text:
Business Economy Colorado Economy
Colorado Economy
In the middle of 19th century, the economy of the state was mainly based on mining of valuable minerals and their processing; breeding of livestock became important towards the late19thcentury. Irrigation of agricultural products like wheat, corn, and hay was done. The industrial and service sectors became important in the middle of the 20th century. The economy of the state varied with its focus more on technical research and high-technology trades. Industries like that of food processing, transport gear, machinery etc. grew in this period. Colorado was the largest beer producer in the country. The estimates of Bureau of Economic Analysis showed that the cost of total state product in 2008 was around $248.6 billion and the Per capita income in the year 2007 was around $41,192 approximately, placing Colorado as the eleventh state getting such per capita income.
Contribution of the Federal Government
One of the key fiscal forces in the state is the Federal Government, which provides many vital services of North American Aerospace Defense Command, U.S. Air Force Academy and Peterson Air Force Base; National Oceanic and Atmospheric Administration and the National Institute of Standards and Technology in Boulder etc. In addition to these, a huge area of land is also under the federal ownership, and amounts to 24,615,788 acres (99,617 km2).
Contribution of Industrial Sector
Denver is both the capital city and financial capital of Colorado. Many nationwide recognized brands have their origin in Colorado. Starting from Qwest, the pioneer in telecommunications, luggage from Samsonite, belts and hoses from Gates, and many other brands have originated in Denver. Other well known brands like that of Kuner, Golden Coors beer, Coors Tek ceramics, Jolly Rancher candy, Celestial Seasonings herbal teas, and many other industries, all have originated in Colorado.
Contribution of Taxes
Taxes in Colorado are charged on taxable income, i.e., the income left after subtracting the central exemptions and standard deductions. The contribution of income tax in the revenue of Colorado amounts to a flat 4.63%irrespective of the income level of the citizens. The contribution of Sales Tax in the revenue of Colorado is of 2.9% on retail sales. Real estate and personal property are liable to be charged in Colorado.
Charity donations also contribute towards the economy. Major parts of donations come from charitable societies based in Colorado, like that of the Daniels Fund, the Gates Family Foundation etc., amounting to nearly $400million each year. Colorado stands at thirty-eighth rank in the country with donations up to 3.4% of income.
Natural Resources & Economy
The land of Colorado is very rich in hydrocarbon and other minerals. As per the Energy Information Administration, Colorado has seven largest Natural gas fields out of 100 in the whole Nation and two largest oil fields out of 100. The output from Colorado's natural gas basins gives around 5 percent to the yearly natural gas production. Its oil deposits are of around1 trillion barrels. Deposits of coal, and quality diamonds can also be found in the mines of Colorado.
Special Tax Districts & Economy
Special tax districts are spread across several counties of Colorado. The special taxes are levied only in the selected counties where they applied. Some of the special tax districts are:
The Regional Transportation District (RTD), affecting the counties of Denver, Boulder, Jefferson, and some other regions of Adams, Arapahoe, Broomfield, and Douglas.
The Scientific and Cultural Facilities District (SCFD), a toll ward with corporal limits adjoining the regions of Adams, Arapahoe, Boulder, Broomfield, Denver, Douglas, and Jefferson Counties
It is a 0.1% of retail sales and use tax (one penny on every 10Dollars).
SCFD allots the cash to native groups on yearly basis. These groups need to provide for the education and amusement of the public through the production, presentation, exhibition of art, music, theater etc.
SCFD recipient groups are divided into three "tiers" among which the receipts are allocated
Tier I comprises local groups: the Denver Art Museum, Botanical Gardens, Museum of Nature and Science, Zoo, and the Center for the Performing Arts. It gets 65.5%.
Tier II includes 26 regional groups. It gets 21%.
Tier III has over 280 local groups such as small theaters, orchestras, art centers, community groups etc. It gets 13.5%.
The Football Stadium District (FD), to help build the stadium of the INVESCO Field
Local Improvement Districts (LID) in Southeast Jefferson and Boulder.
Regional Transportation Districts (RTA)
Occupational Privilege Taxes (OPT) levied on Employers and Workers.
Any employee working in the city is paid over 500 US Dollars for it in a month, then the Employee and Employer, both are liable for the OPT irrespective of their business location.
In Denver, the Employer is responsible for 4 US Dollars per worker monthly and the workers are responsible for 5.75 US Dollar monthly.
In Aurora, both employer and workers are responsible for 2 US Dollars per month.
World Economy Reports
Atlanta Economy
Australia Economy
Boston Economy
Brazil Economy
Canada Economy
Chicago Economy
Denmark Economy
Detroit Economy
Finland Economy
Florida Economy
France Economy
Germany Economy
Houstan Economy
Japan Economy
Malaysia Economy
Netherlands Economy
Russia Economy
San Diego Economy
San Francisco Economy
Sweden Economy
United States Economy
Business Trade Shows and Events
World Company Registers List
GDP Related Topics
» Lists of Countries by GDP
» List of Countries by GDP (Nominal)
» List of Countries by GDP (Nominal) per Capita
» List of Countries by GDP (PPP) per Capita
» List of Countries by Real GDP Growth Rate
» List of Countries by Human Development Index
» List of Countries by Income Equality
» List of Countries by Distribution of Wealth
» Top World Economies
» BRICS Summit 2014
» 8 Things Narendra Modi Said at BRICS Summit
» Special European Summit
Types Of Consumer Goods
Company Secretary Roles
Marketing Mix Strategy
Chartered Accountant Salary
Accounting Schools
Defensive Driving Schools
Motorcycle Driving Schools
Fortune 500 Companies
Memorandum and Association of Articles


Chiliogon advises AfNat Resources Limited (AFRN: LN) on its acquisition by AXMIN Inc. (AXM-TSX Venture) by way of a scheme of arrangement under the laws of Bermuda. AXMIN is a Canadian exploration and development company with a strong focus on central and west Africa. AXMIN has projects in Central African Republic, Mali, Mozambique, Sierra Leone, Senegal and Zambia.


text:
/*
Created by Justin Reese 9/2012
justaddcoffee@gmail.com

This is a simple GFF3 parser that takes a GFF3 file such as this:

Group1.33	maker	gene	245454	247006	.	+	.	ID=maker-Group1%2E33-pred_gff_GNOMON-gene-4.137;Name=maker-Group1%252E33-pred_gff_GNOMON-gene-4.137;
Group1.33	maker	mRNA	245454	247006	.	+	.	ID=1:gnomon_566853_mRNA;Parent=maker-Group1%2E33-pred_gff_GNOMON-gene-4.137;Name=gnomon_566853_mRNA;_AED=0.45;_eAED=0.45;_QI=138|1|1|1|1|1|4|191|259;
Group1.33	maker	exon	245454	245533	.	+	.	ID=1:gnomon_566853_mRNA:exon:5976;Parent=1:gnomon_566853_mRNA;
Group1.33	maker	exon	245702	245879	.	+	.	ID=1:gnomon_566853_mRNA:exon:5977;Parent=1:gnomon_566853_mRNA;

and returns a JSON data structure like this:
{
"parsedData": [ // parsed data is an array
    {
    "ID": "maker-Group1%2E33-pred_gff_GNOMON-gene-4.137",
    "data":[
             {
	      "rawdata" : ["Group1.33","maker","gene","245454","247006",".","+",".","ID=maker-Group1%2E33-pred_gff_GNOMON-gene-4.137;Name=maker-Group1%252E33-pred_gff_GNOMON-gene-4.137"],
	      "attributes" :  {"ID" : ["maker-Group1%2E33-pred_gff_GNOMON-gene-4.137"], "Name" : ["maker-Group1%252E33-pred_gff_GNOMON-gene-4.137"]}
	     }
	     ],
    "children": [
       {
          "ID": "1:gnomon_566853_mRNA",
          "data": [
                     {
              	      "rawdata" : ["Group1.33","maker","mRNA","245454","247006",".","+",".","ID=1:gnomon_566853_mRNA;Parent=maker-Group1%2E33-pred_gff_GNOMON-gene-4.137;Name=gnomon_566853_mRNA;_AED=0.45;_eAED=0.45;_QI=138|1|1|1|1|1|4|191|259"]
        	      "attributes" :  { "ID" : ["1:gnomon_566853_mRNA"], "Parent" : ["maker-Group1%2E33-pred_gff_GNOMON-gene-4.137"], "Name" : ["gnomon_566853_mRNA"], "_AED" : ["0.45"], "_eAED" : ["0.45"],"_QI" : ["138|1|1|1|1|1|4|191|259"] }
		      }
                  ],
          "children": [
            {
            "ID": "1:gnomon_566853_mRNA:exon:5976",
            "data": [
                     {
              	      "rawdata" : ["Group1.33","maker","exon","245454","245533",".","+",".","ID=1:gnomon_566853_mRNA:exon:5976;Parent=1:gnomon_566853_mRNA"]
        	      "attributes" :  {"ID" : ["1:gnomon_566853_mRNA:exon:5976"], "Parent" : ["1:gnomon_566853_mRNA"]}
		      }
		      ],
            "children": [],
            },
            {
	    "ID": "1:gnomon_566853_mRNA:exon:5977",
            "data": [
                     {
              	      "rawdata" : ["Group1.33","maker","exon","245702","245879",".","+",".","ID=1:gnomon_566853_mRNA:exon:5977;Parent=1:gnomon_566853_mRNA"]
        	      "attributes" :  {"ID" : ["1:gnomon_566853_mRNA:exon:5977"], "Parent" : ["1:gnomon_566853_mRNA"]}
		      }
		     ],
            "children": [],
            }
           ]
        }
       ]
     }
   ],
   ... next parent/child/descendants, e.g. gene/mRNA/exons or whatever ...
],
"parseErrors" : [""]
"parseWarnings" : ["no GFF3 pragma"]
}

*/

define("JBrowse/Store/SeqFeature/GFF3/GFF3Parser", [],
       function() {

function GFF3Parser() {
};

GFF3Parser.prototype.parse = function(gff3String) {
    // Right now this method assumes that gff3String is the entire GFF3
    // file in string form. This sucks a bit because it means we'll have to
    // have both the parsed and unparsed GFF3 data in memory which is
    // a waste of memory and will affect performance when the GFF3 files
    // are big. Maybe we can refactor this later to accept a stream instead
    // of a string.

