A QUAKE IN EARTH’S MAGNETIC FIELD: When a CME from the sun struck Earth on April 22nd, our planet’s magnetic field reverberated from the impact. A day later, a stream of solar wind arrived, hit, and had the same effect. In Lancashire, England, a magnetometer operated by Stuart Green captured the quaking of Earth’s magnetic field:
“The data clearly show when the relative calm was shattered on April 21st at around 16:30 (UT) when the CME struck, being quickly followed by fast flowing solar wind from a large and persistent coronal hole,” says Green. “The rumblings have been continuing through the intervening days.”
Vibrations in the magnetic field allow particles normally trapped in our planet’s magnetosphere to rain down around the poles, igniting auroras. Thomas J. Spence was camping in the Boundary Waters Canoe Area of northern Minnesota on April 22nd when the sky suddenly lit up:
“I ventured into the BWCA less than 24 hours after the ice was gone from Kawihiwi Lake–and coincidentally not long after the CME impact,” says Spence. “The aurora began soon after sunset and continued until first light. It was an incredible first spring trip into this amazing wilderness.”
EXITING THE SOLAR WIND STREAM: Earth is beginning to exit a stream of solar wind that has sparked bright auroras around both poles in recent days. We’re not out of the stream yet, though. NOAA forecasters estimate a 60% chance of minor G1-class geomagnetic storms on April 25th and 26th as the solar wind pressure slowly subsides. Free: Aurora Alerts
Sunspot number: 36
Space Weather Conditions
USAF/NOAA 3-day Report of Solar and Geophysical Activity Report and Forecast – Updates
WSA-Enlil Solar Wind Prediction
Recently Reported Solar Events
SolarSoft’s “latest events”
CHYTRID FUNGUS, ZEBRA FISH: EXPERIMENTAL INFECTION
Published Date: 2017-04-24 11:40:36
Subject: PRO/AH/EDR> Chytrid fungus, zebra fish: experimental infection
Archive Number: 20170424.4990724
Date: Thu 20 Apr 2017
Source: Phys.org [edited]
The deadly chytrid fungus has for the 1st time been found to infect and kill species other than amphibians, giving clues on how it causes disease.
The fungus, _Batrachochytrium dendrobatidis_ (Bd), is a type of chytrid that has severely affected over 700 amphibian species worldwide, and has made more species extinct than any other infectious disease known to science — at least 200 so far. It causes chytridiomycosis, a disease that damages amphibian skin and rapidly kills its host.
Until now, chytrid was thought to only affect amphibians, a group that includes toads, newts, salamanders, and frogs. However, researchers from Imperial College London have now demonstrated in the laboratory that Bd can also infect zebrafish at the larval stage — the developmental phase just after they hatch from eggs.
Research into Bd currently relies on studying infected amphibians. However they are difficult to study and also need to be captured in nature, which is not sustainable in the longer term. Amphibians that are sourced from different natural populations also may respond very differently to the fungus.
Zebrafish are some of the most widely-used biological model species owing to their transparency at the larval stage, which allows scientists to use microscopy to easily track infections. Their immune systems also have many parallels with that of humans and other vertebrates such as frogs.
The team behind today’s discovery, which is published in Nature Communications [see reference below], say their work will lead to zebra fish as a new model for studying the disease. This could give scientists the opportunity to understand in more detail how the fungus harms its amphibian hosts.
Professor Mat Fisher, a co-author from Imperial’s School of Public Health, said: “The fact that chytrid is able to infect zebrafish larvae could mean that we now have a more effective animal model with which to study the fungus and continue our research in how to save these amphibians.”
The researchers found that Bd infection took hold in zebrafish larvae in a similar way to how it does in amphibians. Professor Fisher added: “The natural bacterial coating found in young zebrafish appeared to protect them from harm during infection, and meant they could fight off the chytrid. This is a far more humane way to study the fungus than our previous models, and means we now have a new laboratory model.”
Furthermore, because zebrafish breed quickly, the researchers can use many more than they can with frogs. This would help to make research go further and faster.
Co-author Dr Serge Mostowy from Imperial’s Department of Medicine said: “A zebrafish model represents a brand new opportunity to study the disease process of chytrids. Young zebrafish have fully developed innate immune systems, which means we can now easily study host-fungus interactions in real time using non-invasive techniques. We can also control their environment with antibiotics, allowing us to study the role of already-present bacteria in influencing chytrid infection.”
The findings may also offer clues into how the fungus spreads between hosts. The researchers suggest that zebrafish larvae and other fish species could act as environmental reservoirs in the wild, and may pass the infection onto amphibians.
