TOOL FOR ANCIENT WRITING
With time, the record-keepers developed systematized symbols from their drawings. These symbols represented words and sentences but were easier and faster to draw and universally recognized for meaning. The discovery of clay made portable records possible (you can’t carry a cave wall around with you). Early merchants used clay tokens with pictographs to record the quantities of materials traded or shipped. These tokens date back to about 8,500 B.C. With the high volume and the repetition inherent in record keeping, pictographs evolved and slowly lost their picture detail. They became abstract figures representing sounds in spoken communication. The alphabet replaced pictographs between 1700 and 1500 B.C. in the Sinaitic world. The current Hebrew alphabet and writing became popular around 600 B.C. About 400 B.C. the Greek alphabet was developed. Greek was the first script written from left to right. From Greek followed the Byzantine and the Roman (later Latin) writings. In the beginning, all writing system s had only uppercase letters, when the writing instruments were refined enough for detailed faces, lowercase was used as well (around 600 A.D.)
The earliest means of writing that approached pen and paper as we know them today was developed by the Greeks. They employed a writing stylus, made of metal, bone, or ivory, to place marks upon wax-coated tablets. The tablets are made in hinged pairs, closed to protect the scribe’s notes. The first examples of handwriting (purely text messages made by hand) originated in Greece. The Grecian scholar, Cadmus invented the written letter – text messages on paper sent from one individual to another.
Writing was advancing beyond chiselling pictures into stone or wedging pictographs into wet clay. The Chinese invented and perfected ‘Indian Ink’. Originally designed for blacking the surfaces of raised stone-carved hieroglyphics, the ink was a mixture of soot from pine smoke and lamp oil mixed with the gelatin of donkey skin and musk. The ink invented by the Chinese philosopher, Tien-Lcheu (2697 B.C.), became common by the year 1200 B.C. Other cultures developed inks using natural dyes and colours derived from berries, plants, and minerals. In early writings, different coloured inks had ritual meanings attached to each colour.
The invention of inks paralleled the introduction of the paper. The early Egyptians, Romans, Greeks, and Hebrews, used papyrus and parchment papers. One of the oldest pieces of writing on papyrus known to us today is the Egyptian “Prisse Papyrus” which dates back to 2000 B.C. The Romans created a reed-pen perfect for parchment and ink, from the hollow tubular stems of marsh grasses, especially from the jointed bamboo plant. They converted bamboo stems into a primitive form of a fountain pen. They cut one end into the form of a pen nib or point. A writing fluid or ink filled the stem, squeezing the reed forced fluid to the nib
By 400 A.D. a stable form of ink developed, a composite of iron salts, nutgalls, and gum, the basic formula, which was to remain in use for centuries. Its colour when first applied to paper was a bluish-black, rapidly turning into a darker black and then over the years fading to the familiar dull brown colour commonly seen in old documents. Wood-fiber paper was invented in China in 105 A.D. but it only became known about (due to Chinese secrecy) in Japan around 700 A.D. and was brought to Spain by the Arabs in 711 A.D. Paper was not widely used throughout Europe until paper mills were built in the late 14th century.
The writing instrument that dominated for the longest period in history (over one thousand years) was the quill pen. Introduced around 700 A.D., the quill is a pen made from a bird feather. The strongest quills were those taken from living birds in the spring from the five outer left wing feathers. The left wing was favoured because the feathers curved outward and away when used by a right-handed writer. Goose feathers were most common; swan feathers were of a premium grade being scarcer and more expensive. For making fine lines, crow feathers were the best, and then came the feathers of the eagle, owl, hawk, and turkey.
There were also disadvantages associated with the use of quill pens, including a lengthy preparation time. The early European writing parchments made from animal skins required much scraping and cleaning. A lead and a ruler made margins. To sharpen the quill, the writer needed a special knife (origins of the term “pen-knife”.) Beneath the writer’s high-top desk was a coal stove, used to dry the ink as fast as possible.
Plant-fiber paper became the primary medium for writing after another dramatic invention took place: Johannes Gutenberg invented the printing press with replaceable wooden or metal letters in 1436. Simpler kinds of printing e.g. stamp with names used much earlier in China, did not find their way to Europe. During the centuries, many newer printing technologies were developed based on Gutenberg’s printing machine e.g. offset printing.
