Hot Toys 1/6th scale Fantastic Beasts Newt Scamander (Eddie Redmayne) 12-inch Figure

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“You’re too good, Newt. You’ve never met a monster you couldn’t love.” – Leta Lestrange

In an effort to thwart the powerful Dark wizard Gellert Grindelwald’s plans, Albus Dumbledore enlists his former student Newt Scamander, who agrees to help, unaware of the dangers that lie ahead. Lines are drawn as love and loyalty are tested, even among the truest friends and family in an increasingly divided wizarding world…

Wands out, everyone! From the second installment of the Wizarding World series, Sideshow and Hot Toys are extremely thrilled to present sixth scale Collectible Figure of Newt Scamander (Special Edition) from Fantastic Beasts: The Crimes of Grindelwald!

The movie-accurate collectible is masterfully crafted based on the image of Eddie Redmayne as the eccentric magizoologist Newt Scamander in the movie. This figure features a suit and coat in charcoal gray, multiple leading magical creatures and accessories including Pickett the Bowtuckle, Baby Nifflers, a wand, Nicolas Flamel’s card, a vintage briefcase, and a book which is written by…Newt himself!

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Hot Toys MMS512 Fantastic Beasts: The Crimes of Grindelwald 1/6th scale Newt Scamander (Eddie Redmayne) 12-inch (30cm) Figure specially features: Authentic and detailed likeness of Eddie Redmayne as Newt Scamander in Fantastic Beasts: The Crimes of Grindelwald | Newly developed head sculpt with movie-accurate facial expression and skin texture | Brown colored short hair sculpture | Approximately 31 cm tall Body with 30 points of articulation | Six (6) interchangeable hands including: pair of hands for carrying suitcase, pair of relaxed hands, gesturing left hand, right hand with wand

Costume: checkered pattern bow tie, white-colored dress shirt, brown-colored vest, intricately detailed grey-colored suit jacket, grey-colored coat, grey pants with braces, brown-colored boots

Accessories: highly decorated vintage briefcase (openable) with sticker and one (1) lenticular printing card displays two images (clothing and Newt’s room), book (Title: Fantastic Beasts and Where to Find Them), wand, Nicolas Flamel’s card, Bowtruckle (approximately 3.5cm tall), Four (4) baby Nifflers (approximately 1.2-1.3cm tall each), specially designed Fantastic Beasts: The Crimes of Grindelwald themed figure stand with movie logo

Exclusive Bonus Accessory for Special Edition: full grown Niffler (approximately 3.7cm tall)

The Special Edition is only available in select countries and will include a a creature with a penchant for anything shiny – a full grown Niffler!

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Improved Grading of Prostate Cancer Using Deep Learning

Posted by Martin Stumpe, Technical Lead and Craig Mermel, Product Manager, Healthcare, Google AI

Approximately 1 in 9 men in the United States will develop prostate cancer in their lifetime, making it the most common cancer in males. Despite being common, prostate cancers are frequently non-aggressive, making it challenging to determine if the cancer poses a significant enough risk to the patient to warrant treatment such as surgical removal of the prostate (prostatectomy) or radiation therapy. A key factor that helps in the “risk stratification” of prostate cancer patients is the Gleason grade, which classifies the cancer cells based on how closely they resemble normal prostate glands when viewed on a slide under a microscope.

However, despite its widely recognized clinical importance, Gleason grading of prostate cancer is complex and subjective, as evidenced by studies reporting inter-pathologist disagreements ranging from 30-53% [1][2]. Furthermore, there are not enough speciality trained pathologists to meet the global demand for prostate cancer pathology, especially outside the United States. Recent guidelines also recommend that pathologists report the percentage of tumor of different Gleason patterns in their final report, which adds to the workload and is yet another subjective challenge for the pathologist [3]. Overall, these issues suggest an opportunity to improve the diagnosis and clinical management of prostate cancer using deep learning–based models, similar to how Google and others used such techniques to demonstrate the potential to improve metastatic breast cancer detection.

In “Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer”, we explore whether deep learning could improve the accuracy and objectivity of Gleason grading of prostate cancer in prostatectomy specimens. We developed a deep learning system (DLS) that mirrors a pathologist’s workflow by first categorizing each region in a slide into a Gleason pattern, with lower patterns corresponding to tumors that more closely resemble normal prostate glands. The DLS then summarizes an overall Gleason grade group based on the two most common Gleason patterns present. The higher the grade group, the greater the risk of further cancer progression and the more likely the patient is to benefit from treatment.

Visual examples of Gleason patterns, which are used in the Gleason system for grading prostate cancer. Individual cancer patches are assigned a Gleason pattern based on how closely the cancer resembles normal prostate tissue, with lower numbers corresponding to more well differentiated tumors. Image Source: National Institutes of Health.

To develop and validate the DLS, we collected de-identified images of prostatectomy samples which contain a greater amount and diversity of prostate cancer than needle core biopsies, even though the latter is the more common clinical procedure. On the training data, a cohort of 32 pathologists provided detailed annotations of Gleason patterns (resulting in over 112 million annotated image patches) and an overall Gleason grade group for each image. To overcome the previously referenced variability in Gleason grading, each slide in the validation set was independently graded by 3 to 5 general pathologists (selected from a cohort of 29 pathologists) and had a final Gleason grade assigned by a genitourinary-specialist pathologist to obtain the ground-truth label for that slide.

In the paper, we show that our DLS achieved an overall accuracy of 70%, compared to an average accuracy of 61% achieved by US board-certified general pathologists in our study. Of 10 high-performing individual general pathologists who graded every slide in the validation set, the DLS was more accurate than 8. The DLS was also more accurate than the average pathologist at Gleason pattern quantitation. These improvements in Gleason grading translated into better clinical risk stratification: the DLS better identified patients at higher risk for disease recurrence after surgery than the average general pathologist, potentially enabling doctors to use this information to better match patients to therapy.

Comparison of scoring performance of the DLS with pathologists. a: Accuracy of the DLS (in red) compared with the mean accuracy among a cohort-of-29 pathologists (in green). Error bars indicate 95% confidence intervals. b: Comparison of risk stratification provided by the DLS, the cohort-of-29 pathologists, and the genitourinary specialist pathologists. Patients are divided into low and high risk groups based on their Gleason grade group, where a larger separation between the Kaplan-Meier curves of these risk groups indicates better stratification.

