A beach is a beach is a beach… for some, that is. For families, it's a combination of calm, clean water and sand, patrolled by lifeguards and complemented by nearby eats and attractions, such as boardwalks and playgrounds. The ideal beach is also located within short walking distance of accommodations; in fact, a recent Family Vacation Critic survey reveals 50 percent of families prioritize nearby hotels, rental homes and the like. Restrooms and showers add to the appeal, as well.
Coronado Municipal Beach – Coronado, Calif.
A SoCal staple, Coronado Municipal Beach returns to our annual list for many reasons, namely the soothing sand and surf that never feels crowded, even in the thick of summer. Plenty of lifeguards are stationed around the beach, as are hotels, which include the famous Hotel Del Coronado. "Hotel Del" is an attraction in itself, too, with organized historical tours (wait until you hear about the hotel's haunted past) and several onsite eateries available to the public; Moo Time Creamery is a must for frozen treats, though picnic lunches are welcome on the beach, as are pets! You'll also find restrooms at the hotel, and showers are stationed throughout the beach. Located just outside of San Diego, Coronado puts families close to the San Diego Zoo, Balboa Park and SeaWorld San Diego, too.
Insider Tip for Families: There are tide pools located at the rocky sea break by the Hotel del Coronado, ideal for young swimmers. There are also fire pits located on the northernmost stretch of sand on Coronado Beach — perfect for S'mores.
Ocean City Beach – Ocean City, N.J.
Sure, some Jersey Shore towns attract college crowds, but you won't likely find them in Ocean City, which has a dry policy, forbidding the sale of alcohol. Instead, you'll discover eight miles of relaxed oceanfront, complete with lifeguards, restrooms and showers, and enriched by a plethora of nearby activities. These include a boardwalk, where you'll find Adventure Island Water Park & Mini Golf and Gillians Wonderland Pier, along with dozens of places to eat. Additionally, Ocean City offers plenty of budget-friendly accommodations near the beach, such as Seaport Inn Motel, where guests are given two complimentary beach tags.
Insider Tip for Families: Ocean City has numerous free and low-cost events for families throughout July and August: Funtastic Tuesdays (every Tuesday), Beach Walks (every Tuesday and Wednesday), Family Nights (every Thursday), and Character Nights (every Sunday).
Zephyr Cove Resort – Zephyr Cove, Nev.
South Lake Tahoe touts high-rise casinos that might disappoint those who've sought a vacation off the grid. You'll find unspoiled beauty at Zephyr Cove Resort, however, technically located in Nevada, but situated on the border of California. Monitored by a few lifeguards, shallow water provides amazing opportunities to swim, paddleboard (and stand-up paddleboard), or just float on a tube in the water, and a watersports rental service provides the necessary equipment to do so. Also right on the beach, Sunset Bar & Grille offers sandwiches, salads and other pub fare (though we highly recommend Rude Brother Bagels and Coffee, a short drive away). Zephyr Cove Resort offers hotel, cabin and campground accommodations, and a number of other inexpensive hotels and motels are located within walking distance of the beach.
Insider Tip for Families: The M.S. Dixie is a 520-passenger paddlewheel sightseeing cruise that departs from Zephyr Cove daily, offering two-hour narrated tours. Lunch is available for purchase, and there are discounted rates for kids 11 years and under.
Myrtle Beach – Myrtle Beach, S.C.
Myrtle Beach might as well be a distant cousin of Disney World, with plenty of restaurants and an abundance of family-friendly attractions, such as MagiQuest Myrtle Beach, Medieval Times Dinner and Tournament, Family Kingdom Amusement Park, the Myrtle Beach Skywheel and the Hollywood Wax Museum Entertainment Center, slated to open this summer. Also new in 2014, several waterparks will offer outdoor fun – one of them, located at Crown Reef Resort, will even feature the world's first hotel "explosion" slide! Many of the area's oceanfront hotels, such as Sea Watch Resort and Ocean Reef Resort, already offer water playgrounds, too, along with extremely affordable rates. And when it comes to the beach itself, just stake out your spot and rest (literally) assured there are lifeguards, restrooms and showers nearby.
Insider Tip for Families: The downtown boardwalk area of Myrtle Beach hosts free "Hot Summer Nights" events throughout the summer – including concerts, festivals and kids' carnivals. There is a free fireworks display every Wednesday night at the 2nd Avenue Pier.
When you think of Maine, you might envision Bar Harbor, Ogunquit and Kennebunkport. And while all of them offer lovely sand and surf, Short Sands Beach wins our vote. Small in size, it's perfect for families with young children, who enjoy calm, clean water (you won't likely see giant waves), lifeguards and restrooms, along with a playground and basketball court. Downtown York Beach is literally right next to Short Sands Beach, too, providing easy access to shops, restaurants, hotels, and attractions, which include York's Wild Kingdom Zoo and Fun Park. Pack a picnic lunch and head to Nubble Light (Cape Neddick Lighthouse) while you're in the area.
Insider Tip for Families: York Trolley runs between Short Sands and Long Sands for an easy and inexpensive way to explore the area. The Shoreline Explorer's Purple Line picks up from Short Sands to go to Ogunquit – tickets are a few dollars, and kids under 18 ride free.
Crystal Beach – Destin, Fla.
At Crystal Beach, the sand is soft and white, the water is clear and turquoise and the atmosphere is completely calm, with little crowds to be found. Several lifeguards are stationed throughout, though the water is delightfully shallow for little ones — you can walk fairly far out in the water. Restrooms and parking add to the appeal, as do nearby attractions, restaurants and shops at Destin Commons. Plenty of other restaurants, such as Camille's, afford oceanfront views, fresh fish and kid-friendly fare, too. Many families opt to rent a condo right on the beach while in town.
Insider Tip for Families: If you don't want to lug all your gear to the beach, rent two chairs and an umbrella for around $25 per day — if you're visiting for a few days, the rental service staff might give you a discount.
Cannon Beach – Cannon Beach, Ore.
You might recognize this one from "Twilight" – it's the same beach Bella visits with her friends, including Jacob, who tells her about mysterious Edward Cullen and his family. Even if you're not a "Twihard," you've probably heard of iconic Haystack Rock, which rises 235 feet at low tide, or the annual Sandcastle Contest, now in its 50th year. Other attractions include Ecola State Park, ideal for more beach walks, picnics and photo ops. Downtown Cannon Beach, while not a typical beachside town, offers several shops and restaurants; Driftwood and Lumberyard are among the most popular eateries for families. Plenty of oceanfront hotels offer accommodations for every price range. Among them is popular Surfsand Resort, which regularly offers rate discounts and a kids club, and is just steps from Haystack Rock.
Insider Tip for Families: Pack binoculars and visit the beach at low tide, April through mid-May or late-June through July, to see the Tufted Puffins that frequent Haystack Rock.
Mayo Beach – Wellfleet, Mass.
Mayo Beach is quintessential New England, with a peaceful place for families to relax. Close to the harbor, where you can rent a boat, shop and eat, you'll see calm waves and commercial boats in the distance as you collect seashells from the sand. The water, which is protected by lifeguards, is surprisingly warm, too, but when little fingers start to shrivel up, a playground and tennis courts offer more fun across the street, and are accompanied by restrooms. Afterward, grab a bite to eat — and a book — at Wellfleet Bookstore & Restaurant, also located across the street. Many families opt to rent a cottage on or near Mayo Beach; a handful of Wellfleet hotels are located within short driving distance of the beach, and some families opt to stay in neighboring Orleans.
Insider Tip for Families: During the summer, free Shakespeare plays are performed at the Wellfleet Rec Tent near Mayo Beach.
Humiston Beach – Vero Beach, Fla.
Vero Beach, Fla. offers several beaches and parks, all of which are great for families, though our favorite is Humiston Park, since it offers a recently revamped playground, along with restrooms and showers. This stretch of sand, which is free to access, is also a nice place to enjoy a picnic lunch, with tables and grills readily available. Nino's offers delicious pizza right by the beach, as well; after you grab a slice, head to Kilwin's on Ocean Drive for chocolates and ice cream, followed by an evening stroll. Plenty of free parking is available, but oceanfront hotels, such as Holiday Inn Oceanside and Driftwood Resort, are a short and easy walk from the sand.
Insider Tip for Families: Humiston Park hosts a Sunset Saturday Night Concert Series, with free entertainment every Saturday night. Local artists often display and sell their work on weekends, too.
New Buffalo Public Beach – New Buffalo, Mich.
If you don't live in the Midwest, you likely haven't visited (or even heard of) the beaches there. New Buffalo Public Beach, located on Lake Michigan and just over an hour from Chicago, is a standout, however. From the parking lot, a boardwalk allows easy access to the sand and water, which is strikingly shallow and clear – bring your snorkel gear! The waves have the capacity to be strong, but are consistently patrolled by lifeguards. The beach's best asset, however, is the amount of amenities it offers, which include a nature trail, snack stand, playground, restrooms, showers, boat launch ramps and opportunities to fish in the Galien River Nature Preserve. More dining options, along with hotels, are a short walk (just across a bridge) into town.
Insider Tip for Families: Get to the beach before 9 a.m. to avoid a parking fee (normally around $10), or after 8 p.m. to enjoy the sunset.


