text:
\section{Data resources and notations}

\subsection{Data resources}

\noindent (1) Phanerozoic climate change data: ref. [4], [5], [6], [7];

\noindent (2) Phanerozoic sea level fluctuation data: ref. [8], [9], [10];

\noindent (3) Phanerozoic biodiversity variation based on fossil records: ref. [1], [2], [3];

\noindent (4) Genome size databases: Animal Genome Size Database \cite{Animal Genome Size Database}, Plant DNA C-values Database \cite{Plant DNA C-values database};

\noindent (5) Whole genome database: GenBank.

\subsection{Notations}

\begin{equation}
\begin{array}{rcl}
\mbox{Sepkoski curve}&:&\ Curve\_Sepkoski\\
\mbox{teconto-genomic curve}&:&\ Curve\_TectonoGenomic\\
\mbox{time}&:&\ t,T\\
\mbox{biodiversity curves}&:&\ Curve\_BD, BD, Total\mbox{-}BD\\
\mbox{sea level curves}&:&\ Curve\_SL, S^1, S^2, S_w\\
\mbox{climate curves}&:&\ Curve\_CC, C^1, C^2, C^3, C_{w1}, C_{w2}, C_w\\
\mbox{correlation coefficients}&:&\ r^\rho_{\mu\nu}, R^+, R^-, \Delta R, Q, Q',\Delta Q\\
\mbox{climate phases}&:&\ CP I, CP II, CP III\\
\mbox{genome sizes}&:&\ G, G_{sp}, G_{mean\_log}, G_{sd\_log},G^*\\
\mbox{biodiversity variation rates}&:&\ rate\_ori, rate\_ext, rate\_essential\\
\mbox{derivative curves}&:&\ d\_CC, d\_SL, d\_BD\\
\mbox{overall trends}&:&\ OT\mbox{-}BD, OT\mbox{-}GS\\
\mbox{e-folding times and growth rates}&:&\ \tau_{BD}, k_{BD}, \tau_{GS},k_{GS}\\
\mbox{genomes size distributions and matrices}&:&\ D_{gs}, M_{gs}\\
\mbox{codon interval distributions and matrices}&:&\ D_{ci}, \Delta,  M_{ci},  M_{codon}\\
\mbox{genetic code evolutionary curves}&:&\ Barrier, Hurdle.
\end{array}
\end{equation}

\subsection{Math notations}

Let $sum(V)$, $mean(V)$, $std(V)$, $log(V)$ and $exp(V)$ denote respectively the summation, mean, stand deviation, logarithm and exponent of a vector $V(i)$, $i=1,2,...,i_m$:
\begin{eqnarray}
sum(V)&=&\sum_{i=1}^{i_m}V(i)\\
mean(V)&=&\frac{1}{i_m}\ sum(V)\\
std(V)&=&\sqrt{mean((V-mean(V))^2)}\\
log(V)&=&[\log_e(V(1)),\log_e(V(2)),...,\log_e(V(i_m))]\\
exp(V)&=&[\exp(V(1)),\exp(V(2)),...,\exp(V(i_m))].
\end{eqnarray}

Especially, let $nondim(V)$ denote the operation of nondimensionalization for a dimensional vector $V$,
\begin{equation} nondim(V)=(V-mean(V))/std(V).\end{equation}
In this paper, we obtain respectively the dimensionless vectors $Curve\_BD$, $Curve\_CC$, $Curve\_SL$, etc. after nondimensionalization based on the dimensional raw data of biodiversity curve, climate curve and sea level curve in the Phanerozoic eon.

Let $corrcoef(V,U)$, $max(V,U)$, $min(V,U)$ and $[V,U]$ denote respectively the correlation coefficient, maximum and minimum of a pair of vectors $V(i)$ and $U(i)$ ($i=1,2,...,i_m$):
\begin{eqnarray} corrcoef(V,U)&=&\frac{\sum_{i=1}^{i_m}(V(i)-mean(V))(U(i)-mean(U))}{\sqrt{\sum_{i=1}^{i_m}(V(i)-mean(V))^2}\sqrt{\sum_{i=1}^{i_m}(U(i)-mean(U))^2}}\\
max(V,U)&=&[max(V(1),U(1)),max(V(2),U(2)),...,max(V(i_m),U(i_m))]\\
min(V,U)&=&[min(V(1),U(1)),min(V(2),U(2)),...,min(V(i_m),U(i_m))].
\end{eqnarray}

Let $\frac{\mathrm{d}}{\mathrm{d}t}(V)$ denote the discrete derivative of $V(t)$ with respect to time $t$:
\begin{equation}
\frac{\mathrm{d}}{\mathrm{d}t}(V)=[\frac{\mathrm{d}V}{\mathrm{d}t}|_{t=t(1)}, ..., \frac{\mathrm{d}V}{\mathrm{d}t}|_{t=t(i_m)}],
\end{equation}
where $V(t)=[V(1),V(2),...,V(i_m)]$ is an $i_m$-element discrete function of time $t=[t(1), t(2),..., t(i_m)]$.
The linear interpolation of $V$ is denoted by:
\begin{equation}
[V(1),V(2),...,V(i_m')]=interp([t(1),...,t(i_m)],[V(1),...,V(i_m)],[t(1),...,t(i_m')]).
\end{equation}
The concatenation of function $V(t)$ between period $t([P_1])=[t(i_1), t(i_1+1), ..., t(i_2)]$ and period $t([P_2])=[t(t_2+1), t(i_2+2), ..., t(i_3)]$ is denoted by:
\begin{equation}
[V([P_1]),V([P_2])]=[V(i_1),...,V(i_2),V(i_2+1),...,V(i_3)],
\end{equation}
where $P_1=[i_1,i_1+1,...,i_2]$ and  $P_2=[i_2+1,i_2+2,...,i_3]$ are parts of the indices. For a $i_m-$by$-j_m$ array $M(i,j)$, let $M(i,:)$ denote
\begin{equation}
M(i,:)=[M(i,1),M(i,2),...,M(i,j_m)].
\end{equation}

\section{Understanding the Sepkoski curve through the tectono-genomic curve}

The Phanerozoic biodiversity curve has been explained in this paper. We propose a split scenario for the biodiversity evolution:
\begin{equation}
\mbox{Biodiversity evolution}=\mbox{Tectonic contribution}+\mbox{Genomic contribution}.
\end{equation}
We construct a tectono-genomic curve based on climatic, eustatic (sea level) and genomic data, which agrees with the Phanerozoic biodiversity curve based on fossil records very well. We explain the P-Tr extinction by a climate phase reverse event.
And we point out that the biodiversity evolution was driven independently at the species level as well as at the molecular level.

\section{The overall trend of biodiversity evolution}

\subsection{Motivation}

A split scenario is propose to separate the Phanerozoic biodiversity evolution curve into its exponential growth part and its variation part.

\subsection{The exponential outline of the Sepkoski curve}

The Phanerozoic biodiversity curve (namely the Sepkoski curve) can be obtained based on fossil records. We denote the Phanerozoic genus number biodiversity curve in ref. [2] after linear interpolation by (Fig 1):
\begin{equation} Curve\_Sepkoski(t):\ \mbox{ref. [2]}, \end{equation}
which is a $5421$-element function of time $t$, from $542$ million years ago (Ma) to $0$ Ma in step of $0.1$ million of years (Myr):
\begin{equation}
\begin{array}{rcl}
t&=&[t(1),t(2),t(3),...,t(5419),t(5420),t(5421)]\\
&=&[542.0, 541.9, 541.8,...,0.2, 0.1, 0].
\end{array}
\end{equation}

The outline of $Curve\_Sepkoski(t)$ is an exponential function:
\begin{equation} N_{genus}(t)=N^0_{genus}\ \exp (-t/\tau_{BD}), \end{equation}
where the genera number constant is $N^0_{genus}=2690$ genera, and the ``e-folding time'' of the biodiversity evolution is $\tau_{BD}=259.08$ Myr.

\subsection{The split scenario of the Sepkoski curve}

We define the total biodiversity curve $Total\mbox{-}BD$ in the Phanerozoic eon by the logarithm of $Curve\_Sepkoski$:
\begin{equation} Total\mbox{-}BD=log(Curve\_Sepkoski(t)),\end{equation}
which is also a $5421$-element function of time $t$.
According to the linear regression analysis, the regression line of $Total\mbox{-}BD$ on $t$ is defined as the overall trend of total biodiversity curve:
\begin{equation}
\begin{array}{rcl}
OT\mbox{-}BD &=& log(N_{genus}(t)) \\
&=& k_{BD}\cdot(-t)+\log(N^0_{genus}),
\end{array}
\end{equation}
where the growth rate of biodiversity evolution, namely the slope of this regression line, is $k_{BD}=1/\tau_{BD}=0.0038598$ $\mbox{Myr}^{-1}$.

We propose a ``split scenario'' in observing the Phanerozoic biodiversity evolution by separating the Sepkoski curve into its exponential growth part and its variation part. In this scenario, the total biodiversity curve $Total\mbox{-}BD$ can be written as the summation of its linear part $OT\mbox{-}BD$ and its net variation part $BD$ (Fig. 2d):
\begin{equation}
Total\mbox{-}BD=OT\mbox{-}BD+BD.
\end{equation}

Hence, we obtain the biodiversity curve $Curve\_BD$ after nondimensionalization of $BD$:
\begin{equation} Curve\_BD=nondim(BD).\end{equation}

\section{The tectonic cause of mass extinctions}

\subsection{Motivation}

We construct the tectonic curve based on the climatic and eustatic data in consideration of the phase relationships among $Curve\_BD$, $Curve\_CC$ and $Curve\_SL$.

\subsection{The consensus climate curve}

We denote the three independent results on Phanerozoic global climate in ref. [5] [6], [7], [4] as $C^1_0$, $C^2_0$, $C^3_0$ respectively after linear interpolation:
\begin{equation} C^1_0(t):\ \mbox{ref. [5] [6]},\end{equation}
\begin{equation} C^2_0(t):\ \mbox{ref. [7]},\end{equation}
\begin{equation} C^3_0(t):\ \mbox{ref. [4]}.\end{equation}
The missing $^{87}Sr/^{86}Sr$ in ref. [7] in lower Cambrian are obtained from ref. \cite{Veizer et al. 1999} for $C^2_0$. We obtain three dimensionless global climate curves after nondimensionalization:
\begin{equation} C^1(t)=nondim(C^1_0(t)), \end{equation}
\begin{equation} C^2(t)=nondim(C^2_0(t)), \end{equation}
\begin{equation} C^3(t)=nondim(C^3_0(t)). \end{equation}

Hence, we obtain the consensus climate curve $Curve\_CC$ by synthesizing the above three results $C^1$, $C^2$ and $C^3$ (Fig. S1a):
\begin{equation} Curve\_CC=nondim((C^1+C^2+C^3)/3). \end{equation}

\subsection{The consensus sea level curve}

We denote the Phanerozoic sea level curves in ref. [8] and in ref. [9] [10] as $S^1_0$ and $S^2_0$ (via linear interpolation) respectively:
\begin{equation} S^1_0(t):\ \mbox{ref. [8]}, \end{equation}
\begin{equation} S^2_0(t):\ \mbox{ref. [9] [10]}. \end{equation}
And we obtain the dimensionless sea level curves after nondimensionalization:
\begin{equation}S^1(t)=nondim(S^1_0(t)),\end{equation}
\begin{equation}S^2(t)=nondim(S^2_0(t)). \end{equation}

Hence we obtain the consensus sea level curve $Curve\_SL$ by synthesizing the two results $S^1$ and $S^2$ (Fig. S1c):
\begin{equation} Curve\_SL=nondim((S^1+S^2)/2).\end{equation}

We can obtain the derivative curves $d\_CC$, $d\_SL$ and $d\_BD$ respectively as follows (Fig. 2b):
\begin{eqnarray}
d\_CC&=&\frac{\mathrm{d}}{\mathrm{d} t}(Curve\_CC)\\
d\_SL&=&\frac{\mathrm{d}}{\mathrm{d} t}(Curve\_SL)\\
d\_BD&=&\frac{\mathrm{d}}{\mathrm{d} t}(Curve\_BD).
\end{eqnarray}

\subsection{Correlation coefficients among $Curve\_CC$, $Curve\_SL$ and $Curve\_BD$}

So far, we have obtained the first group ($n=1$) of curves $Curve\_CC$, $Curve\_SL$ and $Curve\_BD$ to describe the Phanerozoic climate, sea level and biodiversity. They are all $5421$-element functions of time $t$.

