Poster Session IIIWednesday, December 1. Massachusetts General Hospital, Silver Spring, Maryland Background: Telomeres are nucleoprotein structures located at the ends of chromosomes which shorten with repetitive cell division and replication. Generally, telomeres shorten with age, but this shortening may be accelerated in the presence of cellular oxidative damage or chronic psychological stress. Shorter telomeres have been associated with earlier mortality (Cawthon et al., 2. Therefore, telomere length may reflect the association of psychological well- being on health and longevity. A variety of health related factors have been associated with longer telomeres (non- smoking, healthy diet, exercise, being married; (Sun et al., 2. Yen and Lung, 2. 01. In previous research, meditation practice was associated with higher telomerase activity (the enzyme that helps repair telomeres by adding DNA hexameric repeats to restore telomere length) in a group of individuals at the end of an intensive three- month full- time mediation retreat, compared to a control group (Jacobs et al., 2. Loving- Kindness or metta meditation comes from the Vipassana Buddhist tradition, and focuses on developing a positive intention, unselfish kindness and warmth towards all people (Salzberg, 1. Published work on Loving- Kindness Meditation (LKM) has demonstrated positive effects of this practice on the individual (more positive emotions, greater sense of purpose in life, and decreased illness symptoms such as headaches, congestion or weakness (Fredrickson et al., 2. Because of this potential beneficial effect on psychological and physical health, we hypothesized that experienced LKM meditators would have longer telomeres than age, gender, and education- matched controls. Methods: Individuals (> 1. ![]() LKM practice were recruited from meditation communities in New England. LKM participants had> 4 years of regular, nearly daily LKM practice and overnight meditation retreat experience. Control participants (> 1. Exclusion criteria for both groups included the current diagnosis of schizophrenia/psychosis, intellectual disability, substance abuse/dependence, major depression, anxiety disorder, serious current medical illness, hormonal medication, inflammatory disease, diagnosis of cancer in the last 5 years, pregnancy or lactation. We recruited 7. 5% of the LKM group first, and then recruited controls that resembled the LKM group, targeting the mean age (+/- 5 years), gender proportions, education level, and levels of depression history observed in the LKM group. Several LKM participants had a history of past major depressive episodes, and many had a graduate degree. Therefore, in order to prevent confounding, we over- enrolled control participants that had a past history of depression and a graduate level of education. Blood was obtained by venipuncture from an anticubital vein and collected into tubes containing Ficoll gel for separation of leukocytes by centrifugation. As described previously, the average relative leukocyte telomere length (LTL) is reported as the exponentiated sample ratio of telomere copy number to a 3. B4 copy number (T/S) corrected for a reference sample (Cawthon, 2. A Shapiro- Wilk test showed that our RTL data were not normally distributed (W=0. V=4. 3. 8, z=3. 1, p=0. A log transformation further failed to produce a normal distribution; therefore, we felt that the most conservative approach was to use non- parametric tests; we thus compared RTL between the two groups with a two- sample Wilcoxon rank- sum (Mann- Whitney) test. Results: The meditation practitioners typically practiced LKM as part of a larger set of Vipassana meditation practices. The average number of lifetime hours of LKM was 5. Free Role-Playing Games. This is a collection of links to over 500 free role-playing games (RPGs) on the WWW. RPG here refers solely to narrative (aka "table-top. Type or paste a DOI name into the text box. Click Go. Your browser will take you to a Web page (URL) associated with that DOI name. Send questions or comments to doi. Express Helpline- Get answer of your question fast from real experts. Overall meditation practice lifetime hours was 4,9. There were no significant differences in age, gender, race, education, or exposure to trauma. The control group did have a higher Body Mass Index (BMI); however, in the overall sample, being overweight (BMI≥2. BMI< 2. 5, n=2. RTL (z=- 1. 2. 26, p=. In addition, the LKM group had a higher rate of past depression. Overall, median relative telomere length (RTL) was longer in meditation practitioners (0. Among women participants, telomere length was significantly longer in meditators (0. There was no significant difference between groups among male participants. Conclusions: We found longer relative telomere lengths in individuals with years of experience in LKM, compared to age, gender, education, and trauma- matched individuals, and significantly longer RTL in women meditators than gender matched controls. Our finding suggests that LKM could have beneficial effects on mortality. These findings are in line with other research showing beneficial health effects of other- focused activities (spousal caregiving, community volunteering) and compassion or kindness towards others (altruism, forgiveness). For example, forgiveness of others has also be associated with greater longevity in a longitudinal study of elderly (Toussaint et al., 2. Similarly, Pace et al found relationships between compassion meditation and lower markers of inflammation (Pace et al., 2. Keywords: telomeres, stress, meditation. Disclosure: Nothing to Disclose.
