Friday, December 17, 2010

Danger, Will Robinson!!! or injury prevention with sensors and algorithms

Health is determined by many factors including:
  • Behavior (Physical Activity, Eating habits, Tobacco or substance abuse, responsible sexual choices, etc)
  • Mental Health 
  • Injury and Violence 
  • Environmental Quality 
  • Preventative measures such as immunization 
  • Access to Health Care

All these factors are quantifiable, predictable and preventable. Injuries are most likely to be  perceived as “accidents” and “acts of fate”,  but they depend on the same determinants as other health factors: individual behavior, social and physical environment.  The likelihood of injuries -  unintentional ones and those caused by acts of violence - can be computed from physical location (estimations for USA1 are a good example), gene-environment interactions2, prior medical history, and physical traits3.

There are many ways to prevent injuries - just say "no" to risky behaviors, wear preventative gear while playing sports or fall-optimized shoes for elderly, watch out for others engaged in similar activities... Yet, sometimes we forget to watch, don't have access to histories of others or get diverted.  Would a body sensor or a gadget with smart software be able to warn us about potential accidents ahead to help prevent accidents?  What would it need to measure?

Software and devices automatically detecting and reporting accidents already exist:

Halo Monitoring's fall detection system, for example,  consists of a chest strap and belt clip with motionOnStar or mbrace that "intelligently integrate the driver, the vehicle and the environment" - capabilities such as this will be provided in the area of next-generation health management systems like Aurametrix.
sensors, heart rate and skin temperature monitors. Although the system detects falls only after they happen, a study showed that just the fact of wearing it increases alertness of seniors and reduces the number of falls. Although fall detection systems are not as advanced as telematics for cars - like
Unprecedented accumulation of data  - such as snapshots of driving behavior or 1.2 million person-years of hip fracture observations (Kanis JA) allows development of smarter software able to predict injuries.  Logistic regression models (Kononen et al., 2011) predict seriousness of auto accidents,  first-principles mathematical models (such as AHAAH for the ear) connect forces with injuries, neural net and other data mining approaches foretell which juvenile offenders are likely to return to crime (source of  "intentional injuries"), or allow to calculate risk of fractures based on milk intake, personal history of accidents and body mass index. Self-quantifiers such as René Ghosh are able to figure out how to use their own data to predict future injuries. Using simple math (Riegel equation bringing all running logs on to a comparable level) and trend analysis, he tied his accidents to wanes following waxes in running performance. Researchers keep refining the variables predicting injuries.  Swanenburg et al., for example, predicted multiple falls for those with a history of multiple falls (odds ratio, 5.6) and use of multiple medications (odds ratio, 2.3). And there is another simple measurement of standing position helpful in prediction. Frequent fallers, indeed, have a narrower stance width than non-fallers.

In the always-connected smart-sensor-equipped future, things such as Intel's magic carpet - picking up the weight, angle and pressure of steps - will be a commodity. Gene tests predicting injuries will be integrated with data coming from our carpets, clothing, footwear and location information. And this may be sooner than you think.


1. Centers for Disease Control and Prevention (CDC), National Center for Injury Prevention and Control. Web-based Injury Statistics Query and Reporting System (WISQARS); 2010 Mar 4 Available from:

2. Husted JA, Ahmed R, Chow EW, Brzustowicz LM, Bassett AS. Childhood trauma and genetic factors in familial schizophrenia associated with the NOS1AP gene. Schizophr Res. 2010 Aug;121(1-3):187-92. PMID: 20541371

3. Swanenburg J, de Bruin ED, Uebelhart D, & Mulder T (2010). Falls prediction in elderly people: a 1-year prospective study. Gait & posture, 31 (3), 317-21 PMID: 20047833

4. Kononen DW, Flannagan CA, Wang SC. Identification and validation of a logistic regression model for predicting serious injuries associated with motor vehicle crashes. Accid Anal Prev. 2011 Jan;43(1):112-22. PMID: 2109430

5. Price GR. Predicting mechanical damage to the organ of Corti. Hear Res. 2007 Apr;226(1-2):5-13. Epub 2006 Sep 15.PMID: 16978813

Tuesday, October 12, 2010

You are the Chosen One, at least by your bacteria

Host genomics is not the main decision-making factor for bacteria immigrating into human body, but  it is an important factor. Two papers recently published in the Proceedings of the National Academy of Sciences help to understand why you are chosen and how the choosers make their decisions.

Benson et al studied microbes of mice C57BL/6J, HR and their offspring. BL6 is a common inbred line prone to diet-induced obesity, type 2 diabetes, and atherosclerosis. They also develop age-related hearing loss, if are not following recommended dietary allowance. High runner (HR) mice is lean and fit and loves to exercise - it's in the genes.

