HVAC Repair Services in Manhattan

I don’t know what is wrong with the stupid AC unit, but it is really making me angry that it won’t turn on. I haven’t used it yet this season, but today it got really hot out. Well, at least really hot compared to how it has been the rest of the year. But when I went to turn it on, it just wouldn’t do anything, not even try to turn on. I need to find HVAC services in Manhattan, NY so that I can get someone to come over and take a look at it in the near future. I am not really sure what to make of this situation, and could not even guess if it is a major problem, or something small.

Of course, I hope that it is just a minor thing that can be repaired quickly without costing me a lot of money. I don’t think the air conditioning unit in this house is very old. I know it was here when we moved in, but I think it has been replaced in the last 5-8 years. If so, it really doesn’t seem reasonable that it should break down already. I wonder if it still has some sort of warranty, because air conditioning units are not cheap, and if you get one installed, you should not have to worry about it breaking down for at least 10 years. At least, that is my opinion on the matter.

I need to look for good prices on this repair work too. I don’t know what the easiest way to find good prices on such services. But I guess I could just start calling a bunch of companies in the area, and see who will give me the beast price. But that seems like a lot of work.

It Was My Time to Get out on My Own for the First Time

Living with my parents for so long felt like a drag. I get along with them very well, but they need their privacy and I need mine. And not only that, I often stay out late, and I feel bad when I try to tiptoe in the house, but I learn that I have already accidentally woken my mom up because she heard my car pull into the driveway. I told her that there were a lot of Stockbridge apartments for rent that I was looking into. She was really sad about it.

I was surprised to hear from my dad, after hear learned from my mother that I was moving out. He called me from work, and that is not something he had ever done before. He wanted to know if I was upset with him and that was the reason that I was thinking of moving out. I assured him that was not the issue at all. Read More

Choosing Your First Apartment in Raleigh

Looking for your first apartment when you’ve just graduated from college or graduate school can be a big deal. Many times it is going to be important to have something that is low maintenance since your new job will take most of you time. It can also be important to search for a place with some on-site amenities to ensure that you don’t have to spend a lot of additional time doing some everyday things at other places. When looking for Raleigh NC apartments, keep these things in mind when you begin to look around at your best options.

It is best to sit down and think about the things you want most in an apartment. Some of those might be creature comforts like having a nice kitchen, but others might be more of a necessity, like needing to have a one or two bedroom apartment. When taking size into consideration, think about if you will be living alone or with someone. If there is ever a possibility of a roommate, make certain to aim for 2 bedroom and 2 bathroom apartments, as these will be the most appealing to roommates and also beneficial for you as the main renter since you won’t have to share your bathroom. Other essentials for many are having in-apartment laundry. Read More

Moving to an Apartment in Lewsville

I started looking at apartments in Lewisville not that long ago. I knew that I was going to live there after I graduate from college, and I wanted to have everything ready. I did not want to be left scrambling for a place to live after graduating. I had been saving the majority of my money for the last few years from working at the college bookstore, so I had plenty to put down a deposit and buy the furniture that I would need. Money wasn’t a problem, and the town wasn’t the problem. I just needed to find a nice place to live so I could start the next phase of my life.

I looked online since I am over 400 miles from Lewisville. I have already been promised a good job there, which is why I wanted to get everything ready so I could move there as soon as I graduate. That is the great thing about the Internet. Read More

There is Help for Anyone Struggling

So, I got into affiliate marketing years ago. Back then, I thought what could be simpler than getting paid for routing the people who visit my blog to other websites and getting paid for it? Well, the difference in how much you get paid can be enormous if you do not know what you are doing. And I was the person not getting paid much. That is, until I learned about who Ewen Chia is about six months ago. Now, I am making enough money to put into savings.

