text:
You can learn the basics of uploading images by reading through our image help page . But aside from the technical aspects of adding pictures, there are several factors that affect when, how, and if you should do so.
The Image Itself
The maximum size for an uploaded image is 10 MB, although you should generally avoid adding pictures that large in most cases, as it can affect page load times. The best idea is to upload the image at the largest size possible and with a sufficient quality level that it doesn't appear pixelated or fuzzy. It is possible to resize the image after you add it, so a larger resolution gives more room to play with.
You should think about how best to maximize the image's potential to illuminate the subject of the article you're adding it to. You can use photo editing software to crop, resize, or otherwise tweak an image before you upload it so that the subject of the picture is front and center.
The image should be relevant to the subject of the articles and the wikia and definitely not obscene, pornographic, abusive, offensive, or profane. Images submitted to Wikia are subject to a staff review to make sure they don't go too far.
Think About the License
Wikia's basic rule is that any content you contribute has to comply with our Terms of Use . That means that it must not violate copyright law. To help figure out if the photo you want to add qualifies, ask yourself if it falls into any one of the following:
- Your use of the material will be likely to qualify as a fair use or similar exception.
- You have the permission of the copyright holder.
- The material you want to use is licensed under a Creative Commons or other open license that permits free reuse.
- The material is in the public domain.
- You have created the material yourself.
If it does, you may proceed. If it doesn't, you should rethink using that particular image.
Images from the public domain or released under a free license make the best choice because they have the fewest restrictions. There are several tools online that let you search specifically for images released under the compatible Creatice Commons license.
That said, it is certainly possible to use images that don't fall into either category. Most uses of screencaps, promotional images, or cover art/illustration from a game, movie, or TV show would be likely to qualify as a fair use under U.S. law.
Unlike the text you add, images that contribute to your community are not automatically released under the CC-BY-SA license. They retain whatever copyright protected them before you added them to the wikia. During the upload process, you can add image copyright tags that specify the licensing information for that image. You should choose the correct option that best describes the copyright status of the picture.
Images released under the CC-BY-SA (or similar) license have an attribution requirement. That means that you must include information about the photographer or artist who created it on the image description page. If the license calls for attribution and you fail to do so, you are infringing the owner's copyright.
Even if the image doesn't require attribution as a condition of the license, it is still good etiquette to provide as much information as you can about the creator of the image and the location where you found it. Listing the source can help others verify and learn about the content.
Pay Attention to Local Image Policies
When adding an image, you need to make sure that it complies with all the local rules for picture contribution. Everything we have talked about so far has reflected Wikia's global policies regarding image use. Communities are free (and encouraged) to refine and add to these basic guidelines and many communities have established their own image use policies. It's a good idea to periodically review these guidelines to make sure they are sane and responsible and reflect the community's desire to increase the overall quality of of their articles.
Some communities explicitly forbid certain kinds of images like leaked prototypes or character portraits, while still others allow images only on certain pre-defined pages. They might also have penalties for duplicate images, specific requirements for naming the files, or minimum quality requirements. These are all great things for communities to consider when they are crafting their image policies.
Images can be an invaluable way to make your wikia stand out. They liven up article pages and can help illuminate or expand on the subject of the article in simple visual terms that might take entire paragraphs to explain. I hope this discussion of image use best practices had been helpful. What are some other things to keep in mind? Do you have some great ideas from your home wikia for the best way to govern images? Please share in the comments.


text:
Dedicate time to your health
ias PREVENT is a two day comprehensive medical examination that will provide you with a detailed understanding of your current state of health.
An ias PREVENT programme in the relaxed surroundings of Brenners Park-Hotel & Spa is an extremely comfortable approach, and ensures you dedicate sufficient time to your personal health.
The dedicated care provided by your personal ias PREVENT doctor creates an invaluable atmosphere of confidence. They will consider your lifestyle; the implications of stress and diet, and will conduct cardiology and nutritional tests amongst others.

subset: commoncrawl


text:
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text:
There is no doubt that succulents are trending as the most popular plant this season. So much so that I've heard shoppers say things like, "Oh, these are those plants I read about in the Washington Post." and "Succulents! I heard about these on the radio!" This is great, since when are plants making headlines and getting talked about on the radio.
This is so exciting to me as a long time plant enthusiast, succulents included. What excites me even more is the amount of young people (teenagers) that are excited to buy and learn about succulents. Then I got to thinking.... could succulents be the gateway plant for future gardeners? I'm thinking they will be. We should start thinking of catchy generational name to dub this group awhile. Maybe the Succulent Starter Gardeners or the Succulent Boomers. Well the name needs a little work but I think we have time before all these youngsters grow up into avid gardeners.
For many young people, I would imagine their succulent purchase is their first plant or one of their first plants. First plant experiences are so important to the future of plant in your life. It will make or break your green thumb. My first plant was Pilea mollis 'Moon Valley' or as I called it 20 years ago, Moon Crater Plant. It sat on a stand in my bedroom right under the window sill and right next to my Backstreet Boys poster. To this day I still have a Moon Crater Plant, although it is not the same one.
In 10-20 years this young group of Succulent Stewards (eh, still need to work on that name) will look back on their succulents that had as kids and realize that is what got them into gardening. At least that is my envision. Will they have that eureka moment and go buy their kids one of the then trending plants? What will be the trending plant in 10 -20 years? I guess we will all have to wait and see.


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The Consequences of Texting While Driving
We all have distractions while driving on the road. Pedestrians popping out of nowhere, passengers asking us questions, the radio blaring our favorite tunes, and, of course, other aggressive drivers to avoid are just a few of them. Thanks to modern technology, our cell phones are no exception.
For some mysterious reason, we feel if our cellys go off, we must immediately answer them. From answering a call to texting, we believe that communicating on the phone is imperative. Yet thinking we can answer 'just this once' or 'right quick' may lead to an unexpected catastrophic event.
H2. Defining the Text Laws
The phrase 'texting while driving' legally covers a number of functions involving your cell phone that are carried out during your drive in a moving vehicle. They include:
If any of these are violated, it is considered a moving traffic violation. In certain jurisdictions, it is treated as a criminal misdemeanor. The violation's legal clause and weight varies from state to state.
In 41 different states, along with DC, there are bans on all forms of texting, for any type of driver. For instance, some states only considered it a legal matter when there are persons in the vehicle under 25. Of the 50 states in America, only 33 consider this law a priority for enforcement. This means that police officers can stop anyone who is texting while operating a motor vehicle. For 4 states, the enforcement of this law is secondary, meaning a cop could cite the offending driver for texting, but only if he or she is pulled over for a different violation.
The only 4 states that are without any restrictions on texting while on driving are:
H2. Why Texting on the Road is Bad
Besides the various results that can occur from simultaneously driving and texting on the road, the main reason it's not good to do is because it takes the driver's focus off of the road and onto his cell phone. It distracts him from concentrating on the road and all its conditions that he needs milliseconds to analyze in order to drive well. Being on the phone, for whatever reason, is just not safe for him or his nearby drivers. It impairs his ability to avoid disaster. Statistics have proven that texting while on the road can increase the risk of accident or crashes up to 23 times more than normal. In order to protect those on the road, these laws, meant to prevent or deter this, should be followed and all communication on the phone avoided, unless extreme cases of emergencies.
H2. Main Punishment and Other Consequences
If a driver is found breaking their state laws on texting while driving, his punishment could range from minimal to serious. The actual sentence one can receive will depend on the state the crime was committed, how severe the outcome was, and what the judge decides for his situation. If someone repeats the offense, the punishment also gets more serious.
Punishment can be a combination of the following:
If a driver is working in a commercial or educational business, when they are committing this crime, the fines become a bit steeper. Commercial trucks or school and city buses can be fined over $2000 for violations in some jurisdictions.
H2. What to do if Involved
There are some steps to take if you are caught texting while driving. If you are the offending driver, the best course of action is to find a lawyer for assistance with the charge. It has become a serious crime and you will need to have a professional help you with your case.
If you happen to be the one that was affected by an offending driver, you too, should seek an attorney, but one that deals with personal injury. Your compensation could be one of these three decisions:
1. Compensatory damage awards. This means you are due a monetary recompense. The convicted driver will have to pay you for your trouble, including medical bills and emotional suffering.
Being informed with the enormity of a seemingly insignificant thing as texting on the road, there is only one choice for a driver: don't do it!
What Are You Studying For?