Noninvasive 16S RNA sequencing (Roche 454) showed that the abundance of microbes in "core measurable microbiota" depended on 530 host SNPs, mostly those located in 13 quantitative trait loci and was influenced by 5 more QTLs.

Some of the genetic regions appear to determine what kind of bacteria immigrate and strive in the host, other regions influence the immigration rate, attracting a wide variety pf or specific nationalities. Supplementary material elaborates on  sources of variation and genotype frequencies at given SNP locations. 

How are bacteria making their decisions to colonize or not to colonize?

In another PNAS article, Ben-Jacob and Schultz explain why microbes could be smarter than humans. We may think that our decisions are well thought and sophisticated, but we are, indeed, influenced by other people and our over-interpretations of other people's reactions. Bacteria can assess the noisy and stressful environment around them more objectively and rationally. They anticipate possible drastic changes in the environment and find the best decisions by providing every bacterium with the freedom to choose its own fate. This may look like throwing dice, but the colony manages the odds and effectively programs the effect of the noise on the gene circuit performance.

Our genes may be shaping microbial communities that could, in their turn, control our physical and mental health. Yet our lifestyle choices could break the patterns and let us decide what types of bacteria we want to live with.

And for those whose fight against unwanted microbes is too hard, there may be light in the end of the tunnel: Personal Genomes project has just announced a new collaboration with Rob Knight and Noah Fierer that will enable to explore the microbial diversity of various habitats of the human body and correlate it to the genotype.

  • Andrew K. Benson,, Scott A. Kelly,, Ryan Legge,, Fangrui Ma,, Soo Jen Low,, Jaehyoung Kim,, Min Zhang,, Phaik Lyn Oh,, Derrick Nehrenberg,, Kunjie Hu,, Stephen D. Kachman,, Etsuko N. Moriyama,, Jens Walter,, Daniel A. Peterson,, & Daniel Pomp10.1073/pnas.1007028107 (2010). Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors Proceedings of the National Academy of Sciences of the United States of America,
  • Ben-Jacob E, &; Schultz D (2010). Bacteria determine fate by playing dice with controlled odds. Proceedings of the National Academy of Sciences of the United States of America, 107 (30), 13197-8 PMID: 20660309

Sunday, August 15, 2010

Predicting catastrophic health events - noninvasively and short term

This post was chosen as an Editor's Selection for
"I've just picked up a fault in the AE35 unit. It's going to go 100% failure in 72 hours". These were famous words of the almighty computer HAL in "2001: A Space Odyssey". Few of us believe too much in software forecasts - be it weather, earthquakes or computer hard disk failures. Yet, we all know that sometimes it works. And such systems are very valuable, assuming they continuously improve.
Word cloud generated from QS #15 intros
Wellness is always a common theme of the many presentations and experiments of Quantified Self enthusiasts - citizen scientists quantifying everyday life in order to improve it.  "Health" was the most frequent word among the two-word introductions of QS #15 attendees, followed by variations of sleep and happiness, and ways to measure and understand it.
Are health failures predictable?  Nobody argues they are, although most available services  - such as genomic testing - provide only long term predictions.  Biomarkers that scientists are interested in are obtained through invasive or costly interventions - like blood proteins or intracranial electroencephalograms. Technologies such as Mobile Cardiac Outpatient Telemetry™ (MCOT™) developed by CardioNet  are based on real time EKG focused on heart rhythm abnormalities that can't be detected in small 24 or 48 hour windows. These relatively rare events are correlated with symptoms and used for diagnostics.
Your husband just died, … here’s his black box
(from Gordon Bell's presentation)
Perhaps in the future every one of us will leave a black box holding all the truth about our health, helping next generations to better maintain, test and repair their bodies. Many lives have already contributed to the understanding of causes and effects such as the link between cholesterol levels, diet and heart attack. But what about the short term prediction horizon,  like the 72-hour window of HAL or 30 seconds to 15-to-45 minutes warning by seizure dogs?
Could computer luminary Gordon Bell predict his heart attack if he wore his heart monitor strap while bicycling?  A cardiologist would say "no way", but maybe sometimes it's better to keep quantifying and experiment despite of what medical establishment has to say?
Data from the Women’s Health Initiative study (129,135 postmenopausal women observed over a period of nearly eight years) show that a woman’s resting heart rate may be a good indicator of her risk for a heart attack. Hsia and others (2009) found that women whose resting heart rate is more than 76 beats per minute are significantly more likely to have a heart attack than women with resting heart rate less than 62 beats a minute. This risk factor is particularly strong for women between the ages of 50 and 64, less so for women over the age of 65. (Trial NCT00000611)

Of course, women have different kinds of heart attacks than men do. They are more likely to die from a spasms of heart and the blood vessels leading to the heart, and are more likely to complain of fatigue and sleep disturbances in the weeks and months leading up to a heart attack. Sleep disturbances and decreased physiological differences between day and night are increasing heart attack and stroke risks for males too, at least for shift workers. Exercise tests  - known for their false positive results - have better cardiovascular prognostic value for smokers with high cholesterol than healthy men.