My parents always had financial struggles. I always told myself that I needed to try to do whatever I could to keep that happening in my own house with my own wife and kids. I’ve worked two jobs, worked many long hours above and beyond a simple 40 hours per week. We had tried cutting back to only the basics in order to have a little money left to save for retirement each year, but we kept falling short over and over again. I finally realized that my own parents were doing the best that they knew house. Read More

Regulation of OCT4 in mammalian ES cells

IntegrativeViewSmall

 

 

 

 

 

 

 

 

 

 

Gene regulatory networks provide insight into the mechanisms of differential gene expression at a system level. However, the methods for inference, functional analysis and visualization of regulatory modules and networks require the user to collect heterogeneous data from many sources using numerous bioinformatics tools. This makes the analysis expensive and time-consuming. In this work, the BiologicalNetworks application -the data integration and network visualization environment – was extended with tools for inference and analysis of gene regulatory modules and networks. The backend database of the application integrates public data on gene expression, pathways, transcription factor binding sites, gene and protein sequences, and functional annotations. Thus, all data essential for the analysis can be mined publicly. In addition, the user’s data can either be integrated in the database and become public, or kept private within the application. The capabilities to analyze multiple gene expression experiments are also provided. The generated network, regulatory modules and binding sites can be visualized and further analyzed within this same application.

The developed tools were applied to the OCT4 regulatory network in embryonic stem cells.

Query Language

Introduction

  1. Language Definition.
  2. Query examples.
  3. Querying biological networks.

Query language functionality is implemented as Query Window, from ‘Tools’ menu, where users can type and run their queries. Results of user’s queries are immediately visualized in the main window. Pull-down menu of the query window describes six different types of queries as examples.

To run these queries, start from applying annotations to your graph. To check what kind of annotations are applied to your nodes/edges just right click on them. If ‘Node Attribute Browser’ does not appear, you need to download yeast-context.jar. Save it on your computer and upload through ‘Plugins’->’Load plugins from Jar File’ submenu. Note, for your plugins to work, they should be loaded before opening your graphs.

Apply your ontology annotations: GO, KEGG,.. . Click on the Ontology icon of the menu and add levels you are going to search by your queries. In general case of querying you need to apply all ontology levels.

After all annotations and ontologies are added you can start querying by clicking on ‘Query Engine’ from ‘Tools’ menu.

Be aware of next:
The Query must EXACTLY satisfy the language syntax.
Be sure your terms are correctly spelled.
End your query with ‘;’ and spell ontology terms dividing words by “_” like: “DNA_binding”, “protein_serine/threonine_kinase”, “general_transcriptional_repressor”.
Note, queries searching through all GO tree (like descendants of highter ontology levels binding, enzyme, trancription_regulator) execute longer than simple queries (like children, parent, etc.).

Language Definition.

• //SELECTSELECTStatement
	::= SELECT ("P_UNION" | "P_INTERSECTION" | "P_DIFFERENCE") SELECTSELECT 
	::= "SELECT" ("DISTINCT")? SELECTList FROMClause [WHEREClause]SELECTList	
	::= SELECTItem(","SELECTItem)*SELECTItem::=
	(AggFunction"("ReturnType")")|(ReturnType)

•//FROMFROMClause
	::= "FROM" ( Database(sb) "." DAG(sb) ( "AS" (f = )
	()? )? )*Database::= DAG::=

•//WHEREWHEREClause
	::= "WHERE" SQLExpressionSQLExpression::=SQLPrimaryExpression(sb) 
	(","SQLPrimaryExpression(sb))*SQLPrimaryExpression::=
	((  "."  ) |  ) "="
	((  "."  ) | NodeFunctionExp | 
	("REG_EXP" "(\"" LinkType(sb) ) ) )

Query examples.

