In Crick’s expressed view, the problem was susceptible to X-ray diffraction methods ‘if anyone knew how to use them, which Rosalind did. But it’s slower than model building, and she wouldn’t build models … It was all there. [Maurice] had as much information as we had. He says now he picked up the point in Chargaff’s article [the 1:1 base paring ratio] … but he didn’t see it, and that’s all there is to it. Meanwhile Rosalind was wasting time with Patterson superposition methods, and that took her off in the wrong direction entirely. I don’t know why she did this. I think Luzzati may have advised it … It was a mistake. But absolutely, she’d have got it out sooner or later.’
The National Institute for Health and Care Excellence (NICE) recommends the assessment of health risks due to being overweight or obese should be based on both Body Mass Index (BMI) and waist circumference. It recommends the use of the two measures because although BMI takes account of height, it does not differentiate between mass due to muscle development and mass due to body fat. In addition, BMI does not consider fat distribution, which has been identified as contributing to increased health risk. The health risk consequences of obesity can be significant; an obese man is five times more likely to develop type 2 diabetes and a woman is 13 times more likely. Obese men and women are about three times more likely to develop cancer of the colon, and both have increased risk of a number of other diseases including cardiovascular disease (CVD).
aii and b The smoothed curve should look something like the one in Figure 1 above. The climate became generally colder in the second part of the seventeenth century and then warmer in the first part of the eighteenth century. Notice, however, the exceptionally cold weather in 1740. This corresponds to a severe winter that affected much of Europe and during which as many Irish people died as did in the potato famine of 1845–7. Over the period of 1700 to 1900 the climate fluctuated around the mean of 9.2 °C. The fluctuations seem to reflect a 10 to 15 year cycle. Temperatures began to rise more steadily after 1900, although this is superimposed on top of the existing fluctuations (note a period around the 1960s, which was cold for the twentieth century, but still relatively warm compared with the preceding two and a half centuries). The data shows a continuing rise since 1990. Smoothed curves help to identify trends whilst playing down the effects of anomalous records. Students are unlikely to see this amount of detail; they may identify the pre-1700 dip and the post-1900 rise.
100 young male soccer players aged 10 to 12 years were recruited to the study. Half of the boys played in their soccer club’s best team; these boys formed the elite group. The other half played for the lowest ranked teams; they formed the non-elite group. The mean ages of the groups were 11.9 ± 0.5 (SD) (elite) and 11.6 ± 0.7 years (non-elite). Blood serum testosterone levels were measured three times at half-yearly intervals for all boys. A fourth measurement was taken for a subset of 28 boys (16 from the elite group and 12 from the non-elite group). The results are shown in Figure 1. At the same time, strength measurements were made. These include a board jump taking off and landing with both feet together, and the determination of back and abdomen muscle strength by measuring maximum voluntary contraction using a strain gauge dynamometer. In each case the best of three attempts was recorded as the maximum (Figure 2).
The ridges in the epidermal layer of skin on our fingers form distinct patterns that are unique to every individual. Even identical twins who may share similar patterns have slight variations in the details of their fingerprints, making them valuable in identification. The pattern develops in the early fetus and remains unaltered throughout a person’s life. The ridges’ primary role is to increase friction and hence improve grip. Four main patterns can be identified according to the Henry classification: arch, tented arch, loop and whorl. There are a number of variations in each of these categories. Some are shown below in Figure 1.
Now carefully pick through your sample. Do not neglect the very small organisms that may be on dead leaves, or stuck to rocks. Try to identify your animals using a key (see Figure 4 for one simple example). If you cannot identify an organism show it to your teacher/lecturer. In the end it matters less what the correct name for each organism is than that the whole class calls it the same thing if you are pooling your data. Once you have an idea of what you have, count the individuals in each taxon and record your results in an appropriate format (see Table 1). This is usually easiest if you select one taxon and count the individuals in it into another small container. (This makes recounts easier.) Having one person acting as a scribe while the other counts will help here. Tallies are a useful way of keeping a running total. Put all of your animals back into the stream when you have finished.
The New Zealand Smoke-free Environments Amendment Act (SEAA)  became law on December 10 2003. The SEAA strengthened existing legislation by introducing a range of tobacco control measures including restricting the display and sales of tobacco products, reducing under 18-year-old access to tobacco products, and providing for stronger future regulation of smoking product informa- tion and warnings. The SEAA stipulated that the buildings and grounds of all schools and early childhood centres should be totally smokefree from January 1 2004 and nearly all indoor workplaces from December 10 2004. This included bars, casinos, members' clubs and restau- rants; and non-office workplaces. Several partial exemp- tions were allowed, notably prisons, hotel and motel rooms, and residential establishments such as long-term care institutions and rest homes. Traditional Ma¯ ori indoor settings such as marae (traditional Ma¯ ori community meeting spaces) were only included in the legislation where they were defined as a workplace.
