Learning Better Simulation Methods for Partial Differential Equations

Posted by Stephan Hoyer, Software Engineer, Google Research

The world’s fastest supercomputers were designed for modeling physical phenomena, yet they still are not fast enough to robustly predict the impacts of climate change, to design controls for airplanes based on airflow or to accurately simulate a fusion reactor. All of these phenomena are modeled by partial differential equations (PDEs), the class of equations that describe everything smooth and continuous in the physical world, and the most common class of simulation problems in science and engineering. To solve these equations, we need faster simulations, but in recent years, Moore’s law has been slowing. At the same time, we’ve seen huge breakthroughs in machine learning (ML) along with faster hardware optimized for it. What does this new paradigm offer for scientific computing?

In “Learning Data Driven Discretizations for Partial Differential Equations”, published in Proceedings of the National Academy of Sciences, we explore a potential path for how ML can offer continued improvements in high-performance computing, both for solving PDEs and, more broadly, for solving hard computational problems in every area of science.

For most real-world problems, closed-form solutions to PDEs don’t exist. Instead, one must find discrete equations (“discretizations”) that a computer can solve to approximate the continuous PDE. Typical approaches to solve PDEs represent equations on a grid, e.g., using finite differences. To achieve convergence, the mesh spacing of the grid needs to be smaller than the smallest feature size of the solutions. This often isn’t feasible because of an unfortunate scaling law: achieving 10x higher resolution requires 10,000x more compute, because the grid must be scaled in four dimensions—three spatial dimensions and time. Instead, in our paper we show that ML can be used to learn better representations for PDEs on coarser grids.

Satellite photo of a hurricane, at both full resolution and simulated resolution in a state of the art weather model. Cumulus clouds (e.g., in the red circle) are responsible for heavy rainfall, but in the weather model the details are entirely blurred out. Instead, models rely on crude approximations for sub-grid physics, a key source of uncertainty in climate models. Image credit: NOAA

The challenge is to retain the accuracy of high-resolution simulations while still using the coarsest grid possible. In our work we’re able to improve upon existing schemes by replacing heuristics based on deep human insight (e.g., “solutions to a PDE should always be smooth away from discontinuities”) with optimized rules based on machine learning. The rules our ML models recover are complex, and we don’t entirely understand them, but they incorporate sophisticated physical principles like the idea of “upwinding”—to accurately model what’s coming towards you in a fluid flow, you should look upstream in the direction the wind is coming from. An example of our results on a simple model of fluid dynamics are shown below:

Simulations of Burgers’ equation, a model for shock waves in fluids, solved with either a standard finite volume method (left) or our neural network based method (right). The orange squares represent simulations with each method on low resolution grids. These points are fed back into the model at each time step, which then predicts how they should change. Blue lines show the exact simulations used for training. The neural network solution is much better, even on a 4x coarser grid, as indicated by the orange squares smoothly tracing the blue line.

Our research also illustrates a broader lesson about how to effectively combine machine learning and physics. Rather than attempting to learn physics from scratch, we combined neural networks with components from traditional simulation methods, including the known form of the equations we’re solving and finite volume methods. This means that laws such as conservation of momentum are exactly satisfied, by construction, and allows our machine learning models to focus on what they do best, learning optimal rules for interpolation in complex, high-dimensional spaces.

Next Steps
We are focused on scaling up the techniques outlined in our paper to solve larger scale simulation problems with real-world impacts, such as weather and climate prediction. We’re excited about the broad potential of blending machine learning into the complex algorithms of scientific computing.

Acknowledgments
Thanks to co-authors Yohai Bar-Sinari, Jason Hickey and Michael Brenner; and Google collaborators Peyman Milanfar, Pascal Getreur, Ignacio Garcia Dorado, Dmitrii Kochkov, Jiawei Zhuang and Anton Geraschenko.

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Hot Toys 1/6th scale "Avengers: Endgame" Don Cheadle as James Rhodes / Iron Patriot figure

With blistering success of the epic screenplay by Marvel Studios, Avengers: Endgame is well-received worldwide and the characters have all gained tremendous popularity including James Rhodes a.k.a Iron Patriot. Though the armor gets a few aesthetic changes, yet his new suit is still highly weaponised, with massive machine guns mounted on arms and shoulders.