    // Pseudocode for what I'm doing below:
    // for each line in giant GFF3 string (slurped GFF3 file):
    //    parse data into fields, parse 9th field into attributes hash
    //    if hasParentAttribute
    //       put into hasParent hash where key == id, value = hash of data struct with parsed fields and parsed attributes
    //    else
    //       put into noParent hash where key == id, value = hash of data struct with parsed fields and parsed attributes
    // for each entry in noParent hash:
    //       put into JSON as Parent without any Children (yet)
    // for each entry in hasParent has
    //       make sure Parent ID is in seenIDs, or continue (TODO: put in orphans and put error in parseErrors array)
    //       find Parent in data structure (depth first search)
    //       put into Children array of Parent

    // search for a given ID in children, grandchildren, great-grandchildren, etc.
    var recursion_level = 0;
    var maximum_recursion_level = 200;
    var foundParents = 0;
    var placeChildrenWithParent = function(thisLine, featureArrayToSearch) {
       recursion_level++;
       // first, search each item in featureArrayToSearch
       for ( var j = 0; j < featureArrayToSearch.length; j++ ){

	   // search all parents
	   for ( var k = 0; k < thisLine["data"][0]["attributes"]["Parent"].length; k++ ) {
	       var thisParentId = thisLine["data"][0]["attributes"]["Parent"][k];
	       if ( thisLine["ID"][0] == 'au9.g910.t3' ){
		   console.log("j: " + j + " k: " + k );
		   console.log("comparing "  + thisParentId + " with " + featureArrayToSearch[j]["ID"] );
	       }
	       if ( thisParentId == featureArrayToSearch[j]["ID"] ){
		   featureArrayToSearch[j]["children"].push( thisLine );
		   foundParents++;
	       }
	   }
	   // paranoid about infinite recursion
	   // if ( recursion_level > maximum_recursion_level ){
	   // return false;
	   // }

	   // recurse if there there are children
	   if ( featureArrayToSearch[j]["children"].length > 0 ){
	       if ( placeChildrenWithParent(thisLine, featureArrayToSearch[j]["children"] )){
		   foundParents++;
	       }
	   }
       }
       if ( foundParents > 0 ){
	   return true;
       }
       else {
	   return false;
       }
    }

    // put feature in ["data"] for discontinuous features we've already "filed"
    var df_recursion_level = 0;
    var df_maximum_df_recursion_level = 200;
    var placeDiscontiguousFeature = function(thisLine, featureArrayToSearch) {
       df_recursion_level++;
       var thisId = thisLine["data"][0]["attributes"]["ID"][0];
       // first, search each item in featureArrayToSearch
       for ( var j = 0; j < featureArrayToSearch.length; j++ ){
	   if ( thisId == featureArrayToSearch[j]["ID"] ){
	       featureArrayToSearch[j]["data"] = featureArrayToSearch[j]["data"].concat( thisLine["data"] );
	       featureArrayToSearch[j]["children"] = featureArrayToSearch[j]["children"].concat( thisLine["children"] );
	       return true;
	   }
	   // paranoid about infinite recursion
	   if ( df_recursion_level > df_maximum_df_recursion_level ){
	       return false;
	   }
	   // recurse if there there are children
	   if ( featureArrayToSearch[j]["children"].length > 0 ){
	       if ( placeDiscontiguousFeature(thisLine, featureArrayToSearch[j]["children"] )){
		   return true;
	       }
	   }
       }
       return false;
    }

    var bigDataStruct = {
	"parsedData" : [],
	"parseErrors": [],
	"parseWarnings": []
    }; // parsed GFF3 in JSON format, to be returned

    var lines = gff3String.match(/^.*((\r\n|\n|\r)|$)/gm); // this is wasteful, maybe try to avoid storing split lines separately later
    var hasParent = {}; // child (or grandchild, or whatever) features
    var noParent = {}; // toplevel features without parents

    var seenIDs = {};
    var noParentIDs = [];
    var hasParentIDs = [];

    for (var i = 0; i < lines.length; i++) {

	// check for ##FASTA pragma
	if( lines[i].match(/^##FASTA/) || lines[i].match(/^>/) ){
	    break;
	}
	// skip comment lines
	if( lines[i].match(/^#/) ){
	    continue;
	}
	// skip comment lines
	if( lines[i].match(/^\s*$/) ){
	    continue;
	}
	// make sure lines[i] has stuff in it
	if(typeof(lines[i]) == 'undefined' || lines[i] == null) {
	    continue;
	}
	lines[i] = lines[i].replace(/(\n|\r)+$/, ''); // chomp
	var fields = lines[i].split("\t");
	// check that we have enough fields
	if(fields.length < 9 ){
	    console.log("Number of fields < 9! Skipping this line:\n\t" + lines[i] + "\n");
	    bigDataStruct["parseWarnings"].push( "Number of fields < 9! Skipping this line:\n\t" + lines[i] + "\n" );
	    continue;
	}
	else {
	    if (fields.length > 9 ){
		console.log("Number of fields > 9!\n\t" + lines[i] + "\nI'll try to parse this line anyway.");
		bigDataStruct["parseWarnings"].push( "Number of fields > 9!\n\t" + lines[i] + "\nI'll try to parse this line anyway." );
	    }
	}

	// parse ninth field into key/value pairs
	var attributesKeyVal = new Object;
	if(typeof(fields[8]) != undefined && fields[8] != null) {
	    var ninthFieldSplit = fields[8].split(/;/);
	    for ( var j = 0; j < ninthFieldSplit.length; j++){
		/*
		   Multiple attributes of the same type are indicated by separating the
		   values with the comma "," character, as in:
		   Parent=AF2312,AB2812,abc-3
		*/
		var theseKeyVals = ninthFieldSplit[j].split(/\=/);
		if ( theseKeyVals.length >= 2 ){
		    var key = unescape(theseKeyVals[0]);
		    var valArray = new Array;

		    // see if we have multiple values
		    if ( theseKeyVals[1].match(/\,/) ){ // multiple values
			  if ( !! theseKeyVals[1] && theseKeyVals.length != undefined ){
			      // value can be >1 thing separated by comma, for example for multiple parents
			      valArray = theseKeyVals[1].split(/\,/);
			      if ( !! valArray && valArray.length != undefined ){
				  for ( k = 0; k < valArray.length; k++){
				      valArray[k] = unescape(valArray[k]);
				  }

			      }
			      valArray[0] = unescape(valArray[0]);
			      valArray[1] = unescape(valArray[1]);
			  }
		    }
		    else {  // just one value
			valArray[0] = unescape(theseKeyVals[1]);
		    }
		    attributesKeyVal[key] = valArray;
		}
	    }
	}

	if ( ! attributesKeyVal["ID"] ){
	    attributesKeyVal["ID"] = [];
	    attributesKeyVal["ID"][0] = "parser_autogen_id_" + i;
	}

	var thisLine = {"ID": attributesKeyVal["ID"][0],
			"data": [
	                                                        {"rawdata" : fields,
								 "attributes" :  attributesKeyVal
								}
								],
			"children": []
	};

	if ( attributesKeyVal["Parent"] != undefined ){
	    hasParent[attributesKeyVal["ID"][0]] = thisLine;
	    hasParentIDs.push( attributesKeyVal["ID"][0] );
	}
	else {
	    noParent[attributesKeyVal["ID"][0]] = thisLine;
	    noParentIDs.push( attributesKeyVal["ID"][0] );
	}

	// keep track of what IDs we've seen
	if ( isNaN(seenIDs[attributesKeyVal["ID"][0]]) ){
	    seenIDs[attributesKeyVal["ID"][0]] = 1;
	}
	else {
	    seenIDs[attributesKeyVal["ID"][0]]++;
	}
    }

    var dealtWithIDs = {}; // list of IDs that have been processed

    // put things with no parent in parsedData straight away
    for (var j = 0; j < noParentIDs.length; j++) {
	var thisID = noParentIDs[j];
	var thisLine = noParent[thisID];

	// is this a discontiguous feature that's already been "filed"?
	if ( seenIDs[thisID] > 1 && dealtWithIDs[thisID] > 0 ){ // yes
	    placeDiscontiguousFeature(thisLine, bigDataStruct["parsedData"] );
	}
	else {
	    bigDataStruct["parsedData"].push( thisLine );
	}

	// keep track of what IDs we've dealt with
	if ( isNaN( dealtWithIDs[thisID] ) ){
	    dealtWithIDs[thisID] = 1
	}
	else {
	    dealtWithIDs[thisID]++;
	}

    }

    // now put children (and grandchildren, and so on) in data struct
    for (var k = 0; k < hasParentIDs.length; k++) {
	var thisID = hasParentIDs[k];
	var thisLine = hasParent[thisID];

	// is this a discontiguous feature that's already been "filed"?
	if ( seenIDs[thisID] > 1 && dealtWithIDs[thisID] > 0 ){ // yes
	    placeDiscontiguousFeature(thisLine, bigDataStruct["parsedData"] );
	}
	else {
	    // put this child in the right children array, recursively, or put it on top level and mark it as an orphan
	    if ( ! placeChildrenWithParent(thisLine, bigDataStruct["parsedData"] ) ){
		bigDataStruct["parsedData"].push( thisLine );
		bigDataStruct["parseWarnings"].push( thisID + " seems to be an orphan" );
	    }
	}

	// keep track of what IDs we've dealt with
	if ( isNaN( dealtWithIDs[thisID] ) ){
	    dealtWithIDs[thisID] = 1
	}
	else {
	    dealtWithIDs[thisID]++;
	}

    }
    return bigDataStruct;
};

return GFF3Parser;

});

redpajama_set_name: RedPajamaGithub


The Chantika Jumpsuit is our No 1 jumpsuit style that suits all body shapes and the crosover wrap body is wonderful for bigger and smaller busts. It has an elegance fit-and-flare design, nipped in the waist with a tie-around belt which can be pulled in tigher if desired and worn front or side The cross body and ruffle cap sleeves add to its vintage charm. This is a gorgeous day/night ensamble. Model wears a size S.