We also found that the DLS was able to characterize tissue morphology that appeared to lie at the cusp of two Gleason patterns, which is one reason for the disagreements in Gleason grading observed between pathologists, suggesting the possibility of creating finer grained “precision grading” of prostate cancer. While the clinical significance of these intermediate patterns (e.g. Gleason pattern 3.3 or 3.7) is not known, the increased precision of the DLS will enable further research into this interesting question.

Assessing the region-level classification of the DLS. a: Annotations from 3 pathologists compared to DLS predictions. The pathologists show general concordance on the location and the extent of tumor areas, but poor agreement in classifying Gleason patterns. The DLS’s precision Gleason pattern for each region is represented by interpolating between the DLS’s prediction patterns for Gleason patterns 3 (green), 4 (yellow), and 5 (red). b: DLS prediction
patterns compared to the distribution of pathologists’ Gleason pattern classifications on 41 million annotated image patches from the test dataset. On patches where pathologists are discordant, where the tissue is more likely to be on the cusp of two patterns, the DLS reflects this ambiguity in it’s prediction scores.

While these initial results are encouraging, there is much more work to be done before systems like our DLS can be used to improve the care of prostate cancer patients. First, the accuracy of the model can be further improved with additional training data and should be validated on independent cohorts containing a larger number and more diverse group of patients. In addition, we are actively working on refining our DLS system to work on diagnostic needle core biopsies, which occur prior to the decision to undergo surgery and where Gleason grading therefore has a significantly greater impact on clinical decision-making. Further work will be needed to assess how to best integrate our DLS into the pathologist’s diagnostic workflow and the impact of such artificial-intelligence based assistance on the overall efficiency, accuracy, and prognostic ability of Gleason grading in clinical practice. Nonetheless, we are excited about the potential of technologies like this to significantly improve cancer diagnostics and patient care.

Acknowledgements
This work involved the efforts of a multidisciplinary team of software engineers, researchers, clinicians and logistics support staff. Key contributors to this project include Kunal Nagpal, Davis Foote, Yun Liu, Po-Hsuan (Cameron) Chen, Ellery Wulczyn, Fraser Tan, Niels Olson, Jenny L. Smith, Arash Mohtashamian, James H. Wren, Greg S. Corrado, Robert MacDonald, Lily H. Peng, Mahul B. Amin, Andrew J. Evans, Ankur R. Sangoi, Craig H. Mermel, Jason D. Hipp and Martin C. Stumpe. We would also like to thank Tim Hesterberg, Michael Howell, David Miller, Alvin Rajkomar, Benny Ayalew, Robert Nagle, Melissa Moran, Krishna Gadepalli, Aleksey Boyko, and Christopher Gammage. Lastly, this work would not have been possible without the aid of the pathologists who annotated data for this study.

References

  1. Interobserver Variability in Histologic Evaluation of Radical Prostatectomy Between Central and Local Pathologists: Findings of TAX 3501 Multinational Clinical Trial, Netto, G. J., Eisenberger, M., Epstein, J. I. & TAX 3501 Trial Investigators, Urology 77, 1155–1160 (2011).
  2. Phase 3 Study of Adjuvant Radiotherapy Versus Wait and See in pT3 Prostate Cancer: Impact of Pathology Review on Analysis, Bottke, D., Golz, R., Störkel, S., Hinke, A., Siegmann, A., Hertle, L., Miller, K., Hinkelbein, W., Wiegel, T., Eur. Urol. 64, 193–198 (2013).
  3. Utility of Quantitative Gleason Grading in Prostate Biopsies and Prostatectomy Specimens, Sauter, G. Steurer, S., Clauditz, T. S., Krech, T., Wittmer, C., Lutz, F., Lennartz, M., Janssen, T., Hakimi, N., Simon, R., von Petersdorff-Campen, M., Jacobsen, F., von Loga, K., Wilczak, W., Minner, S., Tsourlakis, M. C., Chirico, V., Haese, A., Heinzer, H., Beyer, B., Graefen, M., Michl, U., Salomon, G., Steuber, T., Budäus, L. H., Hekeler, E., Malsy-Mink, J., Kutzera, S., Fraune, C., Göbel, C., Huland, H., Schlomm, T., Clinical Eur. Urol. 69, 592–598 (2016).

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Hot Toys Fantastic Beasts 1/6th scale Johnny Depp as Gellert Grindelwald Collectible Figure

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“The moment has come to take our rightful place in the world where we wizards are free. Join me, or die.”

The fate of the one. The future of all. At the end of Fantastic Beast and Where to Find Them, the powerful Dark wizard Gellert Grindelwald was captured by MACUSA, with the help of Newt Scamander. But, making good on his threat, Grindelwald escaped custody and has set about gathering followers, most unsuspecting of his true agenda: to raise pure-blood wizards up to rule over all non-magical beings.

To get fans ready for this installment of the Wizarding World, Hot Toys is extremely thrilled to present today the 1/6th scale Collectible Figure of Gellert Grindelwald from Fantastic Beasts: The Crimes of Grindelwald!

The movie-accurate collectible is masterfully crafted based on the image of Johnny Depp as Gellert Grindelwald, featuring a newly developed head sculpt with mismatched eyes and silver short hair sculpture, sophistically tailored costume that recreates the iconic details, two engraved skulls including a skull with LED light-up function and a skull with detachable string, a magic wand, a Grindelwald’s pendent and a specially designed movie-themed figure stand.

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Hot Toys MMS513 Fantastic Beasts: The Crimes of Grindelwald 1/6th scale Gellert Grindelwald Collectible Figure specially features: Authentic and detailed likeness of Johnny Depp as Gellert Grindelwald in Fantastic Beasts: The Crimes of Grindelwald | Newly developed head sculpt with mismatched eyes, movie-accurate facial expression and detailed skin texture | Silver colored short hair sculpture | Approximately 30 cm tall Body with 30 points of articulation | Three (3) pieces of interchangeable left hands including: hand for holding Grindelwald’s pendant, relax hand, gesturing hand | Three (3) pieces of interchangeable right hands including: hand with wand, hand for holding wand, hand for holding skull

Costume: white-colored scarf, navy-colored dress shirt with decorative chains, black-colored vest decorated with chains, long black-colored coat, leather-like black pants with two (2) belts, black-colored boots

The Special Edition includes a fantastic creature Augurey with artificial feathers for sophisticated fans! Exclusive Bonus Accessory for Special Edition: Augurey with artificial feathers (approximately 10cm tall)

Accessories: skull with LED light-up function (yellow light, battery operated), skull with detachable string, wand, Grindelwald’s pendant, specially designed Fantastic Beasts: The Crimes of Grindelwald themed figure stand with movie logo

Release date: Approximately Q4, 2019 – Q1, 2020

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Prime 1 Studio 1:3 scale DC Comics Batman: Hush Huntress (Fabric Cape Edition) Statue

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“Tell Everyone. Huntress did this to you.”