In addition to the official unveil of the Battle Damaged Version of Iron Man Mark LXXXV 1/6th scale collectible figure, Hot Toys is excited to present today the new Iron Patriot 1/6th scale collectible figure from the MMS Diecast Series inspired by the final chapter of the 22-film MCU series, Avengers: Endgame for our passionate fans!

The highly-accurate diescast collectible figure is specially crafted based on the appearance of Don Cheadle as James Rhodes/Iron Patriot in Avengers: Endgame. It features two interchangeable head sculpts including a newly developed head sculpt with remarkable likeness and a helmet head with LED light-up function, metallic blue and reddish-orange painted armor with streamline armor design, LED light-up chest Arc Reactor and repulsors, Iron Patriot’s articulated weapons featuring back-mounted cannons and shoulder-mounted missile launchers, and a specially designed Avengers: Endgame themed figure base!

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Click on them for bigger and better views.

Hot Toys MMS547D34 1/6th scale Iron Patriot Collectible Figure specially features: Approximately 32.5 cm tall Authentic and detailed likeness of Iron Patriot in Avengers: Endgame with Over 30 points of articulations | newly developed head sculpt with authentic likeness of Don Cheadle as James Rhodes in the movie | Movie-accurate facial features with detailed wrinkles and skin texture | interchangeable helmeted head with LED light-up function (white light, battery operated) | Contains diecast material | Special features on armor: Metallic blue, reddish orange and grayish silver colored painting on the sleek and streamline armor design; 6 LED light-up points throughout parts of the armor (white light, battery operated); pair of fully deployable air flaps at back of the armor; detachable chest armor to reveal interior mechanical design; set of attachable cannon (attachable to forearm or back of figure); Two (2) sets of interchangeable forearm cannon (normal and missile firing); Two (2) sets of interchangeable forearm armor (normal and missile firing); Six (6) pieces of interchangeable hands including: pair of fists, pair of hands with articulated fingers and light-up repulsors (white light, battery operated), pair of repulsor firing hands (white light, battery operated) | Articulations on waist armor which allow flexible movement

Weapons: pair of articulated back-mounted cannons, pair of articulated shoulder-mounted missile launchers

Accessories: specially designed movie-themed figure base with movie logo and character name

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Building SMILY, a Human-Centric, Similar-Image Search Tool for Pathology

Posted by Narayan Hegde, Software Engineer, Google Health and Carrie J. Cai, Research Scientist, Google Research

Advances in machine learning (ML) have shown great promise for assisting in the work of healthcare professionals, such as aiding the detection of diabetic eye disease and metastatic breast cancer. Though high-performing algorithms are necessary to gain the trust and adoption of clinicians, they are not always sufficient—what information is presented to doctors and how doctors interact with that information can be crucial determinants in the utility that ML technology ultimately has for users.

The medical specialty of anatomic pathology, which is the gold standard for the diagnosis of cancer and many other diseases through microscopic analysis of tissue samples, can greatly benefit from applications of ML. Though diagnosis through pathology is traditionally done on physical microscopes, there has been a growing adoption of “digital pathology,” where high-resolution images of pathology samples can be examined on a computer. With this movement comes the potential to much more easily look up information, as is needed when pathologists tackle the diagnosis of difficult cases or rare diseases, when “general” pathologists approach specialist cases, and when trainee pathologists are learning. In these situations, a common question arises, “What is this feature that I’m seeing?” The traditional solution is for doctors to ask colleagues, or to laboriously browse reference textbooks or online resources, hoping to find an image with similar visual characteristics. The general computer vision solution to problems like this is termed content-based image retrieval (CBIR), one example of which is the “reverse image search” feature in Google Images, in which users can search for similar images by using another image as input.

Today, we are excited to share two research papers describing further progress in human-computer interaction research for similar image search in medicine. In “Similar Image Search for Histopathology: SMILY” published in Nature Partner Journal (npj) Digital Medicine, we report on our ML-based tool for reverse image search for pathology. In our second paper, Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making(preprint available here), which received an honorable mention at the 2019 ACM CHI Conference on Human Factors in Computing Systems, we explored different modes of refinement for image-based search, and evaluated their effects on doctor interaction with SMILY.