text:
HomeNew YorkNew York City 53 Grand Street
53 Grand Street in New York City
53 Grand Street, NY 11249
Sq. ft. Range: -
About 53 Grand Street
At 53 Grand Street in the Williamsburg neighborhood in Brooklyn, NY 11249 is situated a residential complex. Its construction began in the fall/winter of 2016, and ended relatively recently in 2019. Responsible for this project are the developers company Investmates Real Estate & Development, architects ND Architecture & Design, interior designers of the Meshberg Group, as well as the marketing and trading company Corcoran. This condominium consists of 5 floors, which include 3 spacious apartments for a large family. The number of rooms is 3. An area of 7,800 square feet will provide many amenities for future residents of the complex. For the lower level there will be a private garden, and for buyers of the penthouse there will be a roof. Also, guests cannot do without a fireplace, storage space for bicycles and a virtual doorman. For all questions regarding the rental or purchase of real estate, please contact us, we will be happy to help. Metro stations such as Canal St Station, which is maintained by train number 1, and Canal Street Station, which is maintained by trains A, C, and E, are available in the area. Bus stops are also available, such as 6 Avenue&Canal Street. This is a convenient location for those who enrolled to study at Borough of Manhattan Community College. The nearest cafes, restaurants and bars: Cupping Room Cafe, Jimmy at The James Hotel, Black Tap, Cipriani Downtown, Vin et Fleurs and others.
Amenities of 53 Grand Street
Private Storage
Virtual Doorman
53 Grand Street Team
Investmates Real Estate & Development
ND Architecture & Design
Meshberg Group
What is the Lowest Price for apartment in 53 Grand Street?
The prices on various 53 Grand Street options are available on request. Please contact us for more information.
How many apartments are for sale in 53 Grand Street?
53 Grand Street has 3 gorgeous apartments for sale.
How do I find the gorgeous apartments in 53 Grand Street?
For additional information about apartments in 53 Grand Street - just visit our website inewhomes.com
53 Grand Street
520 West Chelsea
679 Franklin Avenue, NY 11238
58 Metropolitan
58 Metropolitan Avenue, NY 11249
The Rowan
62 North 3rd Street, NY 11249
Austin Nichols House
184 Kent Avenue, NY 11249
from $900k to $2M
56 South Third Street
56 South 3rd Street, NY 11249
112 South 2nd Street
112 South 2nd Street, NY 11249
Northside Piers
142 North 1st Street
142 North 1st Street, NY 11249
338 Berry Street, NY 11249
164 South 1st Street
164 South 1st Street, NY 11211
34 North 7th Street, NY 11249
1SEVENTY
146 South 4th Street, NY 11211
190 South 1st

redpajama_set_name: RedPajamaCommonCrawl


text:
import * as React from 'react';
import { CameraCapturedPicture, CameraNativeProps, CameraPictureOptions } from './Camera.types';
export interface ExponentCameraRef {
    getAvailablePictureSizes: (ratio: string) => Promise<string[]>;
    takePicture: (options: CameraPictureOptions) => Promise<CameraCapturedPicture>;
    resumePreview: () => Promise<void>;
    pausePreview: () => Promise<void>;
}
declare const ExponentCamera: React.ForwardRefExoticComponent<Pick<CameraNativeProps, "type" | "flashMode" | "autoFocus" | "whiteBalance" | "pointerEvents" | "style" | "zoom" | "ratio" | "focusDepth" | "onCameraReady" | "useCamera2Api" | "pictureSize" | "onMountError" | "barCodeScannerSettings" | "onBarCodeScanned" | "faceDetectorSettings" | "onFacesDetected" | "poster" | "onFaceDetectionError" | "onPictureSaved" | "barCodeScannerEnabled" | "faceDetectorEnabled"> & React.RefAttributes<ExponentCameraRef>>;
export default ExponentCamera;
//# sourceMappingURL=ExponentCamera.web.d.ts.map

redpajama_set_name: RedPajamaGithub


This week we'll focus on two composers born this week in history, who have both significantly impacted the Great American Songbook: Irving Berlin, born 130 years ago, and Burt Bacharach who turns 90 on May 12.
Berlin published his first song in 1907 for which he received 33 cents. He went on to become one of the greatest songwriters in history, with countless classics like "Cheek to Cheek," "Alexander's Ragtime Band," and, most notably, "White Christmas,".
Bacharach got an early start on classical piano but was bitten by the jazz bug. Bacharach worked with singers Vic Damone, Steve Lawrence and others before becoming musical director for the actress and singer Marlene Dietrich.


text:
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\begin{document}
\begin{small}

		\title{\flushleft
			{\sf\LARGE \textbf{Biological logics are restricted}} \\
			\vspace{5pt}
			{\sf \textbf{T. M. A. Fink and R. Hannam}} \\
			\vspace{3pt}
			{\sf \small \textbf{London Institute for Mathematical Sciences, Royal Institution, 21 Albermarle St, London W1S 4BS, UK}} \\
			\mbox{}				\vspace{-20pt}
			\mbox{}
		}