Ms Nicole Liew, lead author of the paper from Imperial’s MRC Centre of Molecular Bacteriology and Infection said: “The more we know about how Bd can infect hosts and where it resides in the environment, the better we can prepare for it and prevent more deaths. Our findings today give us an exciting wealth of information to work with, opening a whole new avenue of research. From our experiments, we now know some of chytrid’s hiding places, and present a new lab model highly suited for fluorescent microscopy, enabling us to learn more about the disease process.”
The scientists also managed to infect another species of fish, the guppy, but these fish ended up clearing their infection eventually. The authors say that although their research shows that young zebrafish can be infected, further studies are needed to determine the extent that fish might act as reservoirs of infection in the environment.
Professor Fisher added: “Our knowledge of this devastating fungus is growing in leaps and bounds, and we are excited to see where this new information will take us in terms of saving our amphibian friends.”
[Byline: Caroline Brogan]
ProMED-mail from HealthMap Alerts
[The reference for the scientific article describing the finding is
Liew N, Mazon Moya MJ, Wierzbicki CJ, et al: Chytrid fungus infection in zebrafish demonstrates that the pathogen can parasitize non-amphibian vertebrate hosts. Nat Commun. 2017 Apr 20;8:15048. doi: 10.1038/ncomms15048;
available at https://www.nature.com/articles/ncomms15048.
The abstract reads, “Aquatic chytrid fungi threaten amphibian biodiversity worldwide owing to their ability to rapidly expand their geographical distributions and to infect a wide range of hosts. Combating this risk requires an understanding of chytrid host range to identify potential reservoirs of infection and to safeguard uninfected regions through enhanced biosecurity. Here we extend our knowledge on the host range of the chytrid _Batrachochytrium dendrobatidis_ by demonstrating infection of a non-amphibian vertebrate host, the zebrafish. We observe dose-dependent mortality and show that chytrid can infect and proliferate on zebrafish tissue. We also show that infection phenotypes (fin erosion, cell apoptosis and muscle degeneration) are direct symptoms of infection. Successful infection is dependent on disrupting the zebrafish microbiome, highlighting that, as is widely found in amphibians, commensal bacteria confer protection against this pathogen. Collectively, our findings greatly expand the limited tool kit available to study pathogenesis and host response to chytrid infection.”
This discovery is significant in 2 ways: first, it allows the development of an interesting animal model for this disease, although inference should be made with caution given the taxonomic distance between fish and amphibians and the artificial settings. Second, it hints that the infection should be studied in fish, and perhaps in other animals too. In fact, a few years ago the chytrid fungus was found in crayfish.
_B. dendrobatidis_ is generally thought of as an amphibian specialist that consumes host keratin for sustenance, despite it commonly being maintained in the laboratory on nonkeratinized media, such as tryptone. Numerous vertebrate and invertebrate taxa possess keratin or keratin-like compounds in their gastrointestinal tracts. Hence, it is not surprising that previous researchers have hypothesized that there might be non amphibian hosts or vectors of _B. dendrobatidis_. – Mod.PMB]
Chytrid fungus, crayfish – USA: non-amphibian hosts 20130105.1483017
Chytrid fungus, frogs – worldwide: mechanism of spread
Chytrid fungus, frogs – Worldwide: possible recovery 20101212.4421
Chytrid fungus, frogs – worldwide: review article 20100130.0323
Chytrid fungus, frog – South Korea 20090920.3301
Chytrid fungus, frog – Philippines: (Luzon) 20090527.1976
Chytrid fungus, frogs – Panama 20081014.3246
Chytrid fungus, frogs – Spain (Majorca) 20080928.3065
Chytrid fungus, frogs – Japan (02): wild frogs 20070613.1924
Chytrid fungus, frogs – Japan 20070113.0176
Chytrid fungus, frogs – worldwide: possible source 20060524.1463
Chytrid fungus, frogs – South Africa 20060203.0344
Chytrid fungus, frogs – UK (England) 20050916.2741
Red leg disease, frogs, fatal – UK (02) 20040914.2560
Red leg disease, frogs, fatal – UK 20040912.2542
Frog deformities – USA (02) 20020425.4030
Frog deformities – USA 20020422.4012
Frog mortality, virus – UK 20020201.3458
Chytrid fungus, frogs: background 20001201.2096
Frog deformities – USA (Northeast) 20000420.0579
North America – USA | State of New Jersey, Lower Alloways Creek Township, Salem Nuclear Power Plant, Unit 2
Location: 39°27’46.0″N 75°32’08.0″W
Present Operational Age: ~32 years
Event: UNUSUAL EVENT – HYDRAZINE IN CONTAINMENT
Emergency Class: UNUSUAL EVENT
10 CFR Section: 50.72(a) (1) (i) – EMERGENCY DECLARED
Nuclear Event in USA on Thursday, 20 April, 2017 at 21:10 [EDT].
