#david appelbaum

[kermodefan94-blog]

Citadel. Series Premiere (Episode 1-2) Review

The age of the assumed mega budget, no track record, attempted TV blockbuster is well and truly upon us. The most recent example comes from Amazon..

In terms of their big-ticket projects, Prime Video has effectively two specialities. A brand of action show targeted squarely at red-state America in a distinct throwback vein. In the other category, you have shows Bezos and friends think we’ll be enormous by birthright but aren’t willing to put any creative muscle behind beyond a big marketing push in allowing these to be the great shows they…

[leanstooneside]

Experiencing love for the first time

◊ AT THE U.S. ATTORNEY€™S OFFICE

◊ OF GRAND JURORS COURT STEGANOGRAPHER DAVID HOUSE

◊ OF COUNSEL I

◊ BY DAVID HOUSE GRAND JURY ALEXANDRIA VA

◊ ABOUT DANIEL CLARK

◊ OF RULE 6E

◊ FROM THE FRONTLINE PBS

◊ FROM YOUR DATE

◊ ON THE SCREEN

◊ OF THE FOURTH AMENDMENT

◊ ABOUT JACOB APPELBAUM

◊ WITH DANIEL CLARK

◊ OF JANUARY

◊ IN CAMBRIDGE MA? DH: ALLOW ME

◊ WITH THE BRADLEY MANNING SUPPORT NETWORK

◊ ON THE MATTER WE

◊ OF THE PHALANX

[the-aurora-project]

The Aurora Project "Forever" Written by: John Wetton / Sue Shifrin The fourth John Wetton tribute song from the NJ based band, The Aurora Project. (  / theauroraprojectnj  ) This is a cover of an unreleased John Wetton / Sue Shifrin demo from October of 1987 with the traditional 'Aurora Project' treatment -- the new arrangement has been fully endorsed and supported by Sue Shifrin herself. 'Forever' was re-written by Sue's partner at the time, David Cassidy, as 'Prisoner' for the 1990 David Cassidy solo release. The original demo is included in the John Wetton box set, ‘An Extraordinary Life’, due for release on November 24, 2023. You can pre-order 'An Extraordinary Life' here: https://burningshed.com/john-wetton_a... The original demo of 'Forever' was a candidate for a new Asia record and featured the following performers: John Wetton – Lead Vocals, Bass, and Keyboards Sue Shifrin – Backing Vocals Carl Palmer – Drums Alan Murphy – Guitars The AURORA PROJECT is: Art DeMatteis – Lead and Backing Vocals Kathy ‘Kat’ Francis – Lead and Backing Vocals, Autoharp John Francis – Acoustic & Electric Guitars, Bass Guitar, Taurus Pedals, Backing Vocals Brian O’Sullivan – Keyboards Eric Rocco – Electric Guitar Drums performed by Steve Honoshowsky Arranged by Rick Nelson & 'The Aurora Project' Produced and Engineered by Rick Nelson (NelSongs) – (April thru July of 2023) Mixed at Retromedia Sound Studios – (Red Bank, NJ) Mastered by Maor Appelbaum at Maor Appelbaum Mastering – (Los Angeles, CA) http://www.maorappelbaum.com/ Video Production by Alpha Wave Studios – (Whippany, NJ)

[mirandamckenni1]