Finger arterial pulsatile volume changes or finger blood flow - related to blood pressure - is another medium-to-long term predictor of pending cardiac events. A simple, noninvasive finger sensor test called EndoPAT  can predict major cardiac events such as a heart attack or stroke for people who are considered at low or moderate risk.

AngelMed Guardian System (inventor: Dr. Tim Fischell) is an early warning system - telling 24 hrs to a week before if heart attack is coming. Average time between the cardiac event and hospital arrival is 3 hrs, and about 5 hrs before the surgery starts. By that time 90% of muscles could be dead, so 5 or 10 minute warnings could save lives.  Unfortunately, Guardian is highly invasive - sized as an iPod, it is implanted directly into the patient's chest, monitoring electrogram  waveforms and other crucial heart-signal data 24-7.

Many portable oximeters and ECG are already in the market - and even though consumers are complaining about noise and difficulties in getting readings of diagnostic intervals, the technologies will continue to improve and self-quantifiers will be finding solutions for better health.

Hsia, J., Larson, J., Ockene, J., Sarto, G., Allison, M., Hendrix, S., Robinson, J., LaCroix, A., Manson, J., & , . (2009). Resting heart rate as a low tech predictor of coronary events in women: prospective cohort study BMJ, 338 (feb03 2) DOI: 10.1136/bmj.b219

Mayo Clinic (2009). High resting heart rate could predict heart attack in women. Mayo Clinic women's healthsource, 13 (7) PMID: 19498326

Saturday, July 17, 2010

Collaboration 2.0

Information technology is letting people around the world come together in unprecedented ways. Wikis, blogs and microblogs like twitter, 
crowdsourcing and crowd-task-solving sites continue to flatten the planet.  
Scientific innovation used to be a very private endeavor, with narrowly specialized scientists delving deeply into specific research areas.  The Internet changed some of this giving rise to Wikipedia  - now orders of magnitude larger than the Encyclopedia Britannica, and similar wiki resources for gene annotations, RNA libraries, radiology images, open-source software and other content.
Science funding agencies may appear to be crowdsourcing solutions too - as they employ broad calls for proposals and utilize peer reviews to evaluate the proposed ideas. Their models , however, are not very effective in triggering societal impacts. They impede collaboration in many ways as the researchers are not truly working together and the feedback is not constructive. Reviewers are experts but not direct stakeholders of  proposed projects . They add management overhead (Latour, 1996).

One may argue that science is highly competitive and will always be driven by egos and desire for personal vs collective success. Yet, as Johnston and Hauser note, these very human needs could be met by more efficiently designed open source models, extending beyond snapshots of consensus,  enabling to capture specific contributions of each participant and  permanent record of the life history of the project from conception to completion.

The ease of discovery declines every year - scientists have to search for smaller asteroids, heavier chemical elements and more complicated connections. This has to be matched with either exponential increase in the number of scientists or more innovative collaboration.

People take pleasure in synchronized activities - such as singing or marching together, folding proteins or syncing their brains in a conversation.  Could scientists have meaningful conversations on unimaginable scales, conversations including citizen scientists and people whose health needs could be solved by science?
Some researchers are already using help from crowds collecting their donations to support research - like the recently started open-source research project to develop cure for neglected tropical disease schistosomiasis.
Or the Open Source PCR project supported by the public.

Recent call for collaboration asked for a framework to exchange and disseminate information,  produce guidelines and summarize finding for Participatory health research (PHR) addressing local health issues. Government agencies are using twitter and expect crowds to supply epidemiological metrics to test health policy efficacy.
Meanwhile, many are already utilizing google docs in the quest for collaborators and exchange of ideas. See for example this Folder of Useful Google Docs including:

Or check this call for collaborations in the microbiome and metabolome spaces, to solve neglected medical conditions.

Scientists, let's unite and start collaborating in even more creative ways!