Deerwester et al. (1990) introduced Latent Semantic Indexing (LSI ) to provide associa- tions between similar documents and queries without the necessity of term sharing. LSI uses singular value decomposition to transform a TFxIDF term-by-document frequency matrix to a reduced dimensionality linear subspace. By plotting the distance between term and document points in this subspace, relationships can be reliably established, even be- tween terms or documents that share few common terms. This allows queries with terms not found in all documents to match more relevant documents than TFxIDF . To illustrate: for a given corpus of product reviews for books and apparel, LSI would cluster book reviews and book related terms in one section of the subspace while clustering apparel reviews and apparel related terms in another. When querying the subspace, query terms that match some of the documents or terms in a section of the subspace will be related with other nearby documents and terms. Thus a book query will return most book reviews even if the book query does not use book terms found in all book reviews. In this sense, LSI forms relationships between terms and documents. However, beyond clusters of similar items, this approach cannot provide an understanding of the topics being discussed in each doc- ument, nor does it proscribe a generative model, a model which can be used to generate values for new data.
The core of our solution lies in the heterogeneous topic association between Twitter fo llowee and YouTube video. Typical applications of existing heterogeneous topic association work include cross-med ia retrieval and heterogeneous face recognition, where invariant feature extraction and sub-space learning based solutions are extensively investigated. Invariant feature extraction methods are devoted to reducing the heterogeneous gap by exploring the most insensitive feature patterns. Klare et al. proposed to extract the SIFT and Multiscale LBP for forensic sketch and mug shot photo matching. In the intradiﬀerence and inter-d iﬀerence are jointly considered into a discriminant local feature learning framewo rk. The basic idea of subspace learning is to learn a new space
Our topical and emotion and cognition context features are general across target words. How- ever, the specific features that are informative for metaphor identification may depend on the tar- get word. To account for the specificity of target words, we use multi-level modeling (Daume III, 2007). The idea of multi-level modeling is to pair each of our features with every target word while keeping one set of features independent of the tar- get words. There are then multiple copies of each topic transition and emotion/cognition feature, all paired with a different target word. Thus, if there are N target words, our feature space becomes N + 1 times larger.
Mental illnesses such as depression and anxiety are highly prevalent, and therapy is increasingly being offered online. This new setting is a departure from face-to- face therapy, and offers both a challenge and an opportunity – it is not yet known what features or approaches are likely to lead to successful outcomes in such a dif- ferent medium, but online text-based ther- apy provides large amounts of data for lin- guistic analysis. We present an initial in- vestigation into the application of compu- tational linguistic techniques, such as topic and sentiment modelling, to online ther- apy for depression and anxiety. We find that important measures such as symptom severity can be predicted with compara- ble accuracy to face-to-face data, using general features such as discussion topic and sentiment; however, measures of pa- tient progress are captured only by finer- grained lexical features, suggesting that aspects of style or dialogue structure may also be important.
their resource base; technocentrism does not accept that there are any limits to resource use; resources are presently only limited by lack of technological know-how; technology will increase the effective life of a resource by allowing us to use it more efficiently e.g. fossil fuels; technology will help us find and develop new resources e.g. hydrogen fuel; technology will allow greater resource cycling; 5 max
Our TTN model outperforms all of the base- lines with large gains from 2.1 to 4.9 in the micro F1-score and significant gains from 1.6 to 6.7 in the macro F1-score. Compared with the baselines, TTN not only captures the interactive features at sentence-level, but also considers the topic-level relevance among arguments. This result shows that TTN can recognize the discourse relations at a higher level to improve the performance of Chi- nese implicit discourse relation recognition. Dif- ferent from Liu&Li, TTN not only learns the ar- gument representations by stacking multiple lay- ers with residuals to simulate the repeated read- ing, but also models the deep semantic interactions through factored tensor networks. Different from Guo, TTN not only reduces the complexity of the tensor network using tensor factorization, but also models the sentence-level and topic-level interac- tions together.
The new ideas from the brainstorming sessions were included in the process of developing the main idea in the design phase. The design result in these phases was a low-fidelity 3D model/prototype made from plain material such as: wood, foam, fibreglass and plastic that. We named it “Virtual garden”. The idea was designed as an interaction interface that tried to be simple and intuitive, with technology and wireless media, those functions on the organization of a “normal garden”. If we illustrate how garden interact: with combing with diagnostic tool (4) user (see Fig 1) test if all gadgets in the garden operate. With communication devices users can speak with their relatives or “virtual gardeners” 1 , with information recorders can records