Xeneris "Barbarossa" - Official Music Video From the album "Eternal Rising" - Pre-order/pre-save here: https://orcd.co/xeneris Subscribe To Frontiers Music Srl YouTube Channel: https://ift.tt/63tgQPF "Barbarossa" Lyrics: From the seas they came with their pride Brothers fierce and brave, with swords ablaze Masters of the waves, true corsairs, their legacy will live on for all of our days Brothers, we hail your might Your strength a beacon of light With your souls and ships we’ll fight You conquered the seas, your legend lives on and no one will forget these feats The story’s been written Your courage leads every move Defenders are running, dying one by one Through the storms and the hardest battles Sailing with the strongest pride and honour Without fears marching to the glory Bloody eyes are searching for another win Their enemies trembled, at the sight of their flag raising deep fear in their hearts With each cannon blast, they decree their domain their victories a source of fame The story’s been written Your courage leads every move Defenders are running, dying one by one This is the fight of victory for the power They fight for what they need now and forever They fight for all eternity they fought for their beliefs The village is empty They finally won Their weapons are swinging A new age begun "Eternal Rising" Tracklist: 1. Barbarossa 2. Before The River Of Fire 3. Eternal Rising 4. Pandora's Box 5. A New Beginning 6. To The Endless Sea 7. Shahrazad 8. Scilla And Cariddi 9. Burning Within 10. The Glorious Fight 11. Equinox Xeneris are: Maryan (Vocals) Federico Paolini (Guitars) Roberto Donati (Bass) Stefano Livieri (Drums) CREDITS Music: Federico Paolini, Lars Rettkowitz, Davide Scuteri, Jenny Böhm, Maryan Lyrics: Amina Boumerdassi, Jenny Böhm Produced and Mixed by Lars Rettkowitz at Emperial Sound - Germany https://ift.tt/Gaq0Zor Mastered by Maor Appelbaum at Maor Appelbaum Mastering - California - U.S.A https://ift.tt/djS7kV8 Video Credits: Produced, Directed and Edited by Maurizio Del Piccolo THANK YOU FOR LISTENING! Shop our U.S. webstore: https://ift.tt/gA6mzut & EU webstore: https://ift.tt/PrZ4ysE Follow Our Spotify Playlists: +Rock The World http://spoti.fi/1rQz5Zm +Long Live Metal https://ift.tt/aPdC7SH +New Melodic Rock https://ift.tt/MtT0fk3 Connect with Frontiers: Facebook - https://ift.tt/PdY9mzp Facebook Group - https://ift.tt/TBoaGKZ Instagram - https://ift.tt/J2uSrb1 Twitter - https://twitter.com/FrontiersMusic1 Website - https://ift.tt/58SaFAg #FrontiersRecords via YouTube https://www.youtube.com/watch?v=ufiG5wH4HXg

[puc-puggy]

why is the stop by david appelbaum apparently impossible to find

[hopefulkidshark]

Citadel

2023 ‧ Action ‧ 1 season

Priyanka Chopra as Nadia Sinh

Citadel is an American spy action thriller television series created by Josh Appelbaum, Bryan Oh, and David Weil for Amazon Prime Video, with the Russo brothers acting as executive producers. It stars Richard Madden and Priyanka Chopra Jonas as Citadel agents Mason Kane and Nadia Sinh, respectively, with Stanley Tucci and Lesley Manville among the main cast.

Starring Richard Madden

Priyanka Chopra Jonas

Stanley Tucci

Lesley Manville

Citadel (TV series) - Wikipedia

The global spy agency Citadel has fallen and the memory of its agents has been erased. Now, the powerful Manticore crime syndicate is filling the power vacuum. Citadel agents must remember their past and find the strength to fight back.

First episode date: April 28, 2023 (USA)

Directors: Newton Thomas Sigel, Jessica Yu

Budget: $300 million

Composer: Alex Belcher

Genre: Action-adventure; Drama; Science fiction; Spy thriller; Techno-thriller

Network: Amazon Prime Video

It was all about the job, never got personal.

[playermagic23]

Nick Jonas is proud of Priyanka Chopra as she gets nominated for Critics Choice Super Awards for Citadel

Back in May 2023, Prime Video renewed the series for a second season with Joe Russo set to direct every episode and executive producer David Weil returning as showrunner.

The nominations for the 4th annual Critics Choice Super Awards 2024 have been revealed, showcasing the cream of the crop in action, superhero, horror, and sci-fi or fantasy films and TV shows from the past year. Among the nominees, one name shines particularly bright is Indian actress Priyanka Chopra Jonas. Her recognition comes in the form of a nomination in the Best Actress In An Action Series category, courtesy of her role in the action-packed series, Citadel. Adding to the buzz surrounding Chopra's nomination is the heartfelt support from her husband, singer Nick Jonas.

Taking to his Instagram story, Jonas shared a screenshot of the nomination list featuring Priyanka's name prominently displayed. Alongside the image, he penned an encouraging message, “Let's go Priyanka. #proud,” followed by a fiery emoticon.

The actress is nominated alongside Angela Bassett for 9-1-1, Luciane Buchanan for The Night Agent, Queen Latifah for The Equaliser, Zoe Saldana for Special Ops Lioness, and Maria Sten for The Reacher. The winners will be announced on April 5.

Back in May 2023, Prime Video renewed the series for a second season with Joe Russo set to direct every episode and executive producer David Weil returning as showrunner. The spy thriller—starring Richard Madden and Priyanka Chopra Jonas, and featuring Lesley Manville and Stanley Tucci—premiered on May 26 of last year.