Johnston SC, & Hauser SL (2009). Crowdsourcing scientific innovation. Annals of neurology, 65 (6) PMID: 19562693 
Wright MT, Roche B, von Unger H, Block M, & Gardner B (2010). A call for an international collaboration on participatory research for health. Health promotion international, 25 (1), 115-22 PMID: 19854843

Auer S, Braun-Thurmann H. Towards bottom-up, stakeholder-driven research funding — open science and open peer review: Available at:  Accessed May 21, 2009 

Lawrence PA (2009) Real Lives and White Lies in the Funding of Scientific Research. PLoS Biol 7(9): e1000197. doi:10.1371/journal.pbio.1000197

Marsh A, Carroll D, & Foggie R (2010). Using collective intelligence to fine-tune public health policy. Studies in health technology and informatics, 156, 13-8 PMID: 20543334 

Huss JW 3rd, Lindenbaum P, Martone M, Roberts D, Pizarro A, Valafar F, Hogenesch JB, & Su AI (2010). The Gene Wiki: community intelligence applied to human gene annotation. Nucleic acids research, 38 (Database issue) PMID: 19755503 

Latour, B. 1996. Aramis, or, The love of technology Harvard University Press, Cambridge, Mass 
Butler, D. (2010). Open-source science takes on neglected disease Nature DOI: 10.1038/news.2010.50
Facebook page, Just giving fundraiser page

Scientific collaboration: 
Idea Generation and Solving:

Crowd-Task-Solving and Freelance

  • World4brains, collaboration instead of competition for best ideas, advice and solutions - innovative payment system rewards all valuable input given
  • TaskRabbit,  linking over-stretched consumers with runners for errands, tasks and other to-do’s
  • oDesk - global marketplace for remote work
  • Elance - freelance marketplace
  • Guru - freelance community
  • Ki Work - sourcing online work
  • Amazon Mechanical Turk - micro-task crowdsourcing
  • HumanGrid - small online tasks solving



    Wednesday, July 14, 2010

    Sit less, Move more

    I am typing this standing in front of my computer. My tall chair is aside. 

    About a year ago, I realized that I felt better when I stood while working. It turned out I was not alone in this discovery; more and more people are opting for a vertical approach to work, and the benefits are becoming increasingly evident.

    We're all familiar with the age-old advice to "eat less and exercise". But this may not be enough. As shown in a recent study, exercise does not counteract the ill effects of sedentary lives, we should keep moving (or at least squatting) throughout the day too.  New York Times article about the study (The men who stare at screens) immediately got up-votes from over 100 hackers - those who spend hours staring at screens to code, along with 100+ comments from those staring at screens to read and comment on the news.  Stand up while you read this, asked NYT earlier this year. Prolonged sedentarity affects not only cardiovascular and metabolic health, blood clotting, diabetes and cancer. Countless hours of sitting could cause many other ailments reducing the quality of life such as skewed microbial ecology accompanied by strong body odor, diminishing overall quality of life.

    Health promotion efforts targeting physical inactivity should emphasize both reducing sedentary activity and increasing regular physical activity for optimal health.

    The lead author of the 2010 study says: "Stand up. Pace around your office. Get off the couch and grab a mop or change a light bulb the next time you watch ‘‘Dancing With the Stars.’’ A five-minute stroll is recommended every half hour.

    Stand-up desks and treadmill desk were available years ago, a web site just stand was created for office workers who sit long hours each day, but either the desks are not very usable yet, lobbying your boss for a stand-up workstation is still tricky or most people just like sitting too much. Let's hope this will change. References

    Warren TY, Barry V, Hooker SP, Sui X, Church TS, & Blair SN (2010). Sedentary behaviors increase risk of cardiovascular disease mortality in men. Medicine and science in sports and exercise, 42 (5), 879-85 PMID: 19996993 DOI: 10.1249/MSS.0b013e3181c3aa7e

    Dunstan DW, Barr EL, Healy GN, Salmon J, Shaw JE, Balkau B, Magliano DJ, Cameron AJ, Zimmet PZ, & Owen N (2010). Television viewing time and mortality: the Australian Diabetes, Obesity and Lifestyle Study (AusDiab). Circulation, 121 (3), 384-91 PMID: 20065160 
    DOI: 10.1161/CIRCULATIONAHA.109.894824

    Katzmarzyk PT, Church TS, Craig CL, & Bouchard C (2009). Sitting time and mortality from all causes, cardiovascular disease, and cancer. Medicine and science in sports and exercise, 41 (5), 998-1005 PMID: 19346988 DOI: 10.1249/MSS.0b013e3181930355