text:
Question about Refrigerators
Posted by charlie1985 on
I had a similar problem several months ago.
1) Top freezer compartment is getting TOO cold. The ice gets so thick, it blocks the channels of cool air from the freezer down to the fridge compartment (hence fridge too warm)
SOLUTION: Turn fridge/freezer off to fully defrost, then turn back on.
NB: This might only be a temporary fix, and you will find in a few months time the same thing happens?
2) In my case, I required a replacement of the "controller" board which fixed the defrosting problem of my freezer compartment.
Good luck.
Posted on Oct 27, 2008
SOURCE: I have freestanding Series 8 dishwasher. Lately during the filling cycle water hammer is occurring. How can this be resolved
a 6ya expert can help you resolve that issue over the phone in a minute or two.
best thing about this new service is that you are never placed on hold and get to talk to real repairmen in the US.
the service is completely free and covers almost anything you can think of (from cars to computers, handyman, and even drones).
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Posted on Jan 02, 2017
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Data from: Oxidative stress and the evolution of sex differences in lifespan and ageing in the decorated cricket, Gryllodes sigillatus
Date Published: August 27, 2012
DOI: http://dx.doi.org/10.5061/dryad.cs16k
Content in the Dryad Digital Repository is offered "as is." By downloading files, you agree to the Dryad Terms of Service. To the extent possible under law, the authors have waived all copyright and related or neighboring rights to this data.
|18 times
|There is a key in the first sheet of the spreadsheet to explain column names. The first sheet in the spreadsheet also contains data for inbred animals while the second sheet contains reference data on outbred USA animals (not included in the statistical analyses). The spreadsheet contains information on the sex, age and origin of the animal (i.e. its inbred line). The sheets also show absolute protein and TAC and PC per unit protein. If you want any other information, please do not hesitate to contact me. Many thanks, Ruth Archer
|Data_DryadAnnotated_Archer.xls (62.46 Kb)
|View File Details
When using this data, please cite the original publication:
Additionally, please cite the Dryad data package:
We encourage organizations to:


