Next Generation Sequencing Quotes

Get the ongoing process right and it will keep generating ongoing benefits. In our new era, processes trump products. This shift toward processes also means ceaseless change is the fate for everything we make. We are moving away from the world of fixed nouns and toward a world of fluid verbs. In the next 30 years we will continue to take solid things—an automobile, a shoe—and turn them into intangible verbs. Products will become services and processes. Embedded with high doses of technology, an automobile becomes a transportation service, a continuously updated sequence of materials rapidly adapting to customer usage, feedback, competition, innovation, and wear. Whether it is a driverless car or one you drive, this transportation service is packed with flexibility, customization, upgrades, connections, and new benefits.

[All] modern chatbots are actually trained simply to predict the next word in a sequence of words. They generate text by repeatedly producing one word at a time. For technical reasons, they generate a “token” at a time, tokens being chunks of words that are shorter than words but longer than individual letters. They string these tokens together to generate text. When a chatbot begins to respond to you, it has no coherent picture of the overall response it’s about to produce. It instead performs an absurdly large number of calculations to determine what the first word in the response should be. After it has output—say, a hundred words—it decides what word would make the most sense given your prompt together with the first hundred words that it has generated so far. This is, of course, a way of producing text that’s utterly unlike human speech. Even when we understand perfectly well how and why a chatbot works, it can remain mind-boggling that it works at all. Again, we cannot stress enough how computationally expensive all this is. To generate a single token—part of a word—ChatGPT has to perform roughly a trillion arithmetic operations. If you asked it to generate a poem that ended up having about a thousand tokens (i.e., a few hundred words), it would have required about a quadrillion calculations—a million billion.

Parenting role-play change every ten years from affection to duty to responsibility, and then the cycle must reverse with next generation playing the same in the reverse sequence.

Evolution as a process is powerful because of its cumulative nature. Richard Dawkins offers a neat way to think about cumulative selection in his wonderful book The Blind Watchmaker. He invites us to consider a monkey trying to type a single line from Hamlet: “Methinks it is like a weasel.” The odds are pretty low for the monkey to get it right. If the monkey is typing at random and there are 27 letters (counting the space bar as a letter), it has a 1 in 27 chance to get the first letter right, a 1 in 27 for the next letter, and so on. So just to get the first three in a row correct are 1/27 multiplied by 1/27 multiplied by 1/27. That is one chance in 19,683. To get all 28 in the sequence, the odds are around 1 in 10,000 million, million, million, million, million, million. But now suppose that we provide a selection mechanism (i.e., a failure test) that is cumulative. Dawkins set up a computer program to do just this. Its first few attempts at getting the phrase is random, just like a monkey. But then the computer scans the various nonsense phrases to see which is closest, however slightly, to the target phrase. It rejects all the others. It then randomly varies the winning phrase, and then scans the new generation. And so on. The winning phrase after the first generation of running the experiment on the computer was: WDLTMNLT DTJBSWIRZREZLMQCO P. After ten generations, by honing in on the phrase closest to the target phrase, and rejecting the others, it was: MDLDMNLS ITJISWHRZREZ MECS P. After twenty generations, it looked like this: MELDINLS IT ISWPRKE Z WECSEL. After thirty generations, the resemblance is visible to the naked eye: METHINGS IT ISWLIKE B WECSEL. By the forty-third generation, the computer got the right phrase. It took only a few moments to get there.

UnsafeSequence can be fixed by making getNext a synchronized method, as shown in Sequence in Listing 1.2,[3] thus preventing the unfortunate interaction in Figure 1.1. (Exactly why this works is the subject of Chapters 2 and 3.) [3] @GuardedBy is described in Section 2.4; it documents the synchronization policy for Sequence. Listing 1.2. Thread-safe Sequence Generator. In the absence of synchronization, the compiler, hardware, and runtime are allowed to take substantial liberties with the timing and ordering of actions, such as caching variables in registers or processor-local caches where they are temporarily (or even permanently) invisible to other threads. These tricks are in aid of better performance and are generally desirable, but they place a burden on the developer to clearly identify where data is being shared across threads so that these optimizations do not undermine safety. (Chapter 16 gives the gory details on exactly what ordering guarantees the JVM makes and how synchronization affects those guarantees,

Epigenetics is “the study of heritable changes in gene activity that are not caused by changes in the DNA sequence.” This means that vital information learned by one generation is genetically transferred to the next. Before epigenetics was discovered, it was thought that genes could not learn because the DNA codes that express them do not provide that option. In other words, DNA is the blueprint that determines gene expression, but epigenetics and other mindbody research is showing that the expression is affected by contextual conditions; Mother Nature sets rules to be questioned, not to be blindly obeyed.