    Healy GN, Dunstan DW, Salmon J, Shaw JE, Zimmet PZ, Owen N. (2008). Television time and continuous metabolic risk in physically active adults. Medicine and Science in Sports and Exercise 40, 639-645.(PMID: 18317383

    Khaw K-T, Wareham N, Bingham S, Welch A, Luben R, et al. (2008) Combined Impact of Health Behaviours and Mortality in Men and Women: The EPIC-Norfolk Prospective Population Study. PLoS Med 5(1): e12. doi:10.1371/journal.pmed.0050012 (PMID: 18184033)

    Beasley R, Raymond N, Hill S, Nowitz M, Hughes R. (2003) eThrombosis: the 21st century variant of venous thromboembolism associated with immobility. Eur Respir J.  21(2), 374-6. (PMID: 12608454 )

    Aurametrix is developing next-generation systems for Personal Health Management. Better solutions for a healthier world
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    Additional references post-initial publication:
    Gao W, Sanna M, Chen YH, Tsai MK, Wen CP. Occupational Sitting Time, Leisure Physical Activity, and All-Cause and Cardiovascular Disease Mortality. JAMA Network Open. 2024 Jan 2;7(1):e2350680-.

    Saturday, July 3, 2010

    Microbial sequencing for food applications is gaining momentum, but challenges remain

    Blue Stilton PDO Cheese, one quarter of a half...
    Microbes bring us a wide variety of foods, transforming texture and intensifying flavors. Jake Lahne, posted a great overview of some of the good microorganisms in cheese - like Penicillium molds in Cabrales cheese shown on the right - that adds to other ingredients such as milk, salt and coagulants.

    While modern cheeses are made with preselected cultures, traditional cheeses carry dozens of types of microbes, some highly unusual and uncharacterized.

    Lactic acid bacteria, including lactococci and lactobacilli, not only convert the basic milk sugar, lactose, into lactic acid but also make the cheese inhospitable to many spoilage organisms and is the first step towards deliciousness. Streptococci are also important in cheese and yogurt-making, adding flavor to alpine (Emmental, Gruyere, etc) and Italian hard (Grana Padana, Pecorino Romano, etc) cheeses. Lactococcus lactis, Staphylococcus, Trichococcus, and Monascus are strongly associated with the 36 key aroma compounds of Monascus-fermented cheese. Lactococcus lactis was found to be the dominant bacterium while Monascus was confirmed to be the dominant fungus.

    Propionobacter shermanii, are able to digest acetic acid and convert it to sharp, sweaty-smelling propionic acid and carbon dioxide. Several species of propionibacteria also inhabit human skin, producing less wanted odors.

    Most of the molds that grow on cheese are species of Penicillium, but some cheeses, like St. Nectaire, develop others such as blue mold, P. roqueforti and P. glaucum in blue cheese. Blues include Roquefort, Stilton, Gorgonzola, and Cabrales, and goat cheese Monte Enebro.
    White molds, which are found on the outside of all types of soft-ripened cheeses, are subspecies of P. camembertii (also called P. candidum). These white molds produce enzymes that break down the milk proteins and producing garlicky or earthy, also ammonia smells.

    Room-clearing ability of Epoisses, Münster, and Limburger owe to the smear bacteria officially known as Brevibacter linens. They need salty (up to 15%), moist environments to grow,and create stinky odor compounds, producing oniony or garlicky, fishy, and sweaty aromas. The aroma of the washed-rind cheeses is often compared to smelly feet - and, yes, brevibacter grow well on human skin.

    But it is not only cheese that carries myriads of microbes. There are many other foods. And not all of the bacteria we consume with the foods is good for you.

    Genome sequencing was predicted to bring practical benefits to the field of microbial food safety, identifying and controlling emerging microbial pathogens. It is still not as readily available and inexpensive as needed for practical applications, but a few pilot projects have showed a promise.

    GenomeWeb's Andrea Anderson recently published this article about academic researchers and public health agencies exploring the use of genomics-based approaches to complement existing food safety and surveillance methods.

    Common foodborne pathogens include E. coli 0157:H7, Salmonella, Listeria, and Campylobacter, but there are many more in need of identification. Having effective ways to distinguish between dangerous and neutral microbes is crucial for food safety.

    Many identification methods exist, but whole-genome sequencing could give unprecedented wealth of information, allowing predictions about the nature of organisms, their potential sources and associated risk,

    In a paper published in the Journal of Food Protection in May, USDA's Ward and his colleagues reported on their findings from a multi-locus genotyping study of more than 500 Listeria monocytogenes isolates collected by the USDA-FSIS from a variety of ready-to-eat foods.
    "Integration of PFGE and DNA-sequence-based sub-typing provides an improved framework for prediction of relative risk associated with L. monocytogenes strains from [ready-to-eat] foods," they wrote.