Articles written by hand had resembled printed letters until scholars began to change the form of writing, using capitals and small letters, writing with more of a slant and connecting letters. Gradually writing became more suitable to the speed the new writing instruments permitted. The credit of inventing Italian ‘running hand’ or cursive handwriting with its Roman capitals and small letters, goes to Aldus Manutius of Venice, who departed from the old set forms in 1495 A.D. By the end of the 16th century, the old Roman capitals and Greek letterforms transformed into the twenty-six alphabet letters we know today, both for upper and lower-case letters. When writers had both better inks and paper, and handwriting had developed into both an art form and an everyday occurrence, man’s inventive nature once again turned to improving the writing instrument, leading to the development of the modern fountain pens.
THE SUCCESS OF CELLULOSE
Not too long ago many investors made the bet that renewable fuels from bio-mass would be the next big thing in energy. Converting corn, sugarcane, and soybeans into ethanol or diesel - type fuels lessens our nation’s dependence on oil imports while cutting carbon dioxide emissions. But already the nascent industry faces challenges. Escalating demand is hiking food prices while farmers clear rainforest habitats to grow fuel crops. And several recent studies say that certain biofuel-production processes either fail to yield net energy gains or release more carbon dioxide than they use.
A successor tier of start-up ventures aims to avoid those problems. Rather than focusing on the starches, sugars, and fats of food crops, many of the prototype bioethanol processes work with lignocellulose, the “woody” tissue that strengthens the cell walls of plants, says University of Massachusetts Amherst chemical engineer George W. Huber. Although cellulose breaks down less easily than sugars and starches and thus requires a complex series of enzyme-driven chemical reactions, its use opens the industry to nonfood plant feedstocks such as agricultural wastes, wood chips, and switchgrass. But no company has yet demonstrated a cost-competitive industrial process for making cellulosic biofuels.
So scientists and engineers are working on dozens of possible biofuel-processing routes, reports Charles Wyman, a chemical engineer at the University of California, Riverside, who is a founder of Mascoma Corporation in Cambridge, Mass., a leading developer of cellulosic ethanol processing.” There’s no miracle process out there,” he remarks. And fine-tuning a process involves considerable money and time. “The oil companies say that it takes 10 years to fully commercialize an industrial processing route,” warns Huber, who has contributed some thermochemical techniques to another biomass start-up, Virent Energy Systems in Madison, Wis.
One promising biofuel procedure that avoids the complex enzymatic chemistry to break down cellulose is now being explored by Coskata in Warrenville, Ill., a firm launched in 2006 by high- profile investors and entrepreneurs (General Motors recently took a minority stake in it as well). In the Coskata operation, a conventional gasification system will use heat to turn various feedstocks into a mixture of carbon monoxide and hydrogen called syngas, says Richard Tobey, vice president of Engineering and R&D. The ability to handle multiple plant feedstocks would boost the flexibility of the overall process because each region in the country has access to certain feedstocks but not others.
Instead of using thermochemical methods to convert the syngas to fuel- a process that can be significantly more costly because of the added expense of pressurizing gases, according to Tobey – the Coskata group chose a biochemical route. The group focused on five promising strains of ethanol-excreting bacteria that Ralph Tanner, a microbiologist at the University of Oklahoma, had discovered years before in the oxygen-free sediments of a swamp. These anaerobic bugs make ethanol by voraciously consuming syngas.
The “heart and soul of the Coskata process,” as Tobey puts it, is the bioreactor in which the bacteria live. “Rather than searching for food in the fermentation mash in a large tank, our bacteria wait for the gas to be delivered to them,” he explains. The firm relies on plastic tubes, the filter-fabric straws as thin as human hair. The syngas flows through the straws, and water is pumped across their exteriors. The gases diffuse across the selective membrane to the bacteria embedded in the outer surface of the tubes, which permits no water inside. “We get an efficient mass transfer with the tubes, which is not easy,” Tobey says. “Our data suggest that in an optimal setting we could get 90 percent of the energy value of the gases into our fuel.” After the bugs eat the gases, they release ethanol into the surrounding water. Standard distillation or filtration techniques could extract the alcohol from the water.
Coskata researchers estimate that their commercialized process could deliver ethanol at under $1 per gallon-less than half of today’s $2-per-gallon wholesale price, Tobey claims. Outside evaluators of Argonne National Laboratory measured the input-output “energy balance” of the Coskata process and found that, optimally, it can produce 7.7 times as much energy in the end product as it takes to make it.
The company plans to construct a 40,000-gallon-a-year pilot plant near the GM test track in Milford, Mich., by the end of this year and hopes to build a full-scale, 100-million-gallon-a-year plant by 2011. Coskata may have some company by then; Bioengineering Resources in Fayetteville, Ark., is already developing what seems to be a similar three-step pathway in which syngas is consumed by bacteria isolated by James Gaddy, a retired chemical engineer at the University of Arkansas. Considering the advances in these and other methods, plant cellulose could provide the greener ethanol everyone wants.
