mjpdejong

Nog nergens vertoond: elektrische energie opwekken door zout en zoet water met elkaar in contact te brengen. Op de Afsluitdijk gebeurt het. Vandaag opent de koning een proefinstallatie.

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Cambrian Genomics, which has created a promising DNA-printing technology, has raised a sizable $10 million round of funding, by far the largest in the company’s history.

The funding round was described in a Securities and Exchange Commission filing Thursday. The round has a total of 127 participants, and Cambrian isn’t talking about who led the round or how much the participants contributed.

But there are some known names involved. These include Yammer co-founder Adam Pisoni, Draper Associates, and Cloudera founder Jeff Hammerbacher.

In his wry way, Cambrian founder and CEO Austen Heinz told VentureBeat his company will use the new money to “print more DNA.” But there’s more to it than that.

So far we’ve seen just the outlines of what Cambrian might do with its DNA-printing technology. Much of the discussion about the company so far has focused on the novelty factor of the company’s first DNA product, which makes plants glow in the dark.

Cambrian’s second major product isn’t so much novel as it is sensational, even controversial.

Heinz spoke yesterday at the DEMO conference about one of the companies in Cambrian’s new accelerator program. The company, called Personal Probiotics, uses Cambrian’s “Creature Creator” to print a special virus that kills off microbes in the vagina that cause yeast infections and other sexually transmitted diseases (STDs).

The product, called “Sweet Peach,” also reduces vaginal odor, which is what most in the media have seized upon, and not in a way that was very complimentary, or fair, to Cambrian. The pleasant “peach” odor is created as an indicator that it is working within the woman’s body, Heinz said.

Cambrian’s technology is already being used for far less sensitive, and perhaps more useful, use cases. The company has been doing work printing DNA for the huge pharma company Glaxo Smith Kline. It’s also in talks to formalize a similar business relationship with Roche.

Big pharma companies are asking Cambrian to print various types of DNA that can be used in the drug discovery and testing process.

“We’re helping them make drugs,” Heinz said. For example, Cambrian’s DNA can be used for producing small molecules or for making new screens to find small molecules, Heinz said. “DNA can be used for every part of the process,” Heinz said.

Heinz says that his company also intends to print DNA for customers in the industrial chemical and agricultural industries. He says producing seeds used by consumers is in itself a million-dollar industry.

But perhaps the biggest initiative going on at Cambrian now is the development of its incubator, which supplies resources and work space to companies building products on the DNA-printing technology.

Heinz says the incubator may show more upside for his company in the future, because Cambrian can take an equity stake in them, something that would be impossible with its big pharma customers.

Cambrian isn’t the only company on earth printing the blueprint of life, but it has already filed three patents on its DNA-printing tech, two of which are currently under review.

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Computer programming has quickly become one of the most lucrative industries in the US.

The average salary for a computer programmer just hit an all-time high as it approaches $100,000.

But there are some languages and skill sets that are more valuable than others, and Quartz has compiled some data to break down these differences.

Quartz’s Max Nisen pulled out some figures on the most valuable programming languages based on a larger study from the Brookings Institution that was published in July.

Based on that data, here are programming languages listed next to their average annual salary from lowest to highest:

12. PERL - $82,513

11. SQL – $85,511

10. Visual Basic – $85,962

9. C# – $89,074

8. R- $90,055

7. C – 90,134

6. JavaScript – $91,461

5. C++ – $93,502

4. JAVA – $94,908

3. Python – $100,717

2. Objective C – $108,225

1. Ruby on Rails – $109,460

While some of these coding languages can help you earn $100,000, train to become a Salesforce architect if you want one of the highest-paying jobs in tech.

According to data from IT recruiting firm Mondo that was published in March, Salesforce Architects can earn between $180,000 and $200,000.

This story originally appeared on www.businessinsider.com.

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VIENNA, Austria — Say this for Austen Heinz: His vision for the future will either thrill you or leave you fearing for the future of humanity. There’s not really any room in the middle.

In a pair of interviews, on and off the stage at the Pioneers Festival in Vienna today, the CEO of San Francisco-based Cambrian Genomics explained the mission of his company, which is often benignly described as “laser-printing DNA.”

So what does that mean?

“We want to make everything that is alive on the planet,” he explained. “Everything that is alive is not optimal. It can be made better.”

But he doesn’t have plans for replication: “We want to make totally new organisms that have never existed,” he said. “And replace every existing organism with a better one. It just seems obvious that eventually every human will be designed on a computer.”