There are three eras (Paleozoic, Mesozoic and Cenozoic) in the Phanerozoic eon, the time $t$ in the Phanerozoic eon can be concatenated as follow:
\begin{equation}t=[t([P]),t([M]),t([C])],\end{equation}
where the indices for the Paleozoic, Mesozoic and Cenozoic are as follows respectively:
\begin{eqnarray}
P&=&[(5421-5420),...,(5421-2510)],\ \mbox{for Paleozoic from 542.0 Ma to 251.0 Ma}, \\
M&=&[(5421-2510+1),...,(5421-655)],\ \mbox{for Mesozoic from 251.0 Ma to 65.5 Ma}, \\
C&=&[(5421-655+1),...,5421],\ \mbox{for Cenozoic from 65.5 Ma to today}.
\end{eqnarray}
Similarly, we define the indices for the other periods as follows:
\begin{eqnarray}
PMC&:&\ \mbox{for Phanerozoic from 542.0 Ma to 0 Ma}, \\
PM&:&\ \mbox{for Paleozoic and Mesozoic from 542.0 Ma to 65.5 Ma}, \\
MC&:&\ \mbox{for Mesozoic and Cenozoic from 251.0 Ma to 0 Ma}, \\
P \backslash L&:&\ \mbox{for Paleozoic except for Lopingian from 542.0 Ma to 260.4 Ma}, \\
L&:&\ \mbox{for Lopingian from 260.4 Ma to 251.0 Ma}, \\
L.M.Tr&:&\ \mbox{for Lower and Middle Triassic from 251.0 Ma to 228.7 Ma}, \\
M \backslash L.M.Tr&:&\ \mbox{\small for Mesozoic except for Lower and Middle Triassic from 228.7 Ma to 65.5 Ma}.
\end{eqnarray}

We can calculate the correlation coefficients $r^\rho_{\mu\nu}$ among $Curve\_CC$, $Curve\_SL$ and $Curve\_BD$ in certain periods respectively (Data\_2):
\begin{equation}
r^\rho_{\mu\nu}=corrcoef(\mbox{curve }\mu([\rho]),\mbox{curve }\nu([\rho]))
\end{equation}
where the subscripts
\begin{equation} \mu, \nu= C, S, B \end{equation}
for the curves $Curve\_CC$, $Curve\_SL$ and $Curve\_BD$ respectively, and the superscript
\begin{equation} \rho=P, M, C, PMC, PM, MC, P \backslash L, L, L.M.Tr, M \backslash L.M.Tr \end{equation}
for the corresponding periods respectively.

{\bf Note:} The correlation coefficients generally agree with one other in the calculations between $Curve\_BD$ and any of $Curve\_SL$, $S^1$, $S^2$, or between $Curve\_BD$ and any of $Curve\_CC$, $C^1$, $C^2$, $C^3$, i.e. in general:
\begin{equation}
r^\rho_{\mu\nu}(n)\sim r^\rho_{\mu\nu}(n'),\ n,n'=1,2,...,10.
\end{equation}
Therefore, the phase relationship of $Curve\_CC$, $Curve\_SL$ and $Curve\_BD$ is generally irrelevant with the weights in obtaining $Curve\_CC$ and $Curve\_SL$. The correlation coefficients are also irrelevant whether we nondimensionalize the curves, for instance:
\begin{equation}
\begin{array}{rcl}
&\ &corrcoef((S^1([P])+S^2([P]))/2, BD([P]))\\
&=&corrcoef(nondim((S^1([P])+S^2([P]))/2), nondim(BD([P])))\\
&=&corrcoef(Curve\_SL([P]), Curve\_BD([P]))\\
&=&r^P_{SB}.
\end{array}
\end{equation}

{\bf Note:} The first group ($n=1$) of curves $Curve\_CC$, $Curve\_SL$ and $Curve\_BD$ is the best among the $10$ similar groups of curves to describe the Phanerozoic climate, sea level and biodiversity.

\subsection{Three climate phases}

We propose three climate patterns CP I, CP II and CP III in the Phanerozoic eon based on the positive or negative correlations among $Curve\_CC$, $Curve\_SL$ and $Curve\_BD$. Interestingly, the time between the positive correlation periods and the negative correlation periods agree with the Paleozoic-Mesozoic boundary and the Mesozoic-Cenozoic boundary.

(1) We have
\begin{eqnarray}
r^P_{SB}&=&0.5929>0\\
r^P_{BC}&=&0.1136>0\\
r^P_{CS}&=&0.4942>0
\end{eqnarray}
which indicate the positive correlations among $Curve\_CC$, $Curve\_SL$ and $Curve\_BD$ in the Paleozoic era. This is called the first climate pattern (CP I);

(2) We have
\begin{eqnarray}
r^M_{SB}&=&0.9054>0\\
r^M_{BC}&=&-0.4308<0\\
r^M_{CS}&=&-0.6171<0
\end{eqnarray}
which indicate the negative correlations between $Curve\_CC$ and $Curve\_SL$ and between $Curve\_CC$ and $Curve\_BD$, and the positive correlation between $Curve\_SL$ and $Curve\_BD$ in the Mesozoic era. This is called the second climate pattern (CP II);

(3) We have
\begin{eqnarray}
r^C_{SB}&=&-0.8314<0\\
r^C_{BC}&=&-0.8814<0\\
r^C_{CS}&=&0.9501>0
\end{eqnarray}
which indicate the negative correlations between $Curve\_CC$ and $Curve\_BD$ and between $Curve\_SL$ and $Curve\_BD$, and the positive correlation between $Curve\_SL$ and $Curve\_CC$ in the Cenozoic era. This is called the third climate pattern (CP III).

We define the average correlation coefficient $R^+$ in the positive correlation periods:
\begin{equation}
R^+=\frac{w^P\cdot r^P_{SB}+w^P\cdot r^P_{BC}+w^P\cdot r^P_{CS}+ w^M\cdot r^M_{SB}+ w^C\cdot r^C_{CS}} {w^P +w^P +w^P + w^M + w^C },
\end{equation}
and the average correlation coefficient $R^-$ in the negative correlation periods:
\begin{equation}
R^-=\frac{ w^M\cdot r^M_{BC}+ w^M\cdot r^M_{CS}+ w^C\cdot r^C_{SB}+ w^C\cdot r^C_{BC}} { w^M + w^M + w^C + w^C },
\end{equation}
where the weights $w^{\rho}$ are the durations of Paleozoic, Mesozoic and Cenozoic respectively:
\begin{eqnarray}
w^P&=&542.0-251.0=291.0\ \mbox{Myr}\\
w^M&=&251.0-65.5=185.5\ \mbox{Myr}\\
w^C&=&65.5\ \mbox{Myr}.
\end{eqnarray}
And we denote the difference between $R^+$ and $R^-$ as
\begin{equation}
\Delta R= R^+-R^-.
\end{equation}

We define the average abstract correlation coefficient $Q$ for the positive as well as the negative correlation periods as:
\begin{equation}
Q=\frac{1}{w^{P} + w^{M} + w^{C}}\sum_{\rho=P, M, C} w^{\rho}\cdot (|r^\rho_{SB}| + |r^\rho_{BC}| + |r^\rho_{CS}|),
\end{equation}
and the average abstract correlation coefficient $Q'$ for the mixtures of positive and negative correlation periods as:
\begin{equation}
Q'=\frac{1}{w^{PMC} + w^{PM} + w^{MC}}\sum_{\rho=PMC, PM, MC} w^{\rho}\cdot (|r^\rho_{SB}| + |r^\rho_{BC}| + |r^\rho_{CS}|),
\end{equation}
where the remaining weights $w^{\rho}$ are:
\begin{eqnarray}
w^{PMC}&=&542.0 \ \mbox{Myr}\\
w^{PM}&=&542.0-65.5=476.5\ \mbox{Myr}\\
w^{MC}&=&251.0\ \mbox{Myr}.
\end{eqnarray}
And we denote the difference between $Q$ and $Q'$ as
\begin{equation}
\Delta Q= Q-Q'.
\end{equation}

We found that the abstract correlation coefficients $|r^{PMC}_{\mu\nu}|$, $|r^{PM}_{\mu\nu}|$ and $|r^{MC}_{\mu\nu}|$ in the mixtures of positive and negative periods $\rho=PMC, PM, MC$ are obviously less than the abstract values $|r^{P}_{\mu\nu}|$, $|r^{M}_{\mu\nu}|$ and $|r^{C}_{\mu\nu}|$ in the positive or negative periods, namely in the Paleozoic, Mesozoic and Cenozoic eras. Therefore, the three climate patterns naturally correspond to the Paleozoic, Mesozoic and Cenozoic eras respectively. Based on the data of the first group (n=1) of curves $Curve\_CC$, $Curve\_SL$ and $Curve\_BD$, we have:
\begin{eqnarray}
R^+=R^+(1)&>&0\ (\mbox{tend to be equal to }1)\\
R^-=R^-(1)&<&0\ (\mbox{tend to be equal to }-1)\\
\Delta R=\Delta R(1)&\gg&0\\
Q=Q(1)&\sim&1\ (\mbox{tend to be equal to }1)\\
Q'=Q'(1)&\sim&0\ (\mbox{tend to be equal to }0)\\
\Delta Q=\Delta Q(1)&>&0
\end{eqnarray}
which furthermore shows that the division of three climate patterns CP I, CP II and CP III is essential property of the evolutionary earth's spheres.

{\bf Note:} These relations are still valid for the other groups of curves ($n=2,3,...,10$).

\subsection{The P-Tr extinction was caused by the climate phase reverse between CP I and CP II}

We summarize the reasons to explain the P-Tr extinction by the climate phase reverse event as follows.
\begin{itemize}
  \item Successful explanation of the Sepkoski curve by the tectono-genomic curve based on the climate phase reverse event (Fig 1)
  \item The climate phase reverse event between CP I and CP II happened at P-Tr boundary (Fig 2a)
  \item The sharp peaks of $d\_CC$ at the Guadalupian-Lopingian boundary and at the P-Tr boundary (Fig 2b)
  \item Abnormal climate trend in the Lopingian epoch
  \item Different animal extinction patterns at the Guadalupian-Lopingian boundary and at the P-Tr boundary.
\end{itemize}

\subsection{The tectonic curve and the tectonic contribution to the biodiversity variation}

The phase of $Curve\_SL$ is about the same with the phase of $Curve\_BD$ in the Phanerozoic eon. And the phase of $Curve\_CC$ is about the same with the phase of $Curve\_BD$ in the Paleozoic era (CP I), while it is about the opposite in the Mesozoic era (CP II) and in the Cenozoic era (CP III). Accordingly, we define the associate tectonic curve $Curve\_Tectonic\_0$ by combining the consensus sea level curve and the consensus climate curve as follow (Fig S1e):
\begin{equation}
\begin{array}{rcl}
Curve\_Tectonic\_0 = [(Curve\_SL([P])&+&Curve\_CC([P]))/2,\\
(Curve\_SL([MC])&-&Curve\_CC([MC]))/2].
\end{array}
\end{equation}
We define the tectonic curve $Curve\_Tectonic$ with the same standard deviation of the net variation biodiversity curve $BD$:
\begin{equation}
Curve\_Tectonic=(Curve\_Tectonic\_0 - mean(Curve\_Tectonic\_0)) \cdot a_{std},
\end{equation}
where
\begin{equation}
a_{std}=\frac{std(BD)}{std(Curve\_Tectonic\_0 - mean(Curve\_Tectonic\_0))}.
\end{equation}

The tectonic curve $Curve\_Tectonic$ represents the tectonic (sea level and climate) contribution to the biodiversity evolution. We can calculate the correlation coefficient between the tectonic curve and the biodiversity curve in the Paleozoic era or in the Mesozoic and Cenozoic eras:
\begin{equation}
\begin{array}{rcl}
r^P_{B+}&=& corrcoef(Curve\_Tectonic([P]), Curve\_BD([P]))\\
&=&0.421,
\end{array}
\end{equation}
\begin{equation}
\begin{array}{rcl}
r^{MC}_{B-}&=&corrcoef(Curve\_Tectonic([MC]), Curve\_BD([MC]))\\
&=&0.878.
\end{array}
\end{equation}
Accordingly, we found that the  tectonic curve $Curve\_Tectonic$ is positively correlated with the biodiversity curve $Curve\_BD$ either in the Paleozoic era or in the Mesozoic and Cenozoic eras.