		\maketitle
\noindent
\begin{justify}
{\sf\textbf{Networks of gene regulation govern morphogenesis, determine cell identity and regulate cell function.
But we have little understanding, at the local level, of which logics are biologically preferred or even permitted.
To solve this puzzle, we studied the consequences of a fundamental aspect of gene regulatory networks: genes and transcription factors talk to each other but not themselves.
Remarkably, this bipartite structure severely restricts the number of logical dependencies that a gene can have on other genes.
We developed a theory for the number of permitted logics for different regulatory building blocks of genes and transcription factors.
We tested our predictions against a simulation of the 19 simplest building blocks, and found complete agreement.
The restricted range of biological logics is a key insight into how information is processed at the genetic level.
It constraints global network function and makes it easier to reverse engineer regulatory networks from observed behavior.
}}
\end{justify}
\mbox{} \\ \noindent
{\sf\textbf{\textcolor{red}{\large Introduction}}} \\
The development and maintenance of living organisms requires a lot of computation.
This is mainly performed at the molecular level through gene regulatory networks.
They govern the creation of body structures, regulate cell function, and are responsible for the progression of diseases.
Since the landmark discovery of induced pluripotent stem cells, scientists have identified special combinations of transcription factors which control cell identity \cite{Pawlowski2017, Kamao2014}.
Precision control over cell fate opens up the possibility of manufacturing cells for
drug development \cite{Engle2013},
disease modelling \cite{Kanherkar2014a} and
regenerative medicine \cite{Cherry2013}.
\\ \indent
Models of gene regulatory networks have been investigated for half a century \cite{Kauffman1969,Huang2005}.
They actually predate aspects of our understanding of gene regulation itself, such as the role of transcription factors.
Partly because of this, models of regulatory networks have tended to be overly simplistic \cite{Bornholdt2008}---disregarding, for example,
the complementary roles played by genes and transcription factors \cite{Hannam2019,Hannam2019b}.
Despite the simplicity of these models, however, a theoretical understanding of their typical behavior proved elusive until the mid-2000s \cite{Socolar2003,Samuelsson2003,Shmulevich2004,Mihaljev2006}.
\\ \indent
During the 20th century, various problems in biology have transitioned from a descriptive \cite{Reed2004} to a predictive science \cite{Reed2015}.
Examples include protein folding \cite{Jumper2021,Ahnert2015}, genotype-phenotype maps \cite{Wagner2008} and the segmentation of vertebrates \cite{Gomez2008}.
Yet a predictive understanding of genetic computation remains elusive, despite intense interest from researchers across fields \cite{Rand2021,Yan2017}.
\\ \indent
Predictability comes from mathematical structure, and mathematical structure is the consequence of constraints.
In physics, there is an abundance of constraints, typically expressed in the form of conservation laws.
The role of constraints in biology is less well understood, but modularity \cite{Wagner2007,Ahnert2016} and symmetry \cite{,Dingle2018,Johnston2022} seem to play important roles.
\\ \indent One constraint on gene regulatory networks that is hiding in plain site is their bipartite nature: genes and transcription factors talk to each other but not themselves.
A transcription factor is a protein or complex of proteins which are themselves synthesized from expressed genes.
Thus a transcription factor depends on one or more genes.
A gene is a particular segment of DNA that codes for a protein, flanked by one or more binding sites for different transcription factors.
These transcription factors promote or block the transcription of the gene.
Thus a gene depends on one or more transcription factors.
In this way, the expression levels of genes are determined by those of other genes, but only indirectly, via transcription factors \cite{Buccitelli2020}.
Bipartite models of regulation can reflect key biological details, such as different gene and transcription factor connectivities \cite{Hannam2019,Hannam2019b}.
\\ \indent
Our familiarity with the bipartite constraint belies its importance in determining function.
As we shall see, it severely restricts the range of logical dependencies that any one gene can have on other genes.
Identical arguments apply to the dependencies that a transcription factor can have on other transcription factors, but for brevity we stick to genes.
\\ \indent In this article we do four things.
First, we enumerate the different regulatory motifs that relate one gene to other genes via transcription factor middlemen.
Gene regulatory networks are built out of these regulatory motifs, the 19 simplest of which are shown in Fig. \ref{TableCompositions}.
Second, we develop an exact theory for the number of permitted gene-gene logics, for any regulatory motif.
This number tends to be much smaller than the number of possible gene-gene logics.
Third, for the 19 simplest regulatory motifs, we compared our prediction to a simulation of how  gene and transcription factor logics combine, and found exact agreement.
Fourth, we quantify the benefits of this restriction on logics for reverse engineering gene regulatory circuits from experiments.
\\ \\ \noindent {\sf\textbf{\textcolor{red}{\large Results}}}
\\ \noindent {\sf\textbf{\textcolor{black}{A puzzle}}}
\\ Our key insight is that the bipartite nature of gene regulatory networks
severely limits the number of logical dependencies that one gene can have on other genes.
To understand this conceptually, consider a social network puzzle.
Imagine that men and women talk to the opposite sex but not their own, and each person has one of only two moods, happy or sad.
As a man, your own mood depends on the moods of two women.
For instance, you might be happy only if both women are happy.
Or you might copy the mood of the first and ignore the second.
The mood of each woman depends, in turn, on the mood of two men.
So, ultimately, your mood is governed by the mood of the four men.
In how many ways can your mood depend on them?
\\ \indent
You might guess that there are $2^{2^4}$ = 65,536 ways, which is the number of logical dependencies on four variables.
But in reality there are just 520 ways to depend on the four men.
The hidden variables of the women greatly reduces the range of logical dependencies.
\\ \indent
The solution to this puzzle hints at a fundamental aspect of dynamical systems in which two species depend on each other but not themselves.
It suggests that the logical dependencies observed between a single species are highly restricted.
The preeminent example of such a system is gene regulatory networks, in which genes interact via transcription factors.
As we shall see, the number of permissible gene-gene logics is greatly reduced.
\\ \\ {\sf\textbf{\textcolor{black}{Theory}}} \\
Before we calculate the number of permissible gene-gene logics---which we call biological logics---we review some general properties of logics.
Logics are also known as Boolean functions, and we use the terms interchangeably.
There are $2^{2^n}$ logics of $n$ variables.
For $n=2$, they are
true, false,
$a$, $b$, $\overline{a}$, $\overline{b}$,
$ab$, $a\overline{b}$, $\overline{a} b$, $\overline{a}\overline{b}$,
$a+b$, $a+\overline{b}$, $\overline{a}+b$, $\overline{a}+\overline{b}$,
$ab+\overline{a}\overline{b}$ and $a\overline{b}+\overline{a} b$.
In this notation, $\overline{a}$ means {\sc not} a, $ab$ means $a$ {\sc and} $b$, and $a+b$ means $a$ {\sc or} $b$.
\\ \indent
Notice that two of these functions depend on no variables (true and false),
four depend on one variable ($a$, $b$, $\overline{a}$ and $\overline{b}$),
and the rest depend on two variables.
Let $s(n)$ be the number of logics of $n$ variables that depend on all $n$ variables.
By the principle of inclusion and exclusion,
\begin{equation}
    s(n) = \sum_{i=0}^n (-1)^{n-i} \textstyle \binom{n}{i}2^{2^i}.
    \label{AllVariableLogics}
\end{equation}
The first few $s(n)$ are 2, 2, 10, 218, 64594, starting at $n=0$ (OEIS A000371 \cite{Sloane}).
\def\vspace{0.025in}{\vspace{0.014in}}
\def3.7ex{4.2ex}
\begin{figure}[b!]
\includegraphics[width=0.9\columnwidth]{t1t2tn.pdf}
\setlength{\tabcolsep}{0pt}
\begin{tabularx}{\columnwidth}{@{\extracolsep{\fill}}cccccc}
\emph{}		&					& \emph{Biological} 	&& \emph{Projected} 	& \emph{All}					\\
\emph{Short-}	& \emph{Regulatory} 	& \emph{logics} 	&& \emph{regulatory} 	& \emph{logics}					\\
\emph{hand}	& \emph{motifs} 		& $c(t_1,\ldots,t_n)$ 	&& \emph{motifs}		& $2^{2^{t_1+ \ldots + t_n}}$		\\
 $\{1\}$		& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{1.pdf}				&  4				&& \mbox{}\vspace{0pt}\hspace{0pt}			\includegraphics[height=\lahigh]{g1.pdf}  	 			& 4        					\vspace{0.025in}	\\
$\{2\}$		& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{2.pdf}				& 16   			&& \mbox{}\vspace{0pt}\hspace{0pt}			\includegraphics[height=\lahigh]{g2.pdf}				& 16        					\vspace{0.025in}	\\
$\{3\}$ 		& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{3.pdf}				& 256   			&& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g3.pdf}				& 256        				\vspace{0.025in}	\\
$\{1,1\}$ 		& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{11.pdf}				& 16    			&& \mbox{}\vspace{0pt}\hspace{0pt}			\includegraphics[height=\lahigh]{g11.pdf}				& $16$   		   			\vspace{0.025in}	\\
$\{1,2\}$		& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{12.pdf}				& 88     			&& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g12.pdf}				& $256$   					\vspace{0.025in}	\\
$\{2,2\}$		&\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{22.pdf}			& 520    			&& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g22.pdf}				& $65,536$   	 			\vspace{0.025in}	\\
$\{1,3\}$		& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{13.pdf}			& 1528   	  		&& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g13.pdf}				& $65,536$                       		\vspace{0.025in}	\\
$\{2,3\}$		& \mbox{}\vspace{0pt}\hspace{0pt}	\includegraphics[height=\lahigh]{23.pdf}			& 9160 			&& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g23.pdf}			& $4.3 \times 10^9$ 			\vspace{0.025in}	\\
$\{3,3\}$		& \mbox{}\vspace{0pt}\hspace{0pt}	\includegraphics[height=\lahigh]{33.pdf}			& 161,800    	  	&& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g33.pdf}			& $1.8 \times 10^{19}$ 		\vspace{0.025in}	\\
$\{1,1,1\}$		& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{111.pdf}				& 256 		 	&& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g111.pdf}				& 256         	  			\vspace{0.025in}	\\
$\{1,1,2\}$		& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{112.pdf}			& 1696   			&& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g112.pdf}				& 65,536      			  	\vspace{0.025in}	\\
$\{1,2,2\}$		& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{122.pdf}			& 11,344   		&& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g122.pdf}			& $4.3 \times 10^9$   	 	\vspace{0.025in}	\\
$\{1,1,3\}$ 	& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{113.pdf}			& 30,496  			&& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g113.pdf}			& $4.3 \times 10^9$       		\vspace{0.025in}	\\
$\{2,2,2\}$		& \mbox{}\vspace{0pt}\hspace{0pt}	\includegraphics[height=\lahigh]{222.pdf}			& 76,288 			&& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g222.pdf} 			& $1.8 \times 10^{19}$  		\vspace{0.025in}	\\
$\{1,2,3\}$		& \mbox{}\vspace{0pt}\hspace{0pt}	\includegraphics[height=\lahigh]{123.pdf}			& 204,304 		&& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g123.pdf}			& $1.8 \times 10^{19}$		\vspace{0.025in}	\\
$\{2,2,3\}$ 	& \mbox{}\vspace{0pt}\hspace{0pt}	\includegraphics[height=\lahigh]{223.pdf}			& 1,375,168		&& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g223.pdf}			& $3.4 \times 10^{38}$ 	 	\vspace{0.025in} 	\\
$\{1,3,3\}$ 	& \mbox{}\vspace{0pt}\hspace{0pt}	\includegraphics[height=\lahigh]{133.pdf}			& 3,680,464 		&& \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g133.pdf}			& $1.8 \times 10^{19}$       	\vspace{0.025in}	\\
$\{2,3,3\}$ 	& \mbox{}\vspace{0pt}\hspace{0pt}	\includegraphics[height=\lahigh]{233.pdf}		& 24,792,448 		&& \mbox{}\vspace{0pt}\hspace{0pt}	\includegraphics[height=\lahigh]{g233.pdf}			& $1.2 \times 10^{77}$  	 	\vspace{0.025in}	\\
$\{3,3,3\}$ 	& \mbox{}\vspace{0pt}\hspace{0pt}	\includegraphics[height=\lahigh]{333.pdf}    		& 447,032,128		&& \mbox{}\vspace{0pt}\hspace{0pt}	\includegraphics[height=\lahigh]{g333.pdf}		& $1.3 \times 10^{154}$
\end{tabularx}%
\caption{\footnotesize
\textbf{The number of gene-gene logical dependencies for the 19 simplest regulatory motifs and their projections.}
\textbf{A}
In a regulatory motif, a gene (blue) depends on $n$ transcription factors (red), which depend on $t_1,t_2,\ldots,t_n$ genes (blue), respectively.
As a shorthand for this structure, we write $\{t_1,\ldots,t_n\}$, which counts the number of genes in the $n$ branches of the tree.
\textbf{B}
For the 19 simplest regulatory motifs, we show the number of logical dependencies $c(t_1,\ldots,t_n)$ (left) that a gene can have on other genes.
This tends to be much smaller than the number of logical dependencies in the absence of the transcription factor middlemen, $2^{2^{t_1+ \ldots + t_n}}$ (right), when the regulatory motif is projected.
}
\label{TableCompositions}
\end{figure}
\\ \indent The biological equivalent of our social network puzzle is a gene that depends on two transcription factors, each of which depends on two genes.
This is the sixth regulatory building block, or regulatory motif, in Fig. \ref{TableCompositions}B.
A regulatory motif is the connectivity that a gene has with other genes via transcription factor middlemen.
For a gene that depends on $n$ transcription factors, each of which depends on $t_1,\ldots,t_n$ genes (Fig. \ref{TableCompositions}A),
we use as a shorthand for the regulatory motifs $\{t_1,\ldots,t_n\}$, which counts the number of genes in the $n$ branches of the tree.
\begin{figure*}[t!]
\begin{tabularx}{\textwidth}{@{\extracolsep{\fill}}llll}
\multicolumn{2}{l}{\textbf{Biological logics for \!\!\!\!\!\! \mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{22.pdf}}}																										\vspace{0.04in}			\\
\emph{0 variables}	 				& \emph{3 variables}							& \emph{4 variables}						& \emph{4 variables}					 								\\
1 $\cdot$ 	 true		 				& 8 $\cdot$ $a b c$							&16 $\cdot$ $a b c d$					& \hspace{\digit} 8 $\cdot$ $a b (c d + \overline{c} \overline{d})$									\\
1 $\cdot$ 	 false					& 8 $\cdot$ $a b + c$						&16 $\cdot$ $a b + c d$					& \hspace{\digit} 8 $\cdot$ $(a b + \overline{a} \overline{b}) c d$									\\
								& 8 $\cdot$ $(a+b) c$						&16 $\cdot$ $a b (c+ d)$					& \hspace{\digit} 8 $\cdot$ $a b + c d + \overline{c} \overline{d}$								 	\\
\emph{1 variable}					& 8 $\cdot$ $a + b + c$						&16 $\cdot$ $(a+b) c d $					& \hspace{\digit} 8 $\cdot$ $a b + \overline{a} \overline{b} + c d$				 					\\
2 $\cdot$ $a$						& 8 $\cdot$ $(a+b) c + \overline{a} \overline{b} \overline{c}$				&16 $\cdot$ $a + b + c d$					& \hspace{\digit} 8 $\cdot$ $a + b + c d + \overline{c} \overline{d}$ 	 								\\
								& 4 $\cdot$ $(a b + \overline{a} \overline{b}) c$					&16 $\cdot$ $a b + c + d$					& \hspace{\digit} 8 $\cdot$ $a b + \overline{a} \overline{b} + c + d$	 								\\
\emph{2 variables}					& 4 $\cdot$ $a b + \overline{a} \overline{b} + c$	 				&16 $\cdot$ $a + b + c + d$				& \hspace{\digit} 8 $\cdot$ $(a b + \overline{a} \overline{b}) (c + d)$ 	 								\\
4 $\cdot$ $a b$						& 2 $\cdot$ $(a b + \overline{a} \overline{b}) c + (a \overline{b}+ \overline{a} b) \overline{c}$	&16 $\cdot$ $(a + b)(c + d)$				& \hspace{\digit} 8 $\cdot$ $(a + b) (c d + \overline{c} \overline{d})$ 	 								\\
4 $\cdot$ $a+b$					& 										&16 $\cdot$ $a b (c + d) + (\overline{a} + \overline{b}) \overline{c} \overline{d}$	& \hspace{\digit} 8 $\cdot$ $(a b + \overline{a} \overline{b}) (c + d) + (a \overline{b} + \overline{a} b) \overline{c} \overline{d} $  				\\
2 $\cdot$ $a b + \overline{a} \overline{b}$				&										&16 $\cdot$ $(a+b)(c+d) + \overline{a}\overline{b}\overline{c}\overline{d}$		& \hspace{\digit} 8 $\cdot$ $(a + b) (c d + \overline{c} \overline{d}) + \overline{a} \overline{b} (c \overline{d} + \overline{c} d)$ 				\\
 								&										& \hspace{\digit} 4 $\cdot$ $a b + \overline{a} \overline{b} + c d + \overline{c} \overline{d}$	& \hspace{\digit} 2 $\cdot$  	$(a b + \overline{a} \overline{b}) (c d + \overline{c} \overline{d}) + (a \overline{b} + \overline{a} b) (c \overline{d} + \overline{c} d)$	\\
								&										& \hspace{\digit} 4 $\cdot$ $(a b + \overline{a} \overline{b}) (c d + \overline{c} \overline{d} )$
\end{tabularx}
\caption{\footnotesize
\textbf{Biologically valid gene-gene logics for a gene which depends on two transcription factors, each of which depends on two genes.}
Of the $2^{2^4}$ = 65,536 logics of four variables, only 520 are biologically valid.
In our notation, $\overline{a}$ means {\sc not} a, $ab$ means $a$ {\sc and} $b$, and $a+b$ means $a$ {\sc or} $b$.
We group together logics that depend on $m=$ 0, 1, 2, 3 and 4 variables.
We need not show all 520 logics because of two kinds of symmetry.
In the first, there are $\binom{4}{m}$ ways to choose the $m$ variables.
For example, for $m=2$, there are 6 choices of two variables: $a \, b$; $a \, c$; $a \, d$; $b \, c$; $b \, d$; and $c \, d$.
But the structure of the logics is the same for all choices, and we only show the logics for $a \, b$.
The second symmetry is given by the number before each logic.
It is the number of logics when none or some of its variables are everywhere replaced by their negation, since doing so does not change the structure of the logic.
For example, applying this to $a b$ gives $a b, a\overline{b}, \overline{a} b$ and $\overline{a}\overline{b}$, so we put a 4 in front of the logic $ab$, and don't show the rest.
The column sums are 2, 2, 10, 50 and 250, and $2 \binom{4}{0} + 2 \binom{4}{1} + 10 \binom{4}{2} + 50 \binom{4}{3} + 250 \binom{4}{4} = c(2,2) = 520$.
\label{Valid}
}
\end{figure*}
\begin{figure}[b!]\setlength{\hfuzz}{1.1\columnwidth}
	\begin{minipage}{\textwidth}
	\includegraphics[width=1\textwidth]{TheoryVersusComputation.pdf}