The official synopsis of Citadel season 1 reads, "Eight years ago, Citadel fell. The independent global spy agency—tasked to uphold the safety and security of all people—was destroyed by operatives of Manticore, a powerful syndicate manipulating the world from the shadows. With Citadel’s fall, elite agents Mason Kane (Richard Madden) and Nadia Sinh (Priyanka Chopra Jonas) had their memories wiped as they narrowly escaped with their lives. They’ve remained hidden ever since, building new lives under new identities, unaware of their pasts. Until one night, when Mason is tracked down by his former Citadel colleague, Bernard Orlick (Stanley Tucci), who desperately needs his help to prevent Manticore from establishing a new world order. Mason seeks out his former partner, Nadia, and the two spies embark on a mission that takes them around the world in an effort to stop Manticore, all while contending with a relationship built on secrets, lies, and a dangerous-yet-undying love."

From Amazon Studios and the Russo Brothers’ AGBO, Citadel is executive produced by Anthony Russo, Joe Russo, Mike Larocca, Angela Russo-Otstot, and Scott Nemes for AGBO, with David Weil serving as showrunner and executive producer. Josh Appelbaum, André Nemec, Jeff Pinkner, and Scott Rosenberg serve as executive producers for Midnight Radio. Newton Thomas Sigel and Patrick Moran also serve as executive producers.

[moremedtech]

MIT researchers develop advanced machine learning models to detect pancreatic cancer

MIT researchers develop advanced machine learning models to detect pancreatic cancer. MIT CSAIL researchers develop advanced machine-learning models that outperform current methods in detecting pancreatic ductal adenocarcinoma. Prismatic perspectives pancreatic cancer The path forward The first documented case of pancreatic cancer dates from the 18th century. Since then, researchers have embarked on a long and difficult journey to better understand this elusive and deadly disease. To date, early intervention is the most effective cancer treatment. Unfortunately, due to its location deep within the abdomen, the pancreas is particularly difficult to detect early on. Scientists from the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), as well as Limor Appelbaum, a staff scientist in the Department of Radiation Oncology at Beth Israel Deaconess Medical Center (BIDMC), wanted to better identify potential high-risk patients. They set out to create two machine-learning models for the early detection of pancreatic ductal adenocarcinoma (PDAC), the most common type of cancer. To gain access to a large and diverse database, the team collaborated with a federated network company and used electronic health record data from multiple institutions across the United States. This vast data set contributed to the models' reliability and generalizability, making them applicable to a wide range of populations, geographical locations, and demographic groups. The two models—the “PRISM” neural network and the logistic regression model (a statistical technique for probability)—outperformed current methods. The team’s comparison showed that while standard screening criteria identify about 10 percent of PDAC cases using a five-times higher relative risk threshold, Prism can detect 35 percent of PDAC cases at this same threshold. Using AI to detect cancer risk is not a new phenomenon; algorithms analyze mammograms, CT scans for lung cancer, and assist in the analysis of Pap smear tests and HPV testing, to name a few applications. “The PRISM models stand out for their development and validation on an extensive database of over 5 million patients, surpassing the scale of most prior research in the field,” says Kai Jia, an MIT PhD student in electrical engineering and computer science (EECS), MIT CSAIL affiliate, and first author on an open-access paper in eBioMedicine outlining the new work. “The model uses routine clinical and lab data to make its predictions, and the diversity of the U.S. population is a significant advancement over other PDAC models, which are usually confined to specific geographic regions, like a few health-care centers in the U.S. Additionally, using a unique regularization technique in the training process enhanced the models' generalizability and interpretability.” “This report outlines a powerful approach to use big data and artificial intelligence algorithms to refine our approach to identifying risk profiles for cancer,” says David Avigan, a Harvard Medical School professor and the cancer center director and chief of hematology and hematologic malignancies at BIDMC, who was not involved in the study. “This approach may lead to novel strategies to identify patients with high risk for malignancy that may benefit from focused screening with the potential for early intervention.”