\section{The genomic contribution to the biodiversity evolution}

\subsection{Motivation}

We construct the genomic curve based on the observation of equality between the growth rate $k_{GS}$ in genome size evolution and the growth rate $k_{BD}$ in biodiversity evolution.

\subsection{The overall trend of genome size evolution}

\subsubsection{The log-normal distribution of genome size}

We found that the genome sizes of species in a taxon are log-normally distributed in general, which were verified in the following $7$ taxa (Fig. S2a):
\begin{equation}
log(G(\lambda,sp(\lambda))) \mbox{ are normally distributed},
\end{equation}
where $G(\lambda,sp(\lambda))$ are the genome sizes of all the species $sp(\lambda)$ ($sp(\lambda)=1,2,...,s_m(\lambda)$) in the taxon $\lambda$ in the genome size databases, and
\begin{equation}
\begin{array}{rcl}
\lambda=1 &:& \mbox{Diploblostica}\\
\lambda=2 &:& \mbox{Protostomia}\\
\lambda=3 &:& \mbox{Deuterostomia}\\
\lambda=4 &:& \mbox{Bryophyte}\\
\lambda=5 &:& \mbox{Pteridophyte}\\
\lambda=6 &:& \mbox{Gymnosperm}\\
\lambda=7 &:& \mbox{Angiosperm}.
\end{array}
\end{equation}
Due to the additivity of normal distribution, the genome sizes of animals, plants, or eukaryotes are also log-normal distributed. We obtain the means of logarithm of genome sizes and the standard deviations of logarithm of genome sizes as follows:
\begin{equation} G^P_{mean\_log}(\lambda)=mean(log(G(\lambda,sp(\lambda)))), \end{equation}
and
\begin{equation} G^P_{sd\_log}(\lambda)=std(log(G(\lambda,sp(\lambda)))), \end{equation}
where $sp(\lambda)=1,2,...,s_m(\lambda)$.
Denote $G^*$ as the mean logarithm of genome sizes of all the contemporary eukaryotes:
\begin{equation} G^*=mean(log(G(sp))),\end{equation}
where $sp$ is all the contemporary eukaryotes in the genome size databases.

{\bf Note:} The log-normal distribution of genome size can be demonstrated by the common intersection point $\Omega$ for the following regression lines (Fig 3b):
\begin{eqnarray}
&\mbox{regression line of}& G_{mean\_log}(\lambda')  \mbox{ on } G_{sp}(\lambda')\\
&\mbox{regression line of}& G_{mean\_log}(\lambda') \pm \chi \cdot G_{sd\_log}(\lambda') \mbox{ on } G_{sp}(\lambda')\\
&\mbox{regression line of}& G_{mean\_log}(\lambda') \pm \chi' \cdot G_{sd\_log}(\lambda') \mbox{ on } G_{sp}(\lambda')\\
&\mbox{regression line of}& max(G(\lambda',sp(\lambda'))) \mbox{ on } G_{sp}(\lambda')\\
&\mbox{regression line of}& min(G(\lambda',sp(\lambda'))) \mbox{ on } G_{sp}(\lambda')\\
&\mbox{regression line of}& G_{mean\_log}^P(\lambda)  \mbox{ on } G_{sp}^P(\lambda)\\
&\mbox{regression line of}& G_{mean\_log}^P(\lambda) \pm \chi \cdot G_{sd\_log}^P(\lambda) \mbox{ on } G_{sp}^P(\lambda)\\
&\mbox{regression line of}& G_{mean\_log}^P(\lambda) \pm \chi' \cdot G_{sd\_log}^P(\lambda) \mbox{ on } G_{sp}^P(\lambda)\\
&\mbox{regression line of}& max(G(\lambda,sp(\lambda))) \mbox{ on } G_{sp}^P(\lambda)\\
&\mbox{regression line of}& min(G(\lambda,sp(\lambda))) \mbox{ on } G_{sp}^P(\lambda)
\end{eqnarray}
where $\lambda=1,2,...,7$ for the above $7$ taxa, $\lambda'=1,2,...,19+53$ for $19$ animal taxa and $53$ angiosperm taxa, $\chi=1.5677$ and $\chi_1=3.1867$. The values of $G_{sd\_log}$ tend to decline with respect to $G_{sp}$ that is proportional to the origin time of taxa (Fig S2b).

\subsubsection{The exponential overall trend of genome size evolution}

We assume the approximate origin times $T(\lambda)$ for the taxa $\lambda=1,2,...,7$ as follows:
\begin{equation}
\begin{array}{rcl}
T(1)&=&560.0\ \mbox{Ma}\\
T(2)&=&542.0\ \mbox{Ma, PreCm-Cm}\\
T(3)&=&525.0\ \mbox{Ma}\\
T(4)&=&488.3\ \mbox{Ma, Cm-O}\\
T(5)&=&416.0\ \mbox{Ma, S-D}\\
T(6)&=&359.2\ \mbox{Ma, D-C}\\
T(7)&=&145.5\ \mbox{Ma, J-K}
\end{array}
\end{equation}
We observed a rough proportional relationship between $G^P_{mean\_log}(\lambda)$ and $T(\lambda)$. Because $G^P_{mean\_log}(\lambda)$ is the mean genome size of the ``contemporary species'', we should introduce a new notion (the specific genome size) to indicate the mean genome sizes of the ``ancient species'' in taxa $\lambda=1,2,...,7$ at its origin time $T(\lambda)$. Here, we define the specific genome size $G^P_{sp}$ as:
\begin{equation} G^P_{sp}(\lambda)=G^P_{mean\_log}(\lambda)-\chi \cdot G^P_{sd\_log}(\lambda), \end{equation}
where we let $\chi=1.5677$ such that the intercept of the regression line of $G^P_{sp}(\lambda)$ on $T(\lambda)$ is equal to $G^*$. We found that $G^P_{sp}(\lambda)$ is generally proportional to $T(\lambda)$ (Fig. 3a). We define the regression line of $G^P_{sp}(\lambda)$ on  $T(\lambda)$ as overall trend of genome size curve:
\begin{equation}
OT\mbox{-}GS=k_{GS}(-t)+log(N^0_{genome}).
\end{equation}
This equation is equivalent to the exponential overall trend of genome size evolution:
\begin{equation} N_{genome}(t) = N^0_{genome}\ \exp(-t/\tau_{GS}),
\end{equation}
where the genome size constant is $N^0_{genome}=2.16\times10^9$ base pairs (bp) and the ``e-folding time'' in genome size evolution is $\tau_{GS}=256.56$ (Myr). The growth rate (namely the slope) of $OT\mbox{-}GS$ is $k_{GS} =1/\tau_{GS} =0.0038977$ $\mbox{Myr}^{-1}$.

{\bf Note:} The exponential overall trend of genome size evolution obtained in the Phanerozoic eon can be extrapolated to the Precambrian period. This extrapolation result according to the value of $k_{GS}$ is reasonable to show that the least genome size at $3800$ Ma (about the beginning of life) is about several hundreds of base pairs (Fig 3d).

\subsection{The agreement between the overall trend of genome size evolution and the overall trend of biodiversity evolution}

We found the closely relationship between the genome size evolution and the biodiversity evolution (Fig 3d). Both the overall trend of genome size evolution and the overall trend of biodiversity evolution are exponential; and the exponential growth rate in the genome size evolution ($k_{GS}=0.0038977$ $\mbox{Myr}^{-1}$) (Fig 3a, 3d) is approximately equal to the exponential growth rate in the biodiversity evolution ($k_{BD}=0.0038598$ $\mbox{Myr}^{-1}$) (Fig 2d, 3d):
\begin{equation}
k_{GS} \approx k_{BD},
\end{equation}
which is equivalent to that the e-folding time in the genome size evolution ($\tau_{GS}=256.56$ Myr) is approximately equal to the e-folding time in the biodiversity evolution ($\tau_{BD}=259.08$ Myr):
\begin{equation} \tau_{GS} \approx \tau_{BD}. \end{equation}

\subsection{Explanation of the declining Phanerozoic background extinction rates}

Let $rate\_ori$ and $rate\_ext$ denote the Phanerozoic biodiversity origination rate and extinction rate respectively:
\begin{equation}
rate\_ori:\ \mbox{ref. [2]},
\end{equation}
\begin{equation}
rate\_ext:\ \mbox{ref. [2]},
\end{equation}
which agree with each other in general.
The difference and the average of them are as follows respectively:
\begin{equation}
rate_{o-e}=(rate\_ori-rate\_ext)/2,
\end{equation}
\begin{equation}
rate_{o+e}=(rate\_ori+rate\_ext)/2,
\end{equation}
where $rate_{o-e}$ should agree with $d\_BD$ according to their definitions, and $rate_{o+e}$ represents the variation of biodiversity in the Phanerozoic eon. The outline of $rate_{o+e}$ indicates the declining Phanerozoic background extinction rates \cite{Flessa and Jablonski 1985} \cite{Van Valen 1985} \cite{Sepkoski 1991} \cite{Gilinsky 1994} \cite{Alroy 1998}.

We define an essential biodiversity background variation rate by:
\begin{equation}
rate\_essential=[amp(1) \cdot rate_{o+e}(1),amp(2) \cdot rate_{o+e}(2),...,amp(5421)\cdot rate_{o+e}(5421)],
\end{equation}
where
\begin{equation}
amp=exp(k_{GS}\cdot(-t+542.0)).
\end{equation}
The outline of $rate\_essential$ is generally horizontal (NOT declining). Especially, the peaks of the curve $rate\_essential$ at P-Tr boundary and at K-Pg boundary are very high, which naturally divide the Phanerozoic eon into three climate phases (Fig 2c).

In the split scenario of biodiversification, we can explain the ``declining'' background extinction rates in the Phanerozoic eon. Firstly, there does not exist a tendency in the essential biodiversity background rate curve $rate\_essential$. This essential rate was caused by the random tectonic contribution (no tendency) to the biodiversity evolution:
\begin{equation}
rate\_essential=\frac{\mbox{variation of biodiversity}}{\mbox{tectonic contribution to biodiversity}}.
\end{equation}
Then, the declining tendency in the observed background extinction or origination rates was caused by the genomic contribution to the biodiversity evolution:
\begin{equation}
rate_{o+e}=\frac{\mbox{variation of biodiversity}}{\mbox{tectonic contribution+genomic contribution to biodiversity}}.
\end{equation}
It follows that (Fig 2c):
\begin{equation}
rate_{o+e}=exp(-k_{GS}\cdot(-t+542.0)) \cdot rate\_essential,
\end{equation}
where $rate_{o+e}$ is declining due to the factor $exp(-k_{GS}\cdot(-t+542.0))$.

The genomic contribution to the biodiversity plays a significant role in the robustness of biodiversity evolution: the random tectonic contribution can hardly wipe out all the life on the earth thanks for the exponential growth genomic contribution to the biodiversity evolution.

\subsection{Calculating the origin time of taxa based on the overall trend of genome size evolution}

\subsubsection{The three-stage pattern in Metazoan origination}

We can calculate the origin time of animal taxa according to the linear relationship between the origin time and the specific genome size. We obtained the specific genome sizes of the $19$ taxa in the Animal Genome Size Database (Nematodes, Chordates, Sponges, Ctenophores, Tardigrades, Miscellaneous Inverts, Arthropod, Annelid, Myriapods, Flatworms, Rotifers, Cnidarians, Fish, Echinoderm, Molluscs, Bird, Reptile, Amphibian, Mammal):
\begin{equation} G_{sp}^{animal}(\lambda^{animal})= G_{mean\_log}^{animal}(\lambda^{animal})- \chi\cdot G_{sd\_log}^{animal}(\lambda^{animal}),
\end{equation}
where $\lambda^{animal}=1,2,...,19$.
We can obtain the origin order of these $19$ taxa by comparing their specific genome sizes. Hence, we can classify these $19$ taxa into Basal metazoa, Protostomia and Deuterostomia according to cluster analysis of their specific genome sizes (Data\_3). Our result supports the three-stage pattern in Metazoan origination based on fossil records \cite{Conway-Morris 1993} \cite{Conway-Morris 1989} \cite{Budd and Jensen 2000} \cite{Shu 2005} \cite{Shu et al. 2004} \cite{Valentine 2001} \cite{Shu et al. 2009}.