Lest you think these are the ravings of a mad scientist, or the opening scene of a new sci-fi dystopian thriller, well, it’s not. Heinz is calm and rational in his view. And while his company has not disclosed its total fundraising, it’s backed by such notable Silicon Valley names as Peter Thiel.

The technology at work here is complex, but essentially Cambrian Genomics says it has found a way to dramatically reduce the cost of printing a strand of DNA. Sequencing of the genome is so advanced, Heinz explained, that basically people can take DNA code from libraries and create organic mash-ups, much the way any programmer can pull computer code from code libraries. That new code is then laser printed for a fraction of what it used to cost.

The most notable example of this technology at work was the “glowing plants” campaign on Kickstarter. Using these synthetic biology techniques, the campaign promised to take bioluminescence genes from bacteria and fireflies and insert them into several plants to make them glow in the dark.

The backers raised $484,013 from 8,433 backers. But then Kickstarter responded by changing its rules by banning genetically modified organisms from its platform.

Now Heinz is fighting back. He said on stage that the company will soon launch its own crowd-funding platform for GMOs, called “creature creators.” Cambrian will also help other people create slick videos to promote their synthetic biological creations.

One such project he mentioned on stage was Petomics. The project essentially aims to take genetic material for the odor of bananas and inject it into E. coli bacteria. The modified bacteria would be introduced into pet food and the result would be … poop that smells like bananas.

“It’s a pretty strong smell, too,” Heinz said.

Heinz doesn’t appear to fear the potential backlash such biological tinkering could provoke. For him, there is a simple logic at work at makes such technology inevitable. People are essentially badly designed computers, he said.

“We are running a program that is designed for us to die,” he said. “I think obviously every organism will be designed synthetically. It’s not a stretch to take it to every human and everything that’s alive.”

Posted by Google Research Scientists Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan

“Two pizzas sitting on top of a stove top oven”
“A group of people shopping at an outdoor market”
“Best seats in the house”

People can summarize a complex scene in a few words without thinking twice. It’s much more difficult for computers. But we’ve just gotten a bit closer -- we’ve developed a machine-learning system that can automatically produce captions (like the three above) to accurately describe images the first time it sees them. This kind of system could eventually help visually impaired people understand pictures, provide alternate text for images in parts of the world where mobile connections are slow, and make it easier for everyone to search on Google for images.

Recent research has greatly improved object detection, classification, and labeling. But accurately describing a complex scene requires a deeper representation of what’s going on in the scene, capturing how the various objects relate to one another and translating it all into natural-sounding language.

Automatically captioned: “Two pizzas sitting on top of a stove top oven”

Many efforts to construct computer-generated natural descriptions of images propose combining current state-of-the-art techniques in both computer vision and natural language processing to form a complete image description approach. But what if we instead merged recent computer vision and language models into a single jointly trained system, taking an image and directly producing a human readable sequence of words to describe it?

This idea comes from recent advances in machine translation between languages, where a Recurrent Neural Network (RNN) transforms, say, a French sentence into a vector representation, and a second RNN uses that vector representation to generate a target sentence in German.

Now, what if we replaced that first RNN and its input words with a deep Convolutional Neural Network (CNN) trained to classify objects in images? Normally, the CNN’s last layer is used in a final Softmax among known classes of objects, assigning a probability that each object might be in the image. But if we remove that final layer, we can instead feed the CNN’s rich encoding of the image into a RNN designed to produce phrases. We can then train the whole system directly on images and their captions, so it maximizes the likelihood that descriptions it produces best match the training descriptions for each image.

The model combines a vision CNN with a language-generating RNN so it can take in an image and generate a fitting natural-language caption.

Our experiments with this system on several openly published datasets, including Pascal, Flickr8k, Flickr30k and SBU, show how robust the qualitative results are -- the generated sentences are quite reasonable. It also performs well in quantitative evaluations with the Bilingual Evaluation Understudy (BLEU), a metric used in machine translation to evaluate the quality of generated sentences.

A selection of evaluation results, grouped by human rating.

A picture may be worth a thousand words, but sometimes it’s the words that are most useful -- so it’s important we figure out ways to translate from images to words automatically and accurately. As the datasets suited to learning image descriptions grow and mature, so will the performance of end-to-end approaches like this. We look forward to continuing developments in systems that can read images and generate good natural-language descriptions. To get more details about the framework used to generate descriptions from images, as well as the model evaluation, read the full paper here.