    In another recent paper, Ward and collaborators from Colorado State University used genotyping to show that a virulence-decreasing inlA mutation in L. monocytogenes was more common in isolates from ready-to-eat than from isolates from actual human listeriosis cases.
    Honisch presented a poster outlining work done with collaborators from London's Health Protection Agency at the American Society for Microbiology annual meeting in San Diego this May, describing how the team used the Sequenom MassArray platform to do multi-locus sequence typing, or MLST, on hundreds of Salmonella isolates. Honisch told GWDN that the approach is promising, in part, because mass spec is high-throughput and generates very reproducible data.
    During a session at the recent ASM meeting, Eric Brown, a microbiologist with the US Food and Drug Administration, explained that the FDA has been exploring the use of Roche 454 sequencing to characterize Salmonella isolates and to find markers for tracing outbreak strains back to their source.
    And in Canada, the NML's Gilmour was lead author on a paper appearing in BMC Genomics this February in which researchers used the Roche 454 GS FLX platform to sequence the genomes of two L. monocytogenes strains isolated during a 2008 outbreak of listeriosis in Canada that killed 22 people and caused serious illness in dozens more.
    "This study confirms that the latest generation of DNA sequencing technologies can be applied during high priority public health events," Gilmour and his co-authors wrote, "and laboratories need to prepare for this inevitability and assess how to properly analyze and interpret whole-genome sequences in the context of epidemiology."
    Even so, Gilmour said it will take time for whole-genome sequencing to become a standard traceback method — largely due to remaining bioinformatics challenges.
    "It's our job to learn how to use those [sequencing] technologies and glean the interesting information or the informative information," Gilmour said. "That's kind of the bottleneck we're at right now, is developing those bioinformatics tools to take that raw data and quickly parse through it and find relevant information."

    There is still a long way before genome-sequencing or methods developed based on sequencing results will be standardized and incorporated into practice, but the results look promising and are opening new horizons for health applications.


    Ward TJ, Evans P, Wiedmann M, Usgaard T, Roof SE, Stroika SG, & Hise K (2010). Molecular and phenotypic characterization of Listeria monocytogenes from U.S. Department of Agriculture Food Safety and Inspection Service surveillance of ready-to-eat foods and processing facilities. Journal of food protection, 73 (5), 861-9 PMID: 20501037

    Van Stelten A, Simpson JM, Ward TJ, & Nightingale KK (2010). Revelation by single-nucleotide polymorphism genotyping that mutations leading to a premature stop codon in inlA are common among Listeria monocytogenes isolates from ready-to-eat foods but not human listeriosis cases. Applied and environmental microbiology, 76 (9), 2783-90 PMID: 20208021

    St-Gelais D, Lessard J, Champagne CP, & Vuillemard JC (2009). Production of fresh Cheddar cheese curds with controlled postacidification and enhanced flavor. Journal of dairy science, 92 (5), 1856-63 PMID: 19389943

    Rossetti L, Fornasari ME, Gatti M, Lazzi C, Neviani E, Giraffa G. (2008). Grana Padano cheese whey starters: microbial composition and strain distribution. Int J Food Microbiol. 2008 Sep 30;127(1-2):168-71. Epub 2008 Jun 12.PMID: 18620769

    Flórez AB, Mayo B. (2006) Microbial diversity and succession during the manufacture and ripening of traditional, Spanish, blue-veined Cabrales cheese, as determined by PCR-DGGE. Int J Food Microbiol. 2006 Jul 15;110(2):165-71. Epub 2006 Jun 27.PMID: 16806553

    Aurametrix is developing next-generation diagnostic tools for Personal Health Management. Better solutions for a healthier world

    Thursday, June 24, 2010

    QS#14: There are more questions than answers

    Quantifying self: there are more questions than answers

    Notes from QS Show&Tell #14 held in the San Francisco Bay Area Tech Museum of San Jose on June 22nd.

    Quantifying Productivity

    Bill Jarrold used a simple script to record his Unix activity with timestamps on the commands he typed. By quantifying the number of operations per hour he determined that he is usually on the roll at about 3pm, but is slowing down around 10am and midnight. The talk spurred discussions about other ways to analyze productivity - number of builds per time unit? keystrokes? content analysis of the commands? There also were suggestions on using GUI-based tools. For example, CoScripter Reusable History that records everything one does on the web. Or DeliciousDiscovery that analyzes Delicious bookmarks and tags. There are even commercial software tools such as SpyAgent that capture everything a computer user does: keystrokes typed, websites visited, chat conversations, applications ran, emails sent and received, files opened, and more.