\subsubsection{On angiosperm origination}

Similarly, we can calculate the origin time of angiosperm taxa according to the linear relationship between the origin time and the specific genome size. We obtained the specific genome sizes of the $53$ taxa of angiosperms in the Plant DNA C-value Database (we chose the taxa whose number of species is greater than $20$ in the calculations):
\begin{equation}
G_{sp}^{angiosperm}(\lambda^{angiosperm})= G_{mean\_log}^{angiosperm}(\lambda^{angiosperm})- \chi \cdot G_{sd\_log}^{angiosperm}(\lambda^{angiosperm}),
\end{equation}
where $\lambda^{angiosperm}=1,2,...,53$.
We can obtain the origin order of these $53$ taxa by comparing their specific genome sizes. Hence, we can classify these $53$ taxa into Dicotyledoneae and Monocotyledoneae (Data\_3).

{\bf Note:} The validity of our theory on genome size evolution is supported by its reasonable explanation of metazoan origination and angiosperm origination.

{\bf Notation:} We denote the mean logarithm genome size, the standard deviation genome size and the specific genome sizes by concatenations for all the $19$ animal taxa and the $53$ plant taxa:
\begin{eqnarray}
G_{mean\_log}&=&[\ G_{mean\_log}^{animal},\ G_{mean\_log}^{angiosperm}\ ]\\
G_{sd\_log}&=&[\ G_{sd\_log}^{animal},\ G_{sd\_log}^{angiosperm}\ ]\\
G_{sp}&=&[\ G_{sp}^{animal},\ G_{sp}^{angiosperm}\ ].
\end{eqnarray}

\subsection{The phylogenetic tree based on the correlation among genome size distributions}

We found that the phylogenetic tree for taxa can be easily obtained based on the correlation coefficients among their genome size distributions. We denote the genome size distribution for a taxon $\lambda$ by:
\begin{equation}
D_{gs}(\lambda,:)=[D_{gs}(\lambda,1), D_{gs}(\lambda,2),..., D_{gs}(\lambda,k),...,D_{gs}(\lambda,cutoff_{gs})],
\end{equation}
where there are $D_{gs}(\lambda,k)$ species in taxon $\lambda$ whose genome size is between $(k-1)\cdot step_{gs}$ and $k\cdot step_{gs}$, the genome size step $step_{gs}=0.01$ picogram (pg) and the genome size cutoff is $cutoff_{gs}=2000$.
Hence, we define the genome size distribution distance matrix $M_{gs}(\lambda_1,\lambda_2)$ among taxa by:
\begin{equation}
M_{gs}(\lambda_1,\lambda_2)=1-corrcoef(D_{gs}(\lambda_1,:),D_{gs}(\lambda_2,:)),
\end{equation}
by which, we can draw the phylogenetic tree of the taxa.

We can obtain the genome size distributions $D^P_{gs}(\lambda,:)$ and consequently obtain the genome size distribution distance matrix $M^P_{gs}(\lambda_1,\lambda_2)$ among the above $7$ taxa as follows:
\begin{equation}
M^P_{gs}(\lambda_1,\lambda_2)=1-corrcoef(D^P_{gs}(\lambda_1,:),D^P_{gs}(\lambda_2,:)),
\end{equation}
where $\lambda_1, \lambda_2 = 1,2,...,7$. Hence, we can draw the phylogenetic tree of the $7$ taxa based on $M^P_{gs}$ (Fig S2c).

We can obtain the genome size distributions $D^{animal}_{gs}(\lambda,:)$ and consequently obtain the genome size distribution distance matrix $M^{animal}_{gs}(\lambda_1,\lambda_2)$ among the above $19$ animal taxa as follows:
\begin{equation}
M^{animal}_{gs}(\lambda_1^{animal},\lambda_2^{animal}) = 1-corrcoef(D^{animal}_{gs}(\lambda_1^{animal},:),D^{animal}_{gs}(\lambda_2^{animal},:)),
\end{equation}
where $\lambda_1^{animal}, \lambda_2^{animal} = 1,2,...,19$. Hence, we can draw the phylogenetic tree of the $19$ taxa based on $M^{animal}_{gs}$ (Fig 3c).

We can obtain the genome size distributions $D^{angiosperm}_{gs}(\lambda,:)$ and consequently obtain the genome size distribution distance matrix $M^{angiosperm}_{gs}(\lambda_1,\lambda_2)$ among the $25$ angiosperm taxa (we chose $25$ angiosperm taxa whose number of species is greater than $50$ in the Plant DNA C-value database in order to obtain nontrivial distributions) as follows:
\begin{equation}
M^{angiosperm}_{gs}(\lambda_1^{angiosperm},\lambda_2^{angiosperm}) = 1-corrcoef(D^{angiosperm}_{gs}(\lambda_1^{angiosperm},:),D^{angiosperm}_{gs}(\lambda_2^{angiosperm},:)),
\end{equation}
where $\lambda_1^{angiosperm}, \lambda_2^{angiosperm} = 1,2,...,25$. Hence, we can draw the phylogenetic tree of the $25$ taxa based on $M^{angiosperm}_{gs}$ (Fig S2d).

These phylogenetic trees based on genome size distribution distance matrices generally agree with the traditional phylogenetic trees respectively, which is an evidence to show the close relationship between the genome evolution and the biodiversity evolution.

{\bf Software:} PHYLIP to draw the phylogenetic trees (Neighbor-Joining) in this paper \cite{Felsenstein 1981}.

\subsection{The varying velocity of molecular clock among taxa}

The growth rates $k_{GS}(\lambda)$ of overall genome size evolution $OT_{taxa}(\lambda)$ for taxa $\lambda$ are not constant, though we have an average growth rate $k_{GS}$ for $OT\mbox{-}GS$. We have an approximate relationship that the earlier the origin time $T_{ori}(\lambda)$ is, the slower the growth rate $k_{GS}(\lambda)$ is:
\begin{equation}
(k_{GS}(\lambda)-k_{GS})\cdot T_{ori}(\lambda) \doteq \hat{G},
\end{equation}
where the constant $\hat{G}$ is the difference between the intercept of the overall trend of mean logarithm genome size $OT_{mean\_log}$ and the intercept of $OT\mbox{-}GS$.

\subsection{The genomic curve and the genomic contribution to the biodiversity evolution}

We define the genomic curve by a straight line with slope $k_{GS}$ and the undetermined intercept $b_{today}$:
\begin{equation}
Curve\_Genomic=k_{GS}\cdot (-t)+b_{today},
\end{equation}
which represents the exponential contribution to the biodiversity evolution.

\section{Construction of the tectono-genomic curve}

\subsection{The synthesis scheme for the tectono-genomic curve}

The above undetermined intercept of the genomic curve can be defined as:
\begin{equation}
b_{today}=Curve\_Sepkoski(today)-Curve\_Tectonic(today)
\end{equation}
such that $Curve\_TectonoGenomic(5421)=Curve\_Sepkoski(5421)$.

We define the tectono-genomic curve by synthesizing the tectonic curve $Curve\_Tectonic$ and the genomic curve $Curve\_Genomic$ (Fig 1):
\begin{equation}
Curve\_TectonoGenomic=\exp(Curve\_Tectonic+Curve\_Genomic),
\end{equation}
which agrees very well with the Phanerozoic biodiversity curve $Curve\_Sepkoski$:
\begin{equation}
Curve\_TectonoGenomic \approx Curve\_Sepkoski.
\end{equation}
Thus, the Sepkoski curve based on fossil records can be explained by the tectono-genomic curve based on climatic, eustatic and genomic data.

\subsection{The driving forces of biodiversity evolution at the molecular level and at the species level}

Thus, we have explained the Sepkoski curve in the split scenario. The exponential growth part in the Phanerozoic biodiversity evolution was driven by the genome size evolution on one hand, and the variation of the the Phanerozoic biodiversity evolution was caused by the Phanerozoic sea level fluctuation and climate change on the other hand.

The successful explanation of the Phanerozoic biodiversity curve $Curve\_Sepkoski$ shows that the driving force of the biodiversity evolution is the tectono-genomic driving force. There are two independent tectonic and genomic driving forces in the biodiversity evolution. The first driving force originated from the plate tectonics movement at the species level; while the second driving force originated from the genome evolution at the molecular level.

\section{The error analysis and reasonability analysis}

\subsection{The agreement between the Sepkoski curve and the tectono-genomic curve}

\subsubsection{The error analysis of the consensus climate curve}

We obtain the first weighted average climate curve $C_{w1}$ by choosing the corresponding $\Delta R(n)$, $n=2,3,4$ as the weights $w1$ for $C^1$, $C^2$ and $C^3$ as follows:
\begin{equation}
\begin{array}{rcl}
w1&=&[\Delta R(2),\Delta R(3),\Delta R(4)]/(\Delta R(2)+\Delta R(3)+\Delta R(4))\\
&=&[0.3454,\ 0.1611,\ 0.4935],
\end{array}
\end{equation}
hence,
\begin{equation}
C_{w1}=nondim(w1(1) \cdot C^1+w1(2) \cdot C^2+w1(3) \cdot C^3).
\end{equation}

We obtain the second weighted average climate curve $C_{w2}$ by choosing the corresponding correlation coefficients as the weights $w2$ for $C^1$, $C^2$ and $C^3$ as follows:
\begin{equation}
\begin{array}{rcl}
w2&=&[corrcoef(Curve\_CC,C^1), corrcoef(Curve\_CC,C^2),\\
 & & corrcoef(Curve\_CC,C^3)]/\\
& &(corrcoef(Curve\_CC,C^1)+ corrcoef(Curve\_CC,C^2)+\\
 & &+corrcoef(Curve\_CC,C^3))\\
&=&[0.4865,\ 0.2796,\ 0.2339],
\end{array}
\end{equation}
hence,
\begin{equation}
C_{w2}=nondim(w2(1) \cdot C^1+w2(2) \cdot C^2+w2(3) \cdot C^3).
\end{equation}

We can obtain a weighted average climate curve $C_{w}$ by choosing the average of $w1$ and $w2$ as the weights $w$ for $C^1$, $C^2$ and $C^3$ as follows:
\begin{equation}
\begin{array}{rcl}
w&=&(w1+w2)/2\\
&=&[0.4159,\ 0.2204,\ 0.3637],
\end{array}
\end{equation}
hence,
\begin{equation}
C_{w}=nondim(w(1) \cdot C^1+w(2) \cdot C^2+w(3) \cdot C^3),
\end{equation}
which agrees with $Curve\_CC$.

The weights $w1$ or $w2$ can be referred to as credibilities for the independent curves $C^1$, $C^2$ and $C^3$. Both of $C_{w1}$ and $C_{w2}$ are reasonable estimations of the Phanerozoic climate. So, we can consider the zone between $C_{w1}$ and $C_{w2}$ as the error range of $Curve\_CC$, whose upper range $C_{upper}$ and lower range $C_{lower}$ are about as follows (Fig S1b):
\begin{equation}
C_{upper}=max(C_{w1},C_{w2}),
\end{equation}
\begin{equation}
C_{lower}=min(C_{w1},C_{w2}).
\end{equation}

\subsubsection{The error analysis of the consensus sea level curve}

We obtain the weighted average sea level curve $S_{w}$ by choosing the corresponding $\Delta R(n)$, $n=10,11$ as the weights $w'$ for $S^1$ and $C^2$ as follows:
\begin{equation}
\begin{array}{rcl}
w'&=&[\Delta R(10), \Delta R(11)]/(\Delta R(10)+\Delta R(11))\\
&=&[0.4872,\ 0.5128],
\end{array}
\end{equation}
hence,
\begin{equation}
S_{w}=nondim(w'(1) \cdot S^1+w'(2) \cdot S^2),
\end{equation}
which agrees with $Curve\_SL$.