Prismatic perspectives pancreatic cancer

The journey toward the development of PRISM began over six years ago, fueled by firsthand experiences with the limitations of current diagnostic practices. “Approximately 80-85 percent of pancreatic cancer patients are diagnosed at advanced stages, where cure is no longer an option,” says senior author Appelbaum, who is also a Harvard Medical School instructor as well as radiation oncologist. “This clinical frustration sparked the idea to delve into the wealth of data available in electronic health records (EHRs).” The CSAIL group’s close collaboration with Appelbaum made it possible to understand the combined medical and machine learning aspects of the problem better, eventually leading to a much more accurate and transparent model. “The hypothesis was that these records contained hidden clues — subtle signs and symptoms that could act as early warning signals of pancreatic cancer,” she adds. “This guided our use of federated EHR networks in developing these models, for a scalable approach for deploying risk prediction tools in health care.” Both PrismNN and PrismLR models analyze EHR data, including patient demographics, diagnoses, medications, and lab results, to assess PDAC risk. PrismNN uses artificial neural networks to detect intricate patterns in data features like age, medical history, and lab results, yielding a risk score for PDAC likelihood. PrismLR uses logistic regression for a simpler analysis, generating a probability score of PDAC based on these features. Together, the models offer a thorough evaluation of different approaches in predicting PDAC risk from the same EHR data. One paramount point for gaining the trust of physicians, the team notes, is better understanding how the models work, known in the field as interpretability. The scientists pointed out that while logistic regression models are inherently easier to interpret, recent advancements have made deep neural networks somewhat more transparent. This helped the team to refine the thousands of potentially predictive features derived from EHR of a single patient to approximately 85 critical indicators. These indicators, which include patient age, diabetes diagnosis, and an increased frequency of visits to physicians, are automatically discovered by the model but match physicians' understanding of risk factors associated with pancreatic cancer.

The path forward

Despite the promise of the PRISM models, as with all research, some parts are still a work in progress. U.S. data alone are the current diet for the models, necessitating testing and adaptation for global use. The path forward, the team notes, includes expanding the model's applicability to international datasets and integrating additional biomarkers for more refined risk assessment. “A subsequent aim for us is to facilitate the models' implementation in routine health care settings. The vision is to have these models function seamlessly in the background of health care systems, automatically analyzing patient data and alerting physicians to high-risk cases without adding to their workload,” says Jia. “A machine-learning model integrated with the EHR system could empower physicians with early alerts for high-risk patients, potentially enabling interventions well before symptoms manifest. We are eager to deploy our techniques in the real world to help all individuals enjoy longer, healthier lives.” Jia wrote the paper alongside Applebaum and MIT EECS Professor and CSAIL Principal Investigator Martin Rinard, who are both senior authors of the paper. Researchers on the paper were supported during their time at MIT CSAIL, in part, by the Defense Advanced Research Projects Agency, Boeing, the National Science Foundation, and Aarno Labs. TriNetX provided resources for the project, and the Prevent Cancer Foundation also supported the team. Source: MIT Read the full article

[jcmarchi]

New Hope for Early Pancreatic Cancer Intervention via AI-based Risk Prediction - Technology Org

New Post has been published on https://thedigitalinsider.com/new-hope-for-early-pancreatic-cancer-intervention-via-ai-based-risk-prediction-technology-org/

New Hope for Early Pancreatic Cancer Intervention via AI-based Risk Prediction - Technology Org

The first documented case of pancreatic cancer dates back to the 18th century. Since then, researchers have undertaken a protracted and challenging odyssey to understand the elusive and deadly disease. To date, there is no better cancer treatment than early intervention. Unfortunately, the pancreas, nestled deep within the abdomen, is particularly elusive for early detection. 

Image credit: MIT CSAIL

MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) scientists, alongside Limor Appelbaum, a staff scientist in the Department of Radiation Oncology at Beth Israel Deaconess Medical Center (BIDMC), were eager to better identify potential high-risk patients. They set out to develop two machine-learning models for early detection of pancreatic ductal adenocarcinoma (PDAC), the most common form of the cancer.

To access a broad and diverse database, the team synced up with a federated network company, using electronic health record data from various institutions across the United States. This vast pool of data helped ensure the models’ reliability and generalizability, making them applicable across a wide range of populations, geographical locations, and demographic groups.

The two models — the “PRISM” neural network, and the logistic regression model (a statistical technique for probability), outperformed current methods. The team’s comparison showed that while standard screening criteria identify about 10 percent of PDAC cases using a five-times higher relative risk threshold, Prism can detect 35 percent of PDAC cases at this same threshold. 