	\caption{\footnotesize
	\textbf{Theory and simulation for the number of logics for the 19 simplest regulatory motifs.}
	Our theoretical prediction (circles) is perfectly confirmed by computer simulation (crosshairs).
	Here we have plotted each regulatory motif according to the number of gene-gene logics allowed by it and its projection---in other words, in the presence and absence of the transcription factor middlemen.
	These are the numbers on the left and right of Fig. \ref{TableCompositions}B.
	}
	\label{FigCompare}
	\end{minipage}
\end{figure}
\\  \indent
Our main quantitative result, which we derive in the Methods, is an expression for the exact number of biological (permissable gene-gene) logics for any regulatory motif $c(t_1,\ldots,t_n)$.
(For convenience, we drop the braces around $\{t_1,\ldots,t_n\}$ when it is the argument of a function.)
The result is
\begin{equation}
    c(t_1,\ldots,t_n)  =  \sum_{m=0}^n s(m)	\!\! \sum_{\sigma_1\ldots \sigma_m}   \, \alpha_{\sigma_1} \ldots \alpha_{\sigma_m},
    \label{CA}
\end{equation}
where
\begin{equation*}
\alpha_i =  (2^{2^i}-2)/2.
\end{equation*}
The second sum in eq. (\ref{CA}) is over all of the subsets of size $m$ of the set $\{t_1, \ldots, t_n \}$.
Eq. (\ref{CA}) is simpler than it looks, as some examples illustrate:
\begin{eqnarray*}
    c(i)        	&=& 2 + 2 \, \alpha_i, \\
    c(i,j)     		&=& 2 + 2(\alpha_i + \alpha_j)              + 10 \, \alpha_i \alpha_j, \\
    c(i,j,k)   		&=& 2 + 2(\alpha_i + \alpha_j + \alpha_k)   \\
    			&+& 10 \big(\alpha_i \alpha_j + \alpha_j \alpha_k + \alpha_i \alpha_k \big) + 218 \, \alpha_i \alpha_j \alpha_k.
\end{eqnarray*}
With this, it is easy to calculate the number of biological logics for all of the regulatory motifs in Fig. \ref{TableCompositions}.
Let's calculate $c(2,2)$.
Since $\alpha_2 = (2^{2^2} - 2)/2 = 7$, $c(2,2) = 2 + 2(7+7) + 10 \cdot 7^2 = 520$.
These 520 biological logics are given explicitly in Fig. \ref{Valid}.
\\ \indent
Fig. \ref{TableCompositions}B shows the number of gene-gene logics for the 19 simplest regulatory motifs (left) and their projections (right).
A projection is the connectivity that results from dropping the transcription factors and connecting the genes directly to each other.
The numbers on the left tend to be vastly smaller than those on the right.
The presence of the transcription factor middlemen severely restricts the number of permissible gene-gene logics.
\\ \\ {\sf\textbf{\textcolor{black}{Simulation}}} \\
To test our prediction for the number of biological logics in eq.\ (\ref{CA}),
we wrote a computer simulation to compute how different gene and transcription factor logics combine.
For a given regulatory motif, we assigned all possible logics to the gene at the top and to the transcription factor middlemen---$f$ and the $g_i$ in Fig. \ref{TableCompositions}A.
We call different combinations of logics equivalent if they produce the same logical dependence of the top gene on the bottom genes.
We simulated the architectures in Fig. \ref{TableCompositions}B and compared them to our prediction, and found exact agreement, as shown in Fig. \ref{FigCompare}.
\\ \indent
In general, only a tiny fraction of all logics are biological logics, for a given regulatory motif.
To gain some sense for which logics belong to this select group, we show them explicitly for the regulatory motif $\{2,2\}$ in Fig. \ref{Valid}.
Of the possible $2^{2^4}$ = 65,536 logics of four variables, only 520 are biological.
\newpage \mbox{} \newpage \mbox{} \newpage
\noindent {\sf\textbf{\textcolor{red}{\large Discussion}}}
\\ The restriction of biological logics can be understood as the consequence of two things.
First, most logics cannot be written as a composition of logics, where the composition structure reflects the regulatory motif.
Second, the assignment of logics to genes and transcription factors is redundant, in the sense that different combinations produce the same gene-gene dependence.
We consider each of these in turn before discussing the implications for reverse engineering regulatory networks.
\\  \\ {\sf\textbf{Restriction of gene-gene logics}} \\
Although we have shown that, for a given regulatory motif, most gene-gene logics are not permitted, is not self-evident which ones make the cut.
For example, for the motif $\{2,2\}$ (Fig. \ref{Valid}),
the logic $ab+cd$ is permitted, meaning that the dependent gene is expressed if $a$ {\sc and} $b$ are expressed, {\sc or}  $c$ {\sc and} $d$ are expressed.
But $ac+bd$ is not permitted---swapping $a$ and $c$ in the valid logic invalidates it.
\\ \indent
Ultimately, the condition for a valid logic is being able to express it as a composition of logics.
For the 19 simplest regulatory motifs, brute force enumeration is sufficient to determine the biologically permitted logics.
However, there are some shortcuts for going about this for these and more complex regulatory motifs.
For example, one condition for a logic to be valid for $\{2,2\}$ is that swapping the genes in either branch does not change the logic,
as is the case for $ab + cd$.
But this is not sufficient: $(a+b)c$ is permitted, but $(a+b)c + ab$ is not, even though swapping $a$ and $b$ leaves both unchanged.
Further investigation will likely uncover more comprehensive tests for biological logics.
\begin{figure}[b!]
	\centering
	\includegraphics[width=0.93\columnwidth]{Degenerate.pdf}
	\caption{\footnotesize
	\textbf{Different assignments of logics to genes and transcription factors compose to give the same dependence of a gene on other genes.}
	For a gene which depends on two transcription factors, each of which depends on two genes,
	there are four ways to achieve the logic $f$ = ($a$ {\sc and} $b$) {\sc or} ($c$ {\sc and} $d$).
	}
    \label{Degenerate}
\end{figure}
\def\vspace{0.025in}{\vspace{0.025in}}
\def3.7ex{3.7ex}
\begin{figure}[b!]
\setlength{\tabcolsep}{0pt}
\begin{tabularx}{\columnwidth}{@{\extracolsep{\fill}}cccccc}
\multicolumn{6}{c}{\textbf{Information in bits required to reverse engineer} }				\\
\multicolumn{3}{c}{\emph{Regulatory motifs} ($I_{\rm r}$)}		& \multicolumn{3}{c}{\emph{Proj. regulatory motifs} ($I_{\rm p}$)}	\\
					& \emph{10\! genes}	&  \emph{100\! genes}	& 		& \emph{10\! genes}	&  \emph{100\! genes} \vspace{0.025in}	 \\
\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{1.pdf}				& 5				& 9					&\mbox{}\vspace{0pt}\hspace{0pt}			\includegraphics[height=\lahigh]{g1.pdf}			& 5					& 9 		\vspace{0.025in} \\
\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{2.pdf}				& 9				& 16					&\mbox{}\vspace{0pt}\hspace{0pt}			\includegraphics[height=\lahigh]{g2.pdf}		& 9					& 16		\vspace{0.025in} \\
\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{3.pdf}				& 15				& 25					&\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g3.pdf}		& 15					& 25		\vspace{0.025in} \\
\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{11.pdf}				& 10				& 17					&\mbox{}\vspace{0pt}\hspace{0pt}			\includegraphics[height=\lahigh]{g11.pdf}		& 9					& 16		\vspace{0.025in} \\
\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{12.pdf}				& 15				& 25					&\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g12.pdf}		& 15					& 25		\vspace{0.025in} \\
\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{22.pdf}				& 19				& 34					&\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g22.pdf}		& 24					& 38		\vspace{0.025in} \\
\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{13.pdf}				& 20				& 34					&\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g13.pdf}		& 24					& 38		\vspace{0.025in} \\
\mbox{}\vspace{0pt}\hspace{0pt}	\includegraphics[height=\lahigh]{23.pdf}				& 24 				& 43 					&\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g23.pdf}		& 40					& 58		\vspace{0.025in} \\
\mbox{}\vspace{0pt}\hspace{0pt}	\includegraphics[height=\lahigh]{33.pdf}			& 29				& 52 					&\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g33.pdf}	& 72					& 94		\vspace{0.025in} \\
\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{111.pdf}				& 17 				& 28 					&\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g111.pdf}		& 15 					& 25		\vspace{0.025in} \\
\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{112.pdf}				& 22				& 36					&\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g112.pdf}		& 24					& 38		\vspace{0.025in} \\
\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{122.pdf}				& 26				& 45					&\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g122.pdf}		& 40					& 58		\vspace{0.025in} \\
\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{113.pdf}				& 27				& 45					&\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g113.pdf}		& 40					& 58		\vspace{0.025in} \\
\mbox{}\vspace{0pt}\hspace{0pt}	\includegraphics[height=\lahigh]{222.pdf}			& 30	 			& 53	 				&\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g222.pdf}	& 72 					& 94		\vspace{0.025in} \\
\mbox{}\vspace{0pt}\hspace{0pt}	\includegraphics[height=\lahigh]{123.pdf}			& 31	 			& 54	 				&\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g123.pdf}		& 72 					& 94		\vspace{0.025in} \\
\mbox{}\vspace{0pt}\hspace{0pt}	\includegraphics[height=\lahigh]{223.pdf}			& 35 				& 62	 				&\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g223.pdf}	& 135 				& 162	\vspace{0.025in} \\
\mbox{}\vspace{0pt}\hspace{0pt}	\includegraphics[height=\lahigh]{133.pdf}			& 36 				& 63	 				&\mbox{}\vspace{0pt}\hspace{0pt}		\includegraphics[height=\lahigh]{g133.pdf}	& 135 				& 162	\vspace{0.025in} \\
\mbox{}\vspace{0pt}\hspace{0pt}	\includegraphics[height=\lahigh]{233.pdf}			& 39 				& 71	 				&\mbox{}\vspace{0pt}\hspace{0pt}	\includegraphics[height=\lahigh]{g233.pdf}	& 261 				& 293	\vspace{0.025in} \\
\mbox{}\vspace{0pt}\hspace{0pt}	\includegraphics[height=\lahigh]{333.pdf} 			& 43 				& 80	 				&\mbox{}\vspace{0pt}\hspace{0pt}	\includegraphics[height=\lahigh]{g333.pdf}	& 515 				& 553	\vspace{0.025in}
\end{tabularx}%
\caption{\footnotesize
\textbf{The information in bits required to reverse engineer regulatory motifs and their projections.}
To reverse-engineer a regulatory motif or its projection, we need to deduce two things: the connectivity and the logic.
The number of possible connectivities depends on the size of the entire regulatory network, so we give examples for networks of 10 and 100 genes.
In general, the bipartite structure of regulatory motifs means that significantly less information is required to reverse engineer them than their projections.
}
\label{Info}
\end{figure}
\\  \\ {\sf\textbf{Redundancy of gene-gene logics}} \\
We know that the number of biological logics can be at most the number of assignments of logics to $f$ and the $g_i$ in Fig. \ref{TableCompositions}A.
But we observe that the number of biological logics is less than this.
This is because different assignments of logics to genes and transcription factors can compose to give the same gene-gene logic.
For example, for the regulatory motif $\{2,2\}$ in Fig. \ref{Valid}, 4,096 assignments compose to 520 logics.
An example of different assignments of logics which compose to the same logic is given in Fig. \ref{Degenerate}.
\\ \indent The composition of logics is a preeminent testbed for understanding input-output maps.
Many input-output maps in nature and mathematics are many-to-one, but with a non-uniform redundancy that is exponentially biased towards simple outputs \cite{Dingle2018}.
Examples include RNA secondary structure, protein complexes and model gene regulatory networks \cite{Johnston2022}.
Preliminary evidence suggests that composed logics are similarly biased towards simple logics.
Developed further, our theory for the composition of logics presents an opportunity to give a mathematical explanation of this widely observed empirical trend.
\\ \indent Looking farther afield, while we studied the composition of logics over only two levels, we believe it is possible to generalize our results to multiple levels.
This could give theoretical backing to computational insights into the robustness and evolvability of logic gates, deftly studied by Andreas Wagner and his co-author in the context of genotype-phenotype maps \cite{Raman2011}.
When the number of composition levels is large, the limiting distribution of logics could shed light on the space of functions in some types of neural networks \cite{Mozeika2020}.
\\ \\ \noindent {\sf\textbf{Reverse engineering gene regulation}}
\\ The reduction in the number of gene-gene logics restricts the range of global behavior of gene regulatory networks.
A bound on the range of global behavior is the amount of information required to reverse engineer the regulatory network that gives rise to it.
Reverse engineering is a major goal of systems biology \cite{Yan2017}, and advances in methods for doing so are highly sought.
\\ \indent
As we noted earlier, a global network can be broken down piecewise into its constituent regulatory motifs.
The information required to reverse engineer the whole is the sum of the information required to reverse engineer the parts.
Let's work out the information required to reverse engineer a regulatory motif, on the one hand, and a projected regulatory motif, on the other (Fig. \ref{Info}).
On the face of it, regulatory motifs are more intricate, and ostensibly harder to reverse engineer.
But, as we shall see, the opposite is true.
 \\ \indent
The information required to reverse engineer a projected regulatory motif (Fig. \ref{Info} right), which is derived in the Methods, is
\begin{equation*}
I_{\rm p}  = \textstyle \log_2 \left( \binom{N}{m} 2^{2^m} \right),
\end{equation*}
where $N$ is the number of genes in the network and $m = t_1 + \ldots + t_n$.
The information required to reverse engineer a regulatory motif (Fig. \ref{Info} left), also derived in the Methods, is
\begin{equation*}
I_{\rm r} = \textstyle \log_2 \left( \binom{N}{m} B_m \, c(t_1, \ldots, t_n) \right),
\end{equation*}
where $B_m$ is the $m$th Bell number.
\\ \indent
We show $I_{\rm r}$ and $I_{\rm p}$ for the 19 simplest regulatory motifs in Fig. \ref{Info}, for networks of 10 and 100 genes.
The mean of $I_{\rm r}$ is 24 bits and 42 bits for 10 genes and 100 genes, and
the mean of $I_{\rm p}$ is 80 and 98 bits for 10 and 100 genes.
This translates into a considerable savings in the experimental effort necessary to decode regulatory networks and parts thereof.
\\ \\ \noindent {\sf\textbf{\textcolor{red}{\large Methods}}}
\\ \noindent{\sf\textbf{\textcolor{black}{Derivation of the number of biological logics}}}
\\ Here we derive an exact expression for the number of biologically permitted logics for different regulatory motifs.
This is the number of logics that can be expressed as a composition of logics, according to the dependence implied by each regulatory motif.
\\ \indent
Let $q(t_1,\ldots,t_n)$ be the number of distinct compositions of logics that depend on at least one variable in each and every of the logics $g_i$ (Fig. \ref{TableCompositions}A).
The number of choices of $g_i$ that depend on at least one of its $t_i$ variables is $2^{2^{t_i}}-2$, since only true and false depend on no variables.
But, because of De Morgan duality, the set of logics $g_i$ and $\overline{g}_i$ are identical, so to avoid double counting we must divide this number by two.
Let
\begin{equation*}
    \alpha_{t_i} = ( 2^{2^{t_i}}-2)/2.
\end{equation*}
Then
\begin{equation}
    q(t_1,\ldots,t_n)  =  s(n) \, \alpha_{t_1} \ldots \alpha_{t_n},
    \label{HA}
\end{equation}
where $s(n)$ is defined in eq. (\ref{AllVariableLogics}).
For example,
\begin{eqnarray*}
    q(i)        	&=& 2 	\, \alpha_i,    						\\
    q(i,j)   		&=& 10 	\, \alpha_i \alpha_j,    				\\
    q(i,j,k)  	&=& 218 	\, \alpha_i \alpha_j \alpha_k.
\end{eqnarray*}
We take $q(\emptyset)$ to be $s(0)$, which is 2.
\\ \indent
To calculate the number of distinct logic compositions $c(t_1,\ldots,t_n)$, we just need to sum $q$ over the ways of depending on none of the $g_i$,
plus the ways of depending on just one of the $g_i$, and so on, up to the ways of depending on all $n$ of the $g_i$.
We can write this as
\begin{equation}
    c(t_1,\ldots,t_n)  =  	\!\!\!\!\!\! \sum_{e \in 2^{ \{ t_1,\ldots,t_n\} } } 	\!\!\!\!\!\! 	q(e),
    \label{JA}
\end{equation}
where the sum is over the power set of $\{t_1, \ldots, t_n\}$, that is, all subsets $e$ of the set $\{t_1, \ldots, t_n\}$, denoted by $2^{ \{ t_1,\ldots,t_n\} }$.
Inserting (\ref{HA}) into (\ref{JA}) gives
\begin{equation*}
    c(t_1,\ldots,t_n)  =  	\!\!\!\!\!\! \sum_{e \in 2^{ \{ t_1,\ldots,t_n\} }} 	\!\!\!\!\!\! 	s(|e|) \, \alpha_{\sigma_1} \ldots \alpha_{\sigma_{|e|}},
\end{equation*}
where the $\sigma_i$ are the elements of $e$ and $|e|$ is the number of elements in $e$.
Grouping together subsets of the same size,
\begin{equation}
    c(t_1,\ldots,t_n)  =  \sum_{m=0}^n s(m)	\!\! \sum_{\sigma_1\ldots \sigma_m}   \, \alpha_{\sigma_1} \ldots \alpha_{\sigma_m},
    \label{HI}
\end{equation}
as desired.
The second sum is over all subsets of size $m$ of the set $\{t_1, \ldots, t_n \}$.
For $m=0$, the sum is over the null set and is taken to be 1.
\\ \\ \noindent{\sf\textbf{\textcolor{black}{Simulation of biological logics}}}
\\ To test our predictions, we simulated the logical dependence of one gene on other genes when they interact via transcription factors.
We wrote a program in Mathematica to handle each of the regulatory motifs in Fig. \ref{TableCompositions}B.
In particular, we determined the logical dependence of the top gene on the bottom genes for each possible assignment of logics to $f$ and to $g_1, \ldots, g_n$ (Fig. \ref{TableCompositions}A).
Since $f$ depends on $n$ variable, there are $2^{2^n}$ logics that $f$ must run through.
Since $g_i$ depends on $t_i$ variables, there are $2^{2^{t_i}}$ logics that $g_i$ must run through, for each of the $g_i$.
Thus we must run through a total of
\begin{equation*}
	2^{2^n} 2^{2^{t_1}}\!\!\ldots2^{2^{t_n}}
\end{equation*}
compositions of logics, for each of the regulatory motifs.
As an aside, this implies that $c(t_1,\ldots,t_n)$ is bounded from above by this number, which we indeed observed.
For example, for the regulatory motif $\{1,2,3\}$, we have $c(1,2,3)$ = 204,304 $\leq 2^{2^3} 2^{2^1} 2^{2^2} 2^{2^3} \!$ = 4,194,304.
\\ \\ \noindent{\sf\textbf{\textcolor{black}{Representation and composition of logics}}}
\\ Throughout this article, we write out logics in the disjunctive normal form, which consists of a disjunction of conjunctions.
In other words, we write them as {\sc or}s of {\sc and}s, or sums of products.
As with the product and sum, {\sc and} takes precedence over {\sc or}.
Thus, for example, we have
\begin{center}
($a$ {\sc and} $b$) {\sc or} ($c$ {\sc and} $d$) $\equiv ab+cd$
\end{center}
and
\begin{center}
($a$ {\sc and} $c$) {\sc or} ($a$ {\sc and} $d$) {\sc or} ($b$ {\sc and} $c$) {\sc or} ($b$ {\sc and} $d$) \\
$\equiv ac + ad + bc + bd
\equiv (a+b)(c+d)$.
\end{center}