The long wait until Downton Abbey's fourth season seems less bleak today thanks to a musical parody that has found its way to the Internet. Directed and co-written by John Walton West and featuring music and lyrics by Jason Michael Snow, the production, filmed at Studio 54 in New York, depicts the cast and creator of the costume drama as they develop a musical episode à la Greys Anatomy or Buffy the Vampire Slayer. The result features one thrilling tea-centric duet between Carson and Mrs. Hughes, a gloomy mourning number by Mary, and a hen-fest parlor-room sequence featuring the Dowager Countess and Martha Levinson.
Co-starring Matt Doyle (The Book of Mormon) and Tony-nominated actors Laura Osnes (Anything Goes, Bonnie & Clyde) and Jeremy Jordan (Newsies, West Side Story), the musical is called "brilliant," "haunting," "nougat-y," and "anachronistic" by its own cast. We now leave you, impartial Downton-ites, to review the video in the space below.


text:
Home » News » Dating Patterns Predict Future Commitment
Dating Patterns Predict Future Commitment
By Rick Nauert PhD
Associate News Editor
Last updated: 8 Aug 2018
New research identifies four distinct approaches used by dating couples to develop deeper commitment.
An analysis finds that some strategies work to improve commitments while others are counterproductive to long-term relationship stability.
"The four types of dating couples that we found included the dramatic couple, the conflict-ridden couple, the socially involved couple, and the partner-focused couple," said Brian Ogolsky, a University of Illinois assistant professor of human development and family studies.
Researchers developed the categories after studying graphs showing levels of commitment among 376 dating couples over a nine month period. All participants were in their mid-twenties.
The graphs were a product of reporting by each participant on their level of commitment to marrying their partner and why. During the reporting period, Ogolsky asked participants to explain their reasoning when their commitment level had gone up or down.
From the findings, researchers discovered dramatic daters are twice as likely to break up as other couples.
"These couples have a lot of ups and downs, and their commitment swings wildly. They tend to make decisions based on negative events that are occurring in the relationship or on discouraging things that they're thinking about the relationship, and those things are likely to chip away at their commitment," he said.
"It's not unlike when the transmission goes out on your car, and then your starter goes out. You begin to see little things eroding, and you start to see the relationship in a negative light, and soon you give up," he added.
Dramatic couples also make attribute changes in their commitment to time spent with their own friends or by doing things separately rather than as a couple.
"They were hanging on to their individuality in a way that may be unconventional for early daters," he said.
On the other extreme, partner-focused couples have the highest chance of staying together and being happy over time, Ogolsky.
"These partners are very involved with each other and dependent on each other, and they use what's happening in their relationship to advance their commitment to deeper levels. People in these couples had the highest levels of conscientiousness, which suggests that they are very careful and thoughtful about the way they approach their relationship choices," he said.
Characteristics of a partner-focused couple include sharing a social network, however, the shared contacts are not used to further their commitment.
Conflict-ridden couples may experience decreases in their commitment when they have an argument. Their commitment level may fall, but it doesn't mean they'll break up, he said.
"These couples operate in a tension between conflict that pushes them apart and passionate attraction that pulls them back together.
Nevertheless, this kind of love may not be sustainable in the long term — you'd go crazy if you had 30 to 50 years of mind-bending passion. Partners may change from one group to another over time," he noted.
Socially involved couples — like partner-focused couples — report high levels of satisfaction and stability in their relationships. These couples share a social network and rely on that network to make decisions about their commitment decisions, Ogolsky said.
Researchers believe the ideal long-term relationship should be based on friendship-based love.
"And having mutual friends makes people in these couples feel closer and more committed," Ogolsky said.
Researchers hope the broad categorization of dating relationships will help a couple analyze their current pattern and either maintain or change the pattern to improve commitment and relationship stability.
"The important message is that there are certain ways of making commitment-related decisions that propel you forward, and others push you backward. It can be helpful for couples to think about these patterns and the ways they make important decisions about the future of their relationship," Ogolsky said.
The study, "Pathways of Commitment to Wed: The Development and Dissolution of Romantic Relationships" is found online in the Journal of Marriage and Family and will follow in hard copy.
Source: University of Illinois
Young couple photo by shutterstock.
Rick Nauert PhD
Dr. Rick Nauert has over 25 years experience in clinical, administrative and academic healthcare. He is currently an associate professor for Rocky Mountain University of Health Professionals doctoral program in health promotion and wellness. Dr. Nauert began his career as a clinical physical therapist and served as a regional manager for a publicly traded multidisciplinary rehabilitation agency for 12 years. He has masters degrees in health-fitness management and healthcare administration and a doctoral degree from The University of Texas at Austin focused on health care informatics, health administration, health education and health policy. His research efforts included the area of telehealth with a specialty in disease management.
Nauert PhD, R. (2018). Dating Patterns Predict Future Commitment. Psych Central. Retrieved on January 17, 2020, from https://psychcentral.com/news/2016/02/12/dating-patterns-predict-future-commitment/99023.html
Last updated: 8 Aug 2018 (Originally: 12 Feb 2016)
Last reviewed: By a member of our scientific advisory board on 8 Aug 2018
Symptoms of Codependency Relationship Articles Blog Quizzes Grow Your Relationship Building Assertiveness

redpajama_set_name: RedPajamaCommonCrawl


text: Q: Representing discrete groups in orthogonal groups Suppose that we have a matrix $A$ of a quadratic form $Q_A$ of signature $(n,1)$ and a matrix $B$ of a quadratic form $Q_B$  which also has signature $(n,1)$. Let $O(Q_A)$ be the orthogonal group that preserves the form $Q_A$ and let $O(Q_B)$ be the orthogonal group that preserves the form $Q_B$. Then $O(Q_A)$ and $O(Q_B)$ are isomorphic since $Q_A$ and $Q_B$ have the same signature. Let $G$ be a discrete subgroup of $O(Q_A)$, and suppose that $G$ is given by generators and relations. Does anyone know how to obtain a faithful representation of $G$ into $O(Q_B)$? Equivalently, how does one find generators of $G$ in $O(Q_B)$ that satisfy the defining relations for $G$?

redpajama_set_name: RedPajamaStackExchange


The article below were written in 2007 by me (it's now been 10 years!), after many years of hesitation and doubts about my own capability as an everyday person being able to do "clairvoyant" work.
I have attended several of Bruce Moen's workshops in Ireland, Denmark and Poland, doing both the standard and advanced workshops several times. Basically, Bruce has created a technique of so-called relaxing energy breaths, making you build up the feeling of love in your total being, then using various guided explorations using your imagination as a tool to retrieve verifiable data about non-physical journeys in your mind's eye.


We investigate the possibilities of light detection and ranging (lidar) techniques to study migration of the damselfly species Calopteryx splendens and C. virgo. Laboratory and testing-range measurements at a distance of 60 m were performed using dried, mounted damselfly specimens. Laboratory measurements, including color photography in polarized light and spectroscopy of reflectance and induced fluorescence, reveal that damselflies exhibit reflectance and fluorescence properties that are closely tied to the generation of structural color. Lidar studies on C. splendens of both genders show that gender can be re motely determined, especially for specimens that were marked with Coumarin 102 and Rhodamine 6G dyes. The results obtained in this study will be useful for future field experiments, and provide guidelines for studying damselflies in their natural habitat using lidar to survey the air above the river surface. The findings will be applicable for many other insect species and should, therefore, bring new insights into migration and movement patterns of insects in general.


Runners make their way down Martin Luther King Jr. Boulevard in Cleveland during Sunday's Rock Hall Half Marathon.
CLEVELAND, Ohio -- Hundreds of runners converged on downtown Cleveland on Sunday, taking a nice, scenic 13.1-mile jaunt through the city's streets as part of the Rock Hall Half Marathon.
For many runners it likely marked the culmination of several weeks of training. For others, it was just part of training for other races ahead.
Count me in the latter group. The Rock Hall race marked the halfway point of my training for the New York City Marathon this fall, with a goal of finishing in around four hours.
So, where do I stand at the halfway mark?
Justin Thomas, 31, of Roaming Shores, and Jay "JT" Wittman, 33, of Concord Township, believe I'm right where I need to be.
And I trust their judgment. Both are top-flight runners in Northeast Ohio, competing for the Achilles Running Shop race team, and they both ran Sunday, using the race for training purposes.
I was a bit behind the leaders, finishing in 1:54:05, 272nd overall. Hey, I was 14th out of 57 in my age group (50-54).
Thomas and Wittman believe I've passed the midterm. The final is just over two months away.
Rock on: Chardon resident Angela Pohl, 41, whom I wrote about earlier this month, also ran in Sunday's race, turning in a solid finish of 2:10:52. After running in her first 5k in 2012, Pohl continues to pile up half-marathons.
Barclay reaches the coast: Barclay Oudersluys, 23, the Michigan native who set a goal of running across the country in 100 days to raise money for the Hall Steps Foundation, exceeded both of his goals. Oudersluys, who passed through Northeast Ohio in late July, reached the Atlantic coast on Aug. 15, one day ahead of schedule. Oudersluys had hoped to raise $10,000 for the foundation and ended up with more than $11,000.


HRG research has revealed a 14.3% growth in corporate travel and events in Brazil.
A booming business travel market across Brazil was also detailed. The Global Business Travel Association (GBTA) indicated an expected 14.3% rise in Brazilian business travel spending in 2013.
International business travel to Brazil is experiencing rapid growth, according to HRG, which pointed to an expansion in the number of international airports being added to existing infrastructure across the country.
Susan Lancaster, international development director HRG, said: "Outbound international business travel has been increasing steadily for a number of years now, supported by a significant investment in infrastructure. Five years ago almost 80% of international flights would land in Sao Paulo, 15% in Rio and the remaining 5% in other cities. The opening of international airports in Brasilia, Porto Alegre, Belo Horizonte and across the Northeast of Brazil have helped ease the burden in terms of managing growing demand among business travellers across the country.
"Business travel in Brazil may not be growing quite as quickly as couple of years ago, but the pace of changes is still remarkable. The conference market in particular is booming, and with events like the World Cup and the Olympics on the horizon, there does not seem to be much stopping the region from overtaking mature business travel markets like the UK."


Claire Rowland on interoperability, networks, and latency.
Register to attend our new online conference (May 20, 2015) to hear Claire Rowland and other design leaders discuss experience design and the Internet of Things.
The Internet of Things (IoT) is challenging designers to rethink their craft. I recently sat down with Claire Rowland, independent designer and author of the forthcoming book Designing Connected Products to talk about the changing design landscape.
During our interview, Rowland brought up three points that resonated with me.
"Interoperability is about devices and applications and services being able to interact with other devices, applications, and services regardless of the hardware architecture or who made them or what kind of software they run. Basically, IoT — that means, the devices and services for which we may connect our applications — can discover and communicate and coordinate with other devices and services, no matter who made them.
You can listen to the entire interview in the player below, or through our O'Reilly SoundCloud stream.
Claire is a co-author of the forthcoming book Designing Connected Products, along with Elizabeth Goodman, Martin Charlier, Alfred Lui, Ann Light. Free sample chapters can be found here.
Cropped image on article and category pages by Marc Wathieu on Flickr, used under a Creative Commons license.
This post is part of our ongoing exploration into experience design and the Internet of Things.