LIFE CODE: UNLOCKED!
On an airport shuttle bus to the Kavli Institute for Theoretical Physics in Santa Barbara, Calif., Chris Wiggins took a colleague’s advice and opened a Microsoft Excel spreadsheet. It had nothing to do with the talk on biopolymer physics he was invited to give. Rather the columns and rows of numbers that stared back at him referred to the genetic activity of budding yeast. Specifically, the numbers represented the amount of messenger RNA (MRNA) expressed by all 6,200 genes of the yeast over the course of its reproductive cycle. “It was the first time I ever saw anything like this,” Wiggins recalls of that spring day in 2002. “How to make sense of all this data?”
Instead of shirking from this question, the 36-year-old applied mathematician and physicist at Columbia University embraced it-and now six years later he thinks he has an answer. By foraying into fields outside his own, Wiggins has drudged up tools from a branch of artificial intelligence called machine learning to model the collective protein-making activity of genes from real-world biological data. Engineers originally designed these tools in the late 1950s to predict output from input. Wiggins and his colleagues have now brought machine learning to the natural sciences and tweaked it so that it can also tell a story-one not only about input and output but also about what happens inside a model of gene regulation, the black box in between.
The impetus for this work began in the late 1990s, when high-throughput techniques generated more mRNA expression profiles and DNA sequences than ever before, “opening up a completely different way of thinking about biological phenomena,” Wiggins says. Key among these techniques were DNA microarrays, chips that provide a panoramic view of the activity of genes and their expression levels in any cell type, simultaneously and under myriad conditions. As noisy and incomplete as the data were, biologists could now query which genes turn on or off in different cells and determine the collection of proteins that give rise to a cell’s characteristic features, healthy or diseased.
Yet predicting such gene activity requires uncovering the fundamental rules that govern it. “Over time, these rules have been locked in by cells,” says theoretical physicist Harmen Bussemaker, now an associate professor of biology at Columbia. “Evolution has kept the good stuff.” To find these rules, scientists needed statistics to infer the interaction between genes and the proteins that regulate them and to then mathematically describe this network’s underlying structure-the dynamic pattern of gene and protein activity over time. But physicists who did not work with particles (or planets, for that matter) viewed statistics as nothing short of an anathema. “If your experiment requires statistics,” British physicist Ernest Rutherford once said, “you ought to have done a better experiment.”
But in working with microarrays, “the experiment has been done without you,” Wiggins explains. “And biology doesn’t hand you a model to make sense of the data.” Even more challenging, the building blocks that makeup DNA, RNA, and proteins are assembled in myriad ways; moreover, subtly different rules of interaction govern their activity, making it difficult, if not impossible, to reduce their patterns of interaction to fundamental laws. Some genes and proteins are not even known. “You are trying to find something compelling about the natural world in a context where you don’t know very much,” says William Bialek, a biophysicist at Princeton University. “You’re forced to be agnostic.” Wiggins believes that many machine-learning algorithms perform well under precisely these conditions. When working with so many unknown variables, “machine learning lets the data decide what’s worth looking at,” he says.
At the Kavli Institute, Wiggins began building a model of a gene regulatory network in a yeast-the set of rules by which genes selectively orchestrate how vigorously DNA is transcribed into mRNA. As he worked with different algorithms, he started to attend discussions on gene regulation led by Christina Leslie, who ran the computational biology group at Columbia at the time. Leslie suggested using a specific machine-learning tool called a classifier. Say the algorithm must discriminate between pictures that have bicycles in them and pictures that do not. A classifier sifts through labeled examples and measures everything it can about them, gradually learning the decision rules that govern the grouping. From these rules, the algorithm generates a model that can determine whether or not new pictures have bikes in them. In gene regulatory networks, the learning task becomes the problem of predicting whether genes increase or decrease their protein-making activity.
The algorithm that Wiggins and Leslie began building in the fall of 2002 was trained on the DNA sequences and mRNA levels of regulators expressed during a range of conditions in yeast- when the yeast was cold, hot, starved, and so on. Specifically, this algorithm-MEDUSA (for motif element discrimination using sequence agglomeration) -scans every possible pairing between a set of DNA promoter sequences, called motifs, and regulators. Then, much like a child might match a list of words with their definitions by drawing a line between the two, MEDUSA finds the pairing that best improves the fit between the model and the data it tries to emulate. (Wiggins refers to these pairings as edges.) Each time MEDUSA finds a pairing, it updates the model by adding a new rule to guide its search for the next pairing. It then determines the strength of each pairing by how well the rule improves the existing model. The hierarchy of numbers enables Wiggins and his colleagues to determine which pairings are more important than others and how they can collectively influence the activity of each of the yeast’s 6,200 genes. By adding one pairing at a time, MEDUSA can predict which genes ratchet up their RNA production or clamp that production down, as well as reveal the collective mechanisms that orchestrate an organism’s transcriptional logic.