We can consider the zone between $S^1$ and $S^2$ as the error range of $Curve\_SL$, whose upper range $S_{upper}$ and lower range $S_{lower}$ are about as follows (Fig S1c):
\begin{equation}
S_{upper}=max(S^1,S^2),
\end{equation}
\begin{equation}
S_{lower}=min(S^1,S^2).
\end{equation}

\subsubsection{The error analysis of the Sepkoski curve}

We can consider the zone between $Curve\_S\_AllGenera$ and $Curve\_S\_WellResolvedGenera$ as the error range of $Curve\_Sepkoski$ (Fig 1):
\begin{equation} Curve\_S\_AllGenera:\ \mbox{ref. [3]},
\end{equation}
\begin{equation} Curve\_S\_WellResolvedGenera:\ \mbox{ref. [3]}, \end{equation}
where $Curve\_S\_AllGenera$ is the Phanerozoic biodiversity curve based on all the genera in Sepkoski's data and $Curve\_S\_WellResolvedGenera$ is the Phanerozoic biodiversity curve based on well resolved genera in Sepkoski's data.

\subsubsection{The error analysis of the tectono-genomic curve}

In consideration of the error ranges of $Curve\_CC$ and $Curve\_SL$ as well as their  phase relationships, we define the associate upper tectono-genomic curve $Curve\_TG\_upper\_0$ and the associate lower tectono-genomic curve $Curve\_TG\_lower\_0$ as follow:
\begin{equation}
\begin{array}{l}
Curve\_TG\_upper\_0 = [(S_{upper}([P])+C_{upper}([P]))/2, (S_{upper}([MC])-C_{lower}([MC]))/2],
\end{array}
\end{equation}
\begin{equation}
\begin{array}{l}
Curve\_TG\_lower\_0 = [(S_{lower}([P])+C_{lower}([P]))/2, (S_{lower}([MC])-C_{upper}([MC]))/2].
\end{array}
\end{equation}
Furthermore, in the similar process and with the same parameters in construction of the tectono-genomic curve, we can obtain the upper range and the lower range of the tectono-genomic curve as follows (Fig 1):
\begin{equation}
\begin{array}{l}
Curve\_TG\_upper = \exp(Curve\_Genomic + \\
\hspace{1cm} + a_{std} \cdot (Curve\_TG\_upper\_0  - mean(Curve\_TG\_upper\_0))),
\end{array}
\end{equation}
\begin{equation}
\begin{array}{l}
Curve\_TG\_lower = \exp(Curve\_Genomic + \\
\hspace{1cm} + a_{std} \cdot (Curve\_TG\_lower\_0 - mean(Curve\_TG\_lower\_0))).
\end{array}
\end{equation}

\subsection{The reasonability of the principal conjectures}

\subsubsection{Reasonability of the climate phase reverse based on $r_{\mu\nu}^{\rho}(n)$}

We can obtain the following $10$ groups of curves to describe the Phanerozoic climate, sea level and biodiversity:
\begin{equation}
\begin{array}{lcclclc}
n=1\ &:&\  Curve\_SL&,&\  Curve\_BD&,&\ Curve\_CC\\
n=2\ &:&\  Curve\_SL&,&\  Curve\_BD&,&\ C^1\\
n=3\ &:&\  Curve\_SL&,&\  Curve\_BD&,&\ C^2\\
n=4\ &:&\  Curve\_SL&,&\  Curve\_BD&,&\ C^3\\
n=5\ &:&\  Curve\_SL&,&\  Curve\_BD&,&\ C_{w1}\\
n=6\ &:&\  Curve\_SL&,&\  Curve\_BD&,&\ C_{w2}\\
n=7\ &:&\  Curve\_SL&,&\  Curve\_BD&,&\ C_{w}\\
n=8\ &:&\  S^1&,&\  Curve\_BD&,&\ Curve\_CC\\
n=9\ &:&\  S^2&,&\  Curve\_BD&,&\ Curve\_CC\\
n=10\ &:&\  S_w&,&\  Curve\_BD&,&\ Curve\_CC\\
\end{array}
\end{equation}
And we can obtain the correlation coefficients $r^\rho_{\mu \nu}(n)$ among these groups of curves (Data\_2), where
\begin{equation}
\mu,\nu=S,B,C,C^1,C^2,C^3,C_{w1},C_{w2},C_{w},S^1,S^2,S_w
\end{equation}
for the curves $Curv\_SL$, $Curve\_BD$, $Curve\_CC$, $C^1$, $C^2$, $C^3$, $C_{w1}$, $C_{w2}$, $C_w$, $S^1$, $S^2$ and $S_w$ respectively.

We can define the corresponding average correlation coefficients for all the $10$ groups of curves ($n=1,2,...,10$) as follows:
\begin{equation}
R^+(n), R^-(n), \Delta R(n), Q(n), Q'(n), \Delta Q(n).
\end{equation}

The conclusions on the climate phases CP I, CP II and CP III based on the first group of curves ($n=1$) still hold for the cases of the other groups of curves ($n=2,3,...,10$). Namely, the following equations holds in general:
\begin{eqnarray}
r^P_{SB}(n)&>&0\\
r^P_{BC}(n)&>&0\\
r^P_{CS}(n)&>&0
\end{eqnarray}
for CP I,
\begin{eqnarray}
r^M_{SB}(n)&>&0\\
r^M_{BC}(n)&<&0\\
r^M_{CS}(n)&<&0
\end{eqnarray}
for CP II, and
\begin{eqnarray}
r^C_{SB}(n)&<&0\\
r^C_{BC}(n)&<&0\\
r^C_{CS}(n)&>&0
\end{eqnarray}
for CP III.

Furthermore, we have
\begin{eqnarray}
R^+(n)&>&0\ (\mbox{tend to be equal to }1)\\
R^-(n)&<&0\ (\mbox{tend to be equal to }-1)\\
\Delta R(n)&\gg&0\\
Q(n)&\sim&1\ (\mbox{tend to be equal to }1)\\
Q'(n)&\sim&0\ (\mbox{tend to be equal to }0)\\
\Delta Q(n)&>&0
\end{eqnarray}
which shows that the division of three climate phases is an essential property in the evolution rather than just random phenomenon in math games.

The explanation of the P-Tr extinction based on the phase reverse at P-Tr boundary is therefore valid regardless the disagreement in the raw data of the Phanerozoic climate and sea level. Especially, $\Delta R(1)$ and $\Delta Q(1)$ are relatively the maximum among these $10$ groups of curves, hence we chose the optimal first group of curves to describe the Phanerozoic climate, sea level and biodiversity throughout this paper.

The climate system was not stationary when coupling with the other earth's spheres around P-Tr boundary. We calculate the correlation coefficients $r_{\mu \nu}^{\rho}$, where $\rho= P\backslash L, L, L.M.Tr, M\backslash L.M.Tr$ in detail around P-Tr boundary. The curve $Curve\_CC$ varies instead in the opposite phase with $Curve\_SL$ and $Curve\_BD$ in Lopingian yet; and it varies instead in the same phase with $Curve\_SL$ and $Curve\_BD$ in Lower and Middle Triassic.

\subsubsection{Reasonability of the split scenario}

We summarize the reasons to propose the split scenario in observing the biodiversity evolution as follows.

(1) Evidences to support the close relationship between the genome evolution and the biodiversity evolution:
\begin{itemize}
  \item Exponential growth in both the genome size evolution and the biodiversity evolution
  \item Agreement between genome size growth rate $k_{GS}$ and biodiversity growth rate $k_{BD}$, namely $\tau_{GS}\approx \tau_{BD}$
  \item Favorable phylogenetic trees based on $M_{gs}^P$, $M_{gs}^{animal}$, $M_{gs}^{angiosperm}$, $M_{ci}^{all}$, $M_{ci}^{euk}$
  \item Verification of the three-stage pattern in Metazoan origination and the classification of dicotyledoneae and monocotyledoneae in angiosperm origination based on the overall trend in genome size evolution
  \item Reasonable extrapolation of the overall trend in genome size evolution obtained in Phanerozoic eon to the Precambrian period
  \item The relationship between phylogenetic trees of species by $M_{ci}$ and the evolutionary tree of codons by $M_{codon}$ based on the same matrix $\Delta$.
\end{itemize}

(2) Successful applications of the split scenario:
\begin{itemize}
  \item Explanation of the Sepkoski curve by the tectono-genomic curve in the split scenario
  \item Error analysis agreement between $Curve\_Sepkoski$ and $Curve\_TectonoGenomic$
  \item Explanation of the declining Phanerozoic background extinction rates
  \item Explanation of the robustness of biosphere in the tremendously changing environment.
\end{itemize}

\section{The genetic code evolution as the initial driving force in the biodiversity evolution}

\subsection{The evolutionary relationship between the tree of life and the tree of codon}

\subsubsection{The codon interval distribution $D_{ci}$}

We can obtain both the phylogenetic tree of species and the evolutionary tree of $64$ codons based on the codon interval distributions in the whole genomes. For a certain species $\alpha$ and a certain codon $n_c$ ($n_c=1,2,...,64$ for $64$ codons), we define the ``codon interval'' $I(n_c,\alpha,p)$ as the distance between a pair ($p$) of neighboring codon $n_c$'s in the whole genome sequence. We define the codon interval distribution
\begin{equation}
D_{ci}(n_c,\alpha,:)=[D_{ci}(n_c,\alpha,1), D_{ci}(n_c,\alpha,2),...,D_{ci}(n_c,\alpha,cutoff_{ci})].
\end{equation}
as the distribution of all the codon intervals $I(n_c,\alpha,p)$ in the whole genome sequence (reading in only one direction), where there are $D_{ci}(i)$ pairs of codon $n_c$'s with the distance $i$ (the cutoff of distance in the calculations is set as $cutoff_{ci}=1000$ bases). For a group of $N$ species, there are $64 \times N$ $cutoff_{ci}$-dim vectors $D_{ci}(n_c,\alpha,:)$.

{\bf Example:} The ``GGC'' codon interval distribution of the following ``genome $\alpha_0$'' is $D_{ci}(``GGC\mbox{''},\alpha_0,:)=[0,0,1,3,5,1,0,0,0,0]$, where $cutoff_0=10$.
$$
\mbox{{\color{red}GGC}AU{\color{red}GGC}UU{\color{red}GGC}AUC{\color{red}GGC}A{\color{red}GGC}AU{\color{red}GGC}A{\color{red}GGC}{\color{red}GGC}AU{\color{red}GGC}A{\color{red}GGC}UU{\color{red}GGC}AGCA}
$$
And the ``GCA'' codon interval distribution of the same ``genome $\alpha_0$'' is $D_{ci}(``GCA\mbox{''},\alpha_0,:)=[0,0,1,1,2,1,1,0,1,1]$.
$$
\mbox{G{\color{green}GCA}UGGCUUG{\color{green}GCA}UCG{\color{green}GCA}G{\color{green}GCA}UG{\color{green}GCA}GGCG{\color{green}GCA}UG{\color{green}GCA}GGCUUG{\color{green}GCA}{\color{green}GCA}}
$$
Hence, the correlation coefficient between $D_{ci}(``GGC\mbox{''},\alpha_0,:)$ and $D_{ci}(``GCA\mbox{''},\alpha_0,:)$ is
$$
corrcoef(D_{ci}(``GGC\mbox{''},\alpha_0,:),D_{ci}(``GCA\mbox{''},\alpha_0,:))=0.7235.
$$

\subsubsection{The codon interval correlation matrix $\Delta$}

The codon interval correlation matrix $\Delta(n_c,\alpha,\beta)$ for a group of $N$ species is defined as the $64 \times N \times N$ matrix of the correlation coefficients between pairs of vectors $D_{ci}(n_c,\alpha)$ and $D_{ci}(n_c,\beta)$:
\begin{equation} \Delta(n_c,\alpha,\beta)=corrcoef(D_{ci}(n_c,\alpha,:),D_{ci}(n_c,\beta,:)). \end{equation}

\subsubsection{Calculating the codon interval distance matrix of species $M_{ci}$ according to $\Delta$}

We can obtain the $N \times N$ codon interval distance matrix $M_{ci}(\alpha,\beta)$ of the $N$ species by averaging the $64 \times N \times N$ correlation coefficients with respect to the $64$ codons: \begin{equation}
M_{ci}(\alpha,\beta) = 1- \frac{1}{64} \sum_{n_c=1}^{64} \Delta(n_c,\alpha,\beta).
\end{equation}
Hence, we can draw the phylogenetic tree of $N$ species based on $M_{ci}$.

The method to obtain phylogenetic trees of species based on the codon interval distance matrices is valid not only for eukarya but also for bacteria, archaea and virus. The phylogenetic trees of species based on the codon interval distance matrices generally agree with the traditional phylogenetic trees respectively, which is also an evidence to show the close relationship between the genome evolution and the biodiversity evolution.