Sideshow and Prime 1 Studio are proud to present the 1:3 scale Huntress (Fabric Cape Edition) from Batman: Hush. Batman: Hush is a 2002-2003 comic book story arc published in the Batman monthly series. The Huntress is Helena Rosa Bertinelli, a vigilante operating out of Gotham City, and a member of the Batman Family. She is also a devout Catholic and is very in touch with her Italian heritage. Her career is inspired by a personal vendetta against organized crime, responsible for killing her entire family.

Prime 1 Studio 1:3 scale DC Comics Batman: Hush Huntress (Fabric Cape Edition) Statue features: Size approximately 32 inches tall | Poseable Fabric Cape | Base inspired by Gotham City Rooftop | Three (3) interchangeable right-arms | Two (2) interchangeable right-hand holding Crossbow | Two (2) interchangeable right-hand holding Battle-staff

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Macht die Grippe Impfung Sinn?

Bei all den Zeitungs- und TV-Berichten, die derzeit umgehen, bekommt man den Eindruck, dass “Winter” ein vollständig altmodischer Begriff für die bevorstehende Jahreszeit ist. Eigentlich stehen wir nämlich vor der “Influenza-Saison”. Und laufend sind wir mit der Frage konfrontiert, ob wir die Kinder und uns selbst impfen lassen sollen oder nicht. 


Foto: Andre Borges/Agência Brasília


Das, was wir gemeinhin als „Grippe“ bezeichnen kann vollständig unterschiedliche Ursachen haben. Es gibt mehr als 200 Viren, die Grippe oder grippale Infekte auslösen können.  „Und auch wenn dies oft behauptet wird: Ohne Labortest können Ärzte die beiden Krankheiten nicht seriös auseinander halten“, erklärte mir Tom Jefferson, langjähriger Leiter der Impfabteilung der angesehenen Cochrane-Gruppe. 
In der Tat haben zahlreiche Studien die Ansicht widerlegt, dass nur die „echten“ Influenzaviren ernsthafte Krankheiten machen, die grippalen Infekte hingegen banal verlaufen. Bei kranken älteren Menschen zeigten sich beispielsweise die so genannten RS-Viren genauso oft als Verursacher eines Spitalsaufenthalts wie Influenzaviren. Noch extremer war das Verhältnis bei Kindern. Eine umfangreiche US-Studie zeigte, dass Influenzaviren hier im Jahresschnitt gerade einmal drei Prozent aller Klinikeinweisungen bei Atemwegsinfektionen verursachten und damit in der Rangliste der Krankheitserreger deutlich hinter RS-Viren, Noroviren oder Parainfluenza-Viren lagen. 

Besonders eindrucksvoll war dieser Unterschied während der Schweinegrippe-Pandemie von 2009/2010, wie eine Erhebung des nationalen slowenischen Gesundheitsinstituts in Ljubljana, ergab. Während die Influenza-Viren vorherrschten, kam es bei Kindern kaum zu Fällen von Bronchitits oder anderen Atemwegsinfekten, die einer Behandlung im Krankenhaus erforderten. Als die Grippesaison zu Ende ging und die RS-Viren dominierten, stiegen die stationären Aufnahmen hingegen um das Fünffache an. 
Auch ein historischer Überblick zur Sterblichkeit der vergangenen hundert Jahre in den USA belegt, dass der Einfluss der Influenza grob überschätzt wird. Studienautor Peter Doshi vom Massachusetts Institute of Technology (MIT) in Boston wies nach, dass nicht einmal in den berühmten Grippejahren von 1957/58 (Hongkong-Grippe) und 1968/69 (asiatische Grippe) ein merkbarer Anstieg der Gesamtsterblichkeit erkennbar war. “Deutlich zeigte sich hingegen, dass mit den Fortschritten in der medizinischen Versorgung, der Hygiene und des allgemeinen Lebensstandards das Sterberisiko stark absinkt”, erklärt Doshi. “Die Bedrohung durch eine Influenza-Pandemie wird extrem überschätzt.”

Ebenso wie der Schutzeffekt der Influenza-Impfung. Eine Analyse der Sterblichkeit in den USA während der vergangenen beiden Jahrzehnte ergibt nicht das geringste Indiz dafür, dass die Impfung überhaupt einen Effekt hatte. Obwohl sich die älteren Menschen heute viel öfter impfen lassen und die Impfrate von 15 Prozent im Jahr 1980 auf zuletzt 65 Prozent gestiegen war, ergab sich kein Rückgang bei den Todesfällen durch Influenza. “Unsere Ergebnisse stellen die derzeitigen Konzepte infrage, wie ältere Menschen am besten vor dem Grippetod geschützt werden können”, erklärten die Autoren im Schlusswort ihrer Studie. 
Tatsächlich bestätigen die Zahlen, dass nur die spanische Grippe vom Nachkriegswinter 1918/19 sich überhaupt in einem Anstieg der Gesamtsterblichkeit bemerkbar machte. 


Was machte die Spanische Grippe so gefährlich? 