Here’s the trick to significantly improving your SaaS email marketing skills—you have to become a student of it. This means you should: Start collecting great email copy, CTAs, and designs. Understand the objective behind each and every email that businesses send. Try to understand the rationale behind copy, link, and design decisions. There are great websites like Really Good Emails11, Good Email Copy12, and Good Sales Emails.com13 that you can use for your research. These sites categorize email copy and designs by types. As well as this, you should sign up to receive emails from some of the leading SaaS brands. Those include, among others: Drift MailChimp Pipedrive Shopify SurveyMonkey Trello Wistia Zapier You should also sign up to competing products and mailing lists from companies in your sector. I personally signed up to thousands of products and newsletters. It’s great for benchmarking and research. At the time of writing, I’ve already passively collected more than 60,000 emails. Obviously, don’t sign up to your competitors’ products with a business email address! I have a special email address I use for this. This account allows me to get data, understand what other organizations are doing, and find good copy ideas. For example, here’s what a search for ‘Typeform’ gives me: Figure 18.1 – Inbox Inspiration It’s not uncommon for me to sign up several times to the same product or newsletter. This allows me to see what they have learned and to track the evolution of their email marketing program. At LANDR, we created a shared document to keep track of subject lines, offers, and copy we wanted to test. Our copywriter was even going through his junk mail folder to find ideas and inspiration. There are tests we ran that were inspired by copy found in his spam folder. Some of them turned out to be really successful too—so keep your eyes open for inspiration. You can use Evernote, Paper, or any other platform to collaborate on idea generation. Alternatively, you can subscribe to paid services like Mailcharts14 or Mailody15. These services will help you track and understand your competitors’ email programs. Build processes to find and access copy and design ideas. It will help you create better emails, faster. In the next chapter we’ll get started creating our first email sequences.

Although Sanger Sequencing is still used, it is now increasingly being replaced by newer technologies that are developing at an astounding pace. These technologies, collectively referred to as next-generation or high-throughput sequencing, allow DNA to be sequenced much more quickly and cheaply. The Human Genome Project, which used Sanger sequencing, took ten years to sequence and cost 3 billion US dollars. Using high-throughput sequencing, the entire human genome can now be sequenced in a few days at a cost of 3,000 US dollars. These costs are continuing to fall, making it more feasible to sequence whole genomes.

As strange as it sounds, it is no longer possible to determine how many human genomes have been sequenced. At present the strategy of choice is whole-genome re-sequencing (Chapter 3) whereby next-generation sequence data are mapped onto a reference genome. The results have been breathtaking. The recently concluded (and aptly named) 1000 Genomes Project Consortium catalogued ~85 million SNPs, 3.6 million short insertions/deletions, and 60,000 larger structural variants in a global sampling of human genetic diversity. These data are catalysing research in expected and unexpected ways. Beyond providing a rich source of data for GWA-type studies focused on disease, scientists are also using the 1000 Genomes Project data to learn about our basic biology, something that proved surprisingly difficult when only a pair of genomes was available. For example, a recent GWAS taking advantage of the 1000 Genomes Project data identified ten genes associated with kidney development and function, genes that had previously not been linked to this critical aspect of human physiology. In 2016, Craig Venter’s team reported the sequencing of 10,545 human genomes. Beyond the impressively low cost (US$1,000–2,000 per genome) and high quality (30–40× coverage), the study was significant in hinting at the depths of human genome diversity yet to be discovered. More than 150 million genetic variants were identified in both coding and non-coding regions of the genome; each sequenced genome had on average ~8,600 novel variants. Furthermore, each new genome was found to contain 0.7 Mbp of sequence that is not contained in the reference genome. This underscores the need for methods development in the area of structure variation detection in personal genome data. Overall, however, the authors concluded that ‘the data generated by deep genome sequencing is of the quality necessary for clinical use’.

we must link each of today’s generations with a recurring sequence of four generational archetypes that have appeared throughout all the saecula of our history. These four archetypes are best identified by the turnings of their births: ■ A Prophet generation is born during a High. A Nomad generation is born during an Awakening. A Hero generation is born during an Unraveling. ■ An Artist generation is born during a Crisis.