Sjon shared this story .

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Microsoft maakt de .Net Core Runtime opensource. Daarnaast heeft het concern bekendgemaakt het .Net-platform naar Linux en OS X te gaan brengen. Ook brengt de softwaregigant Visual Studio Community 2013 uit.

Although no licensed vaccines against Ebola exist on the market, 'significant progress' has been made in the last few months, according to immunology experts.

Na de aankondiging van de Nvidia GeForce GTX 970 en GTX 980 is er weinig nieuws van AMD naar buiten gekomen. We concludeerden in onze review van de GeForce GTX 970 en 980 al dat de GTX 970 sneller is dan de duurdere AMD Radeon R9 290X. Ook met Ultra settings was de GTX 970 gemiddeld 3% sneller dan de R9 290X. Om enig tegenwicht te kunnen bieden aan de prestaties van de GTX 970 en GTX 980 zou AMD met een aantal partners de Radeon R9 290X uit gaan rusten met 8 GB GDDR5-geheugen. AMD had in eerste...

Iedere YouTube-uploader kan beelden met snellere framerates zoals 60fps toevoegen aan de site. De videosite heeft die mogelijkheid nu voor iedereen beschikbaar gemaakt. De optie moet vooral de weergave van gamebeelden verbeteren.

Plants rely on sunlight to make their food, but they also need protection from its harmful rays, just like humans do. Recently, scientists discovered a group of molecules in plants that shields them from sun damage. Now scientists report on the mechanics of how these natural plant sunscreens work.

Sjon shared this story from Hacker News 100.

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A new discovery suggests that the number of human genomic loci that might be coding for tRNAs is nearly double what is currently known.

De grootte van het nageslacht van sommige vliegen wordt mede bepaald door eerdere sekspartners van het vrouwtje.

Sjon shared this story from Hacker News 100.

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Sjon shared this story from Hacker News 100.

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Even long stretches of DNA that contain no genes at all can affect the risk of breast cancer, by physically interacting with genes elsewhere, a new study reports.

Een Linux-versie van Counter-Strike: Global Offensive is verkrijgbaar via Steam. Het gaat om een bètaversie van de game die nog niet op de frontpage van Steam te vinden is, maar wel is opgenomen in de database van de downloadsite.

 softpedia: The Linux version of Counter-Strike: Global Offensive seems to have been in the works for ages, but now there is finally some news about it, although it's nothing definitive.

The wait is over. The long sought after data from Oxford Nanopore is here. Almost three months ago, we got a glimpse of a single read from Nick Loman. Now, Nick Loman organizing the really awesome Nanopore themed Google Hangout and announced that Oxford Nanopore MinION data from E.Coli K 12 substrain.

The E. coli genome data from Nanopore MinION data is available from GigaDB. In addition to directly downloading the data from Gigadb, one can also download using Aspera.

• E. coli Whole-genome read data from the Oxford Nanopore MinION

The raw data is about 80 Gb in size, while processed data in fasta format is about 180Mb and easy to download..

Bacterial whole-genome read data from the Oxford Nanopore Technologies MinION™ nanopore sequencer.

Quick, J; Loman N.J (2014): Bacterial whole-genome read data from the Oxford Nanopore Technologies MinION™ nanopore sequencer. GigaScience Database. http://dx.doi.org/10.5524/100102

The MinION is a new, portable single-molecule sequencer developed by Oxford Nanopore Technologies. It measures four inches in length and is powered from the USB 3.0 port of a laptop computer. By measuring the change in resistance produced when DNA strands translocate through and interact with a protein nanopore the device is able to deduce the underlying nucleotide sequence.

Here we present a read dataset from whole-genome shotgun sequencing of the model organism Escherichia coli K-12 substr. MG1655 generated on a MinION device with R7 chemistry during the early-access MinION Access Program (MAP).

Three sequencing runs of the MinION were performed using R7 chemistry. The first run produced 43,656 forward reads (272Mb), 23,338 (125Mb) reverse reads, 20,087 two-direction (2D) reads (131Mb), of which 8% (10Mb) were full 2D. Full 2D reads means that the complementary strand was successfully slowed through the pore.