In general, for the regulatory motif $\{t_1,\ldots,t_n\}$, a logic is biologically permitted only if it can be expressed in the form
\begin{equation*}
\begin{aligned}
& h(x_{1,1},\ldots,x_{1,t_1}; \ldots; x_{n,1},\ldots,x_{n,t_n}) = \\
& f\big(g_1(x_{1,1},\ldots,x_{1,t_1}),\ldots,g_n(x_{n,1},\ldots,x_{n,t_n})\big),
\end{aligned}
\end{equation*}
where $x_{i,j}$ is the $j$th gene in the $i$th branch in Fig. \ref{TableCompositions}A.
For example, consider the bottom right logic in Fig. \ref{Valid}:
\begin{eqnarray*}
h(a,b,c,d) 	&=& (a b + \overline{a} \overline{b}) (c d + \overline{c} \overline{d}) + (a \overline{b} + \overline{a} b) (c \overline{d} + \overline{c} d) \\
	 	&=& (a b + \overline{a} \overline{b}) (c d + \overline{c} \overline{d}) + \overline{(a b + \overline{a} \overline{b})} \,\, \overline{(c d + \overline{c} \overline{d})} \\
		&=& g_1 g_2 + \overline{g_1} \,\, \overline{g_2}
\end{eqnarray*}
where
\begin{equation*}
g_1(a,b) = a b + \overline{a} \overline{b} \quad {\rm and} \quad g_2(c,d) = c d + \overline{c} \overline{d}.
\end{equation*}
Thus we are able to write $h(a,b,c,d)$ as a composition of a logic of two variables, each of which is a logic of two variables,
which corresponds to the regulatory motif $\{2,2\}$.
\\ \\ \noindent{\sf\textbf{\textcolor{black}{Information for reverse engineering}}}
\\ The information associated with realizing a discrete random variable that is uniformly distributed is $\log_2$ of the range of the random variable.
To reverse engineer a projected regulatory motif (Fig. \ref{Info} right), we need to deduce the connectivity and the logic, both of which we take to be uniformly distributed.
(Were the distributions to deviate from uniform, the required information would be less.)
For a network of $N$ genes, the number of ways that a gene can connect to $m = t_1 + \ldots + t_n$ other genes is $\binom{N}{m}$.
The number of logics is $2^{2^m}$.
So the information, in bits, required to reverse engineer a projected regulatory motif is at most
\begin{equation*}
I_{\rm p}  = \textstyle \log_2 \left( \binom{N}{m} 2^{2^m} \right).
\end{equation*}
 \indent
Now let's reverse engineer a regulatory motif (Fig. \ref{Info} left).
For a network of $N$ genes, the number of ways that a gene can connect to other genes via transcription factors is $\binom{N}{m} B_m$, where $m = t_1 + \ldots + t_n$.
The second term is the $m$th Bell number, which is the number of ways to partition a set of $m$ labeled elements.
The number of logics is $c(t_1, \ldots, t_n)$ in eq. (\ref{CA}).
So the information required to reverse engineer a regulatory motif is at most
\begin{equation*}
I_{\rm r} = \textstyle \log_2 \left( \binom{N}{m} B_m \, c(t_1, \ldots, t_n) \right).
\end{equation*}
\\
\begin{scriptsize}%
\noindent{\sf\textbf{Acknowledgements}} \\
{\sf\textbf{Funding:}}
{\sf This research was supported by a grant from bit.bio.}
{\sf\textbf{Author contributions:}}
{\sf T. F. wrote the paper and did the mathematical derivations. T. F. and R. H. did the simulations and made the figures.}
{\sf\textbf{Competing interests:}}
{\sf The authors declare that they have no competing interests.}
{\sf\textbf{Acknowledgements:}}
{\sf The authors acknowledge Andriy Fedosyeyev and Alexander Mozeika for helpful discussions.}
\end{scriptsize}
\vspace*{-0.4in}