    One needs not only sophisticated screening and recording of performance related measures but also more sophisticated data analysis methods. There is a good discussion on HN about More-Hours-Worked not equal to More-Work-Getting-Done (it started from this post: Something Deeply Wrong With Chemistry). Dependence of productivity on hours worked is bell shaped and very individual. Some may be most productive when working 35 hours a week, others could increase workload to 60 hours. Number of keystrokes may not necessarily correlate with meaningful output either. Remember Jack Nicholson in The Shining? Besides, sometimes we need to think before turning ideas into action. And how could we measure what is going on in the brain?

    Quantifying Thoughts

    Mark Carranza is probably the most notable collector of thoughts among the Bay area quantifiers. His database has more than one and a quarter of a million thoughts and keeps growing, with more entries than the diary of Samuel Pepis and the collection of Lion Kimbro, the man who wrote down every though he had.
    Jim, the second presenter of QS#14 is collecting thoughts too - he has 65,000 of them connected by associations and represented by colorful visualizations. He uses spreadsheets and Personal Brain software to create and display the results. TheBrain's display is organized around a central Thought, surrounded by all its Children, Siblings and Jumps - like an ontology - helping to follow a train of hought, flowing from one to the next or just wandering around. Navigation through the data is interesting although rather chaotic. Questions from QS participants addressed the usefulness of the tool. Does it really help to to leverage the power of visual thinking and understand the context of information before taking action?

    Quantifying Stress

    Bharat Vasan of PulseTrace Technologies gave a great impromptu talk on how he is using his watch reading real time heart rate from his wrist. Data may be uploaded via USB or wireless connection. One good application is managing stress. Bharat wants to be a good public speaker - and he certainly is, but his pulse rate always skyrockets during a presentation. Of course, some of us don't need to have a watch like this to know if pulse rate is elevated - the tendency to blush lets everybody around to take the readings. Shortness of breath, nausea and sweating may add to the picture. As Jerry Seinfeld said about delivering the eulogy, most people would rather be in the casket than speaking in public. One the other hand, some increase in pulse when talking is rather good and he trick is to use it as energy to fuel the presentation.
    Pulse rate is a good measure of stress levels. Other measures would complement it and contribute to meaningful analytics. For example, recovery heart rate, a measure of how quickly ones heart could return to resting state. Standing vs sitting - normally there is 5 to 10 beats difference. Temperature in the room. Coffee or spices in food...
    I have not tried the PT100 sensor-based watch, but my complaints about similar monitoring watches include their inability to measure heart rate during running at full speed, sensitivity to humidity, rain, food vapors, limited time intervals between repeated measurements. I also question reliability of measurements in 1-5% of cases.

    Quantifying Motion

    Indeed, quantifiers are improving their lives not only with spreadsheets and software - gadget usage continues to rise. ("Zeo's broken" was among two-word introductions of meetup participants). There were many Fitbit fans proudly showing their devices and less proudly measurements for the day - Wow, we are becoming ashamed for not stepping enough! Popular motion gadgets do not work for everyone and are generation 1.0 or rather 0.5 - some like swimming instead of running and elliptical trainers instead of treadmills, others are surprised to find that rocking baby in their arms was counted as calorie-burning steps. BodyMedia Fit (GoWear Fit), for example, measures not only acceleration, but also skin temperature and galvanic skin response reflective of physical stress, but is not water proof and can't measure emotional stress. Fitbit is less pricey but also not quite ready to do what most people want. The next talk was devoted to fitbit's corporate competitor - DirectLife.

    Alex Bangs, co-founder of Entelos now working on a spinoff - DigitalSelf, likes this little white plastic box and DirectLife program. The device has accelerometers to quantify how much you move. The program starts with a one week assessment to get a good idea of individual's current activity and create a baseline. The plan has daily activity goals that increase slowly week by week (hopefully not as in The Red Shoes by Andersen!). Alex showed how his device was flashing congratulating him with good activity levels for the day. It could be even better, he said, the little box can almost sing a tribute to you if you walked more than your goal. The device fits in the pocket, but can be forgotten at home - which makes every wearer sad as the accomplishments of the day will not be logged and recognized. We love to be tapped on the back and told well done, are not we all?

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    Tuesday, May 4, 2010

    To Ruby or Not to Ruby?

    Ruby on App Engine Meetup (May 4, 2010) proved that Google App Engine is not all about Python and Java, although at present only the last ones are benchmarked and discussed by google app engine developers (try yourself)

    Python is usually fast enough for what you want it to do (and faster than Ruby), while requires a third of the code of an equivalent Java program and is much easier to read. Both Python and Ruby are easy to learn.