\subsubsection{Calculating the distance matrix of codons $M_{codon}$ according to $\Delta$}

We can obtain the $64 \times 64$ distance matrix of codons $M_{codon}$ by averaging the $64 \times N \times N$ correlation coefficients with respect to the $N$ species:
\begin{equation}
M_{codon}(n_c,n_c') = 1- corrcoef(\Delta(n_c,:,:),\Delta(n_c',:,:)).
\end{equation}
Hence, we can draw the evolutionary tree of $64$ codons based on $M_{codon}$.

The evolutionary tree of codons based on $M_{codon}$ agrees with the traditional understanding of the genetic code evolution. Thus, we can obtain both the phylogenetic tree of species and the evolutionary tree of $64$ codons based on the same codon interval correlation matrix $\Delta$. This is an evidence to show the close relationship between the genetic code evolution and the biodiversity evolution. The principal rules in the biodiversity evolution may concern the primordial molecular evolution.

\subsection{The tree of life and the tree of codon (example 1)}

Based on the genomes of $748$ bacteria, $55$ archaea, $16$ eukaryotes and $133$ viruses (GeneBank, up to 2009), we can obtain the codon interval correlation matrices $\Delta^{all}$. For the eukaryotes with several chromosomes, the codon interval distributions are obtained by averaging the codon interval distributions with respect to the chromosomes of the certain species. Consequently, we can obtain the reasonable phylogenetic tree of these speces (Fig S3a) and the reasonable tree of $64$ codons (Fig 4a) by calculating $M_{ci}^{all}(\alpha,\beta)$ and $M_{codon}^{all}(n_c,n_c')$ from $\Delta^{all}$:
\begin{equation}
M_{ci}^{all}(\alpha,\beta) = 1- \frac{1}{64} \sum_{n_c=1}^{64} \Delta^{all}(n_c,\alpha,\beta),
\end{equation}
and
\begin{equation}
M_{codon}^{all}(n_c,n_c') = 1- corrcoef(\Delta^{all}(n_c,:,:),\Delta^{all}(n_c',:,:)).
\end{equation}

\subsection{The tree of life and the tree of codon (example 2)}

Based on the genomes of $16$ eukaryotes, we can obtain the codon interval correlation matrices $\Delta^{euk}$. Consequently, we can obtain the reasonable phylogenetic tree of these $16$ eukaryotes (Fig 4c) and the reasonable tree of $64$ codons (Fig S3b) by calculating $M_{ci}^{euk}(\alpha,\beta)$ and $M_{codon}^{euk}(n_c,n_c')$ from $\Delta^{euk}$.
If there are several chromosomes ($chr(\alpha)=1,2,...,c_m(\alpha)$) in the genome of eukaryote $\alpha$, the codon interval distributions of the chromosomes of species $\alpha$ are $D^{euk}_{ci}(n_c,\alpha,chr(\alpha),:)$. The codon interval correlation matrix is:
\begin{equation}
\Delta^{euk}(n_c,\alpha,chr(\alpha),\beta,chr(\beta)) = corrcoef(D^{euk}_{ci}(n_c,\alpha,chr(\alpha),:), D^{euk}_{ci}(n_c,\beta,chr(\beta),:)). \end{equation}
Consequently, we can calculating the codon interval distance matrix of species: \begin{equation}
M^{euk}_{ci}(\alpha,\beta) = 1- \frac{1}{64} \sum_{n_c=1}^{64} (\frac{1}{c_m(\alpha)}\frac{1}{c_m(\beta)} \sum_{chr(\alpha)=1}^{c_m(\alpha)} \sum_{chr(\beta)=1}^{c_m(\beta)}  \Delta^{euk}(n_c, \alpha,chr(\alpha), \beta,chr(\beta)))
\end{equation}
and the distance matrix of codons:
\begin{equation}
M^{euk}_{codon}(n_c,n_c') = 1- corrcoef(\Delta^{euk}(n_c, :,:,:,:), \Delta^{euk}(n_c', :,:,:,:))
\end{equation}
The phylogenetic tree of eukaryotes by this chromosome average method (for $M_{ci}^{euk}$) generally agrees with the tree by the chromosome average method (for $M_{ci}^{all}$).

\subsection{Three periods in genetic code evolution}

We arrange the $64$ codons in the ``codon\_aa'' order by considering the codon chronology order firstly and considering the amino acid chronology order secondly according to the results in [27]:
\begin{equation}
\begin{array}{cccc}
\mbox{codon chronology:}\\
(1)GGC,GCC,&(2)GUC,GAC,&(3)GGG,CCC,&(4)GGA,UCC,\\
(5)GAG,CUC,&(6)GGU,ACC,&(7)GCG,CGC,&(8)GCU,AGC,\\
(9)GCA,UGC,&(10)CCG,CGG,&(11)CCU,AGG,&(12)CCA,UGG,\\
(13)UCG,CGA,&(14)UCU,AGA,&(15)UCA,UGA,&(16)ACG,CGU,\\
(17)ACU,AGU,&(18)ACA,UGU,&(19)GAU,AUC,&(20)GUG,CAC,\\
(21)CUG,CAG,&(22)AUG,CAU,&(23)GAA,UUC,&(24)GUA,UAC,\\
(25)CUA,UAG,&(26)GUU,AAC,&(27)CUU,AAG,&(28)CAA,UUG,\\
(29)AUA,UAU,&(30)AUU,AAU,&(31)UUA,UAA,&(32)UUU,AAA,
\end{array}
\end{equation}
\begin{equation}
\begin{array}{lcr}
\mbox{amino acid chronology:}\\
(1)\ G,\ (2)\ A,\ (3)\ V,\ (4)\ D,\ (5)\ P,\ (6)\ S,\ (7)\ E,\ (8)\ L,\ (9)\ T,\ (10)\ R,\\
(11)I,(12)Q,(13)N,(14)K,(15)H,(16)F,(17)C,(18)M,(19)Y,(20)W.
\end{array}
\end{equation}

We define the average correlation curves Hurdle curve and Barrier curve as follows:
\begin{equation}
Hurdle(\alpha)=mean(M_{codon}(\alpha,:)),
\end{equation}
\begin{equation}
Barrier(\alpha)=mean(\{M_{codon}(\beta,\beta'):|\beta-\alpha| \leq n_{barr}\ \mbox{and}\ |\beta'-\alpha| \leq n_{barr}\}),
\end{equation}
where $n_{barr}=8$.

According to the observations of the certain positions of the three terminal codons in the evolutionary tree of codons (Fig 4a, S3b) and the certain shapes of the Hurdle curve and the Barrier curve (Fig 4b, S3c), we propose three periods in the genetic code evolution:
\begin{equation}
\mbox{(1) initial period, (2) transition period, and (3) fulfillment period,}
\end{equation}
which are separated by the three terminal codons and correspond to the origination of three terminal codons respectively. We observe that the curve $Barrier$ begins at a level of $Barrier\sim0.4$, then overcome a ``barrier'' of level $Barrier\sim0.5$, and at last reach a low place of level $Barrier\sim0.3$ (Fig 4b). Between the initial period and the fulfillment period, we can observe some considerably higher values in the curves $Barrier$ and $Hurdle$, which indicates a ``barrier'' in the middle period of the genetic code evolution. The overall trend of the curve $barrier$ is declining. This ``barrier'' in the curve $Barrier$ corresponds to the narrow palace in the middle of the tree of $64$ codons based on $M_{codon}$.

\section{A heuristic model on the coupled earth spheres}

\subsection{The strategy of biodiversification}

The robustness of biodiversification was ensured by the genomic contributions, without which the biodiversity on the earth can hardly survive the tremendous environmental changes. The mechanism of genome evolution is independent from the rapid environmental change during mass extinctions, which ensures the continuity of the evolution of life: all the phyla survived from the Five Big mass extinctions; more families (in ratio) survived from the mass extinctions than genera. The mass extinctions had only influenced some non-fatal aspects of the living system (e.g. wipeout of some genera or families), whose influence for the vital or more essential aspects of living system (e.g. the advancement aspect) was limited. The living system seems to be able to respond freely to any possible environmental changes on the earth. The sustainable development of the living system in the high risk earth environment was ensured at the molecular level rather than at the species level.

\subsection{The tectonic timescale coupling of earth's spheres}

The three patterns CP I, CP II and CP III in the Phanerozoic eon indicate the tectonic timescale coupling of earth's spheres. The driving force in the biodiversity evolution should be explained in a tectonic timescale dynamical mechanism. Although the P-Tr mass extinction happened rapidly within several $10^4$ years, its cause should be explained in a broader context at the tectonic timescale. Overemphasis of the impacts of occasional events did not quite touch the core of the biodiversity evolution.

\subsection{A triple pendulum model to explain the climate phase reverse event}

The phase relationship among $Curve\_BD$, $Curve\_SL$ and $Curve\_CC$ can be simulated by a triple pendulum model (Fig S1d) with the coupling constants $k_1$, $k_2$ and a varying coupling $k_3(t)=(1-\epsilon\arctan(t/t_0)/(\pi/2)) \cdot k_3$:
\begin{equation}
\left\{
\begin{array}{l}
\frac{\mathrm{d}^2}{\mathrm{d}t^2} \xi= -\xi - k_1(\xi-\eta)-k_3(t)(\xi-\zeta)\\
\frac{\mathrm{d}^2}{\mathrm{d}t^2} \eta= -\eta - k_2(\eta-\zeta)-k_1(\eta-\xi)\\
\frac{\mathrm{d}^2}{\mathrm{d}t^2} \zeta= -\zeta - k_3(t)(\zeta-\xi)-k_2(\zeta-\eta).\\
\end{array}
\right.
\end{equation}
This model shows that the climate phase reverse can achieve by just varying the coupling $k_3(t)$ from $k_3(1+\epsilon)$ to $k_3(1-\epsilon)$, $\epsilon \ll 1$.

redpajama_set_name: RedPajamaArXiv


text:
7 Lovely Moments from Sridevi's Life!
This is how we will remember her!
On February 24th this year, the world lost an icon. Legendary actress Sridevi was more than what one saw on screen. She was elegance personified, she was a devoted family-oriented person and she was beautiful as she was talented. She was also funny and she saw life and sought to live it on her own terms. She lived as she saw fit and never backed down from going for what she deserved. Rocking the screens from age 4, she even conquered hearts as we can see by the public homage that happened at her final rites. Her death comes as a heartbreak and her life serve as a beautiful chapter in history! Here are 4 most memorable and lovely as well rare moments from Sridevi's life that will touch your heart and let a teardrop!
7. She has played a double role twice in her life
She played Anju and Manju Das in the super hit Hindi film Chaalbaaz. She also played mother and daughter in the Hindi Lamhe, a romantic film that featured her playing a young woman and a young girl.
6. She won her first and only National Award posthumously!
Her performance in Mom, her last film where she played a mother who takes revenge for the rape of her daughter. Her performance was so moving that many laud her last film to be one of her. The film earned her her first ever National Award. Alas, she wasn't alive to receive it, hence her daughters, Jhanvi and Khushi did on her behalf.
5. She was paid as she deserved!
Growing up and transitioning into a full-time heroine from child artiste, she was only 13 years old when cast in Moondru Mudichu by K Balachander. Sridevi recollected in a chat show that her salary was Rs.5000 for the movie when Superstar Rajinikanth's salary was only Rs.2000. She went on to explain that since Kamal Haasan was already an established star by the time they worked on the movie, he received a big amount of Rs.30,000 salary whereas she and Rajini were just starting out so they got what they are supposed to. She said:
"Rajini was very close to my mother. He was like a son. When we were all talking, Rajini would ask how he can become a big star like Kamal. My mother would say, you will definitely become like that. Rajini wanted to earn Rs.30,000," she laughs.
4. When she was so funny during the Iconic Boat scene!
Everybody knows the Vasantha Kala song from Moondru Mudichu when Kamal falls into the water and Rajini refuses to help, singing sinister lyrics as Sridevi screams and looks on in shock. But the actual scene shooting was not so serious and was, in fact, funny and cute! Sridevi told in an interview:
"Instead of saying 'Poitare Poitare', I started yelling 'Pootare Pootare' (a colloquial usage). Kamal sir was holding on to the boat and laughing so much at this that the boat was shaking. And then the Director Balachander was yelling and asking why the boat was shaking," she recalled good-humoredly.
Bigg Boss Tamil: 5 Reasons Why Aari Was People's Favourite This Season!
Confratulations Aari!
EXCLUSIVE: Kamal Haasan Launches His Fashion Brand! Here's All You Need To Know About It!
Happy Birthday Vijay Sethupathi: 8 Characters That Will Live In Our Hearts Forever!
Which one's your favourite?
Paava Kadhaigal Actress Bhavani Sre Roped In By Director Vetrimaaran!
A great lineup for the new actress!
TRENDING: Valimai Director H Vinoth Blessed With A Baby Boy!
Congratulations to the talented director!
Daddy Duties: Selvaraghavan Cannot Leave His New Baby's Side; Check it Out!
Sanam...
Among...