text:
Govt forced to release overdue COVIDSafe report
By Justin Hendry on Jul 23, 2021 11:01AM
But redacts most of it.
The federal government has been forced to release a heavily redacted version of a report evaluating the effectiveness of its COVIDSafe contact tracing app after failing to produce the report itself.
But the document covering the first six months of the app's operation is of little value, with swathes of information relating to its usefulness during the first stage of the pandemic removed.
The independent report, released following a freedom of information request by the Canberra Times, comes almost 18 months after the app was introduced and 12 months after the reporting period.
Under legislation governing COVIDSafe, the Department of Health is required to report on the operation and effectiveness of the app and the national COVIDSafe data store every six months.
Following completion of the report, the Privacy Amendment (Public Health Contact Inforamtion) Act 2020 then requires that the report be handed down in parliament within 15 sitting days.
Despite this requirement, the government has not publicly released the first six-monthly report.
Documents released under FOI show the department engaged global consulting and research firm Abt Associates and its technology partner, Brisbane-based systems integrator Bdna, to evaluate COVIDSafe from September 2020.
The resulting draft report, which cost almost $234,000 and covered the period between 16 May 2020 to 15 November 2020, was received by the department in early December 2020, according to a ministerial information brief released alongside the report.
The external assessors sought to assess the efficiency, including cost of the app against other comparable apps like Singapore's TraceTogether app, as well as appropriateness – such as the design and uptake – and implementation.
"A mixed-methods approach was used drawing on public health evaluation and technology review methodology to examine available evidence on the appropriateness, effectiveness and efficiency of COVIDSafe," the report states.
"Our evaluation focuses on three states, NSW, Victoria and Queensland, chosen in consultation with the [department] as the states with the highest number of Covid-19 cases in the country and with PHUs [public health units] who most use the datastore."
But while the report released under FOI notes "the effectiveness of the app is predicated on its ability to identify individuals", almost all information relating to the app's effectiveness has been redacted.
Most remaining information included is either public or highlights positive aspects of COVIDSafe such as the government's "commendable achievement" developing "a new digital contact tracing tool, for a new virus" in a "matter of weeks".
The redacted report makes not mention of the fact COVIDSafe has only identified 17 close contacts in NSW that would not have been found via any other means or that it is now 'rarely' use for contact tracing in Victoria.
"As our technology review indicates, based on the parameters of knowledge and capabilities at the time of app launch, it is believed that the COVIDSafe app was the correct tool to employ," the report concludes.
Earlier this year, the Digital Transformation Agency refused to release copies of cloud hosting bills or architecture diagrams for COVIDSafe after it came to light that it was spending $100,000 a month with Amazon Web Services.
The DTA has since revised down the hosting bill estimate to approximately $75,000 a month after some performance "tuning", and expects this cost will fall as low as $60,000 a month by the end of July.
app covidsafe department of health federal government governmentit strategy
Promoted Content Critical considerations for moving to the cloud
Promoted Content Why there is no upside to sending data offshore for storage
Promoted Content Albemarle cuts e-waste, capex spend "substantially" by hiring rather than buying
Chamonix lands My Health Record app deal
DTA sheds COVIDSafe app duties
Auditor weighs up review of govt's digital ID system, COVIDSafe app


text:
Phauloppia boletorum är en kvalsterart som först beskrevs av Ewing 1913.  Phauloppia boletorum ingår i släktet Phauloppia och familjen Oribatulidae. Inga underarter finns listade i Catalogue of Life.