UNUSUAL EVENT DECLARED DUE TO HYDRAZINE IN CONTAINMENT
“At 2110 EDT, Salem control room received data that supported unacceptable levels of hydrazine concentration in the U2 Containment atmosphere based on Site Protection atmospheric sampling. The high hydrazine levels were caused due to Steam Generator venting into the Containment atmosphere in support of maintenance for the current Salem Unit 2 Refueling Outage (2R22). The NIOSH habitability limit for hydrazine is 0.03 ppm (2 hour limit). Area samples indicated concentrations as high as 0.25 ppm. Salem Unit 2 Containment has been evacuated while a mitigation plan is being developed. There were no personnel injuries as a result of this occurrence. Salem Unit 2 defueling activities were in progress during this event. All fuel assemblies have been placed in a safe condition. All Salem Unit 2 Containment activities are currently on hold. There has been no impact to the equipment in the Unit 2 Containment, no adverse impact to any equipment located in the vicinity of the high hydrazine concentration, and no operational impact to the plant including Shutdown Cooling which is currently on RHR.”
The Unusual Event was declared under EAL HU3.1, Toxic/Flammable Gas Release Affecting Plant Operations.
The licensee plans to issue a press release.
The licensee notified the NRC Resident Inspector, Lower Alloways Creek Township, State of New Jersey and State of Delaware.
Notified DHS SWO, FEMA Operations Center, DHS NICC, FEMA NWC (email), DHS Nuclear SSA (email), and FEMA NRCC SASC (email).
* * * UPDATED FROM JOHN COOK TO DONALD NORWOOD AT 1305 EDT ON 4/21/2017 * * *
“Salem Unit 2 terminated the Unusual Event at 1258 EDT on 4/21/17. The basis for termination was no longer restricting access to the containment after getting two sets of satisfactory air sample results. With the access restored, normal plant operations can resume and EAL HU3.1 is no longer applicable.
“The details of the sample results are:
Fire Protection performed satisfactory results of no detectable Hydrazine (0.01 ppm with a NIOSH limit of 0.03 ppm) completed both at 1001 EDT and 1247 EDT at the following locations:
– (3) at 130 ft. elevation
– at 78 ft. in the bioshield
– at 78 ft. outside the bioshield.
“Additional mitigating actions taken following U2 Containment evacuation were as follows:
– FME screen installed on open manways for 21/23 S/G with additional plastic covering and tape to prevent further gas release into containment.
– Modified Containment Purge in service to maximize ventilation in Containment.
– 21/24 S/G draining to support filling and draining evolutions to reduce Hydrazine concentrations in the S/G’s.
– Releasing tags on the AFWST to commence filling and further support filling and draining evolutions on the U2 S/G’s.”
The licensee notified the NRC Resident Inspector.
Notified R1DO (Arner), NRR EO (King), and IRD (Stapleton). Notified DHS SWO, FEMA Operations Center, DHS NICC, FEMA NWC (email), DHS Nuclear SSA (email), and FEMA NRCC SASC (email).
“When I use a word,” Humpty Dumpty said in a rather scornful tone, “it means just what I choose it to mean—neither more nor less.” “The question is,” said Alice, “whether you can make words mean so many things.” “The question is,” said Humpty Dumpty, “which is to be Master—that’s all.” ~Through the Looking Glass by Lewis Carroll
“I think, therefore I am.”~René Descartes, Discourse on Method (1637)
“It depends on what the meaning of the word ‘is’ is.” Impeachment of Bill Clinton – Wikipedia
Subject and Object In the Cartesian statement, the subject—I— declares itself in existence on the basis of its own thinking. As an antithesis of the subject, the object is seen and evaluated by the thinking subject.