redpajama_set_name: RedPajamaCommonCrawl


text:
Economic Development | Education | Featured
Digital iHub Opens in Palm Desert
A digital iHub, created to bring an economic boost to the Coachella Valley opened on November 17 across from the Cal State San Bernardino (CSUSB) Palm Desert Campus.
The Palm Desert iHub, which is a collaboration of the city of Palm Desert, the Coachella Valley Economic Partnership, and CSUSB, will feature university programs in cybersecurity, hospitality management, and entrepreneurship as part of expanded educational offerings in the Coachella Valley to increase the level of diversity among enrolled students and allow more members of underrepresented communities to engage in the various fields.
"The addition of these three state-of-the-art programs will greatly enhance our Palm Desert Campus and give our students much-needed experience and opportunities that will help them after graduation as they start their careers," said CSUSB President Tomás D. Morales. "This is part of our partnership with the city of Palm Desert and the Coachella Valley Economic Partnership that we believe will serve as an economic boost to the region."
"Our expansion to the Palm Desert Campus is another part of our efforts to tap hidden talents and help solve the national cybersecurity workforce crisis. This program will help improve the pipeline of critically needed cyber skills coming from the Coachella Valley," said Tony Coulson, a professor of information and decision sciences and executive director of CSUSB's Cybersecurity Center. "CSUSB is helping provide national leadership in these initiatives."
The Cybersecurity Center will be mentoring students, specifically veterans and adults who need "retooling" to meet the needs of cybersecurity and government service, Coulson said.
To help in the recently expanded cybersecurity program, information decision sciences assistant professor Denise Kinsey is teaching cybersecurity full-time at the Palm Desert Campus, said Coulson. The center is planning to hold on campus expos and high-tech research projects at the PDC.
The cybersecurity program along with hospitality management and entrepreneurship will be operated out of the Digital iHub, which is located across the street from the Palm Desert Campus.
The Digital iHub is expected to bring an economic boost to the valley. The Palm Desert City Council voted unanimously to establish the Digital iHub after going through an extensive long-term planning process that identified highly impactful actions that will diversify and elevate the economy of Palm Desert and the entire Coachella Valley.
"The Palm Desert Digital iHub will attract high-paying, environmentally friendly jobs and provide internship opportunities for students, equipping them for similar work," said Palm Desert Mayor Kathleen Kelly. "These gains are huge strides toward the city's goal of creating a more diverse and durable economy with opportunities for all."
The Coachella Valley Economic Partnership (CVEP) was selected by the city of Palm Desert to enter into a five-year agreement to manage the Palm Desert Digital iHub. CVEP has managed the Palm Springs iHub and Accelerator Campus for the entire nine years of their existence and has attracted or inspired nearly 90 technology-based companies to enter the program.
Indio Named One of America's Top Boomtowns
City Government, Economic Development, Public Sector News
The City of Indio ranked number 18 in smartasset's 2021 guide to Boomtowns in America. The online financial information provider ranked cities based on a number of factors, including business growth, housing growth, and change in household income. Indio was the only...
Leeds & Son Jewelers Unveils Remodeled Store
Featured, Retail
Leeds & Son, one of the West Coast's foremost watch, diamond and fine jewelry retailers, has reopened their newly expanded and beautifully remodeled boutique in Palm Desert. The landmark El Paseo retailer reopened its doors to the public on November 9, celebrating...
Economic Development, Featured
Headlining this year's annual Greater Palm Springs Summit will be leading economist Manfred Keil, Ph.D., Professor of Economics at Claremont McKenna College, who will deliver the Annual Economic Report and Forecast at the 17th annual Greater Palm Springs Summit on...


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redpajama_set_name: RedPajamaGithub


Located in western Kenya, Moi University is Kenya's second-oldest public university. The Department of Medical Biochemistry offers a Master's degree in Medical Biochemistry and also teaches biochemistry to the entire College of Health Sciences.
Faculty research interests include diabetes, cardiovascular disease, esophogeal cancer, and linkages between malnutrition and malaria.
There are more than 400 million adults living with diabetes worldwide. The percent of individuals impacted has more than doubled since 1980Approximately three-quarters of people with diabetes live in low- and middle-income countries, and these countries are projected to experience the greatest increase in diabetes burden.. Growth has been fastest in low- and middle-income countries, where the disease is also more likely to be fatal.
Dr. Geoffrey Maiyoh, lecturer at Moi University's Department of Medical Biochemistry, believes he might have found a lead for a more affordable and effective treatment in an unexpected place: stinging nettles.
Often loathed for their irritating, rash-inducing hairs, nettles are packed full of nutrients when cooked. In Kenya, nettle leaves are also commonly used by traditional herbalists to treat diseases, including diabetes. While traditional medicine is both accessible and affordable, treatments like the common nettle have not been scientifically validated.
"Their efficacy, modes of action, dosages, and toxicity profiles remain largely unknown," says Dr. Maiyoh. "I therefore resolved to pursue a career in science with the inspiration of helping to resolve this gap."
After completing his PhD in Molecular Biosciences and Bioengineering at the University of Hawaii, nettles presented a research itch that Dr. Maiyoh just had to scratch.
In seeking to understand how nettle leaves work on a molecular level, Dr. Maiyoh's team has discovered a compound that they named Urticol (derived from the common nettles' Latin name Urtica dioica). When introduced to liver cells, Urticol is more effective at encouraging sugar absorption than other currently used drugs.
Although the biochemical mechanism by which Urticol encourages sugar absorption is still being investigated, Dr. Maiyoh and his colleagues are excited about its promise.
"As a scientist, one of the most valuable assets that I require on top of my knowledge and skills is to have access to laboratory equipment that is adequate for me to be able to design and implement experiments. Without this equipment, I feel incapacitated because sometimes I have this wonderful idea that I may not be able to investigate."
The department needs equipment to improve practical teaching in biochemistry for the entire College of Health Sciences. Equipment is also needed to facilitate basic biomedical research by graduate students in the recently-launched Medical Biochemistry degree program and the small but well-trained core of faculty members, whose current access is limited because existing equipment is primarily dedicated to clinical work.


• BLACKFISH: fishing is very good. Time to fish deeper (40-80 ft) around slack tide, especially with the full moon approaching. Rapid Rock, Race Rock, Sarah's Ledge, inner Bartlett Reef, Two Tree Island (flood tide), various smaller locations east to Watch Hill are still fishing well. You may want to try rock piles or reefs that are away from the more popular areas to increase your chances of finding a keeper sized fish. Green, Asian or Hermit Crabs will work, especially on jigs.
Consider putting the crab (bait) on a chartreuse and orange jig (1.5 – 3.0 ounces in weight/depending on tide and wind). The lighter the jig the better...feel more bites. Use braid in 30-50 lb class and/or a 50 lb fluorocarbon leader (sensitivity to feel the bites is important). Also use a 4/O Gamakatsu hook (Octopus style) when using bait. I would highly recommend planning a trip on a party/charter boat trip to fish for tautog this fall.
• STRIPED BASS fishing is excellent. The Housatonic River is red hot, along with the CT River up to Wethersfield Cove and the upper Thames River (north of Montville). Fish have been moving into the estuaries and tidal rivers to feed heavily on the bait. The reefs in Long Island sound are still producing fish that have begun their annual fall migration. There is still plenty of Atlantic menhaden (bunker) or hickory shad around that can be used as bait. Live bunker on a three-way or slider rig on the local reefs and rip areas can still provide some results.
Other striper spots include the lower Mystic and Thames River, the Race, Sluiceway, Plum Gut, Pigeon Rip, outer Bartlett Reef, Black Point, the "humps' south of Hatchett Reef, lower Connecticut River (Dock & Dine shore fishing access), Long Sand Shoal, Cornfield Point, Southwest Reef (outer), Sixmile Reef, Falkner Island area, the reefs off Branford, New Haven Harbor (Breakwalls) and the upper reaches, Charles Island area, lower Housatonic River, buoys 18 and 20 off Stratford Point, Stratford Shoal/Middle Ground, Milford Point, Penfield Reef, around the Norwalk Islands, and Cable and Anchor Reef.
• BLUEFISH fishing is still very good. Especially inshore along the beaches (coastal state parks) and fishing piers. If you're looking for these "choppers" now's the time before it's too late. They can be found throughout the Sound (find the bait/birds). The top-water bite is fantastic.
Bluefish fishing spots include the reefs off Watch Hill, the Race, Thames River, Sluiceway, Plum Gut, Pigeon Rip, lower Connecticut River, CT DEEP Headquarters, Long Sand Shoal, Sixmile Reef, Falkner Island area, New Haven Harbor and upper reaches, lower Housatonic River, buoys 18 and 20 off Stratford Point, Stratford Shoal/Middleground, Penfield Reef, Norwalk Islands and Cable and Anchor Reef.
• ATLANTIC BONITO & FALSE ALBACORE fishing is amazingly very good. These tunas are getting bigger, with many topping 10 pounds or more. Niantic Bay to Watch Hill is your best bet. You can also find large schools from Millstone Point to Race Rock and the western shore of Fishers Island. Fish the current brakes and choppy water...be ready for a quick strike. Dawn and dusk is typically the best time to fish for these inshore tunas but they can be caught during the daytime. A quiet approach and finding birds (gulls/terns) actively feeding is the key to a successful trip.
• BLACK SEA BASS fishing is red hot on all the major reefs and wrecks throughout Long Island Sound. Fish deep (80' -120') with 8 to 12 ozs of weight. When releasing fish suffering from "barotrauma", a buildup of swim bladder gases, try using a recompression tool to help get them back to the bottom quickly. Moreinformation is available at FishSmart.org.
• PORGY fishing has started to slow down as fish start their migration to offshore overwintering grounds. Some scup can still be caught on the local reefs or from shore. Locate your favorite Enhanced Shore Fishing Opportunities for these hard fighting and excellent eating "Reef Slammers".
• HICKORY SHAD fishing has really picked up. Large schools can be seen on the surface feeding in lower rivers and estuaries. Your best chances are in the lower Connecticut River (DEEP Marine Headquarters fishing pier), Housatonic River, East and West River, Lieutenant River, and Black Hall River.