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Oracle acquired Sun Microsystems in 2010, and since that time Oracle's hardware and software engineers have worked side-by-side to build fully integrated systems and. Excel Data Queries Use MS Query to Treat Excel as a Relational Data Source Excel ranges can work like relational tables. You can join them by common fields. If database design is done right, then the development, deployment and subsequent performance in production will give little trouble. A well-designed database 'just. Get pricing info for the Azure Service Bus, a cloud-based messaging system. No upfront costs. Pay as you go. Try for FREE.![]() I used to have a preacher who made sure to tell us before some sermons that he was preaching to himself as much as he was to the congregation. When I speak, or when I write an article, I have to listen to that tiny little voice in my head that helps filter out my own bad habits, to make sure that I am teaching only the best practices. Hopefully, after reading this article, the little voice in your head will talk to you when you start to stray from what is right in terms of database design practices. So, the list: Poor design/planning. Ignoring normalization. Poor naming standards. Lack of documentation. One table to hold all domain values. Using identity/guid columns as your only key. Not using SQL facilities to protect data integrity. Not using stored procedures to access data. Trying to build generic objects. Lack of testing. Poor design/planning“If you don’t know where you are going, any road will take you there” – George Harrison. Prophetic words for all parts of life and a description of the type of issues that plague many projects these days. Let me ask you: would you hire a contractor to build a house and then demand that they start pouring a foundation the very next day? Even worse, would you demand that it be done without blueprints or house plans? Hopefully, you answered “no” to both of these. A design is needed make sure that the house you want gets built, and that the land you are building it on will not sink into some underground cavern. If you answered yes, I am not sure if anything I can say will help you. Like a house, a good database is built with forethought, and with proper care and attention given to the needs of the data that will inhabit it; it cannot be tossed together in some sort of reverse implosion. Since the database is the cornerstone of pretty much every business project, if you don’t take the time to map out the needs of the project and how the database is going to meet them, then the chances are that the whole project will veer off course and lose direction. Furthermore, if you don’t take the time at the start to get the database design right, then you’ll find that any substantial changes in the database structures that you need to make further down the line could have a huge impact on the whole project, and greatly increase the likelihood of the project timeline slipping. Far too often, a proper planning phase is ignored in favor of just “getting it done”. The project heads off in a certain direction and when problems inevitably arise – due to the lack of proper designing and planning – there is “no time” to go back and fix them properly, using proper techniques. That’s when the “hacking” starts, with the veiled promise to go back and fix things later, something that happens very rarely indeed. Admittedly it is impossible to predict every need that your design will have to fulfill and every issue that is likely to arise, but it is important to mitigate against potential problems as much as possible, by careful planning. Ignoring Normalization. Normalization defines a set of methods to break down tables to their constituent parts until each table represents one and only one “thing”, and its columns serve to fully describe only the one “thing” that the table represents. The concept of normalization has been around for 3. SQL and relational databases are implemented. In other words, SQL was created to work with normalized data structures. Normalization is not just some plot by database programmers to annoy application programmers (that is merely a satisfying side effect!)SQL is very additive in nature in that, if you have bits and pieces of data, it is easy to build up a set of values or results. In the FROM clause, you take a set of data (a table) and add (JOIN) it to another table. You can add as many sets of data together as you like, to produce the final set you need. This additive nature is extremely important, not only for ease of development, but also for performance. Indexes are most effective when they can work with the entire key value. Whenever you have to use SUBSTRING, CHARINDEX, LIKE, and so on, to parse out a value that is combined with other values in a single column (for example, to split the last name of a person out of a full name column) the SQL paradigm starts to break down and data becomes become less and less searchable. So normalizing your data is essential to good performance, and ease of development, but the question always comes up: “How normalized is normalized enough?” If you have read any books about normalization, then you will have heard many times that 3rd Normal Form is essential, but 4th and 5th Normal Forms are really useful and, once you get a handle on them, quite easy to follow and well worth the time required to implement them. In reality, however, it is quite common that not even the first Normal Form is implemented correctly. Whenever I see a table with repeating column names appended with numbers, I cringe in horror. And I cringe in horror quite often. Consider the following example Customer table: Are there always 1. Is the order of payments significant? Does a NULL value for a payment mean UNKNOWN (not filled in yet), or a missed payment? And when was the payment made?!? A payment does not describe a Customer and should not be stored in the Customer table. Details of payments should be stored in a Payment table, in which you could also record extra information about the payment, like when the payment was made, and what the payment was for: In this second design, each column stores a single unit of information about a single “thing” (a payment), and each row represents a specific instance of a payment. This second design is going to require a bit more code early in the process but, it is far more likely that you will be able to figure out what is going on in the system without having to hunt down the original programmer and kick their butt…sorry… figure out what they were thinking. Poor naming standards“That which we call a rose, by any other name would smell as sweet“This quote from Romeo and Juliet by William Shakespeare sounds nice, and it is true from one angle. If everyone agreed that, from now on, a rose was going to be called dung, then we could get over it and it would smell just as sweet. The problem is that if, when building a database for a florist, the designer calls it dung and the client calls it a rose, then you are going to have some meetings that sound far more like an Abbott and Costello routine than a serious conversation about storing information about horticulture products. Names, while a personal choice, are the first and most important line of documentation for your application. I will not get into all of the details of how best to name things here- it is a large and messy topic. What I want to stress in this article is the need for consistency. The names you choose are not just to enable you to identify the purpose of an object, but to allow all future programmers, users, and so on to quickly and easily understand how a component part of your database was intended to be used, and what data it stores. No future user of your design should need to wade through a 5. Consider, for example, a column named, X3. DSCR. What the heck does that mean? You might decide, after some head scratching, that it means “X3. Possibly it does, but maybe DSCR means discriminator, or discretizator? Unless you have established DSCR as a corporate standard abbreviation for description, then X3. DESCRIPTION is a much better name, and one leaves nothing to the imagination. That just leaves you to figure out what the X3. On first inspection, to me, X3. If I subsequently found that, in the organization, there was also an X3. ![]()
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