“’What’s the difference?’ did not ask for difference but meant instead ‘I don’t give damn what the difference is.’ The same grammatical pattern engenders two different meanings that are mutually exclusive: the literal meaning asks for the concept difference whose existence is denied by the figurative meaning…. [G]rammar allows us to ask the question, but the sentence by means of which we ask it may deny the very possibility of asking. For what is the use of asking, I ask, when we cannot even authoritatively decide whether a question asks or doesn’t ask?… The point is as follows. A perfectly clear syntactical paradigm (the question) engenders a sentence that has at least two meanings, one which asserts and the other which denies its own illocutionary mode. It is not so that there are simply two meanings, one literal and the other figural, and that we have to decide which one of these meanings is the right one in this particular situation. The confusion can only be cleared up by the intervention of an extra-textual intervention, such as Archie Bunker putting his wife straight; but the very anger he displays is indicative of more than impatience; it reveals his despair when confronted with a structure of linguistic meaning that he cannot control and that holds the discouraging prospect of an infinity of similar future confusions.” Paul de Man “Semiology and Rhetoric” (1973)
The Deconstructive Position – The subject originates nothing, not even itself. Although one can bring together separate views to create a new view, the subject learns its views from someone or something else. Since knowledge belongs to the subject, it cannot be objective.
Big-data analysis consists of searching for buried patterns that have some kind of predictive power. But choosing which “features” of the data to analyze usually requires some human intuition. In a database containing, say, the beginning and end dates of various sales promotions and weekly profits, the crucial data may not be the dates themselves but the spans between them, or not the total profits but the averages across those spans.
MIT researchers aim to take the human element out of big-data analysis, with a new system that not only searches for patterns but designs the feature set, too. To test the first prototype of their system, they enrolled it in three data science competitions, in which it competed against human teams to find predictive patterns in unfamiliar data sets. Of the 906 teams participating in the three competitions, the researchers’ “Data Science Machine” finished ahead of 615.
In two of the three competitions, the predictions made by the Data Science Machine were 94 percent and 96 percent as accurate as the winning submissions. In the third, the figure was a more modest 87 percent. But where the teams of humans typically labored over their prediction algorithms for months, the Data Science Machine took somewhere between two and 12 hours to produce each of its entries.
“We view the Data Science Machine as a natural complement to human intelligence,” says Max Kanter, whose MIT master’s thesis in computer science is the basis of the Data Science Machine. “There’s so much data out there to be analyzed. And right now it’s just sitting there not doing anything. So maybe we can come up with a solution that will at least get us started on it, at least get us moving.”
Between the lines
Kanter and his thesis advisor, Kalyan Veeramachaneni, a research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), describe the Data Science Machine in a paper that Kanter will present next week at the IEEE International Conference on Data Science and Advanced Analytics.
Veeramachaneni co-leads the Anyscale Learning for All group at CSAIL, which applies machine-learning techniques to practical problems in big-data analysis, such as determining the power-generation capacity of wind-farm sites or predicting which students are at risk for dropping out of online courses.
“What we observed from our experience solving a number of data science problems for industry is that one of the very critical steps is called feature engineering,” Veeramachaneni says. “The first thing you have to do is identify what variables to extract from the database or compose, and for that, you have to come up with a lot of ideas.”
In predicting dropout, for instance, two crucial indicators proved to be how long before a deadline a student begins working on a problem set and how much time the student spends on the course website relative to his or her classmates. MIT’s online-learning platform MITx doesn’t record either of those statistics, but it does collect data from which they can be inferred.
Kanter and Veeramachaneni use a couple of tricks to manufacture candidate features for data analyses. One is to exploit structural relationships inherent in database design. Databases typically store different types of data in different tables, indicating the correlations between them using numerical identifiers. The Data Science Machine tracks these correlations, using them as a cue to feature construction.
For instance, one table might list retail items and their costs; another might list items included in individual customers’ purchases. The Data Science Machine would begin by importing costs from the first table into the second. Then, taking its cue from the association of several different items in the second table with the same purchase number, it would execute a suite of operations to generate candidate features: total cost per order, average cost per order, minimum cost per order, and so on. As numerical identifiers proliferate across tables, the Data Science Machine layers operations on top of each other, finding minima of averages, averages of sums, and so on.
It also looks for so-called categorical data, which appear to be restricted to a limited range of values, such as days of the week or brand names. It then generates further feature candidates by dividing up existing features across categories.
Once it’s produced an array of candidates, it reduces their number by identifying those whose values seem to be correlated. Then it starts testing its reduced set of features on sample data, recombining them in different ways to optimize the accuracy of the predictions they yield.
“The Data Science Machine is one of those unbelievable projects where applying cutting-edge research to solve practical problems opens an entirely new way of looking at the problem,” says Margo Seltzer, a professor of computer science at Harvard University who was not involved in the work. “I think what they’ve done is going to become the standard quickly—very quickly.”
More information: “Deep Feature Synthesis: Towards Automating Data Science Endeavors.”