Degradation of Green Tea Catechins in Tea Drinks Assignment

Degradation of Green Tea Catechins in Tea Drinks - Assignment Example This study is significant because it studies the effect of processing conditions on GTC content. It is important to conserve the GTC content of tea during processing to retain its beneficial effects. Results of the study reveal that the GTC content of processed tea is significantly low compared to traditionally prepared tea. While a cup of traditionally brewed tea contains 400-500 mg of GTC, processed tea contains only 3-60 mg GTC. GTC is stable at room temperature. About 10-15%, GTC is lost when tea is heated up to 98 ?C for 15-30 minutes, with additional 5% loss on prolonged heating. Autoclaving at 120 ?C for 20 minutes, which is yet another step during processing, leads to a loss of 23% GTC. This loss is directly proportional to the pH of the medium. Furthermore, about 80% of the GTC content is degraded in a buffer medium of pH 6. The stability and shelf life of GTC is also found to be dependent on pH content in the absence of other ingredients. Half of the GTC content is lost wit hin 3 months at a pH below 4.5. Sucrose has no effect on the stability of GTC while citric acid leads to a faster degradation. Ascorbic acid has a protective effect in the first month after which it accelerates the degradation. In addition, the results also reveal that at high temperatures, EGCG undergoes epimerization to form GCG. This explains the contrasting GCG content between traditionally prepared tea and processed tea, wherein, the GCG content is higher by 45% in processed tea. Overall, it can be concluded that GTC degrades easily at high temperature and pH, and that the stability of GTC depends on the presence of other ingredients. Further studies that focus on the effect of each ingredient present in processed tea on the stability and speed of degradation of GTC are required. II. Coffee and Green Tea as a Large Source of Antioxidant Polyphenols in the Japanese Population (Fukushima et al 1253-1259) The objective of this study was to evaluate the total consumption of polyphe nols by Japanese people. Polyphenols are well known for their antioxidant activity and beverages are a major source of polyphenols in Japan. The findings of this study suggest that an individual per day consumes an average of 853 mg of polyphenols. The largest source of polyphenols in the Japanese population is coffee, which provides 200 ml of polyphenols in every 100 mL. The average polyphenol consumption through coffee is found to be 426 mg/day. The second largest source of polyphenol is Green tea, which contributes up to 292 mg polyphenols/day. Thus, coffee and green teas constitute up to 70% of the total beverage consumption and are the largest sources of polyphenols. Among fruits and vegetables, satsuma oranges and onions are the most consumed and provided 9 and 4 mg of polyphenols, respectively, per day. Cacao mass (chocolate) and black pepper contribute about 0.8 g and 0.2 g polyphenols per day. Measurement of the antioxidant activities correlates well with the total polyphen ol content of all the tested consumables, suggesting that the in vitro antioxidant activities are proportional to the total polyphenol quantities. Future studies will have to examine whether in vivo antioxidant properties of these polyphenols correlate well with those observed in vitro. III. Content of Potentially Anticarcinogenic Flavonoids of Tea Infusions, Wines, and Fruit Juices (Hertog, Hollman, and Putte 1242-1246) This study attempted to evaluate the concentrations of various anti-carcinogenic flavonoids in beverages such as tea, wines and fruit juices. The study found

The Speckled Band Essay Example for Free

The Speckled Band Essay Our task involves doing an essay on the similarities and differences of three detective fiction stories: The Speckled Band, A Scandal In Bohemia and The Dancing Men all written by the magnificent Arthur Conan Doyle. The genre of these particular stories is all based on Crime/detective. All three stories have the same type of structure although the contents are different. Even though the contents are different, the way they are organised is similar because they are detective stories we should expect similarities between them. Despite the different characters the crime and action in all three is similar. In all three stories there is the same central character Sherlock Holmes-the detective and his great friend Dr John Watson-the first narrator. Helen Stoner approaches Sherlock Holmes about the murder of her sister the night before she was duo to get married. Holmes investigates the cause of death. A Scandal In Bohemia The king of Bohemia comes to see Holmes about a valuable photo, which he needs to get hold of before it ruins his life. The Dancing Men Hilton Cubitt approaches Holmes with a problem; him and his wife are being sent childish drawings of dancing men. Holmes investigates further. The exposition in all three stories has many similarities and differences. There are many similarities I notified in the exposition of these stories, one of which was the dates e. g. in The Speckled Band -Early April in the year 83 Scandal In Bohemia-one night-20th March, 1888. Although the dates are given in two of the stories, I have realised that in The Dancing Men no specific dates are given. This emphasizing a difference in the exposition. In all three stories Dr John Watson is the first (person) narrator and Holmes is always introduced by him. For example in The Speckled Band-In glancing over my notes over the seventy odd cases in which I have during the last eight years studied the methods of my dear friend Sherlock Holmes. The Dancing Men-Holmes had been seated for some hours in silence with his long, thin back curved over a chemical vessel in which he was brewing a particularly malodorous product. A Scandal In Bohemia To Sherlock Holmes she is always the woman.

