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Monthly Archives: March 2022
Examining the genetics of acne – Contemporary Pediatrics
Posted: March 17, 2022 at 2:20 am
The etiology of acne is complex and multifactorial with genetics playing a large role in determining risk, particularly for those individuals with severe acne. The results of a recent study have further elucidated the genetics of acne and advanced the knowledge of the genetic basis of acne risk by identifying versions of the genes that are common among individuals who suffer from this very common inflammatory skin disorder.
It is estimated that more than 85% of teenagers are affected by acne to some degree, and up to 8% have been reported with severe disease, making acne the most prevalent skin disease worldwide. Depending on the severity of the disease, acne can significantly impact self-image and the quality of life (QOL). Major complications of acne include scarring as well as the, at times, significant psychosocial distress that can persist long after active lesions have disappeared.
Approximately 80% of a persons risk of suffering from severe acne can be explained by differences in their genetic makeup, said Miguel E. Rentera, PhD, senior research fellow, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia, and co-author of the study. It is not a single gene but rather variations across hundreds of genes in the genome that determine whether someone is likely to get acne or not, and the extent or severity of the condition.
Rentera and fellow colleagues recently performed a large meta-analysis of genome-wide association studies (GWAS) of acne undertaken in 9 independent cohorts compromising a total of 615,396 study participants. Of the participants, 20,165 were acne cases and 595,231 were controls, making it the largest study of its kind, with the aim of identifying specific genetic variants that are more common among people with moderate or severe acne relative to those who have mild or no acne. Fine-mapping and genome-wide analytical approaches were combined to gain insights into the underlying genes and pathways through which the associated loci contribute to disease susceptibility.
Researchers could identify 29 new genetic variants that are more common in individuals with acne, as well as confirm 14 of the 17 variants already known to be associated with the condition, raising the total number of known acne risk loci to 46. Results also exposed relationships between acne and other complex/common traits, including behavioral, hormonal, inflammatory, and psychiatric traits, as well as shared molecular basis between acne and Mendelian hair and skin disorders, including pustular psoriasis.
Fifteen of these loci have been reported to have an effect in European populations while the remaining 2 have been reported to have an effect in a Han Chinese population. This highlights the potential differences in the genetic architecture of acne between different ethnic populations, warranting further investigation in studies of diverse ancestry. According to Rentera, however, one of the main limitations is the lack of availability of cohorts or biobanks focused on diverse ancestries.
Our study results highlight possible molecular pathways that are implicated in acne, opening new avenues for fundamental research and development of therapeutic targets, Rentera said. Looking forward, we could apply this knowledge to do genetic tests of acne risk and identify those individuals who are likely to present with acne.
Novel research studies will need to be designed and performed to better understand the role of the newly identified acne genes and the mechanisms of action of these new variants need to be characterized and their potential