SMILY Design
The first step in developing SMILY was to apply a deep learning model, trained using 5 billion natural, non-pathology images (e.g., dogs, trees, man-made objects, etc.), to compress images into a “summary” numerical vector, called an embedding. The network learned during the training process to distinguish similar images from dissimilar ones by computing and comparing their embeddings. This model is then used to create a database of image patches and their associated embeddings using a corpus of de-identified slides from The Cancer Genome Atlas. When a query image patch is selected in the SMILY tool, the query patch’s embedding is similarly computed and compared with the database to retrieve the image patches with the most similar embeddings.

Schematic of the steps in building the SMILY database and the process by which input image patches are used to perform the similar image search.

The tool allows a user to select a region of interest, and obtain visually-similar matches. We tested SMILY’s ability to retrieve images along a pre-specified axis of similarity (e.g. histologic feature or tumor grade), using images of tissue from the breast, colon, and prostate (3 of the most common cancer sites). We found that SMILY demonstrated promising results despite not being trained specifically on pathology images or using any labeled examples of histologic features or tumor grades.

Example of selecting a small region in a slide and using SMILY to retrieve similar images. SMILY efficiently searches a database of billions of cropped images in a few seconds. Because pathology images can be viewed at different magnifications (zoom levels), SMILY automatically searches images at the same magnification as the input image.
Second example of using SMILY, this time searching for a lobular carcinoma, a specific subtype of breast cancer.

Refinement tools for SMILY
However, a problem emerged when we observed how pathologists interacted with SMILY. Specifically, users were trying to answer the nebulous question of “What looks similar to this image?” so that they could learn from past cases containing similar images. Yet, there was no way for the tool to understand the intent of the search: Was the user trying to find images that have a similar histologic feature, glandular morphology, overall architecture, or something else? In other words, users needed the ability to guide and refine the search results on a case-by-case basis in order to actually find what they were looking for. Furthermore, we observed that this need for iterative search refinement was rooted in how doctors often perform “iterative diagnosis”—by generating hypotheses, collecting data to test these hypotheses, exploring alternative hypotheses, and revisiting or retesting previous hypotheses in an iterative fashion. It became clear that, for SMILY to meet real user needs, it would need to support a different approach to user interaction.

Through careful human-centered research described in our second paper, we designed and augmented SMILY with a suite of interactive refinement tools that enable end-users to express what similarity means on-the-fly: 1) refine-by-region allows pathologists to crop a region of interest within the image, limiting the search to just that region; 2) refine-by-example gives users the ability to pick a subset of the search results and retrieve more results like those; and 3) refine-by-concept sliders can be used to specify that more or less of a clinical concept be present in the search results (e.g., fused glands). Rather than requiring that these concepts be built into the machine learning model, we instead developed a method that enables end-users to create new concepts post-hoc, customizing the search algorithm towards concepts they find important for each specific use case. This enables new explorations via post-hoc tools after a machine learning model has already been trained, without needing to re-train the original model for each concept or application of interest.

Through our user study with pathologists, we found that the tool-based SMILY not only increased the clinical usefulness of search results, but also significantly increased users’ trust and likelihood of adoption, compared to a conventional version of SMILY without these tools. Interestingly, these refinement tools appeared to have supported pathologists’ decision-making process in ways beyond simply performing better on similarity searches. For example, pathologists used the observed changes to their results from iterative searches as a means of progressively tracking the likelihood of a hypothesis. When search results were surprising, many re-purposed the tools to test and understand the underlying algorithm, for example, by cropping out regions they thought were interfering with the search or by adjusting the concept sliders to increase the presence of concepts they suspected were being ignored. Beyond being passive recipients of ML results, doctors were empowered with the agency to actively test hypotheses and apply their expert domain knowledge, while simultaneously leveraging the benefits of automation.

With these interactive tools enabling users to tailor each search experience to their desired intent, we are excited for SMILY’s potential to assist with searching large databases of digitized pathology images. One potential application of this technology is to index textbooks of pathology images with descriptive captions, and enable medical students or pathologists in training to search these textbooks using visual search, speeding up the educational process. Another application is for cancer researchers interested in studying the correlation of tumor morphologies with patient outcomes, to accelerate the search for similar cases. Finally, pathologists may be able to leverage tools like SMILY to locate all occurrences of a feature (e.g. signs of active cell division, or mitosis) in the same patient’s tissue sample to better understand the severity of the disease to inform cancer therapy decisions. Importantly, our findings add to the body of evidence that sophisticated machine learning algorithms need to be paired with human-centered design and interactive tooling in order to be most useful.