Stereotypes and Stereotyping in Susan Glaspells Trifles Essay

Stereotypes and Stereotyping in Susan Glaspell's Trifles  Ã‚  Ã‚  Ã‚  Ã‚   In the play Trifles, by Susan Glaspell, the male characters make several assumptions concerning the female characters. These assumptions deal with the way in which the male characters see the female characters, on a purely stereotypical, gender-related level. The stereotypical assumptions made are those of the women being concerned only with trifling things, loyalty to the feminine gender, and of women being subservient to their spouses. The first assumption, women being only concerned with trifling things, is seen beginning with line 120 where the men say: Sheriff:  Well, can you beat the women! Held for murder and worryin' about her preserves. County Attorney:  I guess before we're through she may have something more serious than her preserves to worry about. Hale:  Well, women are used to worrying over trifles. These lines show the attitude toward women prevalent throughout the play. It is the men's nonchalance toward the small details t... ...imple things in life, things of little or no significance to the important, male world in which they live. It is here we find the men to be wrong, for it is in the small, seemingly insignificant details that the guilt of a woman is found and stifled. Work Cited Glaspell, Susan. "Trifles." Plays by Susan Glaspell. New York: Dodd, Mead and Company, Inc., 1920. Reprinted in Literature: An Introduction to Fiction, Poetry and Drama. X.J. Kennedy and Dana Gioia Eds. New York: Harper Collins Publisher, 1995. Stereotypes and Stereotyping in Susan Glaspell's Trifles Essay Stereotypes and Stereotyping in Susan Glaspell's Trifles  Ã‚  Ã‚  Ã‚  Ã‚   In the play Trifles, by Susan Glaspell, the male characters make several assumptions concerning the female characters. These assumptions deal with the way in which the male characters see the female characters, on a purely stereotypical, gender-related level. The stereotypical assumptions made are those of the women being concerned only with trifling things, loyalty to the feminine gender, and of women being subservient to their spouses. The first assumption, women being only concerned with trifling things, is seen beginning with line 120 where the men say: Sheriff:  Well, can you beat the women! Held for murder and worryin' about her preserves. County Attorney:  I guess before we're through she may have something more serious than her preserves to worry about. Hale:  Well, women are used to worrying over trifles. These lines show the attitude toward women prevalent throughout the play. It is the men's nonchalance toward the small details t... ...imple things in life, things of little or no significance to the important, male world in which they live. It is here we find the men to be wrong, for it is in the small, seemingly insignificant details that the guilt of a woman is found and stifled. Work Cited Glaspell, Susan. "Trifles." Plays by Susan Glaspell. New York: Dodd, Mead and Company, Inc., 1920. Reprinted in Literature: An Introduction to Fiction, Poetry and Drama. X.J. Kennedy and Dana Gioia Eds. New York: Harper Collins Publisher, 1995.