When these ancient parts of your brain are active or rehearsing the next disaster using the DMN, they effortlessly hijack your attention. You try to meditate and repetitive negative thinking takes over. In the cage match between Caveman Brain and Bliss Brain, Caveman Brain always wins. Survival is a more important need than happiness or self-actualization. You can’t self-actualize if you’re dead. In 2015 the US National Institutes of Health estimated that less than 10% of the US population meditates. One of the primary reasons for this is that meditation is hard. Most people who start a meditation program drop out. GETTING THE BEST OF ALL WORLDS When writing my first best-selling book, The Genie in Your Genes, I experimented with many schools of stress reduction and meditation. Heart coherence. Mindfulness. EFT tapping. Neurofeedback. Hypnosis. One day I had a Big Idea: What happens when you combine them all? I began playing with a routine that did just that. Here’s what I came up with: First, you tap on acupressure points to relieve stress. Second, you close your eyes and relax your tongue on the floor of your mouth. This sends a signal to your vagus nerve, which wanders all over your body, connecting all the major organ systems. It’s the key signaling component of the parasympathetic nervous system, which governs relaxation. 4.8. The vagus nerve connects with all the major organ systems of your body. Third, you imagine the volume of space inside your body, particularly between your eyes. This automatically generates big alpha in your brain, moving you toward the Awakened Mind. Fourth, you slow your breathing down to 6 seconds per inbreath and 6 seconds per outbreath. This puts you into heart coherence. Fifth, you imagine your breath coming in and going out from your heart area, and you picture a sphere of energy in your heart. Sixth, you send a beam of heart energy to a person or place that makes you feel wonderful. This puts you into deep coherence. After enjoying the connection for a while, you send compassion to everyone and everything in the universe. Feeling universal compassion produces the major brain changes seen in fMRI scans of longtime meditators. As we’ll see in Chapters 6 and 8, compassion moves the needle like nothing else. At this point, most people drop into Bliss Brain automatically. They’re in a combination of alpha, heart coherence, and parasympathetic dominance. They haven’t been asked to still their minds, sit cross-legged, follow a guru, or believe in a deity. They’ve just followed a sequence of simple physical steps. After a few minutes of universal compassion, you again focus your beam on a single person or place. You then gently disengage and draw the energy beam back into your own heart. Seventh, you direct your beam of compassion to a part of your body that is suffering or in pain. You end the meditation by returning your attention to the here and now.

Polybius studied the histories of Greco-Roman city-states and noticed a recurring progression of political regimes—from kingship to aristocracy to democracy to anarchy —from which a new kingship would emerge. This progression itself was nothing new: Plato and Aristotle had said something similar. But Polybius went further. He specifically linked it to a pattern of generational succession. In his view, the city-states' first kings are generally powerful and good, but their children so weak and corrupt that an aristocratic rebellion eventually arises among the children's peers. The founding aristocrats govern well enough, but their children sink to oligarchy, prompting a democratic rebellion among their peers. A generation afterwards, the initial democrats' children sink to a mob rule ochlocracy, leading to a state of anarchy. In due course, a new king seizes control, and the cycle repeats. Polybius never says how long it takes for this sequence to occur. Apparently, it could occur slowly, over a period of many centuries—or rapidly, over the course of one saeculum (four generations).

Ideologues are welcome to live with the values they admire, I have no problem with that, but you have to give the next generation the freedom and ability to go their own way. Anything else is fascism.

LLMs are connection machines. They are trained by generating relationships between tokens that may seem unrelated to humans but represent some deeper meaning. Add in the randomness that comes with AI output, and you have a powerful tool for innovation. The AI seeks to generate the next word in a sequence by finding the next likely token, no matter how weird the previous words were. So it should be no surprise that the AI can come up with novel concepts with ease. I asked AI to: Find me business ideas that would incorporate fast food, patent 6,604,835 B2 [which turned out to be for a lava lamp that included bits of crystal], and 14th century England.

Generator functions are regular functions that use a yield statement to provide the next element in the sequence. def my_iterator(limit): for i in range(1, limit+1): yield i for x in my_iterator(4): # 1 2 3 4 print(x) print(list(my_iterator(4))) # [1, 2, 3, 4]