Acknowledgements
This work would not have been possible without Jason D. Hipp, Yun Liu, Emily Reif, Daniel Smilkov, Michael Terry, Craig H. Mermel, Martin C. Stumpe and members of Google Health and PAIR. Preprints of the two papers are available here and here.

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Parrotron: New Research into Improving Verbal Communication for People with Speech Impairments

Posted by Fadi Biadsy, Research Scientist and Ron Weiss, Software Engineer, Google Research

Most people take for granted that when they speak, they will be heard and understood. But for the millions who live with speech impairments caused by physical or neurological conditions, trying to communicate with others can be difficult and lead to frustration. While there have been a great number of recent advances in automatic speech recognition (ASR; a.k.a. speech-to-text) technologies, these interfaces can be inaccessible for those with speech impairments. Further, applications that rely on speech recognition as input for text-to-speech synthesis (TTS) can exhibit word substitution, deletion, and insertion errors. Critically, in today’s technological environment, limited access to speech interfaces, such as digital assistants that depend on directly understanding one’s speech, means being excluded from state-of-the-art tools and experiences, widening the gap between what those with and without speech impairments can access.

Project Euphonia has demonstrated that speech recognition models can be significantly improved to better transcribe a variety of atypical and dysarthric speech. Today, we are presenting Parrotron, an ongoing research project that continues and extends our effort to build speech technologies to help those with impaired or atypical speech to be understood by both people and devices. Parrotron consists of a single end-to-end deep neural network trained to convert speech from a speaker with atypical speech patterns directly into fluent synthesized speech, without an intermediate step of generating text—skipping speech recognition altogether. Parrotron’s approach is speech-centric, looking at the problem only from the point of view of speech signals—e.g., without visual cues such as lip movements. Through this work, we show that Parrotron can help people with a variety of atypical speech patterns—including those with ALS, deafness, and muscular dystrophy—to be better understood in both human-to-human interactions and by ASR engines.

The Parrotron Speech Conversion Model
Parrotron is an attention-based sequence-to-sequence model trained in two phases using parallel corpora of input/output speech pairs. First, we build a general speech-to-speech conversion model for standard fluent speech, followed by a personalization phase that adjusts the model parameters to the atypical speech patterns from the target speaker. The primary challenge in such a configuration lies in the collection of the parallel training data needed for supervised training, which consists of utterances spoken by many speakers and mapped to the same output speech content spoken by a single speaker. Since it is impractical to have a single speaker record the many hours of training data needed to build a high quality model, Parrotron uses parallel data automatically derived with a TTS system. This allows us to make use of a pre-existing anonymized, transcribed speech recognition corpus to obtain training targets.

The first training phase uses a corpus of ~30,000 hours that consists of millions of anonymized utterance pairs. Each pair includes a natural utterance paired with an automatically synthesized speech utterance that results from running our state-of-the-art Parallel WaveNet TTS system on the transcript of the first. This dataset includes utterances from thousands of speakers spanning hundreds of dialects/accents and acoustic conditions, allowing us to model a large variety of voices, linguistic and non-linguistic contents, accents, and noise conditions with “typical” speech all in the same language. The resulting conversion model projects away all non-linguistic information, including speaker characteristics, and retains only what is being said, not who, where, or how it is said. This base model is used to seed the second personalization phase of training.

The second training phase utilizes a corpus of utterance pairs generated in the same manner as the first dataset. In this case, however, the corpus is used to adapt the network to the acoustic/phonetic, phonotactic and language patterns specific to the input speaker, which might include, for example, learning how the target speaker alters, substitutes, and reduces or removes certain vowels or consonants. To model ALS speech characteristics in general, we use utterances taken from an ALS speech corpus derived from Project Euphonia. If instead we want to personalize the model for a particular speaker, then the utterances are contributed by that person. The larger this corpus is, the better the model is likely to be at correctly converting to fluent speech. Using this second smaller and personalized parallel corpus, we run the neural-training algorithm, updating the parameters of the pre-trained base model to generate the final personalized model.