A Bridge to Wiseman’s Cove: James Moleney Essay

Carl Matt – Carl is 15 years of age and is described as a low self-esteemed and obese child. He is the second eldest of the three Matt siblings (Sarah, Carl and Harley). Carl’s mother, Kerry, abandons him and his two other siblings, this makes him feel neglected and at the same time worried for his mother’s well being as she is nowhere to be found. Throughout the story, Carl struggles through many obstacles, however with the help of his new friends soon to meet, he is able to grow and become more confident about himself and towards others. Kerry Matt -Kerry Matt is the mother of the main protagonist, Carl. She is also the mother of Sarah, (Carl’s older Sister) and Harley, (Carl’s younger Brother). Throughout the novel, It is said that Kerry usually leaves her kids behind for more than a day. But when she left again she never came back. Harley Matt – Carl’s younger brother. He is constantly getting into trouble. He gets into fights, steals and gets caught doing graffiti. Skip Duncan – Owner of the barge Carl works on and wife to Joy. Skip initially is reluctant to hire Carl as he is a Matt, but begins to trust and eventually rely on him. Skip is bossy and stubborn, and earns little income from his business of ferrying cars across the cove. [1] Joy Duncan – Skip Duncans wife she is a Kind, Loving motherly figure. Carl Matt is an awkward, lumpy fifteen year-old who just wants to be loved. Sarah, Carl and Harley’s fathers all walked out on the family and their mother, Kerry often finds them too hard to cope with. When his mother walks out on the family, apparently for good, nineteen year-old Sarah, terrified of the responsibility of raising the two boys, heads toEurope, packing the boys off to stay with their Aunt Beryl in Wattle Beach. Beryl is less than delighted at the idea of taking in the boys, until she realizes that she can keep their social services payments. However, far from filling the role of their mother, she shows the boys no love and even attempts to keep Harley chained to the house to prevent him stealing and misbehaving. Carl is miserable in Wattle Beach . His size and awkwardness make him feel self-conscious and unable to make friends at school. Even the townspeople of Wattle Beach regard the boys with suspicion, believing all the Matt clan to be useless no-hopers. All Carl wants is a family and the security of knowing that he is loved. When, at sixteen, Carl stops receiving any social security benefits, Beryl forces him to leave school. He finds work with Skip and Joy Duncan who run a rusty run-down barge from Wattle Bay to the nearby island. When Carl first starts to work for them the business is losing money, largely because of competition from a rival barge company. Before long however, Carl, reveling in finally belonging to something, shows initiative and helps to bring trade to the struggling business. He even begins to find the courage to stand up to his uncaring, manipulative Aunt Beryl. But, just as Carl begins to experience happiness, family secrets come back to haunt him and again bring his world crashing down around him. Finally the truth is revealed and Carl is forced to confront many of his demons. Gradually he begins to let down his defences and allow others in, finally accepting himself as a loved and valued member of the community.