Part 1
Questions 1-2
What two features do record retention possess in nature?
Choose the correct answer
Questions 4-10
Which paragraph contains the following information?
NB You may use any letter more than once.
- The working principle of the primitive pens made of plant stems 4
- A writing tool commonly implemented for the longest time 5
- Liquid for writing firstly devised by Chinese 6
- Majuscule scripts as the unique written form originally 7
- The original invention of today’s correspondences 8
- The mention of two basic writing instruments being invented coordinately 9
- A design to safeguard the written content 10
Questions 11-13
Choose No More than THREE Words and/or a Number from the passage for each answer.
-
What makes it not so convenient to use the quill pens?
11 -
When did one more breakthrough occur following the popularity of paper of plant fibers?
12 -
What inventions were the results of humans’ creative instinct of developing writing tools?
13
Part 2
Questions 14-19
Use the information in the passage to match the people (listed A-D) with opinions or deeds below.
NB you may use any letter more than once.
People:
(A) George W. Huber
(B) James Gaddy
(C) Richard Tobey
(D) Charles Wyman
Opinions/Deeds:
14. A key component to gain success lies in the place where the organisms survive. 14
15. Engaged in separating fixed procedures to produce ethanol in the homologous biochemical way. 15
16. Assists to develop certain skills. 16
17. It needs arduous efforts to achieve highly efficient transfer. 17
18. There is no shortcut to expedite the production process. 18
19. A combination of chemistry and biology can considerably lower the cost needed for the production company. 19
Questions 20-23
Do the following statements agree with the information given in Reading Passage 2?
Write:
TRUE if the statement is True
FALSE if the statement is False
NOT GIVEN if the information is not given in the passage.
- A shift from conventionally targeted areas of the vegetation to get ethanol takes place. 20
- It takes a considerably long way before a completely mature process is reached. 21
- The Coskata group sees no bright future for the cost advantage available in the production of greener ethanol. 22
- Some enterprises are trying to buy the shares of Coskata group. 23
Questions 24-26
Complete the following summary.
Use No More than THREE Words from the Reading Passage for each answer.
Tobey has noticed that the Coskata process can achieve huge success because it utilizes 24 as the bioreactor on whose exterior surface the bacteria take the syngas going through the coated 25 . To produce the ethanol into the water outside which researchers will later 26 by certain techniques. The figures show a pretty high percentage of energy can be transferred into fuel which is actually very difficult to achieve.
Part 3
Questions 27-33
Choose the correct heading for paragraphs A-G from the list below.
List of Headings
(I) The search for the better-fit matching between the model and the gained figures to foresee the activities of the genes
(II) The definition of MEDUSA
(III) A flashback of commencement for a far-reaching breakthrough
(IV) A drawing of the gene map
(V) An algorithm used to construct a specific model to discern the appearance of something new by the joint effort of Wiggins and another scientist
(VI) An introduction of a background tracing back to the availability of mature techniques for detailed research on genes
(VII) A way out to face the challenge confronting the scientist on the deciding of researchable data
(VIII) A failure to find out some specific genes controlling the production of certain proteins
(IX) The use of a means from another domain for reference
(X) A tough hurdle on the way to find the law governing the activities of the genes
Paragraphs
27. Paragraph A 27
28. Paragraph B 28
29. Paragraph C 29
30. Paragraph D 30
31. Paragraph E 31
32. Paragraph F 32
33. Paragraph G 33
Questions 34-36
Do the following statements agree with the information given in Reading Passage 3?
Write:
TRUE if the statement is True
FALSE if the statement is False
NOT GIVEN if the information is not given in the passage.
- Wiggins is the first man to use DNA microarrays for the research on genes. 34
- There is almost no possibility for the effort to decrease the patterns of interaction between DNA, RNA, and proteins. 35
- Wiggins holds a very positive attitude on the future of genetic research. 36
Questions 37-40
Complete the following summary.
Use No More than THREE Words from the Reading Passage for each answer.
Wiggins states that the astoundingly rapid development of techniques concerning the components of genes aroused the researchers to look at 37 from a totally new way. 38 is the heart and soul of these techniques and no matter what the 39 were, at the same time they can offer a whole picture of the genes’ activities as well as 40 in all types of cells. With these techniques, scientists could locate the exact gene which was on or off to manipulate the production of the proteins.