Källor

Spindeldjur
boletorum

redpajama_set_name: RedPajamaWikipedia


text:
Research | Open | Published: 14 November 2015
Identification of potential biomarkers to differentially diagnose solid pseudopapillary tumors and pancreatic malignancies via a gene regulatory network
Pengping Li1,
Yuebing Hu2,
Jiao Yi1,
Jie Li1,
Jie Yang1 &
Jin Wang1
Solid pseudopapillary neoplasms (SPN) are pancreatic tumors with low malignant potential and good prognosis. However, differential diagnosis between SPN and pancreatic malignancies including pancreatic neuroendocrine tumor (PanNET) and ductal adenocarcinoma (PDAC) is difficult. This study tried to identify candidate biomarkers for the distinction between SPN and the two malignant pancreatic tumors by examining the gene regulatory network of SPN.
The gene regulatory network for SPN was constructed by a co-expression model. Genes that have been reported to be correlated with SPN were used as the clues to hunt more SPN-related genes in the network according to a shortest path approach. By means of the K-nearest neighbor algorithm (KNN) classifier evaluated by the jackknife test, sets of genes to distinguish SPN and malignant pancreatic tumors were determined.
We took a new strategy to identify candidate biomarkers for differentiating SPN from the two malignant pancreatic tumors PanNET and PDAC by analyzing shortest paths among SPN-related genes in the gene regulatory network. 43 new SPN-relevant genes were discovered, among which, we found hsa-miR-194 and hsa-miR-7 along with 7 transcription factors (TFs) such as SOX11, SMAD3 and SOX4 etc. could correctly differentiate SPN from PanNET, while hsa-miR-204 and 4 TFs such as SOX9, TCF7 and PPARD etc. were demonstrated as the potential markers for SPN versus PDAC. 14 genes were demonstrated to serve as the candidate biomarkers for distinguishing SPN from PanNET and PDAC when considering them as malignant pancreatic tumors together.
This study provides new candidate genes related to SPN and the potential biomarkers to differentiate SPN from PanNET and PDAC, which may help to diagnose patients with SPN in clinical setting. Furthermore, candidate biomarkers such as SOX11 and hsa-miR-204 which could cause cell proliferation but inhibit invasion or metastasis may be of importance in understanding the molecular mechanism of pancreatic oncogenesis and could be possible therapeutic targets for malignant pancreatic tumors.
Solid pseudopapillary neoplasms (SPN) [1] are uncommon tumors that account for 0.2–2.7 % of all pancreatic tumors and are predominantly seen in young female patients for as-yet-unknown reasons [2]. The fact that SPNs occur predominantly in young women led to the study of gender hormonal receptors by several authors without any evidence of estrogen receptors in pathogenesis of the tumor [3]. Most patients are asymptomatic at diagnosis, and abdominal pain is the most common symptom [3]. SPN shows low-grade malignancy and local surgical excision is usually with a cure rate of greater than 95 % [4, 5]. It is important to distinguish SPN from pancreatic neuroendocrine tumor (PanNET) or pancreatic ductal adenocarcinoma (PDAC) so as to treat them differently, because the treatment of PanNET is usually a selection or combination of surgery, hormone therapy, radiation therapy, and chemotherapy. Similarly, less than 20 % of PDAC patients are suitable for surgery, the gemcitabine or gemcitabine in combination with other chemotherapy agents is thus the main therapeutic measure for PDAC patients [6]. Preoperative diagnosis of SPN will minimize the extent of unnecessary treatment compared with that required for more malignant pancreatic lesions [7, 8]. However, correct diagnosis of SPN is a big challenge because many of the SPN features resembles other types of pancreatic malignant tumors. For example, SPNs are most commonly confused with PanNETs which could occur at pancreatic tail, body and head like SPNs. The difficulty in diagnosis lies in that the two kinds of tumors have histological commonalities, including small- to medium-sized uniform cells with scanty cytoplasm, indiscernible nucleoli, hyaline globules, and numerous small blood vessels with hyalinized walls [9] and both of them behave monomorphous growth and rosette-like structures in morphology [7, 10]. Moreover, both of them can express some neuroendocrine markers, such as CDH1, MME, VIM and CD56 [11]. PDACs which mainly occur at pancreatic head (67 %) with the remaining 33.3 % occur in the body is another type of pancreatic malignancy that has similar radiological features [7, 12] and immunophenotypes to SPN [13].
Recent efforts are devoted to distinguish SPN from other pancreatic tumors at the molecular level and several SPN-related transcription factors (TFs) and other protein-coding genes were exposed. For example, the accumulation of CTNNB1 in nuclear and loss of CDH1 were found to be the characteristic features of SPN. So, immunohistochemical staining of the two proteins could be useful for differentiating SPN from PanNET and PDAC [14, 15]. However, the aberrant behaviors of CTNNB1 and CDH1 in SPN were also observed in some PanNET cases [15, 16]. Similarly, nuclear staining of CTNNB1 and reducing staining of CDH1 could also be positive in some patients with PDAC [13, 17]. Although nearly 30 genes were reported to be SPN related, there have been no genes serving as the gold standard to effectively distinct SPN from malignant tumors in clinical setting.
MicroRNAs (miRNAs) were discovered as a new type of potential biomarker as well as therapeutic targets for diseases in recent years [18]. For example, miR-10b was proposed to be a good diagnostic biomarker for PDAC for its overexpression in the cancer cells [19]. Similarly, upregulation of miR-21 and downregulation of both miR-148a and miR-375 were observed in PDAC relative to adjacent normal tissue and the study therefore proposed that these miRNAs may be used as biomarkers for detecting pancreatic cancer [20]. These small miRNAs encoded by the genome are of about 21nt in length and negatively regulate gene expression by binding to the 3′ UTR of target mRNA and are involved in diverse biological processes, such as differentiation, proliferation, apoptosis etc. [21]. A couple of cancers including colon cancer [22], aggressive B cell lymphoma [23], gastric cancer and invasive endometrial cancer [24] were reported to be associated with specific miRNAs. Park et al. [25] integrated the expression profiles of mRNAs and miRNAs to study the pathogenesis of SPN for the first time. Both mRNAs and miRNAs were shown to be differentially expressed between SPN and PanNET/PDAC. In addition, they found SPN were characterized by three activated pathways, the Wnt/β-catenin, Hedgehog and androgen receptor signaling pathway, with which 17 differently expressed miRNAs were identified to be closely associated by target prediction. However, this work was mainly concerned with the correlation between miRNAs and the pathogenesis of SPNs. It did not elaborate whether miRNAs could be used as biomarkers when discriminating SPNs and the two other pancreatic tumors mentioned above.
Gene regulation is a biological process intertwined by TFs, miRNAs and their target genes, and is vital in controlling gene expression. Abnormal state for certain regulators may affect subsequent regulatory events and thus lead to aberrant cell behaviors. For such a complex system, network has been successfully used as a universal framework to model the biological process in searching genes contributing to the pathogenesis of cancers [26, 27].
In this study, we tried to discover high-quality candidate genes (protein-coding genes and miRNAs) that could diagnose SPN from other malignant pancreatic tumors with the help of the gene regulatory network. The gene regulatory network (GRN) defined here contains three kinds of nodes, including TF, non-TF gene and miRNA. TFs regulate the expression of protein coding genes and miRNAs at the transcriptional level. While miRNAs normally act as negative gene regulators by binding to the 3′UTR of target mRNAs through base pairing, which results in the cleavage of target mRNAs or translation inhibition at the posttranscriptional level. We firstly constructed a GRN by the method that we previously developed and applied to hepatocellular carcinoma [28]. Then we collected the candidate genes related to SPN by searching shortest paths between any pair of known SPN-related genes in the GRN based on the idea that genes interacted together conduct the similar functions [29]. The candidate biomarkers that distinguish SPN from malignant pancreatic tumors were filtered independently from the set of candidate genes by applying K-nearest neighbor algorithm (KNN) on the expression profiles of patient samples. Finally, we evaluated the predictions by the jackknife test. 14 genes including TFs and miRNAs were demonstrated to well separate SPN from PanNET and PDAC samples. The expression patterns of other gene sets were shown to be able to distinguish between SPN and PanNET or SPN and PDAC specifically. So these genes could serve as the potential biomarkers for clinical application. Meanwhile, the discovery of these potential biomarkers may also provide clues to understand the molecular bases of pancreatic tumorigenesis and development as these genes characterize the difference between SPN and PanNET or PDAC.
Microarray data
The mRNA and miRNA expression data were from the SPN study of Park et al. [25] that contained 14 SPN, 6 PanNET, 6 PDAC and 5 non-neoplastic pancreatic samples. We retrieved the data from GEO database [30] with the accession number of GSE 43797. For mRNA, the gene expression profile was obtained using an Illumina HumanHT-12 V4.0 expression beadchip (San Diego, CA). The miRNA data was generated by the Agilent-031181 Unrestricted_Human_miRNA_V16.0_Microarray 030840. Both datasets for mRNA and miRNA expression were log2-transformed and quantile-normalized using the Bioconductor package in R.
Network construction
We applied a two-step integration method [28] to construct the GRN for SPN, which was previously reported for hepatocellular carcinoma network analysis. The overall procedure for constructing the GRN is briefed as follows. Firstly, a candidate network was obtained through the following steps: (1) predicting target genes for TFs and miRNAs using bioinformatics algorithms, i.e. MATCH [31] for TFs and TargetScan [32] for miRNAs; (2) obtaining experimentally-validated regulations for TFs to targets from ChEA [33] and TransmiR 1.2 [34], miRNAs to targets from TarBase 7.0 [35]; (3) integrating all the predicted regulations and experimentally-validated regulations. Since this step produced a lot of noise and was not restricted to the specific tissue, the co-expression model together with the gene expression profile data was then introduced to pick out the regulatory relationships for the corresponding tissue among the candidate network. The Pearson correlation coefficient for each pair of regulation was calculated at this step and the thresholds (cut-off) were determined according to the power law fitness of the degree distribution to result in a scale-free network [36]. That is, the fraction p(k) of nodes having k connections to other nodes decreases exponentially to k as shown in formula 1. In scale-free networks, a minority of nodes dominates most of the connections. The rationality for the selection of threshold is that many studies have shown the biological networks including protein–protein interaction network [37], metabolic network [38] and regulatory network [39] are of hierarchical scale-free nature. So, the network obtained through this filtration is biologically-meaningful.
$${\text{p(k)}} = {\text{k}}^{ - \lambda }$$
where λ is a parameter that typically takes the value of 2 < λ < 3.
Identifying differentially expressed genes
The two integrated statistical methods: (1) Student's t-test; (2) median-ratio fold change were used to identify differentially expressed genes for SPN, PanNET and PDAC versus non-neoplastic pancreas samples respectively, SPN versus PanNET and SPN versus PDAC samples. Genes with P value of <0.01 (t-test) and a fold change ≥ 2 or ≤ 0.5 were recognized as differentially expressed genes.
Previously reported SPN genes and shortest path calculation
Based on the previous studies of immunohistochemical staining, we manually collected genes that have been validated to exhibit abnormal behaviors in SPN using the Polysearch [40], a biomedical text mining tool for extracting relationships between human diseases and genes. After identifying the SPN genes, we calculated the shortest paths for each pair of these genes in the GRN using Dijkstra's algorithm [41], which was developed to construct shortest paths in weighted networks. In our study, the weight of edges was calculated based on Pearson correlation coefficient, α. For convenience, the parameter β = 1−|α| was taken in the study, so that the smaller the β, the stronger the regulation between two genes.
Prediction algorithm
The KNN [42, 43] is widely used in computational biology and bioinformatics for its apparent efficiency and easy-to-use features [44, 45]. In the KNN classifier, the query sample should be allocated to the subset represented by majority of its K nearest neighbors. In this study, the KNN classifier was adopted to predict the performance of shortest path genes to classify the three kinds of pancreatic tumors mentioned before, and Euclidean distance was used to measure the locality of samples based on gene expression profiles. As mentioned above, the KNN classifier contains a parameter K that could affect the prediction result. In other words, different K values may assign the query sample to a distinct subset. The one-dimensional method proposed by Kuo-Chen Chou [46] was used to solve this problem. In this method, all samples were classified into M subsets where each subset Sm (m = 1, 2, …, M) is composed of the same attribute category and its size (the number of samples) is Nm. Given a query sample S, it is predicted to belong to the subset Sm with which its score of the following equation is the highest.
$${\text{Y}}\left( {\text{S}} \right) \, = \sum\limits_{k = 1} {\left[ {{\text{m}}\left( {\text{K,S}} \right) , {\text{S}}_{m} } \right]\left( {{\text{m = 1, 2,}} \ldots , {\text{ M}}} \right)}$$
$$\left[ {m({\text{K}},{\text{S}}),{\text{S}}_{m} } \right] = \left\{ {\begin{array}{*{20}l} {1,} &\quad {if\;m({\text{K}},{\text{S}}) \in {\text{S}}_{m} } \\ {0,} &\quad {otherwise} \\ \end{array} } \right\}$$
Performance validation
In statistical predictions, three kinds of cross-validation methods are widely used to exam the effectiveness of the classifier: the independent dataset test, sub-sampling test (such as fivefold or tenfold cross-validation), and the jackknife test (also called leave-one-out cross-validation) [47]. As demonstrated by Chou.et al. [47], the jackknife test is the least arbitrary and therefore is widely used to examine the performance of various predictors [48–50]. The jackknife test was utilized to evaluate the quality of the prediction model in our study. The prediction accuracy is defined by the percentage of the number of correct prediction events for all classes divided by the number of total prediction events, as follows:
$${\rm Accuracy} = \frac{{Total \, number \, of \, correct \, prediction \, number \, events}}{{The \, number \, of \, prediction \, events}} \% .$$
To identify high-quality candidate biomarkers that could diagnose SPN from PaNET and PDAC, we firstly constructed the candidate regulatory network by integrated all the predicted regulations and experimentally-validated regulations involving TFs and miRNAs as regulators. Then a tissue-specific GRN for SPN was constructed by integrating co-expression model together with the gene expression profile data. Meanwhile we acquired previously reported SPN gene list (protein coding genes and miRNAs) by Polysearch tool [40] and mapped the members of the list to GRN. By linking the members through shortest path method, we obtained new candidate SPN genes which are on the shortest paths. Finally, the candidate biomarkers that could distinguish SPN from malignant pancreatic tumors were filtered from the set of candidate genes by applying KNN classifier on the expression profiles of patient samples. The workflow is shown in Fig. 1.
The workflow. I The candidate regulatory network was constructed by integrating all the predicted regulations and experimentally-validated regulations conducted by TFs and miRNAs. The bioinformatics tools were listed alongside. II To construct the tissue-specific GRN for SPN. III Identifying the potential biomarkers by shortest network paths and KNN classifier
Regulatory network for SPN