Review of New Types of Relation Extraction Methods

This is explained by the fact that patterns do not tend to uniquely identify the given relation. The systems which participated in MUCH and deal with relation extraction also rely on rich rules for identifying relations (Fought et al. 1 998; Gargling et al. 1998; Humphreys et al. 1998). Humphreys et al. 1998) mention that they tried to add only those rules which were (almost) certain never to generate errors in analysis; therefore, they had adopted a low recall and high precision approach. However, in this case, many relations may be missed due to the lack of unambiguous rules to extract them.To conclude, knowledge-based methods are not easily portable to other domains and involve too much manual labor. However, they can be used effectively if the main aim is to get results quickly in well-defined domains and document collections. 5 Supervised Methods Supervised methods rely on a training set where domain-specific examples eave been tagged. Such systems automatically learn extractors for relations by using machine-learning techniques. The main problem of using these methods is that the development of a suitably tagged corpus can take a lot of time and effort.On the other hand, these systems can be easily adapted to a different domain provided there is training data. There are different ways that extractors can be learnt in order to solve the problem of supervised relation extraction: kernel methods (Shoo and Grossman 2005; Bunches and Mooney 2006), logistic regression (Kamala 2004), augmented parsing (Miller et al. 2000), Conditional Random Fields CRY) (Calcutta et al. 2006). In RE in general and supervised RE in particular a lot of research was done for IS-A relations and extraction of taxonomies.Several resources were built based on collaboratively built Wisped (YOGA – (Issuance et al. 2007); Depended – (Rue et al. 2007); Freebase – (Blacker et al. 2008); Wicking (Instates et al. 2010)). In general, Wisped is becoming more and more popula r as a source for RE. E. G. (Opponent and Strobe 2007; Unguent et al. AAA, b, c). Query logs are also considered a valuable source of information for RE and their analysis is even argued to give better results than other suggested methods in the field (Passes 2007, 2009). 5. 19 Weakly-supervised Methods Some supervised systems also use bootstrapping to make construction of the training data easier. These methods are also sometimes referred to as â€Å"huckleberries information extraction†. Bring (1998) describes the DIPPER (Dual Iterative Pattern Relation Expansion) method used for identifying authors of the books. It uses an initial small set of seeds or a set of hand- constructed extraction patterns to begin the training process. After the occurrences of needed information are found, they are further used for recognition of new patterns.Regardless of how promising bootstrapping can seem, error propagation becomes a serious problem: mistakes in extraction at the initial stag es generate more mistakes at later stages and decrease the accuracy of the extraction process. For example, errors that expand to named entity recognition, e. G. Extracting incomplete proper names, result in choosing incorrect seeds for the next step of bootstrapping. Another problem that can occur is that of semantic drift. This happens when senses of the words are not taken into account and therefore each iteration results in a move from the original meaning.Some researchers (Korea and How 2010; Hove et al. 2009; Korea et al. 2008) have suggested ways to avoid this problem and enhance the performance of this method by using doubly- anchored patterns (which include both the class name and a class member) as well as graph structures. Such patterns have two anchor seed positions â€Å"{type} such as {seed} and *† and also one open position for the terms to be learnt, for example, pattern â€Å"Presidents such as Ford and {X}† can be used to learn names of the presidents .Graphs are used for storing information about patterns, found words and links to entities they helped to find. This data is further used for calculating popularity and productivity of the candidate words. This approach helps to enhance the accuracy of bootstrapping and to find high-quality information using only a few seeds. Korea (2012) employs a similar approach for the extraction Of cause-effect relations, where the pattern for bootstrapping has a form of â€Å"X and Y verb Z†, for example, and virus cause Human-based evaluation reports 89 % accuracy on 1500 examples. Self-supervised Systems Self-supervised systems go further in making the process of information extraction unsupervised. The Knolling Web II system (Edition et al. 2005), an example of a self-supervised system, learns â€Å"to label its own training examples using only a small set of domain-independent extraction patterns†. It uses a set of generic patterns to automatically instantiate relation-specif ic extraction rules and then learns domain-specific extraction rules and the whole process is repeated iteratively. The Intelligence in Wisped (IPP) project (Weld et al. 2008) is another example of a self-supervised system.It bootstraps from the Wisped corpus, exploiting the fact that each article corresponds to a primary object and that any articles contain infusions (brief tabular information about the article). This system is able to use Wisped infusions as a starting point for training 20 the classifiers for the page type. IPP trains extractors for the various attributes and they can later be used for extracting information from general Web pages. The disadvantage of IPP is that the amount of relations described in Wisped infusions is limited and so not all relations can be extracted using this method. . 1 Open Information Extraction Edition et al. (2008) introduced the notion of Open Information Extraction, which is opposed to Traditional Relation Extraction. Open information e xtraction is â€Å"a novel extraction paradigm that tackles an unbounded number of relations†. This method does not presuppose a predefined set of relations and is targeted at all relations that can be extracted. The Open Relation extraction approach is relatively a new one, so there is only a small amount of projects using it. Texturing (Bank and Edition 2008; Bank et al. 2007) is an example of such a system.A set of relinquishment's lexicon-syntactic patterns is used to build a relation- independent extraction model. It was found that 95 % Of all relations in English can be described by only 8 general patterns, e. G. â€Å"El Verb E â€Å". The input of such a system is only a corpus and some relation-independent heuristics, relation names are not known in advance. Conditional Random Fields (CRY) are used to identify spans of tokens believed to indicate explicit mentions of relationships between entities and the whole problem of relation extraction is treated as a problem of sequence labeling.The set of linguistic features used in this system is similar to those used by other state of-the-art relation extraction systems and includes e. G. Part-of-speech tags, regular expressions for detection of capitalization and punctuation, context words. At this stage of development this system â€Å"is able to extract instances of the four most frequently observed relation types: Verb, Noun+Prep, Verb+Prep and Infinitive†. It has a number of limitations, which are however common to all RE systems: it extracts only explicitly expressed relations that are primarily word-based; relations should occur between entity names within the same sentence.Bank and Edition (2008) report a precision of 88. 