Posts Tagged ‘quote’

Death of the calorie | 1843

Wednesday, April 17th, 2019

This was pioneering stuff for the 1890s. Atwater eventually concluded that a gram of either carbohydrate or protein made an average of four calories of energy available to the body, and a gram of fat offered an average of 8.9 calories, a figure later rounded up to nine calories for convenience. We now know far more about the workings of the human body: Atwater was right that some of a meal’s potential energy was excreted, but had no idea that some was also used to digest the meal itself, and that the body expends different amounts of energy depending on the food. Yet more than a century after igniting the faeces of Wesleyan students, the numbers Atwater calculated for each macro­nutrient remain the standard for measuring the calories in any given food stuff. Those experiments were the basis of Salvador Camacho’s daily calorific arithmetic.

The Eisenhower Method For Taking Action (How to Distinguish Between Urgent and Important Tasks)

Sunday, April 14th, 2019


“A lot of things that take up mental energy, waste time, and rarely move you toward your goals can easily be eliminated if you apply the Eisenhower Principle. It’s a simple decision-making tool you can use right now. It’s meant to help you question whether an action is really necessary.

You can only benefit from the Eisenhower Method if you can commit yourself to making radical categorization of your daily tasks. This Method requires that you group your tasks and activities into four priorities.

Priority 1 tasks are both urgent and important.
Priority 2 tasks are important but not urgent.
Priority 3 tasks are urgent but not important.
Priority 4 tasks are neither urgent nor important”

The Eisenhower Method For Taking Action (How to Distinguish Between Urgent and Important Tasks) via Instapaper


Here are cognitive scientist Steven Pinker’s 13 tips for better writing / Boing Boing

Sunday, April 14th, 2019

liked particularly:

3. Don’t go meta. Minimize concepts about concepts, like “approach, assumption, concept, condition, context, framework, issue, level, model, perspective, process, range, role, strategy, tendency,” and “variable.”

8. Old information at the beginning of the sentence, new information at the end.

10. Prose must cohere: readers must know how each sentence is related to the preceding one. If it’s not obvious, use “that is, for example, in general, on the other hand, nevertheless, as a result, because, nonetheless,” or “despite.”

12. Read it aloud.

The Best Transcription Services

Monday, April 8th, 2019


If you need the accuracy of a real person doing your transcribing but have only hours of turnaround time to spare, Rev could be a good option. It has the best editor tool (in fact, the same editor as the AI-based Trint) and the easiest upload process of any of the human services we tested. But although it was more accurate than any of the AI-based services we tried, it consistently returned the
hardest-to-read and most error-filled transcriptions (aside from the jargon transcription, on which it tied for the most accurate) while being the costliest of the services we tested. The Rev transcripts were still readable, but we think it’s worthwhile to wait a bit longer for the cheaper and more accurate GoTranscript service if you have the time.

Scribie took the longest of any tested service to return our transcripts, had the worst editor, had the slowest upload process, and sported the poorest user interface. When we submitted our audio sample of a speaker with a foreign accent, Scribie rejected it. A customer service representative stated that the file was too short and too complicated for the service to find someone willing to transcribe it; Scribie rejected a second, longer accented file too. If you need to submit an audio file only on occasion or have lots of clear audio files, Scribie could still be a good option—it’s the least expensive real-people service we tried, and it produced easy-to-read and accurate transcripts for us. But steer clear if you want to be sure your uploads are accepted reliably.”
The Best Transcription Services

Rembrandt in the Blood: An Obsessive Aristocrat, Rediscovered Paintings and an Art-World Feud

Wednesday, April 3rd, 2019

“As he grew in his profession, Six came to feel he had a right to express himself on the family collection. A series of clashes with his father ensued, many of them about providing greater public access, which has always been a difficulty. Currently, tours of the
collection, which are by appointment only, are booked into next year. The picture that the younger Six sketched was of an inward-looking father who is trying to preserve a legacy by keeping the world at bay, who comes to realize over time that he also has to do battle with a gregarious and extroverted son who feels that the way to preserve that legacy is precisely by sharing it with the wider world. The battles left the younger Six progressively more exasperated: “I would cycle home after and think, Jesus, Dad, I’m trying to help you.””

Rembrandt in the Blood: An Obsessive Aristocrat, Rediscovered Paintings and an Art-World Feud

A Decade of GWAS Results in Lung Cancer | Cancer Epidemiology, Biomarkers & Prevention

Monday, April 1st, 2019

The first GWAS on lung cancer were reported in 2008. Three independent studies identified a susceptibility locus on chromosome 15q. Hung and colleagues (14) found two SNPs strongly associated with lung cancer on chromosome 15q25. Further genotyping in this region revealed many SNPs in tight linkage disequilibrium (LD) showing evidence of association. Six genes are located in this region including three nicotinic acetylcholine receptor subunits (CHRNA5, CHRNA3, and CHRNB4). Interestingly, no appreciable variation in the risk was found across smoking categories or histologic subtypes of lung cancer. In a second GWAS, a SNP within the CHRNA3gene was strongly associated with smoking quantity and nicotine dependence (15). The same SNP was also strongly associated with lung cancer. The results suggest that the variant on chromosome 15q25 confers risk of lung cancer through its effect on tobacco addiction.