    Pythonistas think that Ruby syntax is ugly and inheritance distasteful. The philosophy of Ruby is similar to Perl: there is more than one way to do the same thing. This is great for some people, it leads to the creativity and richness of Ruby libraries like Hpricot and Rake. Ruby tries to take the best of Perl, Smalltalk and Lisp but has elements of a C/Java-like syntax. Rubyists are convinced that they have found the most enjoyable interpreted language. Ruby is younger than Python but already has a strong and loyal community and best web tools. Many programmers, however, don't like having too much power to change the syntax and keywords. This can be dangerous if they are not careful.

    Arguments on what language is better resemble disagreements between cultures and ethnicities. For a programmer, it's always fun to learn something new that may be useful in the future. For a startup founder, it's always good to know what is fun and a good investment. Right choices of programming languages, web frameworks and platforms will lead to rapid development, the ability to attract talent, and offer a great expansion potential.

    The Java language is an old favorite in the enterprise. There will be a large pull of candidates to work on Java projects, but this language won't make your startup the most attractive shop in town. Python and Ruby will.

    The Python community has the Django framework while the Ruby language is paired with the Rails framework. DSL Sinatra is also good. What's the difference between the Ruby and Python developer crowds? RoR favors convention over configuration which means that any Rails programmer can drop immediately into any Rails application and have a very good idea of where to get started. Expect rapid development and prototyping and support from community working on updates and plugins. Ruby/Rails developers tend to be expensive but worth it. Complementary Javascript experience is perfect - for dynamic web pages and nice visual effects. PHP developers tend to be a little cheaper than Django and RoR, but watch out for bad coders in this crowd. All hosting providers can deliver a low cost PHP server environment, but there are great platforms for Ruby and Python - such as Heroku (that recently raised $10 million in Series B funding) and Google App Engine that let you develop for free.

    The May 4th meetup was not on Ruby vs other languages. It was about Google App Engine.
    Most Rubyists in the audience were using Heroku for their applications, some will now try to pioneer Google App Engine - or will they?..

    One interesting observation was the "City” versus “the Valley" comparison of preferred languages: Ruby seems to be the City's favorite! Could it be due to cultural differences? Some even think it's as bad as PC vs Mac world - see this short clip.
    There were no Java people in the audience and very few programmed in Python or wanted to learn it. Almost no one tried Google App Engine.

    Matthew Blain presented BulkLoader, a minimal library providing a unified interface for loading, accessing and events notification for different types of content (Try the Bulk Loader sample at:

    John Woodell, Sr Web Developer at Google (twitter: @johnwoodell, e-mail woodie at google), gave the main talk -about running Ruby on the App Engine. Sarah Allen wrote a nice blog about this talk: Another blog post summarizes the sources discussed:

    Check out his SlideShare Presentation:
    App Engine Meetup
    A few pointers and links from this talk:
    • Zero Configuration needs. Scalable services via standard APIs. Built-in application management console
    • Schemaless App Engine datastore: No writing to the filesystem. No relational database. No more than 30 seconds per request. Datastore API + DataMapper
    • Most common tasks: backups and cross-app migration.
    • A big Rails advantage is that it values convention over configuration and lets to avoid lengthy configuration of files. Load everything at once then initialize it. This helps to free more time to focus on business logic. Google App Engine does not work like that yet, but this is on roadmap.

    Key Ruby App Engine Developers (check also this map):

    John Wang - iPhone, Android, and Web Developer at Fresh Blocks in Honolulu, (Twitter: @johntwang Github: jwang) Provided RESTful JSON with Google’s Datastore to iPhone and Android apps.

    Joshua Moore, Assurance Manger at Armorize Technologies (Twitter: @codingforrent, GAE blog). Coauthored the rails_dm_datastore gem, gave presentations on using jRuby on GAE. Rails 2.3.5, Datamapper, HAML, helped to find datamapper bugs

    Leonardo Gallucci, IT Consultant and Software Integrator (Twitter: @leonardog). Wrote tutorial on getting started with Ruby on Rails on Google App Engine (it runs on GAE).

    Vladimir Sizikov (github: vvs) . Developed jruby launcher:

    Sasaki Takeru, a hacker from Japan (Twitter: @urekat). Showed how to run Rails without Ruby Gems. Rails 2.3.5 01/21

    Anagiotis Siatras, a Microsoft .Net/Gae+Rails consultant from Greece (Twitter: @azazeal)
    Developed See his useful Ruby tips at

    More Links:

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