To uncover new genes implicated in the pathogenesis of SPN, we constructed the GRN for SPN [28]. The candidate network was firstly constructed by collecting all the regulations of TFs and miRNAs predicted by bioinformatics tools. Then, the co-expression model was applied to this network. In general, the GRN for SPN is constructed by collecting the regulatory interactions between any pair of genes that are co-expressed (see "Methods" section) in SPN. The cut-off parameter used to decide whether there exists a co-expression relationship between specific gene pairs is basically determined by whether the GRN shows a good power-law behavior in degree distribution (Additional file 1). It is shown that the power-law fitness (see R2 in Fig. 2a, e) of the in-degree distribution for either miRNA or TF in the GRN of SPN rises as the cut-off for co-expression increases. This means the GRN of SPN approaches the scale-free network when the criterion for co-expression goes strict. However, the strictness of the co-expression criterion also affects the size of the GRN. As seen in Fig. 2b, f, the size of GRN decreases as the co-expression cut-off increases. To avoid losing too many effective regulatory interactions, we set the cut-offs for both TF and miRNA regulation at 0.8 for compromising their impact on the power-law fitness and the size of GRN. It is notable that the SPN analyses based on GRN was basically robust when cut-off varied from 0.6 to 0.8. The final GRN contained 7215 nodes (including 180 TFs and 164 miRNAs) and 86,084 interactions, which comprised 11,351 regulations from miRNAs to protein coding genes, 1013 regulations from TFs to miRNAs and 73,720 regulations from TFs to protein coding genes. Among the GRN, 1182 regulations were experimentally validated. The entire GRN is detailed in Additional file 2.
Power-law model fitness to select correlation coefficient thresholds for construction of a GRN in SPN based on the candidate network. The cut-off refers to the absolute value of the correlation coefficient ranging from 0 to 1. The first row represents the sub-GRN where only miRNAs are regulators. The second row represents sub-GRN where only TFs are regulators. a, e Plot the results of power law fitting of in-degrees for different cut-off values. The larger R2 value, the better power-law fitness. b, f Number of targets for different cut-off values. c, g Represent the power-law model of out-degree. d, h Number of regulators for different cut-off values
Candidate genes that are closely related to SPN
We firstly acquired genes which have been reported to be deregulated in SPN by text mining. Previous studies of SPN were mainly conducted by immunohistochemical staining and have identified various SPN-relevant genes such as FLI1 and CCND1 [51], LEF1 [52] and CTNND1 [53], and CTNNB1 and CDH1 in association with the Wnt signaling pathway [25, 54]. A list of 26 previously reported genes including 4 TFs, 7 miRNAs (Additional file 3: Table S1) were manually extracted by using Polysearch tool [40].
To discover more candidate genes involved in SPN, we conducted a search in the GRN of SPN based on the "guilt-by-association" rule [29] which has been widely used to predict gene functions in many biological networks [55, 56]. The rule regarded the neighbors of a given gene as to have similar biological functions. According to such a rule, it can be further inferred that genes in the shortest paths [57] between two known SPN genes (i.e. the path with minimal length between two SPN genes) may have features in common with SPN genes. The shortest paths between each pair of the 26 original SPN-related genes were calculated by the algorithm of Dijkstra [41]. A total of 216 shortest paths were obtained (Additional file 3: Table S2), and 43 genes containing 33 TFs and 10 miRNAs were found to be located in the paths (Additional file 3: Table S3) in addition to those 26 known SPN genes. The 216 shortest paths formed a sub-network (Fig. 3, 25 known genes were shown in the figure, as there was no shortest path between CCDN1 and the other known genes) in which, transcription information is transmitted among known SPN-related regulators and 43 path genes. These 43 genes were believed to be new candidates implicated in the tumorigenesis of SPN according to "guilt-by-association" rule.
Sub-network constructed by shortest paths. Red rectangles represent genes that have already been reported to be related with SPN. Yellow rectangles are newly discovered potential SPN-related genes through shortest paths among red ones
Functional analysis for candidate SPN genes
Functional analyses were made to testify whether the new candidate genes were truly correlated with SPN. Firstly, we performed function enrichment analysis on the candidate TFs. KEGG pathway enrichment analysis demonstrated that all of the candidate TFs were involved in classic cancer-related pathways, such as non-small cell lung cancer, and colorectal cancer (Table 1). Specially, some of genes were enriched in the Wnt signaling pathway whose activation has been reported to be the essential characteristics for the pathogenesis of SPN [13, 25, 51]. Figure 4 illustrates the genes deregulated in Wnt signaling pathway, where SMAD3, c-MYC and c-JUN which participated in cell cycle were found aberrantly expressed in SPN when comparing to non-neoplastic pancreas samples.
Table 1 KEGG enrichment analysis for 33 new candidate TFs
Wnt signaling pathway involved in SPN. Genes in box were found to be deregulated in the Wnt signaling pathway
In addition, the functions of 10 candidate miRNAs were annotated by the tool TAM [58], a web-accessible program which could mine the potential biological processes that a set of miRNAs could be involved in. The results showed miRNA-associated functions were enriched in apoptosis, cell differentiation and epithelial-mesenchymal transition (EMT) (P < 0.05). All of these functions are also closely related to the tumorigenesis.
Candidate genes were enriched in differentially expressed genes
Basically, the genes that contribute to the pathogenesis and development of the disease are prone to be differentially expressed in SPN comparing to the normal state. We checked the expression value of all the candidate genes in both SPN and normal condition and found that most of the candidate genes (TFs and miRNAs) were differentially-expressed (20 out of 33 TFs, 6 out of 10 miRNAs, Tables 2, 3). Taking the 48/41 TFs/miRNAs (see "Methods" section and Additional file 4) that are differentially expressed in SPN as background, the Fisher's exact test showed that both the candidate TFs and miRNAs are significantly enriched in differentially-expressed genes (P < 0.05). This suggests that the candidate genes identified by the shortest-path method are generally essential to the pathogenesis of SPN.
Table 2 Overlap for differentially-expressed TFs and candidate TFs
Table 3 Overlap for differentially-expressed miRNAs and candidate miRNAs
Candidate biomarkers to differentially diagnose SPN and malignant pancreatic tumors
The candidate biomarkers that could separate SPN from malignant pancreatic tumors (including PanNET and PDAC) were searched in the 69 SPN-related genes, i.e. the 26 known genes collected from literatures and the 43 new candidate genes that were predicted in this study. The procedure is as following: (1) the KNN classifier was applied on gene expression profiles of the 69 SPN-related genes to find out the gene set that could independently distinguish SPN and malignant pancreatic tumors; (2) the quality of the KNN classifier was evaluated by jackknife test (see Method). 14 genes were found to discriminate SPN from pancreatic malignancies with 100 % accuracy (Fig. 5a, b) when taking PanNET and PDAC as a whole, among which 8 genes were downregulated in SPN while 6 other genes were upregulated compared with PanNET as well as PDAC. Three genes, TCF7, PPARD and miR-194, were newly found in this study (Additional file 5: Table S6). Obviously, the expression patterns of the genes found here represent the difference between SPN and the other two malignant cancers and may help to understand the molecular mechanisms between benign neoplasms like SPN and malignant tumors.
Biomarkers for SPN and malignant cancers. a Plot of the KNN classification accuracy for 69 genes independently in differentiating SPN from malignant cancers. b Heatmap of expression profiles of potential biomarkers for SPN and malignant cancers. Rows correspond to genes and columns correspond to samples. Color bar gradient represents log2 expression
Particularly, we searched the candidate biomarkers for discriminating SPN from PanNET or PDAC respectively among the 69 SPN-related genes following the same procedure. 17 genes were identified to separate SPN from PanNET with 100 % accuracy (Fig. 6a, b) and 7/10 of them decreased/increased in SPN when comparing with PanNET. The same number of genes and accuracy were obtained for SPN versus PDAC (Fig. 7a, b) with 8/9 genes decreased/increased in SPN compared to PDAC. For the distinction of SPN from PanNET, 9 genes were newly predicted; while for the distinction of SPN from PDAC, 5 genes are newly found (Additional file 5: Table S7/S8). Comparing the two sets of 17 candidate biomarkers, it is found that TCF7 and PPARD are the common members (Figs. 6b, 7b) and both of them were upregulated in SPN compared with PanNET and PDAC (Additional file 5).
Biomarkers for SPN versus PanNET/PDAC. a Plot of the KNN classification accuracy for 69 genes independently in differentiating SPN from PanNET. b Heatmap of expression profiles of biomarkers for SPN and PanNET. Rows correspond to genes and columns correspond to samples, color bar gradient represents log2 expression
Biomarkers for SPN versus PanNET/PDAC. a KNN classification accuracy for 69 genes independently in differentiating SPN from PDAC. b Heatmap of expression profiles of potential biomarkers for SPN and PDAC
In this study, we conducted a first investigation to identify the potential biomarkers for differentially diagnosing SPN from malignant pancreatic tumors, PanNET and PDAC, via a network approach. The shortest paths among 26 previously-reported SPN-related genes in the network were calculated and 43 new candidate genes were identified. Genes from this analysis together with the previously reported SPN-related genes may potentially contribute to the pathogenesis of SPN and help to the precise diagnosis of SPN which is important to improve the prognosis. Thus, we further explored candidate biomarkers for the differentiation of SPN from malignant pancreatic tumors using a nearest neighbor algorithm that was evaluated by the jackknife test.
When considering PanNET and PDAC collectively as malignant pancreatic cancers, PPARD, TCF7 and miR-194 were found to have excellent capabilities to separate SPN from malignant cases. More interestingly, miR-194 was down-regulated in SPN (−2.76-fold, P < 0.01), but was up-regulated in both PanNET (2.06-fold, P < 0.01) and PDAC (2.97-fold, P < 0.01) when all classes were compared with non-neoplastic pancreatic cases. A recent study revealed that up-regulation of miR-194 in PDAC was correlated with increased tumor growth and progression [59], which supports our results.
For SPN versus PanNET, we uncovered a set of 17 genes including 9 new candidate genes and 8 previously-reported genes that could correctly separate the two groups of samples (14 SPN and 6 PanNET). Of the path genes, sex-determining region Y-box 11 (SOX11; 5.62-fold versus normal, 5.02-fold versus PanNET, P < 0.01), which has an out-degree of 690 in the network was reported to be implicated in embryonic development and tissue remodeling [60]. In this study, SOX11 was found to serve as a candidate biomarker to confer a differential diagnosis between SPN and PanNET. SOX11 was also found to be exclusively overexpressed in SPN when the gene expression profiles of SPN with non-neoplastic samples, PanNET with non-neoplastic samples, and PDAC with non-neoplastic samples were compared. Interestingly, in epithelia ovarian cancer (EOC), SOX11 was revealed to be overexpressed when compared with normal ovarian tissues, but loss of expression of SOX11 protein was associated with a more aggressive phenotype [61]. The hypothesis that SOX11 expression in EOC may lead to the aberrant regulation of genes associated with cell survival/death which could promote a pro-apoptotic and less aggressive phenotype was thus postulated in that report. It has also been reported that the overexpression of SOX11 strongly suppresses cell migration/invasion in vitro and in vivo but does not inhibit cell proliferation in gastric cancer [62]. Given these facts, we speculated that overexpression of SOX11 may contribute to the tumorigenesis but less malignant behaviours of SPN, although the detailed mechanisms need further investigation.
Another set of 17 genes consisting of five new candidate genes and 12 reported genes were found to successfully differentiate SPN from PDAC. Of the path genes, sex-determining region Y (SRY) box 9 (SOX9) has an out-degree of 2030 in the network. SOX9 is an important transcription factor required for development and has been implicated in several types of cancer. SOX9 decreased in SPN as compared with PDAC (−12.62-fold, P < 0.01) and non-neoplastic pancreatic tissues (−10.73-fold, P < 0.01), which was consistent with the previous immunohistochemistry data that the detection of SOX9 was observed in the majority (89 %) of PDACs samples but not in SPN (0 %) [63]. Furthermore, SOX9 was also found to be critical for PDAC initiation and involved in their tumorigenesis by regulating the ERBB pathway [64]. These facts implied that SOX9 could act as a candidate biomarker in differentiating SPN and PDAC.
Another path gene that could successfully differentiate SPN from PDAC was miR-204, which increased in SPN compared with PDAC (10.86-fold, P < 0.01) and non-neoplastic pancreatic tissues (5.65-fold, P < 0.01). MiR-204 has been reported to be down-regulated in several human cancers, including gastric cancers, ovarian cancers, breast cancers, malignant peripheral nerve sheath tumors as well as endometrial cancers, and associated with the promotion of tumor invasion and metastasis [65]. Additionally, overexpression of miR-204 dramatically suppressed intrahepatic cholangiocarcinoma cell migration and invasion, as well as the EMT process [66]. Aberrant EMT activation is an important step towards tumor cell invasion and metastasis [67, 68]. From these discoveries, we inferred that miR-204 may be a vital factor to maintain SPN in a low malignant state, and this mechanism requires further study of course. Since miR-204 was down-regulated in PDAC compared with non-neoplastic pancreatic tissues (−2.01-fold, P < 0.01), we speculated whether miR-204 could be of therapeutic usage to suppress tumor metastasis in PDAC. In fact, over-expression of miR-204 was found to cause cell death in malignant pancreatic cancer [69].
The strategy we proposed to discover candidate biomarkers to discriminate SPN from PanNET or PDAC was to search for the candidate SPN-related genes through SPN gene regulatory network firstly, and then to analyze the expression profiles by focusing on the resulted SPN genes in order to decrease the noise that most of the current large scale gene expression analysis are confronted with. The strategy worked well with 100 % accuracy for the dataset available so far. However, it should be noted that more samples should be included to further quantify the importance of each marker gene in the future. Despite the shortage of the amount of samples, the effectiveness of this approach has been demonstrated by showing the biological relevance of the candidate biomarkers as well as the experimental evidence from literatures for certain genes. The potential biomarkers including miRNAs and TFs proposed here need further validation by qRT-PCR, immunohistochemical staining and Western blotting, etc. In fact, mRNA level of one candidate biomarker, androgen receptor (AR) has been verified by qRT-PCR in patient samples [25]. Moreover, both western blotting and immunohistochemical staining analyses revealed that AR was increased in SPN comparing to PanNET and PDAC by Park et al. [25]. Once the potential biomarkers are confirmed, they may be adapted to clinical setting for differential diagnosis between SPN and PanNET/PDAC in which tumor tissue section will be used and the expression level of potential marker genes be checked.