Introduction to Mediation Analysis | University of Virginia Library Research Data Services + Sciences

Sunday, March 31st, 2019

To analyze mediation:
1. Follow Baron & Kenny’s steps
2. Use either the Sobel test or bootstrapping for significance testing. “]]

Final Article — American Scholar Magazine

Tuesday, March 12th, 2019

Decoding DNA
On the hunt for the genetic roots of mental illnesses

By Marcus Banks | March 4, 2019

The model, a form of artificial intelligence, aims to use abstract knowledge gained in the research lab to improve clinical treatments for real patients. The ultimate goal, says Gerstein, is to use the model to develop pharmaceutical treatments that reduce the impact of schizophrenia. Part of the challenge in developing drugs to treat the disease is the fact that it is not a one-size-fits-all condition. “]]

Deep learning and process understanding for data-driven Earth system science | Nature

Tuesday, March 5th, 2019
Perspective | Published: 13 February 2019
Deep learning and process understanding for data-driven Earth system science Markus Reichstein, Gustau Camps-Valls, Bjorn Stevens, Martin Jung, Joachim Denzler, Nuno Carvalhais & Prabhat
Nature volume 566, pages195–204 (2019)

Figure 3 presents a system-modelling view that seeks to integrate machine learning into a system model. As an alternative perspective, system knowledge can be integrated into a machine learning frame- work. This may include design of the network architecture36,79, physical constraints in the cost function for optimization58, or expansion of the training dataset for undersampled domains (that is, physically based data augmentation)80.

Surrogate modelling or emulation
See Fig. 3 (circle 5). Emulation of the full (or specific parts of) a physical model can be useful for computational efficiency and tractability rea- sons. Machine learning emulators, once trained, can achieve simulations orders of magnitude faster than the original physical model without sacrificing much accuracy. This allows for fast sensitivity analysis, model parameter calibration, and derivation of confidence intervals for the estimates.

(2) Replacing a ‘physical’ sub-model with a machine learning model
See Fig. 3 (circle 2). If formulations of a submodel are of semi-empirical nature, where the functional form has little theoretical basis (for example, biological processes), this submodel can be replaced by a machine learning model if a sufficient number of observations are available. This leads to a hybrid model, which combines the strengths of physical modelling (theoretical foundations, interpretable compartments) and machine learning (data-adaptiveness).

Integration with physical modelling
Historically, physical modelling and machine learning have often been treated as two different fields with very different scientific paradigms (theory-driven versus data-driven). Yet, in fact these approaches are complementary, with physical approaches in principle being directly interpretable and offering the potential of extrapolation beyond observed conditions, whereas data-driven approaches are highly flexible in adapting to data and are amenable to finding unexpected patterns (surprises).

A success story in the geosciences is weather
prediction, which has greatly improved through the integration of better theory, increased computational power, and established observational systems, which allow for the assimilation of large amounts of data into the modelling system2
. Nevertheless, we can accurately predict the evolution
of the weather on a timescale of days, not months.

# REFs that I liked
ref 80

ref 57
Karpatne, A. et al. Theory-guided data science: a new paradigm for scientific discovery from data. IEEE Trans. Knowl. Data Eng. 29, 2318–2331 (2017).

# some key BULLETS

• Complementarity of physical & ML approaches
–“Physical approaches in principle being directly interpretable and offering the potential of extrapolation beyond observed conditions, whereas data-driven approaches are highly flexible in adapting to data”

• Hybrid #1: Physical knowledge can be integrated into ML framework –Network architecture
–Physical constraints in the cost function
–Expansion of the training dataset for undersampled domains (ie physically based data augmentation)

• Hybrid #2: ML into physical – eg Emulation of specific parts of a physical for computational efficiency

Mystery RNA spawns gene-activating peptides : Nature News

Saturday, March 2nd, 2019


It should be possible to scan the genome for sequences encoding peptides shorter than 100 amino acids, says Mark Gerstein, a computational biologist at Yale University in New Haven, Connecticut, but sorting through the many ‘hits’ to determine which are functional is likely to be much more difficult.

Meanwhile, Gerstein notes that the polished rice peptides could also have implications for how we view pseudogenes, which have long been thought to be defunct relics of protein-coding genes. Pseudogenes often contain many signals that would stop protein synthesis and, as a result, could only encode short amino-acid chains. “Maybe this would provide a new way for pseudogenes to have some sort of function,” he says.