text:
University of Texas at Austin, Department of Chemistry, 1 University Station A5300, Austin, Texas 78712
Central Glass Co. Ltd., Chemical Research Center, 2-17-5, Nakadai, Kawagoe, Saitama 350-1159, Japan
University of Texas at Austin, Department of Chemistry and Chemical Engineering, 1 University Station A5300, Austin, Texas 78712
One of the major concerns with nanoimprint lithography is defectivity. One source of process-specific defects is associated with template separation failure. The addition of fluorinated surfactants to the imprint resist is an effective way to improve separation and template lifetime. This study focuses on the development of new reactive fluorinated additives, which function as surfactants and also have the ability to chemically modify the template surface during the imprint process and thereby sustain a low surface energy release layer on the template. Material screening indicated that the silazane functional group is well suited for this role. The new reactive surfactant, di-(3,3,4,4,5,5,6,6,7,7,8,8,8-tridecafluorooctyl)silazane (F-silazane) was synthesized and tested for this purpose. The material has sufficient reactivity to functionalize the template surface and acceptable stability (and thus shelf-life) in the imprint formulation. Addition of F-silazane to a standard imprint resist formulation significantly improved template release performance and allowed for significantly longer continuous imprinting than the control formulation. A multiple-imprint study using an Imprio® 100 tool confirmed the effectiveness of this new additive.
Step and flash imprint lithography (S-FIL) has many advantages over traditional optical lithography in terms of procedural simplicity,1,2 low cost,3,4 and high-fidelity pattern transfer5–7 as well as the ability to directly pattern multilevel structures.8–10 However, effective use of S-FIL requires management of the template separation process (Fig. 1).
Three possible outcomes of template separation.
The materials and the interfaces must be engineered so that the cured resist material separates from the imprint template while maintaining tenacious adhesion to the underlying substrate. Adhesive failure at the substrate interface is obviously unacceptable. Cohesive failure of the cured polymer can also occur, which results in significant patterning defects. Both adhesive and cohesive failure must be controlled. The common method for minimizing such defects involves the use of surface treatments as shown in Fig. 2. An adhesion promoter is applied to the substrate prior to dispensing the imprint fluid. A variety of adhesion promoters has been developed to aid in adhesion of resist to oxide and other surfaces commonly encountered in microelectronic device manufacturing. The template is typically treated with a release layer such as fluorinated self assembly monolayer (F-SAM).
Current method to improve separation.
The classical adhesion promoter layers work effectively in most cases as each area of the substrate is only imprinted once during the patterning process. Considerably, more is required of the release layer as the template must perform numerous imprints without interruption. Covalent surface treatments such as perfluoroalkylchlorosilanes11,12 slowly degrade during imprinting, leading to separation failure (Fig. 3). Several mechanisms for this degradation have been proposed, but a definitive mechanism is not currently agreed upon.13–16
Fluorinated self assembly monolayer (F-SAM) treatment and degradation during continuous imprinting.
Addition of surfactant to the imprint formulation is one method to limit separation failure. We recently reported the behavior of the fluorinated surfactant shown in Fig. 4.17 X-ray photoelectron spectroscopy (XPS) studies indicated that the surfactant migrates to the interface of the imprint fluid and template, presumably because of the low surface energy of the F-SAM treated template and similar polarity. This reduces adhesion to the template,17 thereby reducing the likelihood of cohesive failure.
Addition of surfactant reduces separation defects.
With an intact monolayer, migration continues over multiple imprints. However, if the surface treatment degrades, the template becomes sufficiently hydrophilic to disrupt this surfactant migration process and defects begin to occur. As such, the use of a conventional surfactant does not represent a permanent solution to separation failure. In this study, a reactive fluorinated surfactant was added to the imprint formulation. The material was designed to serve both as a surfactant and reagent that functionalizes any silanol sites that are created during imprinting.18
Templates were initially treated with F-SAM to form the nominal release layer as shown in Fig. 5. Additionally, a small amount of reactive surfactant was added to the imprint fluid. During the imprint process, this material is believed to react with exposed areas of the template surface, replenishing the fluorinated monolayer. This allows for continuous imprinting as the requisite hydrophobicity is maintained without periodic cleaning and treatment of the template.
Replenishing the release layer.
There are three requirements that the surfactant must meet in order to be effective. First, it must readily associate or react with the hydrophilic template surface. Second, it must be unreactive toward other components of the imprint formulation. Third, the structure must produce a surface energy similar to that generated by F-SAM treatment.
Several functional groups were auditioned to find a suitable reagent. Figure 6 lists the five candidates: a chlorosilane (Gelest Inc., Morrisville, PA) that is commonly used to form the F-SAM template release layer, N,N-dimethylaminopentamethyldisilane (DMAS) obtained from Gelest Inc., Morrisville, PA, hexamethyldisilazane (HMDS) (Acros Organics, Geel, Belgium), a reagent commonly used to treat surfaces in the semiconductor industry, and alkyl iodides IO and F-IO, which were obtained from Aldrich, St. Louis, MO.
Materials used in initial screening.
The effect on template surface energy was investigated as illustrated in Fig. 7. Glass plates were cut into 1×1 in. squares and cleaned with piranha (H2SO4: 30% H2O2=2:1) for 30 min, which generated a hydrophilic surface. The plates were immersed in 20 wt. % toluene solutions of the respective surfactants for 100 min. After rinsing the plates with toluene and isopropyl alcohol, the water contact angles were measured by goniometry (ramé-hart NRL model 100-00, ramé-hart Inc., USA). Measurements were taken at five different points on the plates and averaged.
Procedure for surface energy experiments.
The results are listed in Fig. 8. The control sample was only exposed to piranha solution. Without additional treatment, the surface was extremely hydrophilic, with a water contact angle of 11.2 deg. F-SAM gave the highest contact angle (106.7 deg). Both materials having Si-N bonds (DMAS and HMDS) showed high values (93.1 and 91.2 deg, respectively), whereas the alkyl iodides (IO and F-IO) generated much lower values (57.9 and 49.0 deg). F-SAM, DMAS, and HMDS appear to meet the surface energy criteria.
Effect on surface energy.
The shelf life of formulations containing the candidate materials was evaluated by gas chromatography-mass spectrometry (GC-MS), Agilent Technologies 6890 N equipped with an Agilent HP-5MS capillary column. Formulations consisting of 33 wt. % of isobornyl acrylate, 33 wt. % n-butyl acrylate, 17 wt. % of ethylene glycol diacrylate and 17 wt. % of one of the three additives (F-SAM, DMAS, or HMDS) were tested as a function of time. Aliquots were taken for 38, 86 and 168 h after addition of the additives and their concentrations were compared to those of the initial formulations (Fig. 9). Xylene was used as an internal standard in the gas chromatographic analysis.
Formulations for stability experiments.
The chlorosilane degraded rapidly, falling to 65% after 168 h. The solution also became turbid upon addition of F-SAM, even though the sample was prepared under nitrogen. The GC-MS data for the DMAS and HMDS formulations suggest considerably greater stability than the F-SAM (Fig. 10). Both solutions remained clear and colorless after 168 h. Based on these results, it was concluded that materials such as DMAS and HMDS show potential for use as reactive surfactants.
Stability in imprint formulation.
F-silazane (Fig. 11) was donated by Central Glass Co. Ltd., Japan. F-silazane is a colorless liquid that readily mixes with imprint formulations. The material is a reactive surfactant.
Chemical structure of F-silazane.
F-silazane was tested in the same manner as previous materials. The water contact angle was 100 deg, a value comparable to F-SAM (Fig. 12). No degradation was apparent by GC-MS, and the sample solution remained clear (Fig. 13).
Effect on surface energy of F-silazane.
Stability of F-silazane in imprint formulation.
A multiple imprint study was carried out to evaluate the performance of F-silazane as a reactive surfactant. Imprinting was performed with an Imprio 100® installed at The University of Texas at Austin using unpatterned templates (25×25 mm2 mesa). The substrates were 8 in. silicon wafers coated with BT20 adhesion promoter (Brewer Science, Rolla, MO). The templates were cleaned and treated with F-SAM to form the initial release layer prior to imprinting. Each wafer was imprinted ∼16 times and then the water contact angle was measured at five different points on the template surface and the values were averaged. A new substrate was prepared and imprinting was then resumed without cleaning the template. This was repeated for a total of 100 imprints. Table 1 shows the two formulations used in this study. One is a common imprint fluid, whereas the other contains 5 wt. % F-silazane.
As shown in Fig. 14, imprinting with the conventional formulation leads to rapid degradation of the surface treatment; the water contact angle decreased significantly after patterning a single substrate. Due to severity of the defects, imprinting with this formulation was discontinued after 81 imprints. When the formulation that included the F-silazane, the template surface energy remained essentially constant even after 100 imprints. No separation failure was observed.
Results of the multiple imprint study.
These results suggest that F-silazane allows for longer template life. Further analysis of the template surface and cured imprint material is necessary to better understand the release mechanism. XPS or electron spectroscopy for chemical analysis is two different methods that can be used to study the migration of a surface treatment. In the multiple imprint study, a nonpatterned template was selected to measure the water contact angle on the template after a set number of imprints. An imprint study using a patterned template also needs to be carried out. Thousands of imprints are necessary to fully investigate the effectiveness of F-silazane for industrial applications.
This study focused on the development of reactive fluorinated surfactants for use in imprint lithography. Initial experiments suggest that materials such as silylamines and silazanes are sufficiently reactive toward the template surface to replenish the degraded monolayer, yet stable in the imprint formulation. One of the materials tested, a perfluoroalkyl silazane (F-silazane) showed an effective combination of reactivity and stability. Multiple imprints were carried out with F-silazane on a commercial imprint tool (Imprio® 100). When the imprint fluid contained F-silazane, it was imprinted 100 times without any significant defects, whereas the conventional formulation began to suffer from increasing surface energy and ultimately suffered separation failure after 81 imprints. We conclude that F-silazane shows great potential for the use as a reactive surfactant in imprint lithography that could reduce defects and extend template life. Further study of this material is clearly warranted.
The authors would like to thank Dr. Marylene Palard, Mr. Gabriel Glenn and Mr. Ricado Garcia for assistance with the S-FIL experiments. The authors would also like to thank Dr. Kazuhiko Maeda, Dr. Koichi Miyauchi and Mr. Kenji Hosoi, Central Glass Co., Ltd. for the donation of the fluorinated silazane and their support of our work.
Biographies and photographs of authors are not available.
Tsuyoshi Ogawa ; Daniel J. Hellebusch ; Michael W. Lin ; B. Michael Jacobsson ; William Bell, et al."Reactive fluorinated surfactant for step and flash imprint lithography", J. Micro/Nanolith. MEMS MOEMS. 12(3), 031114 (Sep 24, 2013). ; http://dx.doi.org/10.1117/1.JMM.12.3.031114
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Incorporating functional inter-relationships into protein function prediction algorithms
Functional classification schemes (e.g. the Gene Ontology) that serve as the basis for annotation efforts in several organisms are often the source of gold standard information for computational efforts at supervised protein function prediction. While successful function prediction algorithms have been developed, few previous efforts have utilized more than the protein-to-functional class label information provided by such knowledge bases. For instance, the Gene Ontology not only captures protein annotations to a set of functional classes, but it also arranges these classes in a DAG-based hierarchy that captures rich inter-relationships between different classes. These inter-relationships present both opportunities, such as the potential for additional training examples for small classes from larger related classes, and challenges, such as a harder to learn distinction between similar GO terms, for standard classification-based approaches.
We propose a method to enhance the performance of classification-based protein function prediction algorithms by addressing the issue of using these interrelationships between functional classes constituting functional classification schemes. Using a standard measure for evaluating the semantic similarity between nodes in an ontology, we quantify and incorporate these inter-relationships into the
We implemented and evaluated a methodology for incorporating interrelationships between functional classes into a standard classification-based protein function prediction algorithm. Our results show that this incorporation can help improve the accuracy of such algorithms, and help uncover novel biology in the form of previously unknown functional annotations. The complete source code, a sample data set and the additional files for this paper are available free of charge for non-commercial use at
A variety of recently available high throughput data sets, such as protein-protein interaction networks, microarray data and genome sequences, offer important insights into the mechanisms leading to the accomplishment of a protein's function. However, the complexity of analyzing these data sets manually has motivated the development of numerous computational approaches for predicting protein function
One of the most popular methods used for predicting protein function from biological data is classification
Standard classification or predictive modeling techniques for function prediction rely on positive and negative examples from functional classification schemes, such as the Gene Ontology
The second important issue that arises is that it is often hard to construct reliable classification models for several functional classes from a given data set due to complex issues including noise in the data, low relevance of the data set for some functional classes, and an insufficient number of training examples for building accurate classification models
Figure 1
Distribution of the number of nearest neighbors of the same class in a neighborhood of size 20 for the member proteins of two functional classes of varying sizes in Mnaimneh
Distribution of the number of nearest neighbors of the same class in a neighborhood of size 20 for the member proteins of two functional classes of varying sizes in Mnaimneh
However, the availability of the same well-defined structure of relationships between functional classes in the form of Gene Ontology raises the following key question: "Can the performance of standard classification algorithms for function prediction be improved by incorporating these inter-relationships into them?". In this paper, we address this question using an approach shown visually in Figure
Figure 2
Conceptual visualization of our approach for incorporating functional inter-relationships into function predictions algorithms, and empirical evidence to show the abundance of proteins carrying labels
Conceptual visualization of our approach for incorporating functional inter-relationships into function predictions algorithms, and empirical evidence to show the abundance of proteins carrying labels
More specifically, using Lin's measure
Note that since the rest of the discussion in this paper will be concerned with classification within the context of GO, the terms (functional)
Related Work
Recently, some approaches have been proposed to address the problem of incorporating inter-relationships between functional classes in GO into function prediction algorithms. These approaches can be categorized using the following two types of relationships between classes constituting the DAG-based functional hierarchies in GO:
Parent-child relationships
The basic structure of the ontologies in GO is constructed from edges between parent and children terms. Some approaches have recently been proposed for enforcing the consistency required by these relationships, namely a protein annotated with a child node must be annotated with the parent node, into function prediction algorithms. Barutcuoglu
Sibling and other distant relationships
An effect of the structure of the ontologies in GO is the formation of sibling relationships between nodes that are children of the same parent. These relationships can be further generalized to extended family relationships, such as cousin and other more distant relationships. King
The incorporation of both these types of relationships is important for making use of the information available in the entire hierarchy. One of the advantages of the direct incorporation of distant functional relationships, which is the focus of the latter set of studies, is that it is possible to incorporate information from nodes farther away in the hierarchy, as compared to the hierarchical incorporation approaches, which only utilize the subgraph of the hierarchy corresponding to the set of target classes. Our work provides a framework for incorporating these distant functional inter-relationships into standard function prediction algorithms. Notably, this task is more challenging than the hierarchical consistency enforcement problem since there is a much larger number of relationships between nodes to be considered than just the parent-child relationships, which are relatively fewer. This factor makes the incorporation of non-hierarchical relationships more challenging. Furthermore, as discussed above, we perform this augmentation using the biological characteristics of proteins captured in high-throughput genomic data, thus addressing one of the limitations of King
Our work is also related to the field of hierarchical and multi-label classification in machine learning and data mining
Semantic Similarity in an Ontology
In GO, nodes (classes or labels) are connected to other nodes through parent-child edges, which impose hierarchical inter-relationships between them. Also, the nodes contain member proteins that have been annotated with the corresponding functional class. Thus, it is possible to compute the similarity between two GO nodes, referred to as
An example of a label similarity matrix computed for the set of functional classes used in this study is shown in Figure
Figure 3
Original and filtered class/label similarity matrices (labels × labels) generated using the labels of genes in one of the training sets from Mnaimneh
Original and filtered class/label similarity matrices (labels × labels) generated using the labels of genes in one of the training sets from Mnaimneh
k-Nearest Neighbor Classifier
One of the most intuitive classification algorithms is the
We chose
Proposed Approach
Modified classification algorithm
It can be observed from Equation 2 that
Equation 3 represents a direct extension of the model described in Equation 2, where, in addition to the label being tested (
Equation 4 makes the implementation of our approach much easier.
Filtering of label similarities
The label similarity matrix contains a value (however small or large) for each pair of labels. Many of these similarities, especially the smaller ones, are likely to be uninformative, since all the labels (functional classes here) are not expected to interact with all the others, particularly in a large diverse set of labels. Indeed, we observed a significant deterioration in the performance of the label similarity-incorporated classifiers when the raw label similarity matrix is used for incorporating inter-class relationships. In order to handle this issue, we used the following heuristic approach for filtering or sparsifying the label similarity matrix. For each label, we determined a filtering threshold using a cross-validation procedure. This threshold was determined by running a grid search over the interval [0, 1] in steps of 0.05. For each such threshold
Note that such a cross-validation-based filtering procedure is expected to produce a more informative version of the original label similarity matrix that is expected to perform well for the unseen test set. However, one issue to consider for this filtering approach is that of consistency between the thresholds computed for the different labels using different training sets. We observed that this issue did not affect our results significantly, but should be considered when applying this approach to other data sets or problems.
Results and discussion
In this section, we compare the functional relationship-incorporated kNN classifiers with the base kNN classifiers for protein function prediction. This evaluation is conducted using both a cross-validation methodology, as well as a quantitative and qualitative evaluation of the predictions made by these classifiers for independent sets of test proteins. However, before presenting these results, we detail the data sets and the experimental methodology used for these evaluations.