The R7 protocol has a modification to increase the relative number of full 2D reads (NONI). To exploit this, two new libraries were produced which included an overnight incubation stage (ONI-1 and ONI-2). Each library was run on an individual flow cell. This resulted in 6,534 & 8,260 forward reads, 2,171 & 2,945 reads and 1,740 & 2,394 2D reads (27%, 29%). In this case, 50% and 41.8% of reads were full 2D respectively. The mean fragment lengths for 2D reads from the three libraries were 6,543 (NONI) 6,907 (ONI-1) and 6,434 (ONI-2).
Raw and assembled sequence data is provided to demonstrate the nature of data produced by the MinION™ platform and to encourage the development of customised methods for alignment and variant calling, de novo assembly and scaffolding. FAST5 files containing event data within the HDF5 container format are provided to assist with the development of improved base-calling methods.

Posted by Christian Szegedy, Software Engineer

The ImageNet large-scale visual recognition challenge (ILSVRC) is the largest academic challenge in computer vision, held annually to test state-of-the-art technology in image understanding, both in the sense of recognizing objects in images and locating where they are. Participants in the competition include leading academic institutions and industry labs. In 2012 it was won by DNNResearch using the convolutional neural network approach described in the now-seminal paper by Krizhevsky et al.^[4]

In this year’s challenge, team GoogLeNet (named in homage to LeNet, Yann LeCun's influential convolutional network) placed first in the classification and detection (with extra training data) tasks, doubling the quality on both tasks over last year's results. The team participated with an open submission, meaning that the exact details of its approach are shared with the wider computer vision community to foster collaboration and accelerate progress in the field.
The competition has three tracks: classification, classification with localization, and detection. The classification track measures an algorithm’s ability to assign correct labels to an image. The classification with localization track is designed to assess how well an algorithm models both the labels of an image and the location of the underlying objects. Finally, the detection challenge is similar, but uses much stricter evaluation criteria. As an additional difficulty, this challenge includes a lot of images with tiny objects which are hard to recognize. Superior performance in the detection challenge requires pushing beyond annotating an image with a “bag of labels” -- a model must be able to describe a complex scene by accurately locating and identifying many objects in it. As examples, the images in this post are actual top-scoring inferences of the GoogleNet detection model on the validation set of the detection challenge.
This work was a concerted effort by Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Drago Anguelov, Dumitru Erhan, Andrew Rabinovich and myself. Two of the team members -- Wei Liu and Scott Reed -- are PhD students who are a part of the intern program here at Google, and actively participated in the work leading to the submissions. Without their dedication the team could not have won the detection challenge.

This effort was accomplished by using the DistBelief infrastructure, which makes it possible to train neural networks in a distributed manner and rapidly iterate. At the core of the approach is a radically redesigned convolutional network architecture. Its seemingly complex structure (typical incarnations of which consist of over 100 layers with a maximum depth of over 20 parameter layers), is based on two insights: the Hebbian principle and scale invariance. As the consequence of a careful balancing act, the depth and width of the network are both increased significantly at the cost of a modest growth in evaluation time. The resultant architecture leads to over 10x reduction in the number of parameters compared to most state of the art vision networks. This reduces overfitting during training and allows our system to perform inference with low memory footprint.
For the detection challenge, the improved neural network model is used in the sophisticated R-CNN detector by Ross Girshick et al.^[2], with additional proposals coming from the multibox method^[1]. For the classification challenge entry, several ideas from the work of Andrew Howard^[3] were incorporated and extended, specifically as they relate to image sampling during training and evaluation. The systems were evaluated both stand-alone and as ensembles (averaging the outputs of up to seven models) and their results were submitted as separate entries for transparency and comparison.

These technological advances will enable even better image understanding on our side and the progress is directly transferable to Google products such as photo search, image search, YouTube, self-driving cars, and any place where it is useful to understand what is in an image as well as where things are.

References:

[1] Erhan D., Szegedy C., Toshev, A., and Anguelov, D., "Scalable Object Detection using Deep Neural Networks", The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 2147-2154.

[2] Girshick, R., Donahue, J., Darrell, T., & Malik, J., "Rich feature hierarchies for accurate object detection and semantic segmentation", arXiv preprint arXiv:1311.2524, 2013.

[3] Howard, A. G., "Some Improvements on Deep Convolutional Neural Network Based Image Classification", arXiv preprint arXiv:1312.5402, 2013.

[4] Krizhevsky, A., Sutskever I., and Hinton, G., "Imagenet classification with deep convolutional neural networks", Advances in neural information processing systems, 2012.