In conclusion, this study provides new insights into the identification of new potential biomarkers to differentiate SPN from PanNET as well as PDAC by a network-based study. 43 new candidate genes involved in the tumorigenesis of SPN were found by using the shortest path analysis among 26 reported SPN-related genes. With the help of KNN classifier and jackknife test, 14 genes were found to discriminate SPN from pancreatic malignancies with 100 % accuracy, and three genes, TCF7, PPARD and miR-194 were newly found in this study. For SPN versus PanNET, 17 genes including SOX11, SMAD3 and miR-194 etc. were identified to separate the two diseases, among which nine were newly discovered. The same number of genes and accuracy were obtained for the distinction of SPN from PDAC, with five genes containing SOX9 and miR-204 etc. were newly predicted. Genes obtained from this study may provide clues to further understanding of the gene regulation mechanism of SPN as well as PanNET and PDAC. Some potential biomarkers, e.g. SOX11 and miR-204 which could cause cell proliferation but inhibit invasion or metastasis could be potential therapeutic targets for malignant pancreatic tumors to lighten their malignant degree.
Frantz VK. Tumors of the pancreas. In: Atlas of tumor pathology, 1st series. Washington DC: Armed Forces Institute of Pathology; 1959.
Mortenson MM, Katz MHG, Tamm EP, Bhutani MS, Wang HM, Evans DB, Fleming JB. Current diagnosis and management of unusual pancreatic tumors. Am J Surg. 2008;196:100–13.
Papavramidis T, Papavramidis S. Solid pseudopapillary tumors of the pancreas: review of 718 patients reported in English literature. J Am Coll Surg. 2005;200:965–72.
Klimstra DS, Wenig BM, Heffess CS. Solid-pseudopapillary tumor of the pancreas: a typically cystic carcinoma of low malignant potential. Semin Diagn Pathol. 2000;17:66–80.
Cai Y, Ran X, Xie S, Wang X, Peng B, Mai G, Liu X. Surgical management and long-term follow-up of solid pseudopapillary tumor of pancreas: a large series from a single institution. J Gastrointest Surg. 2014;18:935–40.
Burris HA, Moore MJ, Andersen J, Green MR, Rothenberg ML, Madiano MR, Cripps MC, Portenoy RK, Storniolo AM, Tarassoff P, et al. Improvements in survival and clinical benefit with gemcitabine as first-line therapy for patients with advanced pancreas cancer: a randomized trial. J Clin Oncol. 1997;15:2403–13.
Baek JH, Lee JM, Kim SH, Kim SJ, Kim SH, Lee JY, Han JK, Choi BI. Small (< = 3 cm) Solid Pseudopapillary Tumors of the Pancreas at Multiphasic Multidetector CT. Radiology. 2010;257:97–106.
Reddy S, Cameron JL, Scudiere J, Hruban RH, Fishman EK, Ahuja N, Pawlik TM, Edil BH, Schulick RD, Wolfgang CL. Surgical management of solid-pseudopapillary neoplasms of the pancreas (Franz or Hamoudi tumors): a large single-institutional series. J Am Coll Surg. 2009;208:950–9.
Kim SA, Kim MS, Kim MS, Kim SC, Choi J, Yu E, Hong SM. Pleomorphic solid pseudopapillary neoplasm of the pancreas: degenerative change rather than high-grade malignant potential. Hum Pathol. 2014;45:166–74.
Takahashi Y, Hiraoka N, Onozato K, Shibata T, Kosuge T, Nimura Y, Kanai Y, Hirohashi Y. Solid–pseudopapillary neoplasms of the pancreas in men and women: do they differ? Virchows Arch. 2006;448(5):561–9.
Liu BA, Li ZM, Su ZS, She XL. Pathological differential diagnosis of solid-pseudopapillary neoplasm and endocrine tumors of the pancreas. World J Gastroenterol. 2010;16:1025–30.
Coleman KM, Doherty MC, Bigler SA. Solid-pseudopapillary tumor of the pancreas. Radiographics. 2003;23:1644–8.
Hong SM, Li A, Olino K, Wolfgang CL, Herman JM, Schulick RD, Iacobuzio-Donahue C, Hruban RH, Goggins M. Loss of E-cadherin expression and outcome among patients with resectable pancreatic adenocarcinomas. Mod Pathol. 2011;24:1237–47.
Chetty R, Serra S. Membrane loss and aberrant nuclear localization of E-cadherin are consistent features of solid pseudopapillary tumour of the pancreas. An immunohistochemical study using two antibodies recognizing different domains of the E-cadherin molecule. Histopathology. 2008;52:325–30.
Kim MJ, Jang SJ, Yu E. Loss of E-cadherin and cytoplasmic-nuclear expression of beta-catenin are the most useful immunoprofiles in the diagnosis of solid-pseudopapillary neoplasm of the pancreas. Hum Pathol. 2008;39:251–8.
Chetty R, Serra S, Asa SL. Loss of membrane localization and aberrant nuclear E-cadherin expression correlates with invasion in pancreatic endocrine tumors. Am J Surg Pathol. 2008;32:413–9.
Li L, Li JS, Hao CY, Zhang CJ, Mu K, Wang Y, Zhang TG. Immunohistochemical evaluation of solid pseudopapillary tumors of the pancreas: the expression pattern of CD99 is highly unique. Cancer Lett. 2011;310:9–14.
Mishra PJ. Non-coding RNAs as clinical biomarkers for cancer diagnosis and prognosis. Expert Rev of Mol Diagn. 2014;14:917–9.
Preis M, Gardner TB, Gordon SR, Pipas JM, Mackenzie TA, Klein EE, Longnecker DS, Gutmann EJ, Sempere LF, Korc M. MicroRNA-10b expression correlates with response to neoadjuvant therapy and survival in pancreatic ductal adenocarcinoma. Clin Cancer Res. 2011;17:5812–21.
Bhatti I, Lee A, James V, Hall RI, Lund JN, Tufarelli C, Lobo DN, Larvin M. Knockdown of microRNA-21 inhibits proliferation and increases cell death by targeting programmed cell death 4 (PDCD4) in pancreatic ductal adenocarcinoma. J Gastrointest Surg. 2011;15:199–208.
Moreno-Moya JM, Vilella F, Simon C. MicroRNA: key gene expression regulators. Fertil Steril. 2014;101:1516–23.
Zhai H, Song B, Xu X, Zhu W, Ju J. Inhibition of autophagy and tumor growth in colon cancer by miR-502. Oncogene. 2013;32:1570–9.
Iqbal J, Shen Y, Huang X, Liu Y, Wake L, Liu C, Deffenbacher K, Lachel CM, Wang C, Rohr J, et al. Global microRNA expression profiling uncovers molecular markers for classification and prognosis in aggressive B-cell lymphoma. Blood. 2015;125:1137–45.
Dong P, Kaneuchi M, Watari H, Sudo S, Sakurag N. MicroRNA-106b modulates epithelial-mesenchymal transition by targeting TWIST1 in invasive endometrial cancer cell lines. Mol Carcinog. 2013;53:349–59.
Park M, Kim M, Hwang D, Kim WK, Kim SK, Shin J, Park ES, Kang CM, Paik YK, Kim H. Characterization of gene expression and activated signaling pathways in solid-pseudopapillary neoplasm of pancreas. Mod Pathol. 2014;27:580–93.
Ye H, Liu X, Lv M, Wu Y, Kuang S, Gong J, Yuan P, Zhong Z, Li Q, Jia H, et al. MicroRNA and transcription factor co-regulatory network analysis reveals miR-19 inhibits CYLD in T-cell acute lymphoblastic leukemia. Nucleic Acids Res. 2012;40:5201–14.
Poos K, Smida J, Nathrath M, Maugg D, Baumhoe D, Korsching E, et al. How microRNA and transcription factor co-regulatory networks affect osteosarcoma cell proliferation. Plos Comput Biol. 2013;9(8):e1003210.
Gu ZG, Zhang CY, Wang J. Gene regulation is governed by a core network in hepatocellular carcinoma. BMC Syst Biol. 2012;6:32.
Nabieva E, Jim K, Agarwal A, Chazelle B, Singh M. Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics. 2005;21:I302–10.
Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson CL, et al. NCBI GEO: archive for functional genomics data data sets–update. Nucleic Acids Res. 2013;41:D991–5.
Kel AE, Gossling E, Reuter I, Cheremushkin E, Kel-Margoulis OV, Wingender E. MATCH: a tool for searching transcription factor binding sites in DNA sequences. Nucleic Acids Res. 2003;31:3576–9.
Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. Elife. 2015;4:e05005.
Lachmann A, Xu H, Krishnan J, Berger SI, Mazloom AR, Ma'ayan A. ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics. 2010;26:2438–44.
Wang J, Lu M, Qiu C, Cui Q. TransmiR: a transcription factor-microRNA regulation database. Nucleic Acids Res. 2010;38:D119–22.
Vlachos IS, Paraskevopoulou MD, Karagkouni D, Georgakilas G, Vergoulis T, Kanellos I, Anastasopoulos IL, Maniou S, Karathanou K, Kalfakakou D, et al. DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions. Nucleic Acids Res. 2015;43:D153–9.
Barabasi AL, Albert R. Emergence of scaling in random networks. Science. 1999;286:509–12.
Han JD, Bertin N, Hao T, Goldberg DS, Berriz GF, Zhang LV, Dupuy D, Walhout AJ, Cusick ME, Roth FP, Vidal M. Evidence for dynamically organized modularity in the yeast protein–protein interaction network. Nature. 2004;430:88–93.
Jeong H, Tombor B, Albert R, Oltvai ZN, Barabasi AL. The large-scale organization of metabolic networks. Nature. 2000;407:651–4.
Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A. Reverse engineering of regulatory networks in human B cells. Nat Genet. 2005;37:382–90.
Cheng D, Knox C, Young N, Stothard P, Damaraju S, Wishart DS. PolySearch: a web-based text mining system for extracting relationships between human diseases, genes, mutations, drugs and metabolites. Nucleic Acids Res. 2008;36:W399–405.
Dijkstra EW. A note on two problems in connection with graphs. Numerische Mathematik. 1959;1:269–71.
Ruiz EV. An algorithm for finding nearest neighbors in (approximately) constant average time. Pattern Recogn Lett. 1986;4:145–57.
Denoeux T. A K-nearest neighbor classification rule-based on Dempster-Shafer theory. IEEE Trans Syst Man Cybern. 1995;25:804–13.
Cai Y, Huang T, Hu L, Shi X, Xie L, Li Y. Prediction of lysine ubiquitination with mRMR feature selection and analysis. Amino Acids. 2012;42:1387–95.
Huang T, Chen L, Cai YD, Chou KC. classification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional property. PLoS One. 2011;6:e25297.
Chou KC. Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol. 2011;273:236–47.
Chou KC, Zhang CT. Prediction of protein structural classes. Crit Rev Biochem Mol Biol. 1995;30:275–349.
Anand A, Suganthan PN. Multiclass cancer classification by support vector machines with class-wise optimized genes and probability estimates. J Theor Biol. 2009;259:533–40.
Nanni L, Lumini A. A further step toward an optimal ensemble of classifiers for peptide classification, a case study: HIV protease. Protein Pept Lett. 2009;16:163–7.
Vilar S, Gonzalez-Diaz H, Santana L, Uriarte E. A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer. J Theor Biol. 2009;261:449–58.
Tiemann K, Heitling U, Kosmahl M, Kloppel G. Solid pseudopapillary neoplasms of the pancreas show an interruption of the Wnt-signaling pathway and express gene products of 11q. Mod Pathol. 2007;20:955–60.
Singhi AD, Lilo M, Hruban RH, Cressman KL, Fuhrer K, Seethala RR. overexpression of lymphoid enhancer-binding factor 1 (LEF1) in solid-pseudopapillary neoplasms of the pancreas. Mod Pathol. 2014;27:1355–63.
Chetty R, Jain D, Serra S. p120 catenin reduction and cytoplasmic relocalization leads to dysregulation of E-cadherin in solid pseudopapillary tumors of the pancreas. Am J Clin Pathol. 2008;130:71–6.
Burford H, Baloch Z, Liu X, Jhala D, Siegal GP, Jhala N. E-cadherin/beta-catenin and CD10: a limited immunohistochemical panel to distinguish pancreatic endocrine neoplasm from solid pseudopapillary neoplasm of the pancreas on endoscopic ultrasound-guided fine-needle aspirates of the pancreas. Am J Clin Pathol. 2009;132:831–9.
Song JM, Singh M. How and when should interactome-derived clusters be used to predict functional modules and protein function? Bioinformatics. 2009;25:3143–50.
Saha S, Chatterjee P, Basu S, Kundu M, Nasipuri M. FunPred-1: protein function prediction from a protein interaction network using neighborhood analysis. Cell Mol Biol Lett. 2014;19:675–91.
Weiss M, Hultsch H, Adam I, Scharff C, Kipper S. The use of network analysis to study complex animal communication systems: a study on nightingale song. Proc Roy Soc B. 2014;281:20140460.
Lu M, Shi B, Wang JA, Cao Q, Cui QH. TAM: A method for enrichment and depletion analysis of a microRNA category in a list of microRNAs. BMC Bioinform. 2010;11:419.
Zhang J, Zhao CY, Zhang SH, Dang-Hui DH, Chen Y, Liu QH, Shi M, Ni CR, Zhu MH. upregulation of miR-194 contributes to tumor growth and progression in pancreatic ductal adenocarcinoma. Oncol Rep. 2014;31:1157–64.
Sock E, Rettig SD, Enderich J, Bosl MR, Tamm ER, Wegner M. Gene targeting reveals a widespread role for the high-mobility-group transcription factor Sox11 in tissue remodeling. Mol Cell Biol. 2004;24:6635–44.
Brennan DJ, Ekc S, Doyle E, Drew T, Foley M, Flannelly G, O'Connor DP, Gallagher WM, Kilpinen S, Kallioniemi OP, et al. The transcription factor Sox11 is a prognostic factor for improved recurrence-free survival in epithelial ovarian cancer. Eur J Cancer. 2009;45:1510–7.
Qu Y, Zhou CF, Zhang JN, Cai Q, Li JF, Du T, Zhu ZG, Cui XJ, Liu BY. The metastasis suppressor SOX11 is an independent prognostic factor for improved survival in gastric cancer. Int J Oncol. 2014;44:1512–20.
Shroff S, Rashid A, Wang H, Katz MH, Abbruzzese JL, Fleming JB, Wang H. SOX9: a useful marker for pancreatic ductal lineage of pancreatic neoplasms. Hum Pathol. 2014;45:456–63.
Grimont A, Pinho AV, Cowley MJ, Augereau C, Mawson A, Giry-Laterriere M, Van den Steen G, Waddell N, Pajic M, Sempoux C, et al. Sox9 regulates Erbb signalling in pancreatic cancer development. Pancreas. 2014;43:1361.
Imam JS, Plyler JR, Bansal H, Prajapati S, Bansal S, Rebeles J, Chen HI, Chang YF, Panneerdoss S, Zoghi B, et al. Genomic loss of tumor suppressor miRNA-204 promotes cancer cell migration and invasion by activating AKT/mTOR/Rac1 signaling and actin reorganization. PLoS One. 2012;7:e52397.
Qiu YH, Wei YP, Shen NJ, Wang ZC, Kan T, Yu WL, Yi B, Zhang YJ. miR-204 inhibits epithelial to mesenchymal transition by targeting slug in intrahepatic cholangiocarcinoma cells. Cell Physiol Biochem. 2013;32:1331–41.
Nieto MA. Epithelial-Mesenchymal Transitions in development and disease: old views and new perspectives. Int J Dev Biol. 2009;53:1541–7.
Wang B, Lindley LE, Fernandez-Vega V, Rieger ME, Sims AH, Briegel KJ. The T box transcription factor TBX2 promotes epithelial-mesenchymal transition and invasion of normal and malignant breast epithelial cells. PLoS One. 2012;7:e41355.
Chen Z, Sangwan V, Banerjee S, Mackenzie T, Dudeja V, Li X, Wang H, Vickers SM, Saluja AK. miR-204 mediated loss of Myeloid cell leukemia-1 results in pancreatic cancer cell death. Mol Cancer. 2013;12:105.
PL, JW conceived the study; PL, YH and JY carried out the analysis and wrote the manuscript. JW, JL and JY supervised the work. All authors read and approved the final manuscript.
This work was supported by the National Science Foundation of China (No. 81250044 and No. 31500674) and the National Basic Research Program of China (973 Program) (No. 2014CB542300) and the funding from NJU-Yangzhou Institute of Optoelectronics. The authors would acknowledge the Center of High Performance Computation of Nanjing University for the support of computational resources.
State Key Laboratory of Pharmaceutical Biotechnology, Collaborative Innovation Center of Chemistry for Life Sciences, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute for Life Sciences (NAILS), School of life sciences, Nanjing University, 163 Xianlin Road, Nanjing, 210023, China
Pengping Li
, Jiao Yi
, Jie Li
& Jin Wang
Department of Neurosurgery, Jinling Hospital, School of Medicine, Nanjing University, 305 East Zhongshan Road, Nanjing, 210002, China
Yuebing Hu
Search for Pengping Li in:
Search for Yuebing Hu in:
Search for Jiao Yi in:
Search for Jie Li in:
Search for Jie Yang in:
Search for Jin Wang in:
Correspondence to Jie Li or Jie Yang or Jin Wang.
Additional file 1: In-degree distribution for GRN. X-axis represents the in-degree for a certain node. A node of in-degree x means that this node is regulated by a total number of x other nodes. Y-axis represents the total number of network nodes which has an in-degree of x. The red curve was the fitting to the power law distribution. (A): The in-degree distribution for sub-GRN in which only miRNAs are included as regulators and the in-degree for each node (miRNAs and protein coding genes) was calculated in this sub-GRN. The in-degree ranges from 0 to 27. (B): In-degree distribution for sub-GRN in which only TFs are included as regulators.
Additional file 2: Gene regulatory network of SPN. The first column represents regulators, while the second column represents corresponding targets.
Additional file 3: Previously reported SPN-relevant genes and shortest paths among path genes. Table S1 lists the 26 genes which have been reported to be related with SPN. Table S2 shows in detail the shortest paths for each pair of 26 SPN-related genes listed in table S1. Table S3 lists 43 new candidate genes on the shortest path, together with the corresponding gene type.
Additional file 4: Differentially expressed genes between SPN and non-neoplastic samples. Table S4 posts differentially expressed TFs with fold change and t-test p-value for each TF. Table S5 posts differentially expressed miRNAs with fold change and t-test p-value too for each miRNA.
Additional file 5: Biomarkers for differential diagnosis of SPN and two malignant pancreatic. Table S6 lists fold change of 14 biomarkers for SPN, PanNET and PDAC versus normal pancreatic tissues as well as SPN versus PanNET and PDAC. Table S7 lists fold change of 17 biomarkers for SPN and PanNET versus normal pancreatic tissues as well as SPN versus PanNET. Table S8 lists fold change of 17 biomarkers for SPN and PDAC versus normal pancreatic tissues as well as SPN versus PDAC.
Solid pseudopapillary neoplasms
Gene regulatory network
Shortest path
Computational Modelling and Epidemiology