We found that training the model with a multitask objective to predict the target phonemes while simultaneously generating spectrograms of the target speech led to significant quality improvements. Such a multitask trained encoder can be thought of as learning a latent representation of the input that maintains information about the underlying linguistic content.

Overview of the Parrotron model architecture. An input speech spectrogram is passed through encoder and decoder neural networks to generate an output spectrogram in a new voice.

Case Studies
To demonstrate a proof of concept, we worked with our fellow Google research scientist and mathematician Dimitri Kanevsky, who was born in Russia to Russian speaking, normal-hearing parents but has been profoundly deaf from a very young age. He learned to speak English as a teenager, by using Russian phonetic representations of English words, learning to pronounce English using transliteration into Russian (e.g., The quick brown fox jumps over the lazy dog => ЗИ КВИК БРАУН ДОГ ЖАМПС ОУВЕР ЛАЙЗИ ДОГ). As a result, Dimitri’s speech is substantially distinct from native English speakers, and can be challenging to comprehend for systems or listeners who are not accustomed to it.

Dimitri recorded a corpus of 15 hours of speech, which was used to adapt the base model to the nuances specific to his speech. The resulting Parrotron system helped him be better understood by both people and Google’s ASR system alike. Running Google’s ASR engine on the output of Parrotron significantly reduced the word error rate from 89% to 32%, on a held out test set from Dimitri. Below is an example of Parrotron’s successful conversion of input speech from Dimitri:

Dimitri saying, “How far is the Moon from the Earth?
Parrotron (male voice) saying, “How far are the Moon from the Earth?

We also worked with Aubrie Lee, a Googler and advocate for disability inclusion, who has muscular dystrophy, a condition that causes progressive muscle weakness, and sometimes impacts speech production. Aubrie contributed 1.5 hours of speech, which has been instrumental in showing promising outcomes of the applicability of this speech-to-speech technology. Below is an example of Parrotron’s successful conversion of input speech from Aubrie:

Aubrie saying, “Is morning glory a perennial plant?
Parrotron (female voice) saying, “Is morning glory a perennial plant?
Aubrie saying, “Schedule a meeting with John on Friday.
Parrotron (female voice) saying, “Schedule a meeting with John on Friday.

We also tested Parrotron’s performance on speech from speakers with ALS by adapting the pretrained model on multiple speakers who share similar speech characteristics grouped together, rather than on a single speaker. We conducted a preliminary listening study and observed an increase in intelligibility when comparing natural ALS speech to the corresponding speech obtained from running the Parroton model, for the majority of our test speakers.

Cascaded Approach
Project Euphonia has built a personalized speech-to-text model that has reduced the word error rate for a deaf speaker from 89% to 25%, and ongoing research is also likely to improve upon these results. One could use such a speech-to-text model to achieve a similar goal as Parrotron by simply passing its output into a TTS system to synthesize speech from the result. In such a cascaded approach, however, the recognizer may choose an incorrect word (roughly 1 out 4 times, in this case)—i.e., it may yield words/sentences with unintended meaning and, as a result, the synthesized audio of these words would be far from the speaker’s intention. Given the end-to-end speech-to-speech training objective function of Parrotron, even when errors are made, the generated output speech is likely to sound acoustically similar to the input speech, and thus the speaker’s original intention is less likely to be significantly altered and it is often still possible to understand what is intended:

Dimitri saying, “What is definition of rhythm?
Parrotron (male voice) saying, “What is definition of rhythm?
Dimitri saying, “How many ounces in one liter?
Parrotron (male voice) saying, “Hey Google, How many unces [sic] in one liter?
Google Assistant saying, “One liter is equal to thirty-three point eight one four US fluid ounces.
Aubrie saying, “Is it wheelchair accessible?
Parrotron (female voice) saying, “Is it wheelchair accecable [sic]?

Furthermore, since Parrotron is not strongly biased to producing words from a predefined vocabulary set, input to the model may contain completely new invented words, foreign words/names, and even nonsense words. We observe that feeding Arabic and Spanish utterances into the US-English Parrotron model often results in output which echoes the original speech content with an American accent, in the target voice. Such behavior is qualitatively different from what one would obtain by simply running an ASR followed by a TTS. Finally, by going from a combination of independently tuned neural networks to a single one, we also believe there are improvements and simplifications that could be substantial.