redpajama_set_name: RedPajamaArXiv


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Kim and Lucas are at one of those points in their lives where everything they have become used to is going to change forever. Lucas just graduated from medical school, they're moving across the country to New York City, AND they're going to have a baby!! I think they are going to love being in New York despite the fact that Kim has some reservations about being a New Yorker for the next five years.
We got together to take some photos of them as Californians before they hit the road for the east coast. The light was perfect during this early morning shoot. We had a blast together. Good luck guys!
Love the pictures! When we hoped you would move East, we meant Colorado, not New York!
You both looked so relaxed! I thought you had been busy lately. I guess I was wrong.
Best of luck in the new chapter in your life.
These are amazing. Kim and Lucas, you are two beautiful people–inside and out! We love you.
Fantastic pictures! What a cute couple. Can't wait to see pictures of Baby!
Will miss you guys! can't wait to see baby pictures, soon soon!
Such awesome photos of such a beautiful couple. Nice job, Mark! Looking forward to the next round of photos with lil King.
Hey, I know you guys are photogenic, but Mark must have touched up something…these are too perfect!!
Love these Mark, great job! I adore the ones in the field.
Real men wear pink! LOVE it!
Wow these are really nice! I hope the unpacking is going well and that "Joe King" waits to come out until your new home is prepared for him/her!
Love these…just an amazing time for you guys!
Love the pics where one of you is in the backround looking at your mate…also the close ups of Kim.
we love all the pix! This guy knew what he was doing! A+ Now have him come to Stony Point for more of the same! and for the new baby!
My favs are the ones in the wheat field! Beautiful!!