Data Sets
We used several high-throughput data sets for evaluating our approach. The first was Mnaimneh
In addition to these data sets, the structure of the GO biological process ontology, and the GO annotations for
Additional file 1
Details of the GO classes used for evaluation. Details of the 138 functional classes from the GO Biological Process ontology whose subsets (classes having at least 10 members in the corresponding data set) are used for evaluation using several genomic data sets in this study.
Click here for file
Table 1
Details of the data sets used for evaluating the label similarity-incorporated classifiers using a cross-validation methodology.
# Proteins
# Features
# Classes
Experimental Methodology
Our overall experimental methodology is shown in Figure
Figure 4
Overall methodology for predicting the functional annotations of a test protein using the label similarity-incorporated k-NN algorithm
Overall methodology for predicting the functional annotations of a test protein using the label similarity-incorporated k-NN algorithm. This methodology is used for our cross-validation experiments, as well as making predictions for test proteins not annotated with any of the target functional classes.
Computation of label similarity matrix
The first step of our experimental procedure is the construction of the similarity matrix between the labels, or the inter-relationship matrix between the corresponding classes, for each of the above data sets. For each set of labels, the original set of annotations are collected from the yeast GO annotations, and the |
Classification and evaluation
Here, for each protein
Using this general classification framework, we evaluated the performance of the base kNN classifiers and the functional or label similarity-incorporated classifiers using two validation methodologies. In the first set of experiments, we used a five-fold cross-validation methodology for this evaluation on the four data sets detailed in Table
Results from cross-validation experiments
In this section, we compare the performance of the label similarity-incorporated classifier with the base
Improvement of performance for a large set of classes
Table 2
Statistics about the comparative performance of the base
Total # classes
# Classes improved
Average improvement over all classes
Maximum improvement
0.0219 (3.57%)
0.1882 (39.92%)
0.0083 (1.33%)
0.2091 (38.66%)
0.0045 (0.63%)
0.1982 (31.82%)
0.0079 (1.02%)
0.1129 (20.39%)
We also examined the effect of our approach on the performance of classification for each class individually. Figure
Figure 5
Comparison of the performance of functional similarity-incorporated
Comparison of the performance of functional similarity-incorporated
Improvement of performance for small classes
One of the primary motivations for the incorporation of label similarity into standard function prediction algorithms was to improve the prediction accuracy for data-poor classes, as discussed in the Background section earlier. Our approach is expected to be useful for this task, as, in our model, the small classes can seek a contribution from classes of bigger sizes that have a high semantic similarity with them. To test this hypothesis, we selected classes of size at most 30 in all the data sets being used, and analyzed the results obtained using label similarity, as against those from basic classification. Table
Table 3
Statistics about the comparative performance of the base
# Small classes
# Classes improved
Average improvement over all small classes
Maximum improvement
0.0358 (6.24%)
0.1882 (39.92%)
0.0225 (3.82%)
0.2091 (38.66%)
0.0129 (1.89%)
0.1982 (31.82%)
0.0197 (2.72%)
0.1129 (20.39%)
The class-by-class improvements for each small class in Mnaimneh
Table 4
Details of the classes most similar to GO:0051049 (
GO Term
Similarity with target class
Vesicle-mediated transport
Gene silencing
Negative regulation of transcription
Regulation of gene expression
Establishment of protein localization
Positive regulation of transcription
Regulation of DNA metabolic process
Regulation of RNA metabolism
Figure 6
Comparison of the performance of functional similarity-incorporated
Comparison of the performance of functional similarity-incorporated
As can be observed from Table
Additional file 2
Arrangement of the functional classes aiding the improvement of the AUC score of the GO:0051049 (regulation of transport) class in the GO biological process ontology. This figure shows the arrangement of the functional classes aiding the improvement of the AUC score of the GO:0051049 (regulation of transport) class (listed in Table
Click here for file
This analysis also supports our hypothesis that the label similarity filtering process is able to capture the most meaningful relationships between functional classes in a given label similarity matrix, and the label similarity-incorporated classification process is able to utilize these relationships to improve the predictions over individual classes.
Incorporating information in the whole GO biological process ontology
Unlike the hierarchical consistency enforcement approach discussed in the related work section, which focuses on the subgraph of the functional hierarchy corresponding to the target classes, one of the advantages of the direct incorporation of relationships into the classification model is that relationships in the entire hierarchy can be incorporated into the classification model, while holding the set of target classes constant. This can be done by simply modifying the label similarity matrix to include the semantic similarities between the target classes and all the other classes in the hierarchy. Thus, instead of using an |
We tested this idea for Mnaimneh
Table 5
Statistics about the comparative performance of the base
Total # classes
# Classes improved
Average improvement over all classes
Maximum improvement
All classes
0.0167 (2.65%)
0.2492 (53.93%)
Small classes
0.0363 (6.32%)
0.2492 (53.93%)
In summary, these cross-validation-based results show that the incorporation of direct relationships between functional classes constituting the GO functional hierarchies, measured using a suitable semantic similarity measure, is a useful method for improving the accuracy of the predictions made over a set of target classes, particularly for classes with a small number of member proteins.
Comparison with Yu
As mentioned before, Yu
In the first experiment, we compared the accuracy of the predictions of our approach and GEST in the case where only the similarities between the target classes are incorporated into the prediction algorithm, corresponding to the results presented in the
Table 6
Statistics about the comparative performance of GEST and our label similarity-incorporated kNN classifiers, measured in terms of the number of classes for which AUC scores are improved by the latter over the former, and the average and maximum improvement in AUC scores.
Total # classes
# Classes improved
Average improvement over all classes
Maximum improvement
0.0116 (1.86%)
0.1788 (37.83%)
0.0004 (0.06%)
0.1854 (36.6%)
0.0059 (0.84%)
0.1307 (23.53%)
0.0081 (1.04%)
0.2117 (48.85%)
The statistics in Table
Additional file 3
Comparison of AUC scores from our approach and GEST. This figure shows the comparison of the performance of our functional similarity-incorporated
Click here for file
In another set of experiments, we compared the accuracy of the predictions made by our method and GEST, using the similarity between the target functional classes for Mnaimneh
Table 7
Statistics about the comparative performance of GEST classifiers and our label similarity-incorporated classifiers using information in the whole GO biological process ontology, measured in terms of the number of classes for which AUC scores are improved by the latter over the former, and the average and maximum improvement in AUC scores.
Total # classes
# Classes improved
Average improvement over all classes
Maximum improvement
0.0235 (3.82%)
0.2171 (42.04%)
0.0144 (2.34%)
0.2137 (47.4%)
Similar to the results discussed earlier, the results in Table
• The semantic similarity measures used in GEST are only computed on the basis of the structure of the ontology, while the formulation of Lin's similarity measure used in our method also takes the set of members at each node into account. This can help uncover similarities between nodes that are otherwise reasonably distant in the ontology, but have very similar set of proteins contained in them, thus indicating a semantic relationship between them that is not captured by the structure of the ontology.
• GEST uses the maximum of the similarities of all the annotations of a neighborhood protein to the target class as the contribution to the score of the target protein for this class. On the other hand, our cross validation-based method for filtering the label similarity matrix is more adaptive, since it determines which classes should contribute to the score of a given target class on the fly. This can help avoid collecting spurious contributions due to small values of similarity between the target and the contributing classes.
These conceptual differences lead to the differences between the results produced by our method and Yu
Validation of function predictions for proteins in the test set
One of the important distinctions between our approach and some other approaches that address the problem of incorporating functional inter-relationships into function prediction algorithms
In the quantitative evaluation, we made predictions for proteins not used in the cross-validation experiments, i.e., those proteins that were not annotated with any of the classes considered, using the set of proteins considered in cross-validation as the training set. In order to use our approach for this task, we randomly split the original training set in an 80 – 20 ratio, with the larger set serving as the final training set. The smaller set is used as a validation set for determining the thresholds to be used for filtering the label similarity matrix. The likelihood scores generated for the test set using both the base kNN and the label similarity-incorporated kNN classifiers for the classes considered were averaged over ten rounds. This computation was performed for the Mnaimneh and Rosetta gene expression data sets to maximize the coverage of the test set, and the same value of
The scores produced by both the classification algorithms (base and label similarity-incorporated) were ranked in descending order independently for each class, and proteins with the same score (mostly in the case when the score is 0) were sorted by their ORF name. The complete ranked lists of predictions for the Mnaimneh and Rosetta data sets are available as Additional Files
Additional file 4
Ranked list of predictions from the Mnaimneh gene expression data set. A detailed list of ranked predictions produced by the label similarity-incorporated kNN classifiers (first worksheet) and base kNN classifiers (second worksheet) for the test genes extracted from the Mnaimneh gene expression data set. The GO terms, arranged in columns, are sorted from left to right in the order of decreasing AUC improvements by incorporating functional relationships into their base classifiers. The genes in each column are ranked in descending order by the score assigned by the corresponding kNN classifier. Genes with the same score (mostly in the case when the score is 0) are sorted by their ORF name.
Click here for file
Additional file 5
Ranked list of predictions from the Rosetta gene expression data set. A detailed list of ranked predictions produced by the label similarity-incorporated kNN classifiers (first worksheet) and base kNN classifiers (second worksheet) for the test genes extracted from the Rosetta gene expression data set. The GO terms, arranged in columns, are sorted from left to right in the order of decreasing AUC improvements by incorporating functional relationships into their base classifiers. The genes in each column are ranked in descending order by the score assigned by the corresponding kNN classifier. Genes with the same score (mostly in the case when the score is 0) are sorted by their ORF name.
Click here for file
Table 8
Statistics about the predictions made by the base kNN classifiers and their label similarity-incorporated versions for the genes in Mnainmeh and Rosetta data sets not covered in the cross-validation experiments, and how many of these predictions could be validated based on the annotations added to the GO database between February–September 2008.
# Test genes
## Validated predictions
Median of ranks in label similarity kNN
Median of ranks in base kNN
Looking further into these results, we examined the distribution of the difference between the ranks assigned by the base and label similarity-incorporated classifiers to the valid annotations, i.e. (rank by base kNN)-(rank by label similarity-incorporated kNN), shown in Figures
Figure 7
Histograms showing the distribution of differences between the ranks assigned by the base kNN and label similarity-incorporated classifiers to functional annotations for a set of test proteins [(rank by base classifier)-(rank by label similarity-incorporated classifier)] that have been validated in the GO database between February–September 2008 for the Mnaimneh and Rosetta data sets
Histograms showing the distribution of differences between the ranks assigned by the base kNN and label similarity-incorporated classifiers to functional annotations for a set of test proteins [(rank by base classifier)-(rank by label similarity-incorporated classifier)] that have been validated in the GO database between February–September 2008 for the Mnaimneh and Rosetta data sets (a) Result from Mnaimneh dataset (b) Result from Rosetta dataset.
As the final test in the historical rollback-based evaluation, we examined how many of the top-
Figure 8
The number of predictions validated by the GO database between February–September 2008 among the top
The number of predictions validated by the GO database between February–September 2008 among the top
These results quantitatively illustrate the effectiveness of incorporating label similarity into protein function prediction algorithms for uncovering the functions of unannotated proteins. To show that this incorporation can also help characterize some proteins for which little functional information is available, we investigated the predicted functions for several uncharacterized proteins to determine whether there is independent evidence to support our predictions. Indeed, we found several instances where predictions unique to the label similarity-incorporated classifiers are supported by compelling independent evidence, which makes these proteins promising candidates for further experimental validation. These examples are discussed in detail below.
Possible novel link between vacuolar [H+]-ATPase dissociation and microtubules supported by YDL123W
YDL123W (Sna4) is a protein that is known to localize to the outer vacuolar membrane, but its biological role has not yet been characterized
YDR233C may play a previously uncharacterized role in mannoprotein metabolism
Glycoproteins are an important class of proteins characterized by the addition of oligosaccharide chains to their polypeptide side-chains. Glycosylation of these proteins typically plays an important role in their function, and they are known to be central for several cellular functions including the recognition of cell structures, inter-cell signaling and transport
Our label similarity-incorporated classifier predicted the previously uncharacterized protein YDR233C (Rtn1) to have a strong association to the term GO:0006056 (
Elucidation of an uncharacterized protein's involvement in RNA processing
YHR156C (Lin1) is a protein whose function is largely uncharacterized (no current biological process or molecular function annotations in GO). Interestingly, Lin1 appeared at the top of several lists of predictions made by the label similarity-incorporated classifiers for processes related to RNA metabolism or processing, including snoRNA metabolism (GO:0016074), RNA 3'-end processing (GO:0031123), and mRNA metabolism (GO:0016071). The same protein was not predicted at a similar confidence level by the base classifiers for these classes (792nd, 865th and 17th respectively).
Interestingly, although this protein's function is not captured by current biological process or molecular function annotations, it is known to be involved in the U5 small ribonucleoprotein (snRNP), which is a component of the spliceosome in yeast
All of these validations represent instances where a protein with relatively shallow previous characterization has been associated with a specific GO term using our prediction methodology, which has allowed us to generate non-trivial hypotheses about its cellular role. Furthermore, all of the functional associations discussed here were unique to the label similarity-incorporated classifiers, which indicates that such an approach can be used to predict reliable, specific, and novel biology. We have provided the ranked prediction lists for both the versions of the classifiers as Additional Files
In summary, the cross-validation experiments and the quantitative and qualitative evaluation of the predictions for previously unannotated proteins shows how the incorporation of inter-relationships between functional classes into standard function prediction algorithms can help expand the set of annotated proteins in
In this paper, we demonstrated the utility of incorporating functional interrelationships into protein function prediction algorithms, in order to improve the predictions made by them. We modeled these relationships using Lin's semantic similarity measure
Our work can be extended in several directions. It will be useful to incorporate the concept of functional similarity into SVMs, which do not have the additive characteristic like
Availability and requirements
• Project Home Page:
• Operating System(s): Platform independent.
• Programming language: Matlab (Tested for version 7.4 and above, but expected to work with earlier versions also).
• License: None.
• Any restrictions to use by non-academics: This paper must be cited.
Authors' contributions
GP, CLM and VK conceived the study and developed the proposed approach and the evaluation methodologies. GP prepared the implementation and experimental results. CLM helped in interpretation and literature validation of the predictions. All the authors participated in the preparation of the manuscript and approved the final version.
We are thankful to Zafer Barutcuoglu, Yves Lussier and Jianrong Li for several fruitful discussions, and for providing us with useful information and data for the project. This work was supported by NSF grant CNS-0551551 and UMR BICB seed grants. Access to computing facilities was provided by the Minnesota Supercomputing Institute.
Prediction of protein function and pathways in the genome era
Contextual inference of protein function
Computational Approaches for Protein Function Prediction: A Survey
Knowledge based analysis of microarray gene expression data by using support vector machines
Gene Classification Using Expression Pro-files: A Feasibility Study
Gene Ontology: tool for the unification of biology
The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes
Semantic Similarity Measures as Tools for Exploring the Gene Ontology
Incorporating Ontology-Driven Similarity Knowledge into Functional Genomics: An Exploratory Study
Correlation between Gene Expression and GO Semantic Similarity
Systematic learning of gene functional classes from DNA array expression data by using multilayer perceptrons
Exploration of Essential Gene Functions via Titratable Promoter Alleles
An Information-Theoretic Definition of Similarity
Functional Discovery via a Compendium of Expression Profiles
Global landscape of protein complexes in the yeast Saccharomyces cerevisiae
Hierarchical multi-label prediction of gene function
Protein classification using probabilistic chain graphs and the Gene Ontology structure
Gene function classification using Bayesian models with hierarchy-based priors
Predicting Gene Function From Patterns of Annotation
Information theory applied to the sparse gene ontology annotation network to predict novel gene function
Broadly predicting specific gene functions with expression similarity and taxonomy similarity
Multi Label Classification: An Overview
Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language
Semantic similarity based on corpus statistics and lexical taxonomy
New kernels for protein structural motif discovery and function classification
A Regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data
How does gene expression clustering work?
Association analysis-based transformations for protein interaction networks: a function prediction case study
BioGRID: a general repository for interaction datasets
Finding function: evaluation methods for functional genomic data
Sorting of proteins into multivesicular bodies: ubiquitin-dependent and -independent targeting
Microtubules Are Involved in Glucose-dependent Dissociation of the Yeast Vacuolar [H+]-ATPase in Vivo
An in Vivo Map of the Yeast Protein Interactome
Glycoprotein biosynthesis in yeast
Cell Wall Architecture in Yeast: New Structure and New Challenges
Saccharomyces cerevisiae Rab-GDI Displacement Factor Ortholog Yip3p Forms Distinct Complexes with the Ypt1 Rab GTPase and the Reticulon Rtn1p
Translocation of lipid-linked oligosaccharides across the ER membrane requires Rft1 protein
Cell biology: Protein sweetener
Exploration of the Function and Organization of the Yeast Early Secretory Pathway through an Epistatic Miniarray Profile
Biochemical and genetic analyses of the U5, U6, and U4/U6 × U5 small nuclear ribonucleoproteins from Saccharomyces cerevisiae
The budding yeast U5 snRNP Prp8 is a highly conserved protein which links RNA splicing with cell cycle progression
The position of yeast snoRNA-coding regions within host introns is essential for their biosynthesis and for efficient splicing of the host pre-mRNA
Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps


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Dionne Warwick has filed for bankruptcy, the New York Daily News reports, with most of her debt stemming from more than $10 million in federal and state tax claims. A publicist for the 1960s and '70s hitmaker ("Walk on By," "Say a Little Prayer," etc.) blamed negligence by former managers for letting things get to this point. Sound familiar? Warwick was the host of an ambitious 2009 inauguration weekend party in Washington that went bust at the last minute, stranding scores of dressed-up ticket buyers. The singer and her publicist said in that case, too, that she was taken advantage of by unscrupulous promoters.
More Reliable Source: Princess Caroline; Bill Clinton; Larry David; Torrey Smith


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Of Montreal
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Although Of Montreal originate from Athens, Georgia, USA, the bands sound is unmistakably rooted in the mid-60s psychedelic era of UK pop, with obvious nods to the Beatles and the Kinks. Formed in the mid-90s, Of Montreal have often been compared to other similarly styled psych pop acts that emerged around the same time, including Apples In Stereo, Olivia Tremor Control, Neutral Milk Hotel, and Elf Power. While Of Montreal is indeed a unit, the bands leader is unquestionably vocalist, guitarist, and main songwriter Kevin Barnes, who originally came from Florida before settling in Georgia. A few musicians came and went, including Elf Powers Bryan Helium (Bryan Poole), but Barnes eventually came across a stable line-up by the late 90s and was joined by Derek Almstead (bass), James Huggins (drums), Andy Gonzales (guitar), and Dottie Alexander (keyboards, clarinet, flute, percussion). Over 40 additional vocalists and musicians were added to the...
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Granite Stater,
This has been an exciting week for Citizens for a Strong New Hampshire with the announcement of our 1st annual 'Strong New Hampshire Conference,' which will be held on October 12th at the Executive Court in Manchester from 10 a.m. - 2 p.m. You can click here to purchase and print your ticket(s) online.
We've also released the names of our first round of outstanding speakers who are dedicated to the same values of strong families and a strong economy that our organization has been fighting for each and every day.
Here is the first list of confirmed speakers for our event:
- Congressman Steve King (IA-04) (tentatively confirmed) is a strong conservative who has been fighting for limited government, less spending and lower taxes in Congress for a decade.
- Louisiana State Senator Elbert L. Guillory (District 24) released this video and received much national press and internet fame for his announcement this year.
- Boston talk radio and nationally acclaimed journalist and author, Howie Carr.
- Executive Director of the American Center for Law and Justice, Jordan Sekulow.
In addition to hearing from these great speakers and receiving an update of what Citizens for a Strong New Hampshire has accomplished over the past year, lunch is included in the price of your ticket.
You can read more about our 1st annual "Strong New Hampshire Conference" in both the national and local press by visiting the "News" section of our website by clicking here.
We look forward to seeing you on October 12th. Remember to purchase your ticket(s) today.
Michael Biundo
Citizens for a Strong New Hampshire