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Holiday House Le Hameau de Gassin
2-room terraced house 35 m2 on 2 levels. Object suitable for 2 adults + 2 children. Living/dining room with 1 sofabed (140 cm, length 190 cm), TV (flat screen). Exit to the terrace. Kitchenette (2 hot plates, microwave, electric coffee machine). Upper floor: (steep stair), 1 room with 2 beds (90 cm, length 190 cm), bathroom and separate WC. Electric heating. Terrace. Terrace furniture. Facilities: washing machine. Internet (WiFi, extra). Reserved parking space n 20. Please note: smoke alarm. Offer by a private individual according to Art. 155, IV of CGI. 83065000034WS
Gassin 10 km from Saint Tropez: Residence "Le Hameau de Gassin". Outside the resort, 5 km from the centre of Gassin, 2.5 km from the sea. For shared use: swimming pool (10 x 5 m, depth 100 - 200 cm, seasonal availability: 01.Jun.-30.Sep.). Shop 5 km, supermarket 2 km, restaurant 2 km, bakery 500 m, sandy beach "Les Marines de Gassin" 2.5 km. Marina 2.5 km, surf school 2.5 km, sailing school 2.5 km, tennis 5 km, minigolf 1.5 km, cycle lane 1.5 km. Nearby attractions: Parc d'attractions Azur Park 2 km, Port Grimaud 5 km, Plages de Pampelonne 13 km, Sainte-Maxime 14 km, Cannes 84 km, Nice 110 km. Well-known lakes can easily be reached: Lac de Sainte Croix 89 km. Hiking paths: Massif de l'Esterel 50 km. Please note: car recommended.
International Capital of tourism which has become a myth, Saint Tropez contributes to the reputation of the Côte d'Azur. Its streets scents of Provence and its artistic atmosphere make it a resort for the jet set international as well as tourists looking for authenticity. Small, busy, picturesque fishing village with 5'500 inhabitants, directly by the sea, 68 km north-east from Toulon, 83 km south-west from Cannes, 112 km south-west from Nice. All shops, restaurants etc. in the resort. Disco, evening entertainment, museum, cinema. Fitness centre. More possibilities for shopping 5 km. In the summer this little fishing port becomes a majestic marina and home to the most beautiful yachts in the world.
Sandy beach. Diving, sailing, sailing school, water skiing. Tennis. Minigolf 6 km. Riding in Grimaud 14 km. Bicycles and mountain bikes for hire. Children's playground, leisure park "Azur Park" 5 km. Famous beaches in Pampelonne, 6 km (Ramatuelle). Events throughout the year: regattas, "bravade" (festival), antiques, art exhibitions, polo championships in Gassin.
France, Cote d'Azur, Saint-Tropez
Customer reviews - Holiday House Le Hameau de Gassin


Dan leads product marketing for SharePoint, Yammer and Microsoft Stream. He brings a customer-centric perspective from a 20-year career as a consultant for Fortune 500 organizations, during which time he served as the Microsoft Technologies Consultant for NBC for the broadcast of the Olympics in Rio, Sochi, London, Vancouver, Beijing and Torino. Dan is a native of Colorado and graduate of Yale and Thunderbird. Learn more about Dan at http://danholme.com.
With a career spanning more than 20 years, Dan Holme, a native of Colorado and graduate of Yale, earned a reputation as one of the world's most respected consultants, authors, thought leaders, and experts on Microsoft technologies. In April of 2016, Dan joined Microsoft as the Director of Product Marketing.
Dan is thrilled to be part of the brilliant and talented team committed to the future of SharePoint, Yammer and Microsoft Stream, which power collaboration and communication for organizations.
Prior to joining Microsoft, Dan was the co-founder of IT Unity, a media, events, and digital marketing company serving the Office 365 and SharePoint communities worldwide. As CEO, Dan helped to build IT Unity's strong brand, customer base, and reputation among users, partners, and experts around the globe.
Dan has trained and guided hundreds of thousands of IT professionals, executives, and users at almost every Fortune 100 enterprise, and more than half of the Fortune 500 and Global 1000. He has authored hundreds of articles for IT Unity and SharePointProMag.com, and numerous best-selling books for Microsoft Press.
For these contributions, Dan was awarded Microsoft MVP status for ten years across four technologies, named one of the top ten partner MVPs in the world, and recognized as one of the top influencers in the SharePoint and Office 365 community.
New to Seattle after ten years on Maui, Hawaii, Dan is passionate about nature, the performing arts, and the Olympic Games. Dan has served as the Microsoft Technologies Consultant for NBC Sports for the broadcast of the Olympics in Rio, Sochi, London, Vancouver, Beijing and Torino.
(c) 2016 Dan Holme. All Rights Reserved.


The office of Rent Control assists landlords and tenants in understating the impact the Municipal Rent Control Ordinance of the City of Newark, may have on their properties and apartments.Technical assistance is provided on a wide variety of rental issues which cover such matters as: which apartments are subject to local rent control laws; what is the legal base rent; what rent increases the law permits; tax surcharges;water/sewer surcharges; major new improvement surcharges; hardship increases;and annual registration requirements. Files on all residential properties are maintained for public inspection if owners and/or tenants have questions regarding a particular property.
The Newark Rent Ordinance regulates rents for certain residential rental units in Newark, New Jersey. There is no commercial rent control in Newark.
The Rent Control Board's primary function is to conduct hearings and mediation of tenant and landlord petitions regarding the adjustment of rents under the City's rent control laws.
The Rent Control Board cannot arbitrate matters that are not part of the Title 19,Chapter 2, Rent Control Regulations, Rent Control Board, of the Revised General Ordinances of the City of Newark. For example, we do not have jurisdiction to adjudicate alleged breaches of a rental agreement, which must be decided in court. The Rent Control Board also cannot hear cases concerning issues of discrimination, harassment or retaliation, as such matters are outside of our jurisdiction. Please contact the Department of Community Affairs regarding these issues.
The Rent Control Board provides counseling information on subjects that are covered by the Rent Ordinance ONLY.Please understand that our staff CANNOT give legal advice and they are instructed to let you know when your question is one that should properly be answered by a lawyer.
While we cannot refer you to individual attorneys, our staff will be glad to direct you to the appropriate resources for advice and assistance. A list of these resources is available through the referral listing on our website. Please note that you do not need a lawyer to file a petition at the Rent Control Office.
Due to high demand and a limited number of staff, there can sometimes be delays in speaking with a representative. It is helpful if you have your questions written down before you speak with a representative.
No information for this category.
File a petition with Rent Control for an adjustment in his/her rent.
A. No. All Public Housing, and existing owner-occupied 1-4 family dwellings are exempt from Rent Control but are still required to register annually.
B. All newly constructed dwellings of four or more units can be exempt from Rent Control for 30 years, as per State statue; however, the Landlord/Owner must apply for a certificate of exemption prior to occupancy in order to authenticate their exemption status.
C. Substantially rehabilitated dwellings can be exempt for five years (if they were previously vacant for 18 months) or one year (initial rent) if occupied, and if the cost of rehabilitation exceeds 50% of the dwelling's value. (Note: landlord must apply for and receive a certificate of exemption from the Rent Control Board).


The Deadbolt Mystery Society Mr. Mindgame Full Reveal + Coupon!
The Deadbolt Mystery Society is a monthly subscription box service for lovers of mystery and suspense. We have THE STRANGE CASE OF MR. MINDGAME full reveal! Thanks Greg for the tip and photos!
– Ready to solve the Case?


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In 8th Grade, Separate Algebra is Unequal Algebra for Black Students
By Sarah D. Sparks — June 11, 2020 6 min read
Algebra is considered the gateway to advanced mathematics, and school districts across the country have hoped to diversify access to college-preparatory math by increasing the number of students who take algebra by the end of 8th grade. But calling a course "Algebra" doesn't guarantee black students are getting equal access to the math content they need to succeed in high school.
A study released this week in the journal Educational Researcher found teachers cover significantly less algebra material in those classes at predominately black schools than their peers in schools that are mostly white or have no racial majority.
"You know, districts have opened up algebra access to more students, but the question is, what are they getting access to?" said Karisma Morton, an assistant professor of mathematics education at the University of North Texas. She co-wrote the study with Catherine Riegle-Crumb of the University of Texas, Austin.
The researchers used data from the U.S. Trends in International Mathematics and Science Study, which included a survey of 8th grade teachers in 111 schools nationwide on their teaching practices. Teachers reported how much time they spent covering various concepts in their 8th grade algebra classes, from algebra and more advanced concepts like geometry to basic numeracy.
In schools where black students made up 60 percent or more of the student body, teachers reported spending on average 72 percent of their class time on content covering algebra or more advanced topics, versus 28 percent of class time spent on more basic numeracy topics, such as fractions, Morton and Riegle-Crumb found. By contrast, schools that were majority white spent 82 percent of class time on algebra or more advanced concepts and10 percentage points less time on more basic skills. Schools with a Latino majority did not show significant differences in their algebra course content from those without a racial majority.
"I was not surprised ... but I found the results jarring," Morton said. "I feel like especially in today's climate, we need to be very much focused on what's happening and not assume that opening up access guarantees opportunities to learn."
Access Versus Opportunities
For nearly a decade, states and districts have pressed inititiatives to include more traditionally underrepresented students in algebra by the end of 8th grade as a means of widening the pipeline of such students to advanced math in high school and, eventually, to careers in science, technology, engineering, and math.
The EdTrust, a nonprofit education advocacy group, found that based on national civil rights data, black students were underrepresented in 8th grade algebra in 37 out of 41 states with sufficient data in 2015-16. That disconnect continued into high school, where black students made up 15 percent of high school students, but 9 percent of those enrolled in any Advanced Placement courses, incuding advanced math such as calculus.
"At each of these critical stages, black and Latino students have been locked out of advanced coursework opportunities," said Kayla Patrick, the EdTrust's pre-K-12 policy and data analyst, who conducted the group's study. "And what's really important is that each one of the opportunities opens up the door for the next, so they build on top of each other like blocks."
But Morton's current findings may help to explain why some high-profile initiatives, such as California's former "algebra for all by 8th-grade" requirement, rolled out to mixed results. One study by Jian-Hua Liang, an education research and evaluation consultant at the California Department of Education found that California students who passed 8th-grade algebra in that state were not necessarily more likely to take and succeed in advanced math courses in high school, and a 2015 study tracking California's algebra initiative found that higher enrollments in middle-school algebra were linked to drops in students' scores on the state math test.
"A lot of times students, particularly students from disadvantaged populations, don't even recognize that these courses are in fact gatekeeper courses," Morton said. "It could be that a student in a predominantly black school is getting not as much algebra, but still getting an 'A' and then transitioning into high school.
"And so these students are at a disadvantage when they get to high school because they don't have as much algebra under their belt. And guess what happens? They get to high school, they may be enrolled in a geometry course and they don't have the foundation. And eventually, they don't see the same favorable outcomes as their peers who are prepared," Morton said. "So, yeah, [8th grade algebra] might be a gatekeeper, It might get you through the door, but then you may not continue. ... You fall through the cracks."
Morton and her colleague controlled for the typical challenges: schools with fewer resources for curriculum materials; teachers with less math expertise or confidence in teaching the subject; students with little interest in math or poor math achievement coming into the class. But even when all those factors were controlled, majority-black schools still covered significantly less algebra content in their 8th grade classes than schools where black or Latino students made up a smaller share of the student body.
"At the end of the day, we as teachers make a committment to educating all children well. This is a wake-up call that we may not be doing that as well as we think we are," she said.
Morton recommended that school and district leaders could help improve course equity by bringing together algebra teachers not just within schools, but across the district or beyond, to analyze different sections of courses and compare how much material they are covering, and how deeply.
Robert Berry III, a professor of math education at the University of Virginia and the immediate past president of the National Council of Teachers of Mathematics, said teachers need to look more closely at both what and how they are presenting material to different groups of students.
"If teachers have autonomy to emphasize certain things in courses, could that affect the kinds of tasks students are afforded? Are they just procedural in nature, or do students have tasks in problem-solving, reasoning, justification, and digging deeper," he said. "This can matter as much as the content."
EdTrust's Patrick said that high schools can mitigate some opportunity gaps among students by automatically enrolling students in Advanced Placement math courses, rather than requiring prerequisites, but added that educators must be aware of potential differences in what concepts students have been exposed to and provide tutoring or other supports for students to thrive in challenging courses.
Photo: Crosby High School algebra teacher Jennifer Desiderio works with Lyndon Frederic, right, in her class in Waterbury, Conn. in 2019. Students in the Waterbury public school district are predominantly black and Hispanic; A new study suggests in schools where most students are black, as several are in the Waterbury school district, 8th grade algebra courses cover less algebra material than similar classes in majority-white schools, which can put students at a disadvantage when they reach high school math courses. Photo Source: AP Photo/Jessica Hill
Assistant Editor, Education Week
Sarah D. Sparks covers education research, data, and the science of learning for Education Week.
Equity Research Instruction
A version of this news article first appeared in the Inside School Research blog.
Inside School Research
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Fruitvale man dies in single-vehicle accident
A 28-year-old Fruitvale man was pronounced dead at the scene of a single-vehicle accident in Fruitvale early Saturday.
Val Rossi
Fire and ambulance responded to a call at nearly 1 a.m. at the top of Nine Mile Road, where the fatal accident occurred. Driver Tyler Tjader rolled his truck and was pronounced deceased at the scene but his passenger walked away from the accident, according to RCMP.
"He was just a young guy, it's just a tragedy," said Cpl. Darryl Orr of the Trail and Greater District Detachment.
"It looked like the truck had been taken off road, and somehow that played a contributing factor," he explained.
Trail RCMP continue to investigate the cause of the incident, which is a rare one on Orr's account.
"An off-roading accident in the winter, in my experience, is somewhat rare," he added. "You hear about people getting into Ski-Doo accidents but off-road vehicles at this time of year, you don't hear much of that."
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