The most cutting-edge startup in the lawn care industry is already operating in over 100 US cities and they're not stopping there.
Lawn Love, an app similar to Uber or Lyft, but for professional lawn care services is launching in Tennessee this week.
The service enables its users to instantly schedule, review, and pay for various types of yard work through the use of a mobile app or website. The platform is powered by satellite imaging software which reviews a property and generates a quote in less than two minutes.
"Traditionally consumers have had to wait for a lawn care worker to physically come to their property, offer a quote, and schedule the service. It can take weeks to get the job done after initial contact was made. We're excited to be bringing lawn care into the 21st century," said Founder and CEO, Jeremy Yamaguchi.
Lawn Love has partnered with hundreds of existing lawn care businesses across the state. The company aims to provide these businesses with innovative scheduling, job routing, and payment software that will help them streamline their operations.
"The average lawn care company in Tennessee has only two employees and lacks the resources to modernize their business models. We are trying to democratize software for small businesses that will allow them to compete with dominating, corporate lawn care providers such as TruGreen," said Mr. Yamaguchi.
The company was founded in 2014 to revolutionize lawn care services for the 80% of American households that have a lawn. Since then, Lawn Love has partnered with over 2,500 independent lawn care companies to deliver services across the country.
Each independent contractor goes through a rigorous background check to assess their level of lawn care experience before they can begin work. Users can schedule a wide range of services including lawn mowing, weeding, aeration, gutter clearing and more.
Lawn Love was Founded in 2014 by Jeremy Yamaguchi, the San Diego based startup has completed 300,000 lawn care jobs in over 100 US cities. Since it's humble beginnings, the company has seen massive growth. Lawn Love employs a team of 90 based out of Scripps Ranch, San Diego. Original investors include Y Combinator, Bullpen Capital, Joe Montana, Alexis Ohanian, Barbara Corcoran and more.
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Showing posts with label movies. Show all posts
MST3K Watch: The Day Time Ended (1979)
Format: MST3K on Netflix
Viewing: First
This movie is a bizarre mess with no story. Now, sometimes MST3K cuts these movies for time, so its possible we lost some key moments or elements on the floor, but... I don't think so.
The MST3K episode DOES feature an astonishing musical number lifting music from The Music Man. The current crew has really hit their stride. And - there's a Kim Cattrall callback.
But, yeah, it's a bunch of unmotivated special FX and a cast that I can't not talk about.
Labels: 1970's, First viewing, movies, movies 2019, mst3k, sci-fi
Espionage Comedy Watch: Spy (2015)
Format: Amazon Streaming
This movie is exactly (exactly) what you think it's going to be. That's not a knock, it's just a statement.
It's weird. I feel like Paul Feig would do really well managing a network TV comedy. It seems better suited to his sense of humor where he could have fun with characters than trying to cram in an actual story in 90-120 minutes.
This was part of my "I have a cold, so let's just watch some inconsequential stuff" viewing from Friday night, and it fit the bill.
Labels: 2010's, First viewing, movies, movies 2019
Disney Watch: Ralph Breaks the Internet (2018)
No write up. I'm a bit under the weather, but I really enjoyed it. And I can't believe Disney went off-script with their own IP to that degree. A lot of good stuff. And, of course, Vanellope's song - just brilliant.
Late Edit: Our own NathanC wrote a great review over at the TPR site, so go check that out.
Labels: 2010's, cartoons, Disney, First viewing, movies, movies 2019
80's Watch: Hollywood Shuffle (1987)
Viewing: No idea. At least third.
Back when Hollywood Shuffle (1987) first showed up on home video, it was a movie I recall renting and really liking. I know for a fact I only sorta got what the movie was saying and doing and was more interested in the fact that some of the sketches and spoofs played well to even a 13 year old. After all, the movie is about an actor's journey through casting and into his first day on set of a film, loaded with cut-away scenes where they lampoon Hollywood movies.
Labels: 1980's, movies, movies 2019
Noir Watch: Devil in a Blue Dress (1995)
Format: TCM on DVR
I'd intended to see Devil in a Blue Dress (1995) during it's theatrical run, and I don't really know how I didn't. It was a wide release and ran for a bit. In the intervening years I've watched more noir of the original era, not necessarily watching what came out as noir and neo-noir at the theater.* The 90's and 00's saw a fair number of mid-century crime and costume dramas and glossy neo-noir films that I think a lot of folks today see in their mind's eye more than actual films of the original noir era. Some of the films were pretty good (I love LA Confidential), others were less so (I really struggled with The Black Dahlia).
There's a lot to recommend Devil in a Blue Dress, even if it feels like writer/ director Carl Franklin was more intent on establishing a string of movies based on the protagonist's exploits than he was in actually getting into the why's and wherefore's of the story's central mystery. It's one of the extremely rare Black-focused noir films, and does a phenomenal job of world building, leaning on familiar noir tropes and giving us new spin based on the Black experience of mid-Century LA.
Labels: 1990's, movies, movies 2019, neo-noir, noir
Noir Watch: The Hitch-Hiker (1953)
Format: BluRay from Kino Lorber
I told myself that this year I was going to watch all of the films I could obtain which were directed by Ms. Ida Lupino.
I primarily know Ida Lupino as an actor who sort of radiates a certain razor sharp intellect in roles as hero or villain, whether she's vicious or kind. She's up there in my list of actors whose films I'll give a go even if the movie isn't to my taste.*
But as she is not *in* the movies she directs (understandably), I've not gotten around to seeing what she did standing behind the lens (less understandably). Of the films, the most famous is likely the 1953 noir thriller, The Hitch-Hiker, which I recently picked up as a BluRay edition released by Kino Lorber, made from a restoration print struck at the Library of Congress.
Labels: 1950's, lupino, movies, movies 2019, noir
Jackie Watch: Police Story (1985) and Police Story 2 (1988)
Format: Austin Film Society
Viewing: second/ first
Thanks to some good pals my first year of college, I started watching Jackie Chan movies. Unfortunately, lo these many years later, because I watched many of them in the space of one academic year, I have no idea which is which, what I have seen and what I haven't. The conversation usually went more like "There's a Jackie Chan movie playing at The Hogg Auditorium. We're going after dinner." "Okay."
It turned out I had seen Police Story, but not Police Story 2 - but I have, in the past, seen Police Story 3: Supercop. Which was not part of the double-bill at the Austin Film Society that SimonUK and I attended.
But, yeah, like all of you, when I first saw his movies, I loved everything about Jackie - his sense of humor, his incredible stunts, his loyalty to his stunt team, the fact he wrote, directed and starred in his movies, and that he even sang his own theme songs. And, yeah, you can see the influence of the comedy greats in Jackie - if you love Buster Keaton or Chaplain, you should like Jackie's movies.
If the movies have a weakness, imho, it's that they often can't quite settle on tone. That said, by the end of Police Story, the shift from goofy antics and wacky set-pieces to wanting to see the bad guys get punched just real, real hard is more than earned.
Chan's energy is just different from anyone else in cinema. He's got the finesse of Bruce Lee, but - instead of Lee's eye of the storm focused energy, ready to unleash, he sort of is the storm.
Maggie Cheung plays May in both films, Jackie's long-suffering girlfriend, and she has some terrific comedic bits and really takes some hits for the team doing her own stunts.
The plot is some boiler-plate 1980's cop-movie stuff, and that's okay. It's all a skeleton upon which to hang cool action scenes and showcase the work of Jackie and his crew.
I dunno. I really like Police Story, maybe the second one a bit less, but they're both hugely watchable movies. I just found Police Story 3: Supercop on Amazon, so I'm going to watch it ASAP. It has Michelle Yeoh, so... you know...
20th Anniversary Watch: Office Space (1999)
Format: a very, very old DVD
Viewing: 8th or 9th
In February 2019 I was about 9 months post-graduation and working in a very strange job for - what I figured out - was literally poverty wages (the job required a 4 year bachelor's degree, so... don't major in radio-TV-film, kids). This week marks not just the 20th anniversary of the release of Office Space (2019), but late 2018- early 2019 marks the start of my 20th year in the workforce as an FTE, I suppose.
Office Space was a product of Austinite Mike Judge, who had risen to fame first with Beavis & Butthead on MTV circa 1993, and brought Arlen, Texas to the small screen via King of the Hill. Upon arrival, the movie mostly flopped. Critics were relatively kind, but the film had no major stars except Jennifer Aniston in the era of Big Stars = Big Profits, and a workplace comedy about hating your job wasn't exactly groundbreaking. But at the time I felt a certain loyalty to the Texas film scene and Mike Judge, so we went to see it around opening weekend and... yeah.
As Jamie said when we were talking about the movie after: this was the first movie I saw that I may not have related to 100%, but it was the first movie I saw about adults that I could relate to as an adult.
Labels: 1990's, movies, movies 2019, personal, work
PODCAST! "Captain America: The Winter Soldier"! Avengers Chronological Countdown #09 - w/ Jamie and Ryan
Format: Bluray
Viewing: unknown
We reach one of Jamie's all-time favorite movies, a tale of a handsome man who is neck-deep in issues he thought he fixed before he went down for a long nap. Cap returns in an espionage thriller, but - also - a story of friendship. And blowing things up real good. It's one of the big turning points for Marvel as they put their best foot forward with a solid story that takes things up a notch.
Avengers - Alan Silvestri, Avengers OST
Captain America - Henry Jackman, Captain America: The Winter Solider OST
Labels: 2010's, agent carter, avengers, Captain America, Marvel, movies, movies 2019, podcast
Post-War Watch: The Best Years of Our Lives (1946)
Viewing: second
The Best Years of Our Lives (1946) rightfully won accolades and awards upon its release, telling the story of three returning GI's in terms that try not to gloss over the hardships and adjustments those who went to war in WWII must make as they come home and attempt to re-enter civilian life. Perhaps as much or more importantly, the movie doesn't ignore the adjustments and expectations of those who were safe at home, including arcs for the folks who didn't go, for whom life was not on pause as their loved ones - or even former coworkers - disappeared for a few years.
Labels: 1940's, actual history, movies, movies 2019, myrna loy
Valentine's Day Watch: Coffy (1973)
Format: Amazon Prime Streaming
Jamie and I went out for a lovely dinner for Valentine's Day, followed by catching up on Star Trek: Discovery, and then - somehow - I wound up watching this movie, and I think for the fashion choices alone, Jamie didn't object. I thought I'd previously seen Coffy (1973) when I started it - because it's currently streaming free to Amazon Prime subscribers, and, I didn't intend to actually watch it. But I hadn't seen it and the next thing I knew I was an hour in.
Labels: 1970's, dames, First viewing, movies, movies 2019, neo-noir, noir
PODCAST! It's High School Musicals with "Grease" (1978) and "High School Musical" (2006) - Maxwell, Mrshl and Ryan
Viewing: Easily my 15th or 16th, maybe more
Decade 1970's
Maxwell and Ryan welcome Marshall to the PodCast as they discuss "Grease" and "High School Musical", two movies that are about finding love and finding out who you are in the nightmare factory that is the American Public High School. One of these films is definitely for kids, and the other... really, not for kids, no matter what America wants to think.
Summer Nights - Grease OST - cast
We Go Together - Grease OST - cast
Grease - performed by The Signaltones
High School Movies:
Labels: 1970's, 2000's, First viewing, movies, movies 2019, music, podcast
Relevant Watch: All the President's Men (1976)
Viewing: third
Decade: 1970's (obvs)
It's too hard to unpack both the film All the President's Men (1976) and the actual events of the Watergate scandal without writing a full treastise, so I won't. But in 2019, the events of this movie have both an echo that sounds all too familiar, but one which it is difficult to believe would actually register with at least half the voting population. If the movie is *about* anything, it's showing how goddamn hard it is to build a newspaper story that will stick, the near-impossible job of the press, and, of course, the responsibility of the press in a free and open democratic society, something this blogger firmly believes in.
Labels: 1970's, actual history, movies, movies 2019
Comedy Watch: Dirty Rotten Scoundrels (1988)
Viewing: 7th or so
So, here's a curious one: could Dirty Rotten Scoundrels (1988) be made today?
I'm going to say "no".
Is the movie still still as oddly brilliant and funny as it was in 1988? More so, I think. The older I get, the more I relish Caine's role and dialog in particular. And Glenne Headley's breathy, wide-eyed Ohioan is, of course, absolutely terrific knowing what we know at the film's conclusion.
But I suspect the twitter scold-squad would be up in arms about pretty much everything Steve Martin does in this movie if it arrived now.
However, I am pretty sure in another 20 years, this movie will still be around and revered as a classic comedy by those in the know, part of Martin's filmography of rediscovered classics and Caine's occasional and often successful dive into comedy. And, of course, Headley will be rightfully mourned.
I mean, Ruprecht is timeless, and I look forward to future generations wondering where he got the trident.
Noir Watch: The Burglar (1957)
I recently read the David Goodis novel, which Goodis himself adapted as a screenplay for The Burglar (1957). Surprise: the book is better. An existential noir thriller that *really* piles on Goodis' weirdness with women, the book is singularly bleak piece of fiction that, honestly, would probably not work terribly well as a film (the ending would be, also, logistically unfilmable in 1957*).
The movie hits a lot of the same beats and maintains the motivations of the book, but it's just not as well fleshed out, and they clearly were worried about the audience getting lost along the way so they're more concrete in trying to state the vague mess of issues plaguing Duryea's titular burglar.
Jayne Mansfield is about as far from the Gladden on the novel as one could get in personality and build, but it does shake up the mix a bit and puts a point on the creeping sexual stress as the story shows up on the screen - it's simply different from the frail, skinny girl of the novel.
There's some terrific imagery and cinematography in the film, and pitch perfect noir-esque build of sweaty claustrophobia once the game is revealed, all of which is mind-boggling, as this was the director's first time out (Paul Wendkos, who went on to make Gidget movies!), and a DP who, really, doesn't show much on his filmography to show how he got to this point.
Honestly, I think they cut too much from the book to give the other burglars any real personality or show why Duryea's character is so wound up, but it's still basically intact, and as a B picture, it's got some good stuff going for it.
*it involves a lot of stuff of people swimming in the choppy Atlantic. Sort of.
Labels: 1950's, First viewing, movies, movies 2019, noir
Catching-Up Watch: Eighth Grade (2018)
Watched: 02/07/209
Format: Amazon Streaming (Prime)
A lot of ink got spilled and a lot of gushing occurred when Eighth Grade (2018) hit cinemas last year. And, of course, you'll note the trend that this kneecaps movies for me when I do get around to seeing them, so I won't linger on that too much, but all of the praise certainly colored how I went into the film (cheerfully, willingly, curiously!), and my reaction by the film's conclusion.
Musical Watch: High Society (1956)
So, this is a musical version of The Philadelphia Story - the classic flick starring Hepburn, Jimmy Stewart and Cary Grant. Apparently that play became the movie of The Philadelphia Story, which became the stage musical High Society, which became this movie.
This movie isn't... great. It's not bad, and I laughed out loud at a number of things, but on the whole, I'd rather be in Philadelphia. And I say that as someone who is a fan of Bing Crosby movies and likes Cole Porter.
What this movie does do is let Louis Armstrong play himself, and give him time to appear quite a bit in the movie. And he's not bad!
This is also the movie that dares to remind you that Grace Kelly was very, very good looking* - which, as she is not Kate Hepburn, seems to be the primary driver for why men are after her (ladies, believe it or not, personality and wit go really, really far. Be a Kate Hepburn.).
I dunno. I wish the music had more zip and it didn't feel like an echo of something else, but Bing looks like he's having a ball with Frank, and a gentleman in tophat and tails, suffering from a hangover, yells at a bird, and that was one of the funniest things I've seen in, like, a week.
*again, very attractive, that Grace Kelly
Labels: 1950's, movies, movies 2019, music
Noir Watch: The Stranger (1946)
Format: TCM on DVR (Noir Alley from months ago)
This is an oddball one to slot in with noir in some ways - but I think it fits. It's just sort of a weird set-up to have your antagonist of the film a Nazi war criminal.
I really don't want to say too much or give too much away - I really didn't know much going into The Stranger (1946), and if you've not yet seen it - try not to learn too much and go give it a shot.
PODCAST: Noir Watch: "The Long Goodbye" (1973) - w/ SimonUK and Ryan
Format: Kino Lorber BluRay
Simon and Ryan delve into Film Noir via Neo-Noir, Altman-directed entry "The Long Goodbye" (1973), an oddball of a film with a lot to offer. We explore the role of Philip Marlowe in the world of fiction, some of the mechanics of noir, and whether or not any of this actually works as a movie.
The Long Goodbye - Music by John Williams/ Lyrics by Johnny Mercer/ performed by Jack Sheldon
For more about The Signal Watch PodCast
The Signal Watch Patreon
Labels: 1970's, books, crime fiction, First viewing, movies, movies 2019, neo-noir, noir, podcast
Ida Lupino at 101
Today marks what would have been the 101st birthday of screen actor, director and producer Ida Lupino. Ida Lupino passed in 1995.
I first came to note Lupino in High Sierra, I believe (I can't recall anymore), and have gone on to try and watch whatever I see going by on TCM. Yes, she's a terrific actor and has a presence that stills like the one above don't always capture. There's an intelligence to her work that - when I learned she had gone on to do work behind the camera and established her own production company, just sort of made sense. She had the misfortune of being a woman born two or three decades too early, who still managed to carve out a place for herself in a field controlled by men.
In 2018, a few retrospectives took place honoring her work and legacy. Did I watch any of her films from these retrospectives on my own time? No. Something I need to rectify.
But I am glad that Lupino's reputation is getting elevated and the strides she made during her career are being seen by today's film fans and makers.
Anyway, I hereby pledge that before Ms. Lupino's 102nd, and pending availability, I will watch the following projects which she directed:
Never Fear (1950)
Hard, Fast and Beautiful (1951)
On Dangerous Ground (1951)
The Hitch-Hiker (1953)
The Bigamist (1953)
The Twilight Zone: The Masks (1964)
Labels: birthday, creators, lupino, movies
Signal Watch is now a "Cats: The Movie" Stan Site


