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Welcome to The Visible Embryo, a comprehensive educational resource on human development from conception to birth.

The Visible Embryo provides visual references for changes in fetal development throughout pregnancy and can be navigated via fetal development or maternal changes.

The National Institutes of Child Health and Human Development awarded Phase I and Phase II Small Business Innovative Research Grants to develop The Visible Embryo. Initally designed to evaluate the internet as a teaching tool for first year medical students, The Visible Embryo is linked to over 600 educational institutions and is viewed by more than one million visitors each month.

Today, The Visible Embryo is linked to over 600 educational institutions and is viewed by more than 1 million visitors each month. The field of early embryology has grown to include the identification of the stem cell as not only critical to organogenesis in the embryo, but equally critical to organ function and repair in the adult human. The identification and understanding of genetic malfunction, inflammatory responses, and the progression in chronic disease, begins with a grounding in primary cellular and systemic functions manifested in the study of the early embryo.

WHO International Clinical Trials Registry Platform

The World Health Organization (WHO) has created a new Web site to help researchers, doctors and
patients obtain reliable information on high-quality clinical trials. Now you can go to one website and search all registers to identify clinical trial research underway around the world!




Pregnancy Timeline

Prescription Drug Effects on Pregnancy

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Female Reproductive System

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Disclaimer: The Visible Embryo web site is provided for your general information only. The information contained on this site should not be treated as a substitute for medical, legal or other professional advice. Neither is The Visible Embryo responsible or liable for the contents of any websites of third parties which are listed on this site.
Content protected under a Creative Commons License.

No dirivative works may be made or used for commercial purposes.


Pregnancy Timeline by SemestersFetal liver is producing blood cellsHead may position into pelvisBrain convolutions beginFull TermWhite fat begins to be madeWhite fat begins to be madeHead may position into pelvisImmune system beginningImmune system beginningPeriod of rapid brain growthBrain convolutions beginLungs begin to produce surfactantSensory brain waves begin to activateSensory brain waves begin to activateInner Ear Bones HardenBone marrow starts making blood cellsBone marrow starts making blood cellsBrown fat surrounds lymphatic systemFetal sexual organs visibleFinger and toe prints appearFinger and toe prints appearHeartbeat can be detectedHeartbeat can be detectedBasic Brain Structure in PlaceThe Appearance of SomitesFirst Detectable Brain WavesA Four Chambered HeartBeginning Cerebral HemispheresFemale Reproductive SystemEnd of Embryonic PeriodEnd of Embryonic PeriodFirst Thin Layer of Skin AppearsThird TrimesterSecond TrimesterFirst TrimesterFertilizationDevelopmental Timeline
CLICK ON weeks 0 - 40 and follow along every 2 weeks of fetal development
Google Search artcles published since 2007

Home | Pregnancy Timeline | News Alerts |News Archive Dec 16, 2013


Three ways to view the same molecule.
Left - as mass; Middle - as atoms; Right - as a calculation

A molecule is an electrically neutral group of two or more atoms held together by chemical bonds.
Molecules are distinguished from ions by their lack of electrical charge.

WHO Child Growth Charts




Programming smart molecules

Computer scientists have put powerful probabilistic reasoning algorithms into the hands of bioengineers.

The Harvard School of Engineering and Applied Sciences (SEAS) and the Wyss Institute for Biologically Inspired Engineering at Harvard University have joined forces to give bioengineers some workng tools of advanced math.

In a new paper presented at the Neural Information Processing Systems conference on December 7, Ryan P. Adams and Nils Napp have shown that an important class of artificial intelligence algorithms could be implemented using chemical reactions.

These algorithms, which use a technique called “message passing inference on factor graphs,” are a mathematical coupling of ideas from graph theory and probability.

They represent the state of the art in machine learning and are already critical components of everyday tools ranging from search engines and fraud detection to error correction in mobile phones.

Adams’ and Napp’s work demonstrates that some aspects of artificial intelligence (AI) could be implemented at microscopic scale using molecules. In the long term, the researchers say, such theoretical developments could open the door for “smart drugs” that can automatically detect, diagnose, and treat a variety of diseases using a cocktail of chemicals that can perform AI-type reasoning.

“We understand a lot about building AI systems that can learn and adapt at macroscopic scales; these algorithms work behind the scenes in many of the devices we interact with every day,” says Adams, an assistant professor of computer science at SEAS whose Intelligent Probabilistic Systems group focuses on machine learning and computational statistics. “This work shows that it is possible to also build intelligent machines at tiny scales, without needing anything that looks like a regular computer.

'This kind of chemical-based AI will be necessary for constructing therapies that sense and adapt to their environment. The hope is to eventually have drugs that can specialize themselves to your personal chemistry and can diagnose or treat a range of pathologies.”

Ryan P. Adams, assistant professor computer science, Harvard School of Engineering and Applied Sciences (SEAS).

Adams and Napp designed a tool that can take probabilistic representations of unknowns (probabilistic graphical models, in the language of machine learning) and compile them into a set of chemical reactions to estimate quantities otherwise unobservable.

The key insight is that the dynamics of chemical reactions can be mapped directly into two computational steps computer scientists normally perform via computer simulation [in silico].

This insight opens up interesting new questions for computer scientists working on statistical machine learning. One being, how to create algorithms and models tackling the uncertainty molecular engineers typically face. In addition, addressing the long-term possibilities of smart therapeutics. Basically, open the door for converting natural biological reaction pathways and regulatory networks into datasets useful for drawing conclusions.

Just like robots, biological cells must estimate external environmental states and act on them; designing artificial systems that perform these tasks could give scientists a better understanding of how problems might be solved on a molecular level inside living systems.

“There is much research on how to develop chemical computational devices,” says Napp, a postdoctoral fellow at the Wyss Institute, working on the Bioinspired Robotics platform, and a member of the Self-organizing Systems Research group at SEAS. Both groups are led by Radhika Nagpal, the Fred Kavli Professor of Computer Science at SEAS and a Wyss core faculty member.

At the Wyss Institute, a portion of Napp’s research involves developing new types of robot devices that move and adapt like living creatures.

Explains Napp: “What makes this project different is that, instead of aiming for general computation, we focused on translating particular algorithms already successful at solving difficult problems in areas like robotics — into molecular descriptions.

“For example, these algorithms allow today’s robots to make complex decisions and reliably use noisy sensors. It is really exciting to think how these tools might be able to help build better molecular machines.”

The field of machine learning is revolutionizing many areas of science and engineering by extracting useful insights from vast amounts of weak and incomplete information. It is not only fueling an interest in “big data,” but is also enabling rapid progress in more traditional disciplines such as computer vision, estimation, and robotics, where data is available but difficult to interpret.

Bioengineers often face similar challenges. Many molecular pathways are still poorly characterized and available data is corrupted by random noise. Using machine learning, these challenges can now be overcome by modeling the dependencies between random variables and accumulate any small amount of information each random event can provide.

“Probabilistic graphical models are efficient tools for computing estimates of unobserved phenomena,”
says Adams. “It’s very exciting to find that these tools map so well to the world of cell biology.”