Zunächst einmal verlief die Krankheit so ungewöhnlich, dass viele Ärzte sie zunächst gar nicht für eine Influenza hielten, sondern an eine Rückkehr von Cholera oder Typhus dachten. Das begann schon mit dem Beginn des Ausbruchs, der nicht in die normale Grippesaison fiel, sondern bereits im Spätsommer 1918 startete und bis zum späten Frühjahr 1919 andauerte. 
Eines der ersten Todesopfer war der spanische König. Tuberkulosekranke – wie etwa Franz Kafka – traf die Grippe besonders hart. Aderlässe und Blutegel kamen zu neuen Ehren. Naturheiler brauten Wundermittel aus Tollkirschen. Und dennoch war kein Kraut gewachsen. Edith Schiele starb als sie im 6. Monat schwanger war. Ihr Mann Egon steckte sich bei ihr an und folgte ihr kurz darauf ins Grab. 
Während normale Grippewellen die Gefährdungskurve eines „U“ bilden – also für Kleinkinder und alte Menschen das höchste Risiko bedeuten, wurden hier speziell Menschen in der Blüte ihres Lebens, zwischen 20 und 40 Jahren dahingerafft. Im Jahr 2005 isolierten Wissenschaftler der US-Behörde CDC Originalviren von einem Influenzaopfer, das im Permafrost von Alaska konserviert worden war. Und hier zeigte sich bei Experimenten, dass die Viren bei Affen noch immer dieselben Symptome auslösen konnten, wie damals. 
Todesursache der “spanischen Grippe” war ein sogenannter Zytokinsturm – eine extrem heftige Reaktion des Immunsystems, welche befallenes Gewebe im Körper regelrecht verwüstet. Also eine Überreaktion, die zwar die Infektion stoppt, dabei aber so großen Schaden – vor allem an den Lungen – anrichtet, dass sie für die Betroffenen gefährlicher war als die Viren selbst. 
Auch der Grund, warum das Immunsystem – speziell bei jungen Erwachsenen – derart hysterisch reagierte, scheint nun geklärt. Eine Arbeitsgruppe um den Evolutionsbiologen Michael Worobey an der Universität von Arizona in Tucson rekonstruierte das Virus von 1918 und klärte auch dessen Entstehung durch Genanalysen auf. Dabei wurde klar, dass sich kurz vor dem Ausbruch eine vollständig neuartige Mischung mit Anteilen von Pferde- und Vogelviren gebildet hatte, die später als H1N1 Typus kategorisiert wurde. Kinder und Jugendliche unter 20 Jahren hatten schon mit deren Vorgängern Bekanntschaft gemacht und hatten deshalb einen gewissen Schutz vor der neuen Variante. Die Altersgruppe der über 20-jährigen war in ihrer Kindheit – im letzten Jahrzehnt des 19. Jahrhunderts – jedoch ausschließlich mit H3N8 Viren konfrontiert. Und hier reagierte das Immunsystem nun vollkommen überrascht und in fataler Weise falsch. 
Worobey schließt daraus, dass der frühe virale Kontakt in der Kindheit den wichtigsten Schutz vor neuartigen Virenvarianten darstellt. „Das wird in den derzeitigen Impfstoff-Strategien aber überhaupt nicht bedacht.“ 


Wenn Geimpfte ein höheres Risiko haben 

Mediziner und Schulbehörden in Kanada machten während der Schweinegrippe-Pandemie eine interessante Beobachtung, die für zukünftige Ereignisse lehrreich sein könnten: Sie bemerkten, dass die meisten Kinder mit dem neuartigen Virentyp, der damals die Welt umrundete, problemlos zurecht kamen. Es gab jedoch eine Ausnahme: Vergleichsweise schwer erkrankten nämlich jene Kinder, die zuvor jährlich eine Grippe-Impfung erhalten hatten. Auch hier waren die – zum Glück seltenen Todesfälle – meist wieder durch einen Zytokinsturm des Immunsystems ausgelöst. 
In Kanada wurden gleich vier Studien durchgeführt, um diesen Verdacht zu prüfen und ihn schließlich auch bestätigten: Anscheinend ist es für das Immunsystem der Kinder von Vorteil, die Viren – ohne pharmazeutische Schützenhilfe – kennen zu lernen. Dann erwerben sie über den immunologischen Kontakt auch das Rüstzeug, mit stark veränderten Viren klarzukommen. Jene Kinder, die keine saisonale Grippe-Impfung erhielten, profitierten über den Kontakt mit anderen Influenza-Stämmen und hatten gegenüber der neuartigen Variante zumindest einen Teilschutz erworben. Das Immunsystem war vorgewarnt und die Krankheit verlief zumeist mild. Die Impfung hingegen stört offenbar diesen Lerneffekt des Immunsystems. 

Eine Gruppe von Virologen und Kinderärzten der Erasmus Universität Rotterdam untersuchten diesen Zusammenhang mit einem recht drastischen Experiment. Dafür setzten die Wissenschaftler Mäuse verschiedenen Impfungen und nachfolgenden Infektionen aus. Der entscheidende „Elchtest“ für die Tiere war eine Konfrontation mit Vogelgrippe Viren vom Typ H5N1. Dieser besonders gefährliche Virustyp galt als Dummy für eine neuartige tödliche Mutation der Influenzaviren. 
Die Überlebenschancen der Mäuse, die in diesem Experiment eingesetzt wurden, standen nicht sonderlich gut: 

  • Wurden die Mäuse mit dem saisonalen Impfstoff geimpft und danach mit den Vogelgrippe-Viren konfrontiert, so starben sie. 
  • Wurden die Mäuse nicht geimpft und dann mit den Vogelgrippe Viren konfrontiert, so starben sie ebenfalls. 
  • Wurden die Mäuse mit saisonalem Impfstoff geimpft, danach mit saisonalen Viren infiziert, so überstanden sie im Normalfall die saisonale Grippe, starben aber ebenfalls wieder, wenn sie anschließend mit H5N1 infiziert wurden. 


Was denken Sie, war die einzige Variante, bei der die armen Versuchsmäuse dieses Experiment überlebten? 
Folgendes: Das Überlebensrezept bestand darin, dass die Mäuse ungeimpft eine normale Grippe durchmachten. Sie wurden krank und erholten sich wieder. Und siehe da: Danach waren sie plötzlich gegen die ansonsten stets tödlichen H5N1 Vogelgrippe-Viren gewappnet. 
Sie hatten weniger Viren in der Lunge, erkrankten weniger heftig und die meisten Tiere überlebten den Kontakt mit der Influenza-Mutation. 
Und dieser Lerneffekt des Immunsystems erklärt nach Ansicht der holländischen Mediziner auch die unterschiedlichen Verläufe, die während der Schweinegrippe Pandemie beobachtet wurden. Länder mit geringer Impfmoral bei der saisonalen Grippe-Impfung – wie beispielsweise Österreich, Holland oder Deutschland, kamen mit der Pandemie am besten zurecht. 
Länder wie die USA, wo die „Flu-Shots“ bereits ab einem Alter von 6 Monaten empfohlen und von der Bevölkerung auch angenommen werden, hatten hingegen während der Schweinegrippe-Pandemie eine vergleichsweise hohe Sterblichkeit. 
Wer seine Kinder gegen Grippe impft, geht demnach also das Risiko ein, dass diese nur eine “Scheinimmunität” gegen die in der Impfung enthaltenen Antigene erhalten, sich jedoch keine breitere Immunität gegen nachfolgende andersartige Grippeviren ausbilden kann. Und wenn dann doch einmal mutierte Viren kommen, verkehrt sich der vermeintliche Schutz ins Gegenteil. 