text:
Commissioners vote to expand public access hours at Wakulla County beaches
After a lively debate, Wakulla County Commissioners voted to expand public access hours at all county beaches.
By Jacob Murphey
Published: Apr. 4, 2022 at 10:52 PM EDT
CRAWFORDVILLE, Fla. (WCTV) - After a lively debate, Wakulla County Commissioners voted to expand public access hours at all county beaches.
The new hours will be from 6 a.m. to midnight. The beaches were previously closed from sunset to sunrise.
The vote was split 3-2, with Commissioners Ralph Thomas and Quincee Messersmith voting against.
The move represented a second attempt in as many meetings to expand beach hours. Commissioner Thomas previously proposed 24/7 access to the beaches, but the motion failed to gain a second and died.
Monday's proposal came from Commissioner Mike Kemp, who said he felt it was a compromise after talking with many people on both sides of the issue.
"I talked to so many people who are for it in Shell Point, against it in Shell Point, 24/7, curfew, no curfew -- it's all over the place," he said during the debate.
Commissioner Thomas voted against the move, saying he felt the compromise was still too restricting on personal freedoms.
The debate has split the county's coastal communities.
Perry Morris has lived in Shell Point for 30 years. He's in favor of expanding the hours, arguing some of the area's most spectacular sights only come out at night.
"The dark sky you don't get in a Tallahassee setting, the beautiful stars. The Milky Way," he said.
But others worry about what a relaxed curfew might mean for their community. Anne Henson was one of several who spoke out against the move during the meeting.
"Having people down there, Wednesday night to 10, midnight, doing their music, whatever -- doesn't work for us," she said.
In the end, commissioners decided not to change the county ordinance, but instead simply direct the county's park director to change the hours. A future vote could just as easily modify the hours again.