Je kunt alleen een topuniversiteit maken door geld en talent weg te halen bij de andere universiteiten, schrijven Roland Bal, hoogleraar aan de

Amsterdamse ouderen met een laag inkomen maken massaal gebruik van het openbaar vervoer, dat sinds vorig jaar gratis is voor hen. De gemeente

New study tests whether invertebrates produce similar DNA-based traps to neutrophil extracellular traps (NETs)to prove theory that they are an ancient immune technique...

UPDATE OCT. 7:  Emmanuel (my “son”) and his family, in Liberia, are still all healthy! The post below is from mid-August, when much of the US still didn’t care about Ebola because it wasn’t here. It includes the immune system basics that rarely make it into media coverage.

Sunday, August 17:

Eman’s emails arrive hours ahead of the news here.

“An Ebola quarantine site was  attacked and looted. News is that most of the patients have escaped. This is going to put more fear into the population. All this because people are denying the virus.  More people might get exposed.  I’m so weak I can’t wake up this morning. Its 6:00 pm and I am still in bed listening to the news.  All this happened in a very populated area called West Point. Got pain all over my body. Keep me in your meditations.”

Emmanuel is a medical student in Liberia whom my husband and I have been supporting since he contacted me after reading my human genetics textbook in 2007. Until the fever hit him last weekend, he dedicated himself to “sensitization,” educating the public about how to stay safe. But now he’s too sick and weak to venture out.

His email from Monday, August 18, said only “Need help!”

Eman is our son in the African sense, not based on his DNA. And our families have grown close. Some of the funds we sent to see him through medical school helped put his mother through nursing school. It costs a fraction of medical education here.

The emails and texts from Liberia are eerie in the face of the crumbling infrastructure, the abandoned hospitals and schools. Eman taps on a phone these days, too terrified to use an Internet cafe as he has in the past. We know he’s in trouble when his brother Joseph takes over — it means Eman is in the hospital. It’s happened for cholera, amoebiasis, and cerebral malaria more than once. Fighting infection is a way of life in Liberia.

I’m mortified when the news here focuses on the deaths of individuals — tragic as they may be — while the populations of African nations like Liberia, of the entire continent, are under threat. Eman wants to know why the US didn’t pay attention until the arrival here of two patients, who were treated. So do I.

JUST 7 GENES

The stark seeming-simplicity of the Ebola virus flashes across my mind whenever I receive an email from Eman.

Ebola virus has a mere 7 protein-encoding genes, but the RNA that is its genetic material holds hidden information. One key gene (GP, for glycoprotein) has an overlapping reading frame so that an alternate form harbors a stretch of added adenines. And the encoded protein is cut after translation, generating a mature secreted form that sits on the surfaces of viral particles, as well as a sugar-coated smaller part, like a moon carved from a planet.

The irony of it all is stunning. Genetics and genomics journals overflow with data. Always more exomes, more genomes, meta-analyses of meta-analyses that search for meaning among the nearly limitless combinations of variants of our 20,000 or so genes. And yet a 7-gene “infectious particle,” so streamlined it isn’t even a cell, isn’t even alive, can reduce a human body to a puddle, inner barriers dissolving into nothingness, within days.

How does Ebola virus, so much simpler than influenza, than HIV, do it?

AN EBOLA TIMELINE
Ebola virus homes to certain immune system cells as well as the boxy epithelial cells that aggregate into layers and the single bathroom-tile-like endothelial cells. Inside the body, the virus first tackles innate immunity – the immediate and generalized response to infection. Ebola commandeers monocytes and macrophages, the wandering cells that travel around the body, dividing, distributing its deadly cargo.

Meanwhile, the virus replicates like crazy.

In those who will not survive, the innate immune response goes on a tad too long. The virus also invades dendritic cells. These are the sentries that “present” the pathogen’s provoking antigens to the parts of the immune system that carry out the second phase, the slower and targeted adaptive response. And indirectly, mysteriously, lymphocytes die en masse, instead of producing antibodies.

Yet at the same time, a “cytokine storm” erupts, sending other arms of the immune response into overdrive. Levels of gamma interferon, interleukins 2 and 10, and tumor necrosis factor soar, triggering fever and flu-like symptoms. Yet it’s as if there’s no interferon at all. Viruses do not see it.

The bizarre immune response during Ebola infection is rightfully termed “paradoxical,” at once too slow, too little, too intense.

Then the body’s barriers begin to break down.

The endothelial cells that curl into the tiny tubes that are the capillaries, and also line the interiors of larger blood vessels, contort into blobs. Holes appear. Barriers melt away, and the fluids that they contained redistribute. The still-crazily-replicating virus now has direct access to organs, favoring the adrenal cortex (plunging blood pressure), the kidneys, gonads, spleen, and most dangerous, the liver.