Glücksspiel Influenza Impfung 

Kaum eine Impfung hat einen so schlechten Ruf wie die Influenza-Impfung. Besonders skeptisch sind hier die Österreicher. Die Durchimpfungsrate, errechnet auf Basis der abgegebenen Impfdosen, betrug während der Saison 2017/18 magere 6,4 Prozent. „Im Vergleich zum Vorjahr war das zwar eine Steigerung von fast einem Prozent, insgesamt ist die Rate aber nach wie katastrophal”, hieß es von Seiten des Verbands der Impfstoffhersteller. 
Die Grippewelle von 2017/18 war eine der stärksten der vergangenen Jahrzehnte. Sie begann rund um Weihnachten und dauerte ungewöhnlich lange bis Ende März. In Deutschland wurden bis Jahresmitte 2018 mehr als 270.000 Influenza-Fälle gemeldet. Im gesamten Jahr 2017 waren es dagegen nur 95.977 Meldungen. Nach Angaben der „Arbeitsgemeinschaft Influenza“ sind 1.287 Menschen in Deutschland an Grippe verstorben. „Diese offiziellen Zahlen zur aktuellen Grippesaison beschreiben wohl längst nicht das tatsächliche Infektionsgeschehen“, erklärte STIKO-Vorsitzender Thomas Mertens gegenüber der Ärztezeitung. Er geht von zehnmal höheren tatsächlichen Grippezahlen aus und von rund 12.000 Influenza bedingten Todesfällen. Bei derartigen Ungenauigkeiten stellt sich allerdings die Frage, warum überhaupt eine bundesweites – aus Steuergeldern finanziertes Influenza-Überwachungssystem notwendig ist, wenn dann die „tatsächlichen Zahlen“ ums Zehnfache abweichen. 

Der Großteil der Erkrankungen wurde vom Influenza Typ B – vom Stamm Yamagata – ausgelöst. Die H1N1 Variante vom Influenza Typ A, die in den Vorjahren vorgeherrscht hatte, war nur mehr für rund 25 bis 30% der Fälle verantwortlich. Blöd war allerdings, dass von den Impfstoffen nur ein einziger auch diesen Stamm abdeckte. Und der war – als sich dies herumsprach – bald nicht mehr lieferbar. Die Schutzwirkung der Impfung lag nach Angaben der Behörden insgesamt zwischen 25 und 52%. Für die nächste Saison soll nun jedenfalls auch der Stamm Yamagata in allen Impfstoffen enthalten sein. Man wird sehen, ob diese Nachjustierung den entscheidenden Erfolg beschert, oder ob sich auch diesmal wieder ein unvorhergesehener Virenstamm breit macht. 


Schwarze Löcher der Wirksamkeit 

Die Wirksamkeit der Impfung war nicht nur in der vergangenen Saison schlecht. Die Influenza-Impfung ist ein Dauer-Sorgenkind. Laut der unabhängigen Cochrane Collaboration bestehen bezüglich der Wirksamkeit der Influenza Impfung zwei schwarze Löcher. Für Kinder unter 2 Jahren gibt es gar keinen Nachweis der Wirksamkeit. Ebenso schlecht ist die Datenlage für Personen im Alter über 65 Jahren. Und sogar bei Menschen in Gesundheitsberufen schließen die Cochrane-Autoren:

Unsere Übersichtsarbeit fand keine vernünftige Basis, um die Impfung der Menschen in Gesundheitsberufen zu empfehlen.“ 

Es gibt demnach keine zuverlässigen Belege, dass die Patienten davon profitieren, wenn die Ärzte und Krankenpfleger geimpft sind. Derzeit, so der Cochrane Impfexperte, Tom Jefferson, “gleicht die Werbung für die Influenza-Impfung eher den Praktiken von Staubsauger-Verkäufern auf Jahrmärkten”. 

In manchen Jahren bezieht sich das schwarze Wirksamkeitsloch der Impfung nicht nur auf Kleinkinder und Senioren, sondern dehnt sich auf die gesamte Bevölkerung aus. Als Grund für diese Abstürze nennen die Influenza-Experten die zeitverzögerten Herstellung. Der Impfstoff wird nämlich ein halbes Jahr im voraus – nach den vorherrschenden Influenza-Viren auf der Südhalbkugel – konzipiert. Und welche Typen sich dann sechs Monate später tatsächlich im Norden zeigen, ist Glückssache. 
Seit Jahren fordert die Cochrane Collaboration, dass die Auswirkungen der Influenza Impfung in einer gut gemachten Vergleichsstudie zwischen Geimpften und Ungeimpften gemessen werden. Nur so wäre eine objektive Bewertung der Impfung möglich. Tatsächlich weiß man bisher nicht einmal annähernd, wie viele Krankheitstage sich ein durchschnittlicher gesunder Erwachsener durch eine Impfung erspart. Wenn man sich überhaupt etwas erspart. 

Die Hersteller der Impfstoffe sehen keinen Anlass, so eine Studie zu finanzieren. Offenbar erscheint ihnen das Risiko zu groß, dass die Resultate einer derartigen Arbeit ihnen einen nachhaltigen finanziellen Schaden zufügen könnten. Es läge also an den Gesundheitsbehörden, hier tätig zu werden und so eine Studie öffentlich zu finanzieren. Schließlich werden auch viele Millionen an Steuergeld an Zuschüssen für die jährlichen Influenza-Impfaktionen bezahlt. Höchste Zeit, sollte man meinen, dass diese Ausgaben auf ihre Sinnhaftigkeit geprüft werden. Bislang konnten sich die Behörden jedoch nicht zu einer relevanten Aktivität aufraffen.