text:
Public Domain Picture: Pagosa Skyrocket Habitat
Courtesy: US Fish & Wildlife Service
Views: 180 | Downloads: 1
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Pagosa Skyrocket Habitat
Because of its limited range and overlap with urban and agricultural areas, Pagosa skyrocket habitat includes partially developed lots and roadsides in Archuleta County, Colorado. Photo credit: USFWS ........................................................................... BACKGROUND: Pagosa skyrocket (Ipomopsis polyantha) is an extremely rare plant endemic to two populations on Mancos shale outcrops in and around the Town of Pagosa Springs in Archuleta County, Colorado. This member of the phlox family (Polemoniaceae) grows as a rosette for one to many years; then flowers, produces seeds, and dies in its final year of life. In June and July when the species is flowering, numerous bees and other insects can be seen visiting its clusters of white flowers with small purple spots. These pollinators are essential to the survival of the species and are comprised primarily of native ground-dwelling solitary bees. Pagosa skyrocket habitat is unique, being comprised of barren shales where conditions are harsh and difficult for growth. The species was discovered in 1899 by Charles Fuller Baker, an American naturalist, and described as a new species in 1904 by the well-known botanist Axel Rydberg. No comprehensive surveys were conducted until the mid-1980s, when it became a candidate for listing under the Endangered Species Act. Today the Pagosa skyrocket is threatened by commercial, residential, and agricultural property development; the associated new utility installations and access roads; and from the potential effects of climate change. Because of Pagosa skyrocket's limited range, the fact that there are only two populations, its small population size, and numerous threats to the species, it was proposed for Endangered Species Act protection in 2010. A final listing is expected in 2011. Eighty-eight percent of the areas where Pagosa skyrocket are found are private lands. The Endangered Species Act does not protect plants on private lands and therefore, all conservation actions on private land are voluntary. Funding is available to landowners wanting to conserve the species. Please contact Gina Glenne or Ellen Mayo at 970-243-2778 for further information on this species and ways that you can help with conservation and recovery.
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Though it's unlikely you'll ever find yourself in a game of Dungeons and Dragons with Nina Arsenault, if you do, make sure you don't break character.
Arsenault always played high-priestess characters.
Our discussion of the ultimate nerd pastime has come up in talking about Arsenault's SummerWorks show I W@s B*rbie, which is based on the transsexual writer/performer's real-life experience of being asked to play Barbie at the Mattel Corporation's 50th birthday party for the much loved (and hated) doll.
When she talks in the script about arriving at Fashion Week, Arsenault delivers the poignant line, "I am on a pilgrimage into the pink plastic temple of patriarchy, and like it or not, I am its highest priestess," which has led us to a discussion of her previous roles as "Priestess," as well as the subject of patriarchy.
Not surprisingly, Arsenault has even more to say about the latter subject.


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Pursuant to Standing Rule XIV of the Paralegal Division of the State Bar of Texas, notice is hereby given of an election for the office of 2016-2017 President-Elect. This election will be held by mail during the month of January 2016 by the Board of Directors.
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