You can see the language syntax from the list of following examples:

1.a

SELECT 
	path_bindings(a)
FROM 
	dag(x,y,z) 
WHERE 
	a = idlist(1|2|3|999)
1.b

SELECT 
	path_bindings(a//b)
FROM 
	dag(x,y,z) 
WHERE 
	a = idlist(1|3),
	b = idList(16|)
2.a

SELECT 
	path_expression(a//b)
FROM 
	dag(x,y,z) 
WHERE 
	a = idlist(1|3),
	b = idList(16|)
2.b

SELECT 
	full_path_expression(a//b)
FROM 
	dag(x,y,z) 
WHERE 
	a = idlist(1|3),
	b = idList(16|)
3.a

SELECT 
	full_path_expression(a//b/c)
FROM 
	dag(x,y,z) 
WHERE 
	a = nameEquals(nervous_system) union nameEquals(pns), 
	b = idlist(31|57|), 
	c=idlist(22|21)
3.b

SELECT 
	path_bindings(a//b/c)
FROM 
	dag(x,y,z) 
WHERE 
	a = nameEquals(nervous_system) union nameEquals(pns), 
	b = idlist(31|57|), 
	c = idlist(22|21)
4

SELECT 
	full_path_expression(a//b/c//d)
FROM 
	dag(x,y,z) 
WHERE 
	a = idlist(1|) difference idList(2|), 
	b = idlist(48|), 
	c = idlist(5|31|67|3),
	d = idlist(12|)
5.a

SELECT 
	path_bindings(a//b, c//d)
FROM 
	dag(x,y,z) 
WHERE 
	a = nameEquals(nervous_system), 
	b = idList(19|20|), 
	c = idlist(46|),
	d = nameEquals(dendrite)
5.b

SELECT 
	path_expression(a//b), full_path_expression(c//d)
FROM 
	dag(x,y,z) 
WHERE 
	a = nameEquals(nervous_system), 
	b = idList(19|20|), 
	c = idlist(46|),
	d = nameEquals(dendrite)
5.c

SELECT 
	path_bindings(a{l1}//b{l2}//c//d{l3}/e//f, g//h{l4}//i), path_expression(j{l5}//k/l) 
FROM 
	dag(database,username,passwd) 
WHERE 
	a = idlist(12|13|14) intersect nameEquals(protein) union idList(4|), 
	l3= "isa", 
	b = idList(23|24|25)
6.

SELECT
	full_path_expression(a{l1}//b)
FROM 
	dag(x,y,z) 
WHERE 
	a = idlist(1|), 
	b = idlist(31|57|), 	
	l1 = "isa"	
7.

SELECT 
	path_expression(a{l1}//b{l2}/c)
FROM 
	dag(x,y,z) 
WHERE 
	a = idlist(1|), 
	b = idlist(49|50|48|52|), 
	c = idlist(5|31|67|3),
	l2 = "has"
8.

SELECT 
	full_path_expression(a{l1}//{l2}//{l3}b{l4}//d)
FROM 
	dag(x,y,z) 
WHERE 
	a = idlist(23),
    b = idList(49),
	d = idlist(12),
    l2 = "has"
    ,
    l3 = "has"
    l4 = "has"

Querying biological networks.

 

  • 1) SELECT
    	path(a)
    FROM
    	Yeast.go
    WHERE
    a = descendant(nameEquals$DNA_binding);
    

    Select a subnetwork of genes whose GO annotations fall under descendants of ‘DNA_binding’ term in GO hierarchy.

  • 2) SELECT
    	path(a)
    FROM
    	Yeast.go
    WHERE
    a = children (nameEquals$protein_kinase);
    

    Select a subnetwork of genes whose GO annotations fall under children of ‘protein_kinase’ term in GO hierarchy.

  • 3) SELECT
    	path(a)
    FROM
    	Yeast.go
    WHERE
    a = parent (nameEquals$acid_phosphatase);
    

    Select a subnetwork of genes whose GO annotations fall under parent of ‘acid_phosphatase’ term in GO hierarchy.

  • 4) SELECT
    	path_bindings(a//b)
    FROM
    	Yeast.go
    WHERE
    a = children (nameEquals$protein_kinase);
    b = parent (nameEquals$protein_threonine/tyrosine_kinase);
    

    Select a binding (union) of two subnetworks one of genes whose GO annotations fall under children of ‘protein_kinase’ term in GO hierarchy and another whose GO annotations fall under parent of ‘protein_threonine/tyrosine_kinase’ term in GO hierarchy .