Conclusion
Parrotron makes it easier for users with atypical speech to talk to and be understood by other people and by speech interfaces, with its end-to-end speech conversion approach more likely to reproduce the user’s intended speech. More exciting applications of Parrotron are discussed in our paper and additional audio samples can be found on our github repository. If you would like to participate in this ongoing research, please fill out this short form and volunteer to record a set of phrases. We look forward to working with you!

Acknowledgements
This project was joint work between the Speech and Google Brain teams. Contributors include Fadi Biadsy, Ron Weiss, Pedro Moreno, Dimitri Kanevsky, Ye Jia, Suzan Schwartz, Landis Baker, Zelin Wu, Johan Schalkwyk, Yonghui Wu, Zhifeng Chen, Patrick Nguyen, Aubrie Lee, Andrew Rosenberg, Bhuvana Ramabhadran, Jason Pelecanos, Julie Cattiau, Michael Brenner, Dotan Emanuel, Joel Shor, Sean Lee and Benjamin Schroeder. Our data collection efforts have been vastly accelerated by our collaborations with ALS-TDI.

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Deutschlands Regierung gibt grünes Licht für Masern-Impfpflicht

Heute hat das deutsche Bundeskabinett den Gesetzesentwurf von Gesundheitsminister Jens Spahn zur Einführung einer Impfpflicht gegen Masern bestätigt. 
Zur Aufnahme in eine Kita oder die Volksschule soll ab Herbst der Nachweis der zweimaligen Masernimpfung Pflicht sein. Für bereits aufgenommene Kinder muss der Nachweis bis spätestens Sommer 2020 vorgelegt werden, sonst drohen Strafen bis zu 2.500 Euro. 
Die schwarz-rote Regierung hat damit ein Gesetz auf den Weg gebracht, vor dem zahlreiche Fachexperten – die meisten davon Impfbefürworter – eindringlich warnen. Den Ausschlag für die Initiative gaben populistische Kampagnen in zahlreichen Medien und Umfragen in der Bevölkerung, welche sich klar für die Zwangsimpfung ausgesprochen haben. 


Fehlende Einzelimpfstoffe ergeben gleich auch eine Impfpflicht gegen Mumps und Röteln

Um die Masern ausrotten zu können wird von der WHO ein Grenzwert von 95% genannt. Damit ist der Anteil der Menschen gemeint, die gegen Masern immun sein sollten.
Deutschland liegt diesbezüglich darunter, heißt es. Zwar konnten bei den Schuleingangs-Untersuchungen zuletzt 97,1% der Kinder die Bestätigung für die erste Masern-Impfung vorweisen. Allerdings haben im Bundes-Durchschnitt nur 93% der Kinder die 2. MMR. Und deshalb braucht es – laut Jens Spahn & Co. – die Impfpflicht.

93% ist weniger als 95%, das stimmt.
Aber heißt das nun, dass 7% der Kinder nicht immun sind?

Rechnen wir das kurz mal nach:

  • 97,1% der Kinder haben die erste MMR. Das heißt 2,9% der Kinder sind vollkommen ungeschützt.
  • 93% der Kinder haben die zweite MMR. Diese Gruppe hat einen offiziell angegebenen Schutz von 99% – das heißt hier sind 0,9% ungeschützt.
  • Die Differenz der nur einmal geimpften Kinder beträgt 4,1 Prozent. Diese Gruppe ist zu 95% geschützt. Hier sind also weitere 0,2% ungeschützt.
  • Zählen wir das zusammen, so ergibt sich, dass derzeit 96% der Kinder nach den offiziellen Kriterien einen ausreichenden Maserntiter haben und nur 4% ungeschützt sind. 

Deutschland erfüllt bei den Kindern also längst die Kriterien der WHO.

Wie es bei den Erwachsenen aussieht – speziell bei den Masern-geimpften Erwachsenen, die langsam ihre Titer einbüßen – wäre die wohl wesentlich wichtigere Frage, wenn man die Chancen der Ausrottung der Masern realistisch einschätzen möchte.
Doch die Beantwortung dieser Frage würde eine komplexe Analyse der Gesamt-Situation erfordern. Und das ist nicht im Sinn einer Gesundheitspolitik, die nichts mehr fürchtet als eine objektive wissenschaftliche Prüfung und eine kritische Selbstreflexion der getroffenen Entscheidungen im Impfwesen.