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This week is Holy Week - the week we journey with Jesus to the cross and the tomb in preparation of celebrating the greatest day in history next Sunday, when our Savior conquered death and rose from the grave! In order to experience the triumph of the Resurrection, we must prepare our hearts with the gravity of the price that was paid. Walking with Jesus to the cross opens our hearts to all He endured for us.
"God made Him who had no sin to be sin for us, so that we might become the righteousness of God." 2 Corinthians 5:21
Jesus endured spiritual, emotional and, of course, physical suffering for us. Feel the tension building as we go through this week. May we grow in the depth of our understanding of His love.
Palm Sunday – The Triumphal Entry
(Matthew 21:1-1, Mark 11:1-10, Luke 19:29-44 and John 12:12-19)
Jesus makes it very clear that He is the Messiah! He comes at Passover ("Behold the Lamb of God who takes away the sins of the world" John 1:29) and on the back of a donkey (Messianic prophecy from Zechariah 9:9).
Monday – Jesus Curses the Fig Tree and He Cleanses the Temple
(Matthew 21:18-19 & 12-13 and Mark 11:12-14 & 15-18)
Jesus shows that it is not about what you look like on the outside, but what God does in your heart. He makes it very clear that it is not about "religion," but about a "relationship" with God through Jesus Christ.
Tuesday – Jesus Teaches in the Temple & His Authority is Questioned
(Matthew 21:23-23:39; Mark 11:27-33 & 12:1-44 and Luke 20:1-21:4)
The drama is building. The Religious Leaders are confronting Jesus, and He is not backing down. The confrontation is coming. Everyone must give an answer: What do you believe about Jesus? Is he just a great man, prophet and teacher OR is He the Messiah, the Son of God?
Tuesday Evening – Jesus Anointed at Bethany
(Matthew 26:6-13; Mark 14:3-9 and John 12:2-11)
Those who are committed to Jesus stay faithful. When we are forgiven much, we love much. This extravagant act of love is still talked about today. Is Jesus the Love of our life and the Joy of our heart?
Wednesday – The Religious Leaders Plot to Kill Jesus
(Matthew 26:14-16; Mark 14:10-11 and Luke 22:3-6)
Jesus lived 33 sinless years. By His goodness and grace, He forces everyone to make a decision – our will or His will. Do we want religion OR are we willing to accept Jesus?
Thursday – The Last Supper and the Garden of Gethsemane
(Matthew 26:17-29 & 36-46, Mark 14:12-25 & 32-42; Luke 22:7-20 & 40-46 and John 13:1-38)
Jesus shares the Passover meal with the disciples, whom He loves. He makes it very clear that "His Body" is the bread broken for us and "His blood" is the new covenant poured out for us. Jesus is willing to pay the price for our sins, so that we can have an eternal relationship with God.
Thursday Night – Jesus' Arrest & Trial
(Matthew 26:47-27:26; Mark 14:43-15:15; Luke 22:47-23:25 and John 18:2-19:16)
Make no mistake – it was not the religious leaders or the soldiers who arrested Jesus that night. Jesus could have called down legions of angels and stopped this at any moment. But, the Love and Grace of God took Jesus to the cross.
Good Friday – Jesus' Crucifixion and Death
(Matthew 27:27-56; Mark 15:16-41; Luke 23:26-49 and John 19:17-30)
Jesus suffered and died for you and for me. He laid down His life, so that you and I can truly live. "For the wages of sin is death" Romans 6:23, and death is eternal separation from God. Jesus took our place; He stood in the gap. He made a way for us to have eternal life. Thank You, Jesus!
As we read what Jesus endured for us, may we be forever grateful for His love, grace and mercy. This week should draw us closer to our God. My prayer is that we all grow deeper in our faith and more passionate about our relationship with the God who loves us more than we can ever dream or imagine.
May this Holy Week prepare us for living each day in the Love and Grace of our Savior!