Using AI to detect cancer risk is not a new phenomena — algorithms analyze mammograms, CT scans for lung cancer, and assist in the analysis of Pap smear tests and HPV testing, to name a few applications. “The PRISM models stand out for their development and validation on an extensive database of over 5 million patients, surpassing the scale of most prior research in the field,” says Kai Jia, an MIT PhD student in electrical engineering and computer science (EECS), MIT CSAIL affiliate, and first author on an open-access paper in eBioMedicine outlining the new work.

“The model uses routine clinical and lab data to make its predictions, and the diversity of the U.S. population is a significant advancement over other PDAC models, which are usually confined to specific geographic regions, like a few health-care centers in the U.S. Additionally, using a unique regularization technique in the training process enhanced the models’ generalizability and interpretability.” 

“This report outlines a powerful approach to use big data and artificial intelligence algorithms to refine our approach to identifying risk profiles for cancer,” says David Avigan, a Harvard Medical School professor and the cancer center director and chief of hematology and hematologic malignancies at BIDMC, who was not involved in the study. “This approach may lead to novel strategies to identify patients with high risk for malignancy that may benefit from focused screening with the potential for early intervention.” 

Prismatic perspectives

The journey toward the development of PRISM began over six years ago, fueled by firsthand experiences with the limitations of current diagnostic practices. “Approximately 80-85 percent of pancreatic cancer patients are diagnosed at advanced stages, where cure is no longer an option,” says senior author Appelbaum, who is also a Harvard Medical School instructor as well as radiation oncologist. “This clinical frustration sparked the idea to delve into the wealth of data available in electronic health records (EHRs).”

The CSAIL group’s close collaboration with Appelbaum made it possible to understand the combined medical and machine learning aspects of the problem better, eventually leading to a much more accurate and transparent model. “The hypothesis was that these records contained hidden clues — subtle signs and symptoms that could act as early warning signals of pancreatic cancer,” she adds. “This guided our use of federated EHR networks in developing these models, for a scalable approach for deploying risk prediction tools in health care.”

Both PrismNN and PrismLR models analyze EHR data, including patient demographics, diagnoses, medications, and lab results, to assess PDAC risk. PrismNN uses artificial neural networks to detect intricate patterns in data features like age, medical history, and lab results, yielding a risk score for PDAC likelihood. PrismLR uses logistic regression for a simpler analysis, generating a probability score of PDAC based on these features. Together, the models offer a thorough evaluation of different approaches in predicting PDAC risk from the same EHR data.

One paramount point for gaining the trust of physicians, the team notes, is better understanding how the models work, known in the field as interpretability. The scientists pointed out that while logistic regression models are inherently easier to interpret, recent advancements have made deep neural networks somewhat more transparent. This helped the team to refine the thousands of potentially predictive features derived from EHR of a single patient to approximately 85 critical indicators. These indicators, which include patient age, diabetes diagnosis, and an increased frequency of visits to physicians, are automatically discovered by the model but match physicians’ understanding of risk factors associated with pancreatic cancer. 

The path forward

Despite the promise of the PRISM models, as with all research, some parts are still a work in progress. U.S. data alone are the current diet for the models, necessitating testing and adaptation for global use. The path forward, the team notes, includes expanding the model’s applicability to international datasets and integrating additional biomarkers for more refined risk assessment.

“A subsequent aim for us is to facilitate the models’ implementation in routine health care settings. The vision is to have these models function seamlessly in the background of health care systems, automatically analyzing patient data and alerting physicians to high-risk cases without adding to their workload,” says Jia. “A machine-learning model integrated with the EHR system could empower physicians with early alerts for high-risk patients, potentially enabling interventions well before symptoms manifest. We are eager to deploy our techniques in the real world to help all individuals enjoy longer, healthier lives.” 

Written by Rachel Gordon

Source: Massachusetts Institute of Technology

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[phoenixprjct]

🍿 Citadel is flashy and fun but neglects its substance - In-Depth Review 🍿

Rating: ⭐⭐⭐.5

Creators (Platform): Josh Appelbaum, Bryan Oh, and David Weil (Amazon Prime Video)

Publishers (Release): Amazon Studios (2023)

Citadel undoubtedly needed more time to set itself up, let it stories cook, and better deliver the global ramifications of an epic spy thriller, but with capable leads, a schlocky but irresistible romance, and slick action beats, it can be forgiven (a little) for its focus on style over substance. An anticlimactic finale delivers the series's greatest twist, setting up solid and rocky ground for an announced second season.

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