Die Schwächen des Masern-Impfprogramms

Bei der Einführung von Impfungen gibt es meist keine objektivierbaren Zielvorgaben, die mit der Maßnahme erreicht werden sollen. Und wenn sich im Nachhinein zeigt, dass das Programm Schwächen hat, so werden diese überall gesucht, bloß nicht bei den eigenen behördlichen Maßnahmen. Dies gilt für Impfprogramme allgemein – und auch für an sich erfolgreiche und sinnvolle Impfungen wie z.B. jene gegen Masern.

Wer trägt also die Schuld daran, dass die von der WHO mehrfach angekündigte Ausrottung der Masern heute irrealer scheint denn je?

Sind es tatsächlich die paar Impfgegner, die nicht erreicht werden können? Und liegt die einzige Chance tatsächlich in der Impfpflicht wie Ärztekammer-Funktionäre, Gesundheitspolitiker und viele Medien suggerieren?
Oder hat möglicherweise das Impfprogramm selbst Schwächen und es wird bloß ein Sündenbock gesucht, um davon abzulenken?
Für diese Variante spricht tatsächlich einiges.

So wurde bei der Einführung des Masern-Impfprogramms nicht bedacht, dass damit neue Risikogruppen entstehen können. Dass geimpfte Mütter ihren Babys einen geringeren Nestschutz mitgeben und diese deshalb ein höheres Risiko haben, im ersten Lebensjahr zu erkranken. Es wurde auch übersehen, dass es für die Aufrechterhaltung einer lebenslangen Immunität wichtig ist, dass Erwachsene immer mal wieder einem masernkranken Kind begegnen, welches rundum massenhaft Viren ausstreut und mit jedem Hustenstoß seiner ganzen Umgebung eine Auffrischungsimpfung verpasst. Der Wegfall dieser “natürlichen Durchseuchung” mündete in einem überraschenden Abfall der Wirksamkeit der Impfstoffe. Die epidemiologische Aufarbeitung der letzten Masern-Ausbrüche zeigt, dass der Anteil Erwachsener bereits bei knapp 50 Prozent liegt. Und die meisten waren einst geimpft worden.

Dieses Problem wird künftig immer deutlicher hervortreten. Insofern wird das Ziel einer erfolgreichen weltweiten Ausrottung der Viren mit jedem Jahr unwahrscheinlicher. Speziell wenn in den europäischen “Hotspots der Impfverweigerung” – in Ländern wie der Ukraine und in Teilen Rumäniens beinahe schon Zustände herrschen wie in der Vorimpf-Ära. An den Umtrieben von Impfgegnern, wie in unseren Medien dargestellt, liegt das aber sicherlich nicht. In beiden Ländern gibt es enorme Versorgungsprobleme. Die Ausbrüche in Rumänien betreffen vor allem die Volksgruppe der Roma. Diese haben in ihren ghettoartigen Siedlungen – auch abseits der Impfungen – kaum Zugang zu Gesundheitsleistungen.
In der krisengebeutelten Ukraine hat die Bevölkerung längst das Vertrauen in ihr Gesundheitssystem verloren. Korruption und Versorgungsmängel sind Alltag. Die Menschen fürchten sich vor abgelaufenen Impfstoffen zweifelhafter Qualität. Überall grassieren Gerüchte von schweren Schäden. Wohlhabendere Ukrainer lassen ihre Kinder mit importierten Impfstoffen impfen oder reisen dafür eigens in den Westen. Ohne internationale Hilfe werden diese strukturellen Probleme nicht zu lösen sein.

Verbesserte Impfungen – statt Zwang

Das in Deutschland geplante Gesetz bezieht sich exklusiv auf die Impfung gegen Masern. Am Markt ist jedoch bloß die Dreierimpfung MMR – gegen Masern, Mumps und Röteln, oder die Viererimpfung, die auch noch die Windpocken-Komponente enthält, erhältlich. Das ist ein weiterer schwerer Mangel des Impfprogramms: Es gibt kaum Alternativen – etwa Einzelimpfstoffe oder Impfstoffe, die man schlucken oder Inhalieren kann.
Und zwar nicht deswegen, weil es diese Impfstoffe nicht gibt. Prinzipiell stünden Einzelimpfstoffe ebenso zur Verfügung wie Impfstoffe, die man inhalieren kann. Sie müssten jedoch mit einigem Aufwand importiert werden, oder sind gar nicht behördlich zugelassen. Mit innovativen Impfstoffen wäre es längst möglich gewesen, die Impfquote weiter zu erhöhen. Impfkritische Personen oder Menschen mit Nadelphobie (immerhin rund 10 Prozent der Bevölkerung) wären damit sicherlich leichter erreichbar.