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Facebook's AI boss: Facebook could fix its filter bubble if it wanted to http://www.recode.net/2016/12/1/13800270/facebook-filter-bubble-fix-technology-yann-lecun
Now the head of Facebook's artificial intelligence research division, Yann LeCun, says this is a problem Facebook could solve with artificial intelligence. "We believe this is more of a product question than a technology question," LeCun told a group of reporters last month when asked if artificial intelligence could solve this filter-bubble phenomenon. "We probably have the technology, it's just how do you make it work from the product side, not from the technology side." A Facebook spokesperson clarified after the interview that the company doesn't actually have this type of technology just sitting on the shelf. But LeCun seems confident it could be built. So why doesn't Facebook build it? - - - --Lauren-- REPORT Fake News Here! - https://vortex.com/report-fakes CRUSHING the Internet Liars - https://vortex.com/crush-net-liars STOP President Trump - https://vortex.com/stop-president-trump - - - Lauren Weinstein (lau...@vortex.com): https://www.vortex.com/lauren Lauren's Blog: https://lauren.vortex.com Founder: Network Neutrality Squad: https://www.nnsquad.org PRIVACY Forum: https://www.vortex.com/privacy-info Co-Founder: People For Internet Responsibility: https://www.pfir.org/pfir-info Member: ACM Committee on Computers and Public Policy Google+: https://google.com/+LaurenWeinstein Twitter: https://twitter.com/laurenweinstein Tel: +1 (818) 225-2800 I have consulted to Google, but I am not currently doing so -- my opinions expressed here are mine alone. - - - The correct term is "Internet" NOT "internet" -- please don't fall into the trap of using the latter. It's just plain wrong! _______________________________________________ nnsquad mailing list https://lists.nnsquad.org/mailman/listinfo/nnsquad