The final stage is the bleeding, as the liver’s output of clotting factors becomes unhinged. One protein in particular goes by various names: in the older literature it’s simply “tissue factor,” but is also known as thromboplastin, CD142, and factor III.

Whatever it’s called, this cell surface glycoprotein converts prothrombin into thrombin, the essential final step in blood clotting. The fact that no deficiency of thromboplastin is known – the others cause hemophilias and other clotting disorders – belies its importance.

In Ebola infection, thromboplastin is too active, ushering in disseminated intravascular coagulation. Tiny clots form in blood vessels everywhere. Organ necrosis sets in as the blood supply ebbs, and clotting factors needed to stanch greater breaches as the blood vessels come apart become depleted. Hemorrhaging begins as the biochemical balance so critical to appropriate clotting vanishes.

BACK TO THE GENES
A human body overwhelmed with Ebola virus is like a castle whose defenses fail, from the inside out, all orchestrated by that puzzling handful of genes.

GP targets the virus to certain cell types, deforms the endothelium, and destroys antigen presentation. ZMapp, the drug being given to a handful of infected people ahead of human testing because it worked in macaques, counters GP. It consists of three monoclonal antibodies produced in tobacco cells.

VP24 cuts off the host transcription factor STAT1, which is required to use gamma interferon, according to a recent report in Cell Host & Microbe. And VP40 protein, because it forms the outside of the virus, should elicit an antibody response, only it usually doesn’t.

The power of a virus such as Ebola tends to evoke anthropomorphism. But the virus isn’t intentionally trying to kill people, as one prominent researcher told the New York Times, calling the virus “a survivor. It does what it can to avoid the human immune system.” It doesn’t think.

Another type of survivor might provide the clues necessary to stop the current epidemic: people whose immune systems can fight off the virus.

LEARNING FROM SURVIVORS
Just as HIV antivirals were developed using clues from people who never became infected despite repeated exposure, a solution to Ebola hemorrhagic fever might lie among individuals who recover.

Survivors have 10 million viruses per milliliter of blood serum; people who succumb have 10 billion. So far we know that the immune response in people who survive is subtly distinctive, the innate response turning off at a specific point and the adaptive response beginning in time to help, neither becoming overactive. Identifying biomarkers may reveal the specifics that drive resistance, such as an adhesion factor that re-attaches torn endothelium.

Ebola hemorrhagic fever is the consequence of runaway viral replication against a backdrop of a strangely deranged immune response. We know the viral genome sequence, and I’m sure the genome sequences of survivors are being or will soon be sequenced. I hope it is only a matter of time until researchers deduce how variations of the 20,000-gene human genome or its expression resist the 7-gene genome of Ebola virus, and figure out how to replicate the response.

Until they do, I’m petrified. I just got an email sent from Eman’s phone — from Joseph, August 20.

“Eman walked to the hospital today because according to him, he is not doing well. He called me up in pain. Luckily, its not Ebola. We were so scared. He’s admitted. No word yet. I will keep you informed. Joseph”

Update Sunday August 24: Eman is still in the hospital, but he “only” has hookworms and malaria (which he always seems to have). When he gets out tomorrow, he plans to volunteer with MSF to fight Ebola.

 

The post Update: How Ebola Kills appeared first on DNA Science Blog.

Gene-based personalized medicine has many possibilities for diagnosis and targeted therapy, but one big bottleneck: the expensive and time-consuming DNA-sequencing process. Now, researchers have found that nanopores in the material molybdenum disulfide could sequence DNA more accurately, quickly and inexpensively than anything yet available.

Robin William’s suicide was a tragic, but all too familiar happening amongst humorists of this period. New research is showing that comedians, performers in the arts, and even people with better senses of humor are prone to more health problems as adults. Depression , cardiovascular, pulmonary and stress related problem are not uncommon in comedians, and even children and adults that have better sense of humor are susceptible to obesity and cardiovascular disease. The irony is that this occurs in the very people that are helping our health by making us laugh. Several recent studies and reviews indicate that mirthful laughter has health benefits, from increased immune function to improving vascular wall stiffness. Reduced cortisol and increased endorphin release improve both our mental and physical health, although no studies have directly linked laughing to increased longevity. ...

An alternative use for beer that will "get under your skin"!...

 tecmint: A database is an information organized in such a fashion that a computer program can access the stored data or a part of it.