Eine Impfpflicht allein ist dazu jedenfalls kaum in der Lage. Sie schürt vielmehr die allgemeine Skepsis gegen Impfungen. In Frankreich, wo die Regierung Macron Pflichtimpfungen gegen elf Krankheiten gesetzlich verankert hat, bezeichnen sich in Umfragen rund 40 Prozent der Bevölkerung als impfkritisch. Kein Land der EU hat einen ähnlich hohen Anteil.
Und auch der Erfolg der Zwangsimpfungen ist zweifelhaft. Im Jahr 2018 verzeichnete Frankreich 2913 Masernfälle, fünfmal mehr als Deutschland (532 Fälle).

Weitere Informationen zur Impfpflicht findet Ihr auf der Webseite der “Ärzte für Individuelle Impfentscheidung”. Der Verein hat kürzlich in Berlin eine Petition mit 143.000 Unterschriften gegen die Impfpflicht an das Gesundheitsministerium übergeben. 

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Hot Toys Avengers: Endgame-1:6 Iron Man Mark LXXXV (Battle Damaged Version) 12-inch figure

“I love you three thousand.” – Tony Stark

Avengers: Endgame was always going to mark the final Marvel Cinematic Universe appearance for some of the original Avengers. Since the very first Iron Man movie back in 2008, fans have been captivated by the incredible story of the genius billionaire Tony Stark and it’s sad to see the beloved character leaving the silver screen!

Inspired by the big moments in Avengers: Endgame, Hot Toys is excited to present today the battle damaged version of 1/6th scale Iron Man Mark LXXXV collectible figure as the latest addition to the ever-expanding Marvel Cinematic Universe lineup.

Meticulously crafted based on Tony Stark/Iron Man in the movie, the one-of-a-kind collectible figure features two interchangeable head sculpts including a newly developed head sculpt with battle damages and an interchangeable helmeted head with LED-light up function, Iron Man armor has been faithfully reproduced with red, gold and charcoal grey coloring with battle damage and weathering effects, LED light-up functions scattered throughout the upper body, back and Arc Reactor on chest, a battle damaged 1/6th scale Nano Gauntlet as seen in the movie equipped with LED light up function and matching interchangeable hands mounted with all six Infinity Stones, an energy blade, a pair of claw blasters and a highly elaborated rock diorama dynamic figure stand.

A Special Edition available in selected markets will include a Holo Shield as bonus item exclusively for collectors.

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Hot Toys "Star Wars: The Rise of the Skywalker" 1/6th scale Sith Trooper 12" Collectible Figure

Today Hot Toys is pleased to officially present the new 1/6th scale collectible figure of the Sith Trooper from the upcoming Star Wars: The Rise of the Skywalker! With a modern and more menacing look befitting its namesake, the Sith Trooper collectible figure Advance Release Edition will be available for purchase at San Diego Comic-Con International 2019!

The highly-accurate collectible figure is expertly crafted based on the appearance of Sith Trooper from Star Wars: The Rise of Skywalker featuring brand-new helmet and armor designs, skillfully applied paint applications throughout the armor, newly developed body and under-suit, two styles of blasters, a variety of interchangeable hands, and a character-theme figure stand!

Hot Toys MMS544 Star Wars: The Rise of the Skywalker 1/6th scale Sith Trooper Collectible Figure specially features: Authentic and detailed likeness of Sith Trooper in Star Wars: The Last Jedi | Newly developed helmet with fine details | Specially applied glossy red painting on armor | Approximately 31 cm tall Body with over 30 points of articulations | Seven (7) pieces of interchangeable gloved hands including: pair of fists, pair of relaxed hands, pair of hands for holding blaster rifle, opened left hand

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Costume: newly designed Sith Trooper armor, black colored under-suit, red colored belt, red colored boots | Weapons: Two (2) blaster rifles | Accessory: Specially designed figure stand with nameplate and Star Wars logo

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