Dieser Artikel ist ein kurzer Ausschnitt aus dem Influenza-Kapitel in meinem aktuellen Buch “Gute Impfung – Schlechte Impfung“, das im Oktober 2018 im Verlag Ennsthaler erschienen ist. (417 Seiten, 24,90€)



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Mezco Toyz One:12 Collective (1:12 scale) Freddy Krueger 16cm tall action figure

“Whatever you do, don’t fall asleep!”

The One:12 Collective Freddy Krueger figure features four head portraits capturing the fearsome expressions of the dream demon, including one with a removable faceplate that reveals a skull sculpt – from the iconic scene where Freddy & Tina have a fatal encounter. Freddy is outfitted in his infamous striped sweater and comes complete with his signature clawed gloves and a trash can lid, straight from Tina’s nightmare.

In Wes Craven’s classic slasher film, ‘A Nightmare on Elm Street’, several teenagers fall victim to Freddy Krueger as he preys on them in their dreams. Dark secrets start to unravel as the teens suspect their parents may be the key to solving the puzzle before it’s too late.

THE ONE:12 COLLECTIVE FREDDY KRUEGER FIGURE FEATURES: Four (4) head portraits, Approximately 16cm tall One:12 Collective body with over 30 points of articulation, Hand painted authentic detailing. Six (6) interchangeable hands including: claw hand (R), pointing claw hand (R), posing hand (L), grappling hand (L), severed finger hand (L), fist (L), Fedora, Striped sweater, Slacks, Work boots, trash can lid, One:12 Collective display base with logo, One:12 Collective adjustable display post

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Night Sight: Seeing in the Dark on Pixel Phones

Posted by Marc Levoy, Distinguished Engineer and Yael Pritch, Staff Software Engineer

Night Sight is a new feature of the Pixel Camera app that lets you take sharp, clean photographs in very low light, even in light so dim you can’t see much with your own eyes. It works on the main and selfie cameras of all three generations of Pixel phones, and does not require a tripod or flash. In this article we’ll talk about why taking pictures in low light is challenging, and we’ll discuss the computational photography and machine learning techniques, much of it built on top of HDR+, that make Night Sight work.

Left: iPhone XS (full resolution image here). Right: Pixel 3 Night Sight (full resolution image here).

Why is Low-light Photography Hard?
Anybody who has photographed a dimly lit scene will be familiar with image noise, which looks like random variations in brightness from pixel to pixel. For smartphone cameras, which have small lenses and sensors, a major source of noise is the natural variation of the number of photons entering the lens, called shot noise. Every camera suffers from it, and it would be present even if the sensor electronics were perfect. However, they are not, so a second source of noise are random errors introduced when converting the electronic charge resulting from light hitting each pixel to a number, called read noise. These and other sources of randomness contribute to the overall signal-to-noise ratio (SNR), a measure of how much the image stands out from these variations in brightness. Fortunately, SNR rises with the square root of exposure time (or faster), so taking a longer exposure produces a cleaner picture. But it’s hard to hold still long enough to take a good picture in dim light, and whatever you’re photographing probably won’t hold still either.

In 2014 we introduced HDR+, a computational photography technology that improves this situation by capturing a burst of frames, aligning the frames in software, and merging them together. The main purpose of HDR+ is to improve dynamic range, meaning the ability to photograph scenes that exhibit a wide range of brightnesses (like sunsets or backlit portraits). All generations of Pixel phones use HDR+. As it turns out, merging multiple pictures also reduces the impact of shot noise and read noise, so it improves SNR in dim lighting. To keep these photographs sharp even if your hand shakes and the subject moves, we use short exposures. We also reject pieces of frames for which we can’t find a good alignment. This allows HDR+ to produce sharp images even while collecting more light.

How Dark is Dark?
But if capturing and merging multiple frames produces cleaner pictures in low light, why not use HDR+ to merge dozens of frames so we can effectively see in the dark? Well, let’s begin by defining what we mean by “dark”. When photographers talk about the light level of a scene, they often measure it in lux. Technically, lux is the amount of light arriving at a surface per unit area, measured in lumens per meter squared. To give you a feeling for different lux levels, here’s a handy table:

Smartphone cameras that take a single picture begin to struggle at 30 lux. Phones that capture and merge several pictures (as HDR+ does) can do well down to 3 lux, but in dimmer scenes don’t perform well (more on that below), relying on using their flash. With Night Sight, our goal was to improve picture-taking in the regime between 3 lux and 0.3 lux, using a smartphone, a single shutter press, and no LED flash. To make this feature work well includes several key elements, the most important of which is to capture more photons.

Capturing the Data
While lengthening the exposure time of each frame increases SNR and leads to cleaner pictures, it unfortunately introduces two problems. First, the default picture-taking mode on Pixel phones uses a zero-shutter-lag (ZSL) protocol, which intrinsically limits exposure time. As soon as you open the camera app, it begins capturing image frames and storing them in a circular buffer that constantly erases old frames to make room for new ones. When you press the shutter button, the camera sends the most recent 9 or 15 frames to our HDR+ or Super Res Zoom software. This means you capture exactly the moment you want — hence the name zero-shutter-lag. However, since we’re displaying these same images on the screen to help you aim the camera, HDR+ limits exposures to at most 66ms no matter how dim the scene is, allowing our viewfinder to keep up a display rate of at least 15 frames per second. For dimmer scenes where longer exposures are necessary, Night Sight uses positive-shutter-lag (PSL), which waits until after you press the shutter button before it starts capturing images. Using PSL means you need to hold still for a short time after pressing the shutter, but it allows the use of longer exposures, thereby improving SNR at much lower brightness levels.

The second problem with increasing per-frame exposure time is motion blur, which might be due to handshake or to moving objects in the scene. Optical image stabilization (OIS), which is present on Pixel 2 and 3, reduces handshake for moderate exposure times (up to about 1/8 second), but doesn’t help with longer exposures or with moving objects. To combat motion blur that OIS can’t fix, the Pixel 3’s default picture-taking mode uses “motion metering”, which consists of using optical flow to measure recent scene motion and choosing an exposure time that minimizes this blur. Pixel 1 and 2 don’t use motion metering in their default mode, but all three phones use the technique in Night Sight mode, increasing per-frame exposure time up to 333ms if there isn’t much motion. For Pixel 1, which has no OIS, we increase exposure time less (for the selfie cameras, which also don’t have OIS, we increase it even less). If the camera is being stabilized (held against a wall, or using a tripod, for example), the exposure of each frame is increased to as much as one second. In addition to varying per-frame exposure, we also vary the number of frames we capture, 6 if the phone is on a tripod and up to 15 if it is handheld. These frame limits prevent user fatigue (and the need for a cancel button). Thus, depending on which Pixel phone you have, camera selection, handshake, scene motion and scene brightness, Night Sight captures 15 frames of 1/15 second (or less) each, or 6 frames of 1 second each, or anything in between.1

Here’s a concrete example of using shorter per-frame exposures when we detect motion:

Left: 15-frame burst captured by one of two side-by-side Pixel 3 phones. Center: Night Sight shot with motion metering disabled, causing this phone to use 73ms exposures. The dog’s head is motion blurred in this crop. Right: Night Sight shot with motion metering enabled, causing this phone to notice the motion and use shorter 48ms exposures. This shot has less motion blur. (Mike Milne)

And here’s an example of using longer exposure times when we detect that the phone is on a tripod:

Left: Crop from a handheld Night Sight shot of the sky (full resolution image here). There was slight handshake, so Night Sight chose 333ms x 15 frames = 5.0 seconds of capture. Right: Tripod shot (full resolution image here). No handshake was detected, so Night Sight used 1.0 second x 6 frames = 6.0 seconds. The sky is cleaner (less noise), and you can see more stars. (Florian Kainz)

Alignment and Merging
The idea of averaging frames to reduce imaging noise is as old as digital imaging. In astrophotography it’s called exposure stacking. While the technique itself is straightforward, the hard part is getting the alignment right when the camera is handheld. Our efforts in this area began with an app from 2010 called Synthcam. This app captured pictures continuously, aligned and merged them in real time at low resolution, and displayed the merged result, which steadily became cleaner as you watched.

Night Sight uses a similar principle, although at full sensor resolution and not in real time. On Pixel 1 and 2 we use HDR+’s merging algorithm, modified and re-tuned to strengthen its ability to detect and reject misaligned pieces of frames, even in very noisy scenes. On Pixel 3 we use Super Res Zoom, similarly re-tuned, whether you zoom or not. While the latter was developed for super-resolution, it also works to reduce noise, since it averages multiple images together. Super Res Zoom produces better results for some nighttime scenes than HDR+, but it requires the faster processor of the Pixel 3.

By the way, all of this happens on the phone in a few seconds. If you’re quick about tapping on the icon that brings you to the filmstrip (wait until the capture is complete!), you can watch your picture “develop” as HDR+ or Super Res Zoom completes its work.

Other Challenges
Although the basic ideas described above sound simple, there are some gotchas when there isn’t much light that proved challenging when developing Night Sight:

1. Auto white balancing (AWB) fails in low light.

Humans are good at color constancy — perceiving the colors of things correctly even under colored illumination (or when wearing sunglasses). But that process breaks down when we take a photograph under one kind of lighting and view it under different lighting; the photograph will look tinted to us. To correct for this perceptual effect, cameras adjust the colors of images to partially or completely compensate for the dominant color of the illumination (sometimes called color temperature), effectively shifting the colors in the image to make it seem as if the scene was illuminated by neutral (white) light. This process is called auto white balancing (AWB).

The problem is that white balancing is what mathematicians call an ill-posed problem. Is that snow really blue, as the camera recorded it? Or is it white snow illuminated by a blue sky? Probably the latter. This ambiguity makes white balancing hard. The AWB algorithm used in non-Night Sight modes is good, but in very dim or strongly colored lighting (think sodium vapor lamps), it’s hard to decide what color the illumination is.

To solve these problems, we developed a learning-based AWB algorithm, trained to discriminate between a well-white-balanced image and a poorly balanced one. When a captured image is poorly balanced, the algorithm can suggest how to shift its colors to make the illumination appear more neutral. Training this algorithm required photographing a diversity of scenes using Pixel phones, then hand-correcting their white balance while looking at the photo on a color-calibrated monitor. You can see how this algorithm works by comparing the same low-light scene captured using two ways using a Pixel 3:

Left: The white balancer in the Pixel’s default camera mode doesn’t know how yellow the illumination was on this shack on the Vancouver waterfront (full resolution image here). Right: Our learning-based AWB algorithm does a better job (full resolution image here). (Marc Levoy)

2. Tone mapping of scenes that are too dark to see.

The goal of Night Sight is to make photographs of scenes so dark that you can’t see them clearly with your own eyes — almost like a super-power! A related problem is that in very dim lighting humans stop seeing in color, because the cone cells in our retinas stop functioning, leaving only the rod cells, which can’t distinguish different wavelengths of light. Scenes are still colorful at night; we just can’t see their colors. We want Night Sight pictures to be colorful – that’s part of the super-power, but another potential conflict. Finally, our rod cells have low spatial acuity, which is why things seem indistinct at night. We want Night Sight pictures to be sharp, with more detail than you can really see at night.

For example, if you put a DSLR camera on a tripod and take a very long exposure — several minutes, or stack several shorter exposures together — you can make nighttime look like daytime. Shadows will have details, and the scene will be colorful and sharp. Look at the photograph below, which was captured with a DSLR; it must be night, because you can see the stars, but the grass is green, the sky is blue, and the moon casts shadows from the trees that look like shadows cast by the sun. This is a nice effect, but it’s not always what you want, and if you share the photograph with a friend, they’ll be confused about when you captured it.

Yosemite valley at nighttime, Canon DSLR, 28mm f/4 lens, 3-minute exposure, ISO 100. It’s nighttime, since you can see stars, but it looks like daytime (full resolution image here). (Jesse Levinson)

Artists have known for centuries how to make a painting look like night; look at the example below.2

A Philosopher Lecturing on the Orrery, by Joseph Wright of Derby, 1766 (image source: Wikidata). The artist uses pigments from black to white, but the scene depicted is evidently dark. How does he accomplish this? He increases contrast, surrounds the scene with darkness, and drops shadows to black, because we cannot see detail there.

We employ some of the same tricks in Night Sight, partly by throwing an S-curve into our tone mapping. But it’s tricky to strike an effective balance between giving you “magical super-powers” while still reminding you when the photo was captured. The photograph below is particularly successful at doing this.