3 % and a recall of 45. 2 Even though the system shows very good results the relations are not pacified and so there are difficulties in using them in some other systems. Output Of the system consists Of tepees stating there is some relation between two entities, but there is no generalization of these relations. Www and Weld (2010) combine the idea of Open Relation Extraction and the use of Wisped infusions and produce systems called Weepers and Weeps . Weepers improves Texturing dramatically but it is 30 times slower than Texturing.However, Weeps does not have this disadvantage and still shows an improved F-measure over Texturing between 1 5 % to 34 % on three corpora. Fader et al. 201 1) identify several flaws in previous works in Open Information Extraction: â€Å"the learned extractors ignore both â€Å"holistic† aspects of the relation phrase (e. G. , is it contiguous? ) as well as lexical aspects (e. G. , how many instances of this relation are there? )†. They target these problems by introducing syntactic constraints (e. G. , they require the relation phrase to match the POS tag 21 pattern) and lexical constraints.Their system Revere achieves an AUK which is 30 % better than WOE (Www and Weld 201 0) and Texturing (Bank and Denton 2008). Unshackles et al. (AAA) approach this problem from another angle. They try to mine for patterns expressing various relations and organism then in hierarchies. They explore binary relations between entities and employ frequent items mining (Augural et al. 1993; Syrians and Augural 1 996) to identify the most frequent patterns. Their work results in a resource called PATTY which contains 350. 69 pattern sunsets and substitution relations and achieves 84. 7 % accuracy. Unlike Revere (Fader et al. 201 1) which constrains patterns to verbs or verb phrases that end with prepositions, PATTY can learn arbitrary patterns. The authors employ so called syntactic- ontological-lexical patterns (SOL patterns). These patterns constitute a sequence of words, POS-tags, wildcats, and ontological types. For example, the pattern â€Å"persons [ads] voice * song† would match the strings my Heinousness soft voice in Rehab and Elvis Presley solid voice in his song All shook up.Their approach is based on collecting dependency paths from the sentences where two named entities are tagged (YACHT (Hoffa et al. 2011) is used as a database of all Ones). Then the textual pattern is extracted by finding the shortest paths connecting two entities. All of these patterns are transformed into SOL (abstraction of a textual pattern). Frequent items quinine is used for this: all textual patterns are decomposed into n-grams (n consecutive words). A SOL pattern contains only the n-grams that appear frequently in the corpus and the remaining word sequences are replaced by wildcats.The support set of the pattern is described as the set of pairs of entities that appear in the place Of the entity placeholders in all strings in the corpus that match the pattern. The patterns are connected in one sunset (so are considered synonymous) if their supporting sets coincide. The overlap of the supporting sets is also employed to identify substitution relations between various sunsets. . 2 Di stant Learning Mint et al. (2009) introduce a new term â€Å"distant supervision†. The authors use a large semantic database Freebase containing 7,300 relations between 9 million named entities.For each pair of entities that appears in Freebase relation, they identify all sentences containing those entities in a large unlabeled corpus. At the next step textual features to train a relation classifier are extracted. Even though the 67,6 % of precision achieved using this method has room for improvement, it has inspired many researchers to further investigate in this direction. Currently there are a number of papers ring to enhance â€Å"distant learning† in several directions. Some researchers target the heuristics that are used to map the relations in the databases to the texts, for example, (Takeouts et al. 01 2) argue that improving matching helps to make data less noisy and therefore enhances the quality of relation extraction in general. Hay et al. (2010) propose us ing an undirected graphical model for relation extraction which employs â€Å"distant learning' but enforces selection preferences. Ridded et al. (2010) reports 31 % error reduction compared to (Mint et al. 2009). 22 Another problem that has been addressed is language ambiguity (Hay et al. 01 1, 2012). Most methods cluster shallow or syntactic patterns of relation mentions, but consider only one possible sense per pattern.However, this assumption is often violated in reality. Hay et al. (201 1) uses generative probabilistic models, where both entity type constraints within a relation and features on the dependency path between entity mentions are exploited. This research is similar to DIRT (Line and Panatela 2001 ) which explores distributional similarity of dependency paths in order to discover different representations of the same semantic relation. However, Hay et al. (2011) employ another approach and apply IDA (Belie et al. 2003) with a slight modification: observations are re lation tepees and not words.So as a result of this modification instead of representing semantically related words, the topic latent variable represents a relation type. The authors combine three models: Reel-LAD, Reel-LDAP and Type-LAD. In the third model the authors split the features of a duple into relation level features and entity level features. Relation level features include the dependency path, trigger, lexical and POS features; entity level features include the entity mention itself and its named entity tag. These models output clustering of observed relation tepees and their associated textual expressions.