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Wilfred Carter (25th May 1908 - 5th February 1998) appeared in Coronation Street in March 1972 as Freddie Baxter, the secretary of the Weatherfield Sailing Club.
He also appeared in Dixon of Dock Green, Emergency-Ward 10, No Hiding Place, ITV Television Playhouse, Z Cars, Softly Softly, Take Three Girls, Doctor Who, The Regiment and Barlow.
Radio work includes The Faceless Ones, Personal Column and The Man Born to Be King.


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SYNOPSIS: Two seemingly dumb teens struggle to prepare a historical presentation with the help of a time machine. REVIEW: "The funniest comedy in the history of history" A fun film for its time, the now cult film "Bill & Ted's Excellent Adventure" was a silly adventure-comedy directed by Stephen Herek starring future A-lister Keanu Reeves and "The Lost Boys" actor …Read More »
Home | Tag Archives: Fee Waybill
Tag Archives: Fee Waybill
Film Review: Ladies and Gentlemen, the Fabulous Stains (1982)
SYNOPSIS: Corrine Burns retreats far into plans for her band, The Fabulous Stains, after her mother's death. So far that she gets them (she and two cousins) on a tour with a washed-out glam-rock group and a rising British punk band, radically changes her appearance, attracts a cult following and national media attention. With luck like this, what could go …Read More »


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Originally published January 20, 2017
Contractors will start the last phase of the planned Hawthorne Street Pedestrian-Bike Enhancement Project during the weekend of Jan. 21, 2017. The southern portion of the street between Rim Road and Schuster Avenue will be closed to all traffic, while the northern portion of the street closer to Rim will reopen.
The work, which should be finished around the end of February 2017 (weather permitting), will necessitate the temporary move of the Miner Metro Shuttle stop to the southwest corner of Rim and Randolph Drive from Rim and Hawthorne for the remainder of the project. Parking officials will set up signs to alert shuttle users of the change.
The work along Hawthorne will involve some demolition and reconstruction. Detour signs will help drivers and pedestrians navigate around the construction zone. Drivers are encouraged to slow down in the area or avoid it if possible.
Motorists can access the V-1 (Mike Loya Academic Services Building) parking lot and the Highlander Apartments, 1700 Hawthorne St., via driveways closer to Rim.
The enhancement project, which stretches from Rim to Crosby Avenue, began in early October 2016. When finished, Hawthorne will have one lane in each direction that will be marked for co-use with bicyclists. There will be no center lane.
The goal is to make this campus entrance safer, more efficient and aesthetically pleasing. The road will continue the look of Hawthorne on campus. It will feature wide sidewalks with ADA-approved ramps, native and drought-resistant trees, shrubs and flowering plants with an irrigation system, and additional lighting. The V-1 lot will have one wide driveway.
On a related note, the City of El Paso has made the stretch of Hawthorne from Schuster to Crosby a "No Parking" area on the west (P-1 parking lot) side of the street and no parking from 7 a.m. to 6 p.m. weekdays on the east side of the street. The University has set up warning signs on A-frames to alert motorists about this change.
The UTEP Public Information Office can assist media personnel with interview requests to speak with University administrators, faculty expert sources, or students; or to arrange campus visits for photography / videography shoots.
Call or email one of our media contacts with your name, outlet, deadline, and story idea or request to start an inquiry.
Elizabeth Ashby
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Team Video Project-
English in Contemporary Society
Basic Idea
Our Target Audience
Children who are about 4-10
Time it will show
Tiltle of the show
Chinglish Tubbies and the Hairspliting Princess
Let's see our video :)
After the children have finished their school
About 0400pm-0500pm
Peak Hours of Cartoons
"Chinglish" nowadays are
getting more serious
good education program to correct their mindset
Presented by:
Cherry Hong (13052862d)
Jenny Chin (13049181d)
Kitty Mak (13049015d)
Tam Chui Ying (13083812d)
Shirley Wong (13084314d)
Present Remotely
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Basic Idea
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Cherry Hongon 24 September 2013
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Spice Islands Pumpkin Pie Spice, 2 Oz (Pack of 3)
Spice Islandssku: 040100002818
$ 27.13
Est. 1941. www.spiceislands.com. This traditional favorite is a compelling blend if cinnamon, ginger, nutmeg, allspice, mace, and cloves. Try our Pumpkin Pie Spice in fruity quick breads, cookies and muffins. Warm honey with a dash of Pumpkin Pie Spice and drizzle over baked yams, sweet potatoes, pumpkin and winter squash. Dollop whipped cream over pies, cakes, hot chocolate or cider, then dust with Pumpkin Pie Spice.


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So here's this random photo from Reddit we ran into. Earlier this year, the City of Sweetwater commissioners approved a request by the Sweetwater police department themselves, to accept a donation from the U.S military……….a mine resistant armored vehicle. This war tank, as we like to call it, holds a 10 person capacity and is valued at $465,000.
C'mon seriously? As if the hoods of Sweetwater weren't shady enough. This whole ordeal obviously caused up a stir in the area and it even made it to a couple of websites, One in specific being reddit.com, where we got the picture courtesy of one if its users.
We're talking about Sweetwater here. Do yourselves a favor and please go read thoughts shared about this pointless tank roaming the streets of Sweetwater by clicking the link below. Guaranteed laughs.
Photo Source: reddit.com


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So now that we're over the Thanksgiving weekend, we can now talk about the history that was made last Wednesday night at LIV. The 2 Live Crew reunited in full for the first time in over two decades. 23 years to be exact.
Brother Marquis and Fresh Kid Ice were joined by Mr. Luke Skywalker himself to give willing fans a chance to witness history. Flo Rida (pictured below), among other high profile guests were in attendance as well.
Shouts to Dave Grutman, Abebe Lewis and everyone else who put on for Miami and reunited one of our most iconic claims to music from Miami.
Photos By WorldRedEye.Com


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