  • 5) “Find colocalized proteins grouped by location”.
    SELECT p.location, set(p)
    FROM yeastGraphDB G(N, E)
    WHERE p:N and p.type = ’protein’
    GROUP BY p.location;
    

    Here, the from clause refers to a graph. Further, thanks to the grouping condition, the output is a nested relation instead of a graph, where due to the inner structuring element set, this query produces a set of tuples (genepairs) for every binding of location.

  • 6) In the next query, we use graph operations in the body of the query, and the return data type is a graph. “Find networks of colocalized proteins that are parts of some protein complex and are connected by either a 2-hybrid (y2h) edge or a comimmunoprecipitation (coIP) edge.”
    SELECT graph(N2(n.name, n.source), E2(e.label,e.source))
    FROM yeastGraphDB G1(N, E)
    WHERE n:N and c:N and e:E
    and n.type << ’protein’
    and c.type = ’protein complex’
    and (e.label = ’y2h’ or e.label = ’coIP’)
    and pathExpr(G1, c//[member of]n) = true
    

    The query declares a variable c whose type is protein complex.
    The query returns a graph, whose nodes n should be tuples with the attributes name and source (i.e., data source), and whose edges e should have a label and a source from which that edge was known.
    Recall that the system will convert this to a query on a connector node.
    The << operation specifies that the type of the node should be “under” protein in the node type hierarchy
    The last line should be read as “n has an edge whose label has the value member, and this edge points to c”, where c is declared before. Note that we did not mention the relationship between nodes n and edges e, namely, an instance of the returned edge set e connects instances of the returned node set n. This constraint, expressed as n.edge = e, is implied by the construct of line 2, where n and e are constrained to be parts of the same graph.

  • 7) Finally, we present an example from systems biological analysis that uses the graph-theoretic attributes. In this example, we take two subnetworks b1 and b2 produced by two subqueries, each using an aggregate graph function. For each network, the query computes the distribution of the betweenness centrality of the nodes of the respective graphs, and then uses the F-test to compare them.
    WITH b1 AS (
    SELECT distribution(betweenness centrality(*,0.05))
    FROM yeastGraphDB G1(N, E)
    WHERE n:N and e:E and
    n.source IN (’Gavin-DB’, ’Ito-DB’, ’Tong-DB’)
    and n.degree() > 2),
    b2 AS (...)
    SELECT F-test(b1, b2)
    FROM b1, b2, stat-source;
    

    Due to a heavy use of statistical operations, a number of statistical operations have been packaged in a source called stat-source. The function between-centrality produces a bag of values corresponding to the betweenness centrality of all nodes satisfying the remaining constraints. The function distribution takes a set of values and a bucket size and outputs a histogram, which is known to the system as a basic statistical data type defined as a table of 2-tuples {category, count} – here the category comes from the number of distinct values of the centrality measure with a bucket size of 0.05.

 

Canonical Pathways Collection

Pathway diagrams from PubMed and World Wide Web contain valuable highly curated information difficult to reach without specialized tools. There is currently no search engine or tool that can analyse pathway images, extract their pathway components (genes, proteins, cells, organs, etc.), and indicate their relationships.

We present a resource of pathway diagrams retrieved from article and web-page images through optical character recognition (OCR), in conjunction with data-mining and data integration methods. The recognized pathways are integrated into the BiologicalNetworks research environment linking them to a wealth of data available in the IntegromeDB knowledgebase, which integrates data from >100 public data sources and the biomedical literature. Multiple search and analytical tools are available that allow the recognized cellular pathways, molecular networks and cell/tissue/organ diagrams to be studied in the context of integrated knowledge, experimental data and the literature.

We scanned a collection of >150 journals, 50,000 articles, and 150,000 figures (new articles are downloaded daily) available in PubMed Central and World Wide Web. The downloaded figures are stored on a remote server and the Lucene open-source search engine is used to index, retrieve, and rank the image text descriptions (using the default statistical ranking). In case of publication, the image description is the image legend, whereas in the case of a web page, the specifically designed algorithm retrieves the most appropriate description from the web page text surrounding the image. Image publication date and source journal are stored as separate fields that can also be used to sort the results. The constantly growing ‘Imaging Pathways’ repository currently contains 1,025 pathways, which is more than in any existing public repositories, e.g. BioCarta contains 354 and the KEGG contains 345 reference pathways. Taking into account that BiologicalNetworks’ back-end database IntegromeDB integrates Reactome, KEGG, BioCarta, NCI-Nature pathways, WikiPathways and HumanCyc this makes the BiologicalNetworks the richest compendium of currently available pathways.

BiologicalNetworks Software

BiologicalNetworks is a Systems Biology software platform for biological pathways analysis, querying and visualization of gene regulation and protein interaction networks, metabolic and signaling pathways. It is equipped with filtering and visualization tools, to provide high quality, easily understood scientific presentation of your pathway analysis results.

The system includes a general-purpose scalable warehouse of biological information, which integrates over 20 curated and publicly contributed data sources, biological experimental and PubMed data for the 8 representative genomes (S. cerevisiae, D.melanogaster, etc.). BiologicalNetworks is also supported with curated pathways from a number of public databases like KEGG and different scientific studies.

BiologicalNetworks identifies relationships among genes, proteins, small molecules and other cellular objects, and draws pathways, linked to the original sources of information.

The system is equipped with enhanced graph manipulation and a query language, data mining and filtering tools, a storage mechanism and a generic data-importing mechanism through schema-mapping.

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Pathways Analysis

  • Import and export your data
  • Create, save, edit your pathways and produce high quality diagrams for your publications
  • Meta-Network (network in which a node, Meta-Node, itself has its internal network structure): multi-scale visualization of bio-networks, ideal for network of functional modules.
  • Find relationships among genes/proteins, cell processes and other objects
  • Optimize the view by filtering, pathway expansion, and protein classification.
  • Perform graphic drawing and layout optimization

Navigation and Tools

  • Build Molecular Interaction Networks for gene lists imported from microarray and other experiments
  • Specify and visualize upstream and downstream events
  • Find a path between two or several molecules
  • Detect common targets or/and regulators for a group of proteins.

Pathways and Interaction Networks from public Databases and Literature

  • Browse pathway database system compiled from over 20 databases
  • Access more than 140,000 facts of regulation, interaction and modification
  • Get the original sentence or paper abstract to validate the facts of interaction and biological phenomenon.

BiologicalNetworks pathways analysis software supports Windows® 2000 and XP, Linux and Macintosh operating systems.

 

Yeast meiosis

DEMO_MeiosisPPNet

Diploid cells of Saccharomyces sporulate when starved of nitrogen and fermentable carbon source. These nutritional signals converge on to the upstream regulatory region of IME1 gene. Ime1p interacts with Ume6p, a general repressor of sporulation-specific genes during mitotic growth and the complex activates the transcription of IME2 and other early meiotic genes unleashing a cascade of gene regulatory events that lead to initiation of pre-meiotic DNA replication, meiotic recombination, meiotic divisions and subsequently spore maturation.

A gene list comprising of approximately 1800 genes identified by microarray expression profiling studies to be involved in early sporulation and additional genes shown to have a sporulation phenotype upon deletion was used to derive a putative network underlying the poorly understood developmental pathway of sporulation. The resulting network with 1461 genes and 5247 interactions can be further filtered using querying utilities to selectively view the DNA-protein interactions or protein-protein interactions, various known functional modules operating during sporulation such as DNA replication/recombination/repair, Peptidolysis and proteolysis, Cell cycle regulation, regulation of transcription from Pol II promoter, ribosome complexes, etc.

GO Biological Process annotations show that several of tanscription factors are involved in regulation of glucose repression, starvation response to various nutrients, physiological stress response and cellular processes such as autophagy. This information can further be used to create hypothesis-based wet-lab experiments involving deletion of the specific binding site for each of these transcriptional regulators and monitoring the effect on IME1 transcription in response to the corresponding environmental condition.