Pixel 3, 6-second Night Sight shot, with tripod (full resolution image here). (Alex Savu)

How Dark can Night Sight Go?
Below 0.3 lux, autofocus begins to fail. If you can’t find your keys on the floor, your smartphone can’t focus either. To address this limitation we’ve added two manual focus buttons to Night Sight on Pixel 3 – the “Near” button focuses at about 4 feet, and the “Far” button focuses at about 12 feet. The latter is the hyperfocal distance of our lens, meaning that everything from half of that distance (6 feet) to infinity should be in focus. We’re also working to improve Night Sight’s ability to autofocus in low light. Below 0.3 lux you can still take amazing pictures with a smartphone, and even do astrophotography as this blog post demonstrates, but for that you’ll need a tripod, manual focus, and a 3rd party or custom app written using Android’s Camera2 API.

How far can we take this? Eventually one reaches a light level where read noise swamps the number of photons gathered by that pixel. There are other sources of noise, including dark current, which increases with exposure time and varies with temperature. To avoid this biologists know to cool their cameras well below zero (Fahrenheit) when imaging weakly fluorescent specimens — something we don’t recommend doing to your Pixel phone! Super-noisy images are also hard to align reliably. Even if you could solve all these problems, the wind blows, the trees sway, and the stars and clouds move. Ultra-long exposure photography is hard.

How to Get the Most out of Night Sight
Night Sight not only takes great pictures in low light; it’s also fun to use, because it takes pictures where you can barely see anything. We pop up a “chip” on the screen when the scene is dark enough that you’ll get a better picture using Night Sight, but don’t limit yourself to these cases. Just after sunset, or at concerts, or in the city, Night Sight takes clean (low-noise) shots, and makes them brighter than reality. This is a “look”, which seems magical if done right. Here are some examples of Night Sight pictures, and some A/B comparisons, mostly taken by our coworkers. And here are some tips on using Night Sight:

- Night Sight can’t operate in complete darkness, so pick a scene with some light falling on it.
- Soft, uniform lighting works better than harsh lighting, which creates dark shadows.
- To avoid lens flare artifacts, try to keep very bright light sources out of the field of view.
- To increase exposure, tap on various objects, then move the exposure slider. Tap again to disable.
- To decrease exposure, take the shot and darken later in Google’s Photos editor; it will be less noisy.
- If it’s so dark the camera can’t focus, tap on a high-contrast edge, or the edge of a light source.
- If this won’t work for your scene, use the Near (4 feet) or Far (12 feet) focus buttons (see below).
- To maximize image sharpness, brace your phone against a wall or tree, or prop it on a table or rock.
- Night Sight works for selfies too, as in the A/B album, with optional illumination from the screen itself.

Manual focus buttons (Pixel 3 only).

Night Sight works best on Pixel 3. We’ve also brought it to Pixel 2 and the original Pixel, although on the latter we use shorter exposures because it has no optical image stabilization (OIS). Also, our learning-based white balancer is trained for Pixel 3, so it will be less accurate on older phones. By the way, we brighten the viewfinder in Night Sight to help you frame shots in low light, but the viewfinder is based on 1/15 second exposures, so it will be noisy, and isn’t a fair indication of the final photograph. So take a chance — frame a shot, and press the shutter. You’ll often be surprised!

Acknowledgements
Night Sight was a collaboration of several teams at Google. Key contributors to the project include: from the Gcam team Charles He, Nikhil Karnad, Orly Liba, David Jacobs, Tim Brooks, Michael Milne, Andrew Radin, Navin Sarma, Jon Barron, Yun-Ta Tsai, Jiawen Chen, Kiran Murthy, Tianfan Xue, Dillon Sharlet, Ryan Geiss, Sam Hasinoff and Alex Schiffhauer; from the Super Res Zoom team Bart Wronski, Peyman Milanfar and Ignacio Garcia Dorado; from the Google camera app team Gabriel Nava, Sushil Nath, Tim Smith , Justin Harrison, Isaac Reynolds and Michelle Chen.



1 By the way, the exposure time shown in Google Photos (if you press “i”) is per-frame, not total time, which depends on the number of frames captured. You can get some idea of the number of frames by watching the animation while the camera is collecting light. Each tick around the circle is one captured frame.

2 For a wonderful analysis of these techniques, look at von Helmholtz, “On the relation of optics to painting” (1876).

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AC Play 1/6th scale Lightning Warrior Armor with Head sculpt set – Jane Foster THOR 12" figure

“This is not She-Thor. This is not Lady Thor. This is not Thorita. This is Thor. This is the Thor of the Marvel Universe. But it’s unlike any Thor we’ve ever seen before.”

Jane Foster is a fictional character appearing in American comic books published by Marvel Comics, most commonly depicted as a supporting character of the superhero Thor Odinson. In a 2014 storyline Foster is revealed to be deemed worthy to wield Thor’s hammer Mjolnir when the former is no longer able. During this temporary period, she adopts the name Thor, the Goddess of Thunder, and joins the Avengers.

AC Play 1/6th scale Lightning Warrior Armor with Head sculpt set Features: Female Head Sculpt, Helmet (removable), Black undershirt, Arm sleeve, Vambrace, Breastplate, Cloak, Belt, Leather pants, Cuisses, High-heeled shoes (solid), Battle ax. Note: Body Not Included (Ideal for TBLeague S10D Body). Lightning effects not included.

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SUPER DUCK SET038 1/6th scale Sexy Fighter Girl aka Poison Head Sculpt + Costume Set

Poison (ポイズン Poizun) is a fictional character in the Final Fight and Street Fighter series of video games. Poison is shown to be a woman with long, rugged, pink hair. She wears a black cap, a choker, cutoff, blue jean-shorts, red high-heels, and a tanktop cut just below her breasts. She wears several armbands around her right arm and has chains and a pair of handcuffs suspended off her shorts. Final Fight Revenge features her also possessing a whip used in attacks.

SUPER DUCK SET038 1/6th scale Sexy Fighter Girl Head Sculpt + Costume Set Parts List: Head Sculpt, Hat, Tight vest, Denim shorts, Leather riding crop, Handcuffs, Waist chain, Heels, Elbow pads, Wrist ring, Armband, Choker. Note: Body not included. Model is posed with TBleague S07c Pale body in the photos

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