,100 a month by drug maker Insys Therapeutic Inc. ("Insys") for speaking engagements and consulting services, all the while prescribing to patients Subsys, a tightly-restricted oral spray medication approved by the federal Food and Drug Administration ("FDA") strictly for use in treating breakthrough cancer pain in opioid-tolerant patients.
In a Complaint filed with the state Board of Medical Examiners ('the Board") yesterday, the State alleges that a reasonable person would recognize that Sun received those payments for his prescribing of Subsys, in violation of Board regulations.
Sun wrote more than 775 Subsys prescriptions from 2012 through 2016. The Complaint alleges that he indiscriminately prescribed the drug to eight patients who were not suffering from breakthrough cancer pain and/or who were already on stable pain management prescribing routines, the State alleges. His prescriptions generated more than $4.8 million in revenues for Insys and earned Sun $136,768 in compensation from the drug company, the State alleges.
Earlier this month the State filed suit against Insys, charging the company unlawfully directed its sales force to push Subsys for prescription to a broader patient population – patients suffering any type of chronic pain – and at higher doses.
The actions against Sun and Insys are the latest in the State's ongoing investigation into the "off-label" prescribing of Subsys – a fentanyl-based opioid medication approximately fifty times more powerful than heroin and one hundred times more powerful than morphine.
Subsys is one of six transmucosal immediate release fentanyl ("TIRF") medications that instantly deliver the powerful painkiller fentanyl through the oral membranes. The only federally approved use for TIRF substances, including Subsys, is for the management of breakthrough pain in adult cancer patients who are already receiving, and who are tolerant to, around-the-clock opioid therapy for their underlying persistent cancer pain.
The State began investigating the use of Subsys in October 2016 after Cherry Hill family physician Vivienne Matalon prescribed Subsys to three patients who did not meet the criteria for receiving the drug, one of whom died.
In response to the death, Attorney General Porrino and the Division of Consumer Affairs took the unprecedented step of announcing investigations and issuing a public alert warning patients and doctors alike of the risks associated with prescribing Subsys and other TIRFs medications outside their approved use.
As part of the State's initiative to determine whether doctors have been prescribing Subsys appropriately, and as was announced publicly in October 2016, investigators with the Enforcement Bureau of the Division of Consumer Affairs inspected patient records at numerous doctors' offices statewide, and subpoenaed patient records from others.
An inspection of Sun's patient records allegedly showed he had prescribed Subsys to several non-cancer patients for whom such a drug was "contraindicated" because of the associated risks of addiction, overdose and death.
In the wake of those alleged findings, Sun agreed to the temporary suspension of his license.
to his patients. The Complaint also alleges Sun engaged unlawful conduct by accepting money from Insys intended to induce his prescribing and engaged in fraud by mischaracterizing patient diagnoses to ensure insurance coverage for patients' prescriptions.
According to the Complaint, Sun's' relationship with Insys began on March 30, 2012 when an Insys sales representative called him to discuss Subsys. Sun wrote his very first Subsys prescription on April 10, 2012.
Sun received money from Insys purportedly for being part of a Subsys speaker program in which physicians who utilized Subsys were paid for being speakers at promotional forums encouraging the use of the drug; serving as a consultant for the company; and participating on its advisory board.
During this course of his relationship with Insys, Sun allegedly was made aware that his prescribing was being monitored by the drug company and that he was expected to keep prescribing Subsys.
When Sun could not attend a July 2012 speaker training, he told a sales representative the he "owes her a few [Subsys] scripts for missing the web training".
When the sales representative's supervisor informed her via email in March 2015 that Sun was "$30K short still just of breaking even" for that quarter," she forwarded that email to him.
When a mail-order pharmacy which frequently filled Subsys prescriptions, contacted the sales representative expressing concern that "we have not seen many scripts from (Sun)" she forwarded the email to Sun.
Whenever the sales representative received an email from a supervisor informing her that Sun had written a Subsys prescription for a "low strength", i.e. not as profitable, she would contact Sun.
When Insys obtained Subsys insurance coverage for Sun's patients, the sales representative would forward emails conveying the news of the coverage approval to Sun.
In March 2013, the sales representative forwarded Sun an email informing him that Insys had obtained approval for a patient and exclaimed "Keep them rolling" with a smiley emoticon.
By enrolling in the Program, Sun acknowledged he had read the risks associated with Subsys, and understood that it was approved only for narrow uses as a cancer pain medication and that any "off-label" use could result in a fatal overdose, according to the State's Complaint.
Nonetheless, the State alleges, Sun prescribed Subsys to eight patients, identified in the Complaint by their initials, who did not fit the criteria for receiving the drug. The State alleges Sun's actions constitute professional misconduct and gross negligence which endangered the life, health, welfare, and safety of his patients.
If proven, Sun's actions provide the Board with grounds to suspend or revoke his license, according to the State's allegations.


text:
Q: Evaluating $\frac{\sum_{n=1}^{99}\sqrt{10+\sqrt{n}}}{\sum_{n=1}^{99}\sqrt{10-\sqrt{n}}}$
Evaluate $$\frac{\sum_{n=1}^{99}\sqrt{10+\sqrt{n}}}{\sum_{n=1}^{99}\sqrt{10-\sqrt{n}}}$$

Both the top and bottom do not telescope and and last and first do not add up to something nice, so I do not know how to proceed. Thanks!

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text:
\section{Introduction}
Beginning with the pioneering work of Lorenz on convectional instabilities [1], there has been a lot of research work on systems of differential equations which yield bounded nonperiodic trajectories converging to an attractor of rather complicated nature. A chaotic attractor has a kind of ``hank of string" like appearance and does not have a integral dimension. The chaotic trajectory is not one-dimensional as it does not return over itself, nor is it two-dimensional because it is not a surface uniformly covered by trajectories, nor is it a volume uniformly filled by the trajectories to become a three-dimensional structure.\par
In this communication, we have studied the Newton-Leipnik chaotic system [2] which possesses more than one attractor. Newton and  Leipnik obtained the  set of  differential equations from Euler rigid body equations which were modified with the addition of a linear feedback. Two strange attractors starting from different  initial conditions and same parameter conditions were obtained. B. Marlin established the existence of closed orbits which were not asymptotically stable [3]. Chen et al.[4]  devised a stable-manifold-based method for chaos control and synchronization. H. Richter studied the stabilization of a desired motion  within one attractor as well as taking the system dynamics from one attractor to another[5].
\par
We have shown that  when the parametric conditions are varied keeping the initial conditions intact, the system showed the presence of multiple attractors. The chaoticity of these attractors are then analyzed through phase space analysis, power spectrum[6,7] and lyapunov exponents[8,9]. Dimension of the attractor which is fractal is estimated. Owing to the  low dimensionality of the attractors, the peak-to-peak dynamics[10,11] is analyzed and a suitable controller [12,13]has been designed for the system which reduces it to a purely periodic state.
\section{Formulation}
\subsection{Analysis of Chaos}Newton Leipnik system is characterised by the following differential equations.
\begin{equation}
\dot{x_1}=-\alpha x_1+x_2+10 x_2 x_3
\end{equation}
\begin{equation}
\dot{x_2}=- x_1-0.4 x_2+5 x_1 x_3
\end{equation}
\begin{equation}
\dot{x_3}=\beta x_3-5 x_1 x_2
\end{equation}
This system has two strange attractors originating from the initial states $(0.349, 0.0,  -0.160)^T$, $(0.349,  0.0,  -0.180) ^T$ and system parameters $\alpha =0.4, \beta =0.175,$ which  have been studied and analyzed previously[2,4].\par But while exploring the dynamical system with respect to the parameters we came across three other chaotic attractors. These three chaotic attractors emerged from a totally different parameter state and initial condition. The two dimensional projection of these attractors are shown in figure 1(a), 1(c), 1(e) and their corresponding   power spectrum is illustrated in figure 1(b), 1(d), 1(f)  respectively. The initial values of the variable, in our case, is taken as $ x_{10}=0.35$,  $x_{20}=0.0$, $x_{30}=0.16$. The parameter $`\beta '$ is kept constant at 0.17 whereas $`\alpha '$ is varied. The attractor in figure 1(a),  appears for $\alpha =0.550$ whereas that of  figure 1(c) is for $\alpha =0.7689$ and the last attractor of figure 1(e) appears at $\alpha =0.809$.  Changing of the parameter $`\alpha '$ causes a  rapid change in the appearance of the strange attractor and also  in its nonlinear properties.\par The first attractor is quite but not exactly similar to the one described in [4]. The second one represents much like a chaotic torus. The third one is carrying an entirely  different structure. It is also clear from the power spectrum also that in comparison to the other two the second  figure shows a little bit ordered behaviour which is also observable in the phase space diagram. We have also studied the lyapunov spectrum, which forms the most useful tool for the detection and quantification of chaos. Lyapunov exponents are the average exponential rates of divergence or convergence of nearby orbits in phase space. Any system containing atleast one positive lyapunov exponent is defined to be chaotic, with the magnitude of the exponent reflecting the  time scale on which  system dyanamics becomes unpredictable. The lyapunov exponents calculated for the attractors are tabulated as follows.\\
\begin{center}
{\bf TABLE I}\\
\vskip 10pt
\begin{tabular}{|c|c|c|c|} \hline
{}& {}& {}& {}\\
 & $\lambda _1$ &$\lambda _2$&$\lambda _3$\\
{}& {}& {}& {}\\
\hline
{}& {}& {}& {}\\
Attractor 1&0.25554&0.03268&-1.18911\\
{}& {}& {}& {}\\
\hline
{}& {}& {}& {}\\
Attractor 2&0.18986&0.1039005&-1.19466\\
{}& {}& {}& {}\\
\hline
{}& {}& {}& {}\\
Attractor 3&0.21343&0.15888&-1.27319\\
{}& {}& {}& {}\\
\hline
\end{tabular}
\end{center}
The Lyapunov spectrum of a strange attractor is closely associated to its fractional dimension. Kaplan and Yorke [14--17]  conjenctured that the information dimension $d_f $ is related to the lyapunov spectrum by the equation
$$ d_f = j + \frac{\sum_{i=1}^j \lambda _i}{\mid \lambda _{j+1}\mid }, $$
where $j$ is defined by the condition that
$$\sum_{i=1}^j\lambda_i >0   \; \;\mbox{and}\;\;\sum_{i=1}^{j+1}\lambda_1<0   $$
The information dimension for the three attractors are obtained as,
                        $$d_{f_1}=2.24 \;\;\;\;\;\; d_{f_2}=2.25 \;\;\; \;\;\;d_{f_3}=2.29$$
It is seen that with the increase in parameter  $`\alpha '$, the information dimension also increases. From the lyapunov spectrum it is observed that the largest positive lyapunov decreases in the case of the second attractor and again rises for the third. So the expansion rate of the trajectories of the second attractor is less than that of the other two attractors whereas the third attractor displays the  largest  information dimension.

\par After three attractors have been displayed with different value of parameter $`\alpha '$ we explore the whole spectrum of the system with  the variation of  $`\alpha'$.  This is shown by means of  the bifurcation diagram of figure (2).  There is period doubling route  to chaos which is also depicted through phase space diagrams shown in figure (3a)-(3d). The first figure shows a single period with $\alpha =0.8045$. As we decrease the parameter value of $\alpha $ to 0.798 the phase space shows a double period. Further decrement of $\alpha $ to 0.7944 quadrapules the period which switches to a chaotic situation after a few more doublings and at a value $\alpha =0.7689$.
\par \par After this analysis on the chaotic dynamics of the system, we have taken the analysis of the peak-to-peak dynamics of the system which has been further used to design a controller for controlling the system.
\subsection{Peak-to-peak Dynamics}
It was first shown by E.N. Lorenz that for a chaotic attractor the intensity of the forthcoming peak and its time of occurrence can be predicted with sufficient accuracy by one or more curves in the plane $(x_i, x_{i+1})$, where $x_i$ denotes a maxima in the temporal evolution of the variable $X(t)$. This characteristic of the attractor depends on the lower dimensionality of the chaotic attractor, which must be near about two. The subject of peak- to-peak dynamics have been discussed in the various field of science and engineering. R\"{o}ssler system, the Chua circuit and the Duffing oscillator are found to display peak-to-peak dynamics under suitable parameter conditions. A system which display peak-to-peak dynamics can be described by a reduced order model which is the peak-to-peak map and a return time map. The former predicts the value of the next peak, while the second one gives the time interval separating a peak from the next peak. The methodology of obtaining a peak-to-peak plot is that, from the time series of a particular variable $X$, one collects the peak points as $x_i$ and plots them as $x_i$ vs $x_{i+1}$. Thus the plot is composed of distinct points. The attractor is periodic if the PPP shows only a few distinct  points. The points of the PPP are all distinct and may show a definite curve if it is a case of torus or strange attractor with a dimension close to two. On the other hand for a high dimensional strange attractor one obtains a cloud like set. For a quasi-periodic orbit the points of PPP form a closed regular curve. In the similar way a return time plot can be obtained from the set of all pairs $(x_i, \tau_i)$ with $\tau_i= t_{i+1}-t_i$. \par
The peak-to-peak plot and the return time plot for the three attractors are depicted in figures 4(a)--4(f). The PPP and RTP for the first attractor, which occurs for $\alpha =0.55$ and has a fractal dimension $2.24$, shows a well behaved structure as shown in figure 4(a) and 4(b). 4(c) and 4(d) is for the attractor with $\alpha =0.7689$ and fractal dimension $2.25$ and a distinct curve is obtained. 4(e) and 4(f) is for the attractor with $\alpha =0.809$ and fractal dimension $2.29$ and this is reflected in the PPP and RTP which has a cloud like appearance. This indicates that peak-to-peak dynamics is dependent on the low dimensionality of the system and is clearly visible from the above analysis.
\subsection{Control of Chaos}
The idea of control of chaos was enunciated by Ott, Grebogi and Yorke [22] and after that various methodologies and strategies have been developed theoretically and experimental realization of control have been achieved [23]. Control of chaos have been effectively observed in a magnetoelastic ribbon [24], a heart [25], a thermal convection
loop [26], a yttrium iron garnet oscillator [27], a diode oscillator [28], an optical multimode
chaotic solid-state laser [29], a Belousov-Zhabotinski reaction diffusion chemical system [30], and
many other experiments.
\par Peak-to-Peak plot and return time plot can be used to device optimal feedback controllers of chaotic systems. If the PPP is a smooth curve then one can attain a map from it. In our observation the attractor with $\alpha =0.7689$ showed a perfect curve (parabola) and it has been targeted to achieve the controlled state.  This control procedure is adopted easily once the peak-to-peak map is derived. Our analysis has been considered in an ideal environment where noise is absent and the sampling has been made on sufficiently high frequency. In our system we introduce a control variable $u(t)$ in the third equation as follows:
\begin{equation}
\dot{x_1}=-\alpha x_1+x_2+10 x_2 x_3
\end{equation}
\begin{equation}
\dot{x_2}=- x_1-0.4 x_2+5 x_1 x_3
\end{equation}
\begin{equation}
\dot{x_3}=\beta x_3-5 x_1 x_2-bu(t)
\end{equation}
where b is a real constant to be introduced later. For any constant control $u(t) = \bar u \in U = [u_{\mbox{min}}, u_{\mbox{max}}]  \subset R$  the system has a chaotic attractor. In our case for $u(t)=0.0$,  three chaotic attractor were obtained. In order to control the system, one of the requisites is that the control law $u(t)$ is constant between two subsequent peaks i.e., $u(t)=u_k \;\;\forall \;\;t \in (t_k, t_{k+1}]$. We consider the third state variable as output variable.
A peak-to-peak map $$x_{k+1}=F(x_k, \bar u)$$ have been derived, through simulation, for 20 equally spaced values of $u$ in the set $U = [0.0, 0.0095]$.
The reduced order model
\begin{equation}
x_{k+1}=F(x_k,u_k)
\end{equation}
  have been obtained by linear interpolation of these maps. Thus, the dynamics of variable $y$ of a system can be easily described by equation (2.7). The fixed point        $$\bar x=F(\bar x,\bar u),$$  identifies a  unstable periodic orbit of
the system $$ \dot{x}(t)=f(x(t),u(t)) $$
The output $x(t)$ associated with the unstable periodic orbit has one peak per period at $\bar x$. The linear approximation of the map $F$ around $\bar x$ is given by
$$x_{k+1}-\bar x=A(x_k-\bar x) + b(u_k-\bar u)$$
where,
$$A=\left[\frac{\partial F}{\partial x}\right]_{\bar x, \bar u}\;\;\;\;\;\mbox{and}\;\;\;\;\; b=\left[ \frac{\partial F}{\partial u}\right]_{\bar x, \bar u} $$
 are two real numbers that we assume to be not equal to zero. The law for control is then given by
$$u_k= \left\{ \begin{array}{c} \bar u+ h(x_k- \bar x) \;\;\;\;\; \mbox{if} \mid x_k- \bar x\mid \leq \epsilon \\
\\
\bar u \;\;\;\;\;\;\;\;\hskip 50pt \mbox{if} \mid x_k- \bar x\mid > \epsilon  \end{array}\right.$$
where the scalar $h$ must be such that $ \mid A+bh <1\mid $. So the control $u_k$ is allowed to vary from $(\bar u-h \epsilon )$ to $(\bar u+h \epsilon )$ and
$$ h=-\frac{A}{b}\;\;\;\; \Rightarrow \;\;\;\;A+bh = 0$$
is optimal because the response time of the linear approximation is minimum (i.e., equal to one), since, $x_{k+1}= \bar x$.\par
In the case of Newton Leipnik System, at $\bar u=0.007$  the  PPM  $x_{k+1}=F(x_k, \bar u)$ has an unstable fixed point at $\bar x=0.3$ with $A=-3.6$ , $b=0.601$. The nature of the controlled system with $h=-A/b=6.0$ ,  $\epsilon=0.5$ is shown in figure (5). The nature of the phase space is depicted in the inset  that  represents a single periodic orbit.
\section{Conclusion}
Three new attractors of the  Newton Leipnik system have been analyzed, which was found to evolve upon changing the parameter condition of the system, keeping the initial conditions fixed. Observation shows that they not only differ in structure and properties but also show varying dimensionality. Peak-to-peak dynamics have been obtained  and the peak-to-peak plot is found to be useful in determining the controller function which reverts the chaotic situation to a periodic state.\\
\\
{\bf Acknowledgement:}\\
Authors, P. Saha and B. Rakshit, are thankful to CSIR, Govt. of India for a senior research fellowship and a junior research fellowship respectively. They would also like to thank Dr. Benjamin Marlin of North Western Oklahoma State University, for providing the background of the literature and many helpful references.
\section{References:}
{[1]}.  E.N. Lorenz - {\it J. Atmos. Sci.} {\bf \underline {20}}  {(1963)} {130}.\\
{[2]}.  R.B. Leipnik and T.A. Newton - {\it Phys. Lett. A} {\bf \underline {86}}  {(1981)} {63}.\\
{[3]}.  B.A. Marlin - {\it Int. J. Bifurc. \& Chaos} {\bf \underline {12}}  {(2002)} {511}.\\
{[4]}.  S. Chen et. al. - {\it Chaos, Solitons \& Fractals} {\bf \underline {20}}  {(2004)} {947}.\\
{[2]}.  H. Richter - {\it Phys. Lett. A} {\bf \underline {300}}  {(2002)} {182}.\\
{[6]}. D.Farmer et.al - {\it Ann.N.Y.Acad.Sci.}{\bf\underline{357}} {(1980)} {453}\\
{[7]}. R.J.Higgins - {\it Ann.J.Phys.}{\bf\underline {44}} {(1976)} {766}\\
{[8]}. A.Wolf , J.Swift , H.Swineny and J.Vastano - {\it Physica D} {\bf \underline {16}}  {(1985)}{285}\\
{[9]}. Hao.Bai-lin , Elementary Symbolic Dyanmics and Chaos in Dissipative Systems.(World Scintific , Singapore , 1989).\\
{[10]}.  M. Candaten and S. Rinaldi - {\it Int. J. Bifurc. \& Chaos} {\bf \underline {8}}  {(2000)} {1805}.\\
{[11]}.  C. Piccardi and S. Rinaldi - {\it Int. J. Bifurc. \& Chaos} {\bf \underline {6}}  {(2003)} {1579}.\\
{[12]}.  C. Piccardi and S. Rinaldi - {\it Int. J. Bifurc. \& Chaos} {\bf \underline {12}}  {(2002)} {2927}.\\
{[13]}.  C. Piccardi and S. Rinaldi - {\it Physica D} {\bf \underline {144}}  {(2000)} {298}.\\
{[14]}. J. Kaplan and J. Yorke, ``Chaotic behaviour of multidimensional difference equations"  in Functional Differential Equations and the approximation of fixed points, lecture Notes in Mathematics, vol 730.  H.O.peitgen and H.O. Walther eds. (Springer, Berlin) p.228. \\
{[15]}. P.Frederickson, J.kaplan, E.Yorke and J.Yorke
- {\it J. Diff. Eqs. } {\bf\underline{49}} {(1983)} {185}\\
{[16]}. L. S. Young - {\it Ergodic theory and Dyanamical Systems } {\bf\underline{2}} {(1982)} {109}\\
{[17]}. F.Ledrappier - {\it Comm. Math. Phys.} {\bf\underline{81}} {(1981)} {229}\\
{[18]}.  J.P. Eckmann, S.O. Kamphorst and D. Ruelle  - {\it Europhys. Lett.} {\bf \underline {4}}  {(1987)} {973}.\\
{[19]}.  A. Giuliani and C. Manetti - {\it Phys. Rev. E} {\bf \underline {53}}  {(1996)} {6336}.\\
{[20]}.  J.P. Zbilut and C.L. Webber. Jr. - {\it Phys. Lett. A} {\bf \underline {171}}  {(1992)} {199}.\\
{[21]}. P. Grassberger and I. Procaccia - {\it Physica D } {\bf \underline{7}} {(1983)} {473}\\
{[22]}. E. Ott, C. Grebogi, J.A. Yorke - {\it Phys. Rev. Lett. } {\bf \underline{ 64 }} {(1990} {1196}\\
{[23]}. S. Boccaletti et al. - {\it Physics Reports} {\bf \underline{329}} {(2000)} {103}\\
{[24]}. W.L. Ditto,  S.N. Rauseo and M.L. Spano - {\it Phys. Rev. Lett. } {\bf \underline{65}} {(1990)} {3211}\\
{[25]}. A. Garfinkel et al.- {\it Science } {\bf \underline{257}} {(1992)} {1230}\\
{[26]}. J. Singer and H.H. Bau - {\it Phys. Fluids A} {\bf \underline{3}} {(1991)} {2859}\\
{[27]}. A. Azevedo and S. Rezende - {\it Phys. Rev. Lett.} {\bf \underline{66}} {(1991)} {1342}\\
{[28]}. E. R. Hunt - {\it Phys. Rev. Lett. } {\bf \underline{67}} {(1991)} {1953}\\
{[29]}. R. Roy et al. - {\it Phys. Rev. Lett. } {\bf \underline{68}} {(1992)} {1259}\\
{[30]}. V. Petrov et al. - {\it Nature } {\bf \underline{361}} {(1993)} {240}\\

\section{Figure Captions}
Figure (1) - 2D phase space diagram and the coresponding power spectrum for Newton-Leipnik System. (a) \& (d) : $\alpha =0.55$   (b) \& (e) :  $\alpha =0.7689$ (c) \& (f) :  $\alpha =0.809$.\\
\\
Figure (2) - Bifurcation diagram of the system with respect to the variation of the parameter $\alpha $.\\
\\
Figure (3) - Period doubling route to chaos. (a) : $\alpha =0.8045$, (b) : $\alpha =0.798$, (c) : $\alpha =0.7944$, (d) : $\alpha =0.7689$.\\
\\
Figure (4) - Peak -to-peak plot and return time plot. (a) \& (b): $\alpha =0.55$,  (c) \& (d): $\alpha =0.7689$, (e) \& (f): $\alpha =0.809$.\\
\\
Figure (5) - Variation of $x_3(t)$ with the application of the controller showing transition to a periodic state. Inset picture shows the phase space diagram during the controlled stage.

\end{document}

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text:
July 17: Civil Rights Activist Lawrence Guyot Was Born on This Date in 1935
0 Posted by Victor Trammell - July 17, 2022 - CIVIL RIGHTS, LATEST POSTS, On This Date
By Victor Trammell
Photo credits: Leni Sinclair/Getty Images
Lawrence Guyot (pictured), a civil rights advocate, was born in Pass Christian, Mississippi, on July 17, 1935.
Guyot's brutal beatings at the hands of white police officers as a young civil rights activist helped lay the groundwork for the Voting Rights Act of 1965. His life as a young adult was defined by the extreme injustice he fought against and experienced himself. This is what made his commitment to advocacy so genuine.
Guyot traveled to free civil rights leader Fannie Lou Hamer and two other people who had been detained in Winona, Mississippi when they entered an all-white bus terminal. After asking about violent treatment while in custody, nine police officers pistol-whipped Guyot. He was stripped naked by the white officers who brutalized him.
The dirty cops also threatened to apply heated metal to Guyot's genitals. The relentless assault continued until a doctor told the rogue officers to stop. Guyot received another vicious beating at the hands of the corrupt law enforcers when the doctor left the scene.
Guyot founded the Student Nonviolent Coordinating Committee while a college student, and he encouraged African-Americans in Mississippi to cast ballots. Guyot graduated from Rutgers University with a law degree in 1971, after which he relocated to Washington, D.C., where he worked to elect Marion Barry as mayor.
In 2004, he said there was nothing like risking his life alongside others for something that was immensely important, such as the Civil Rights Movement. Guyot passed away in 2012 at his Mount Rainer, Maryland, home.
He was 73 years of age at the time of his death.
Lawrence Guyotracist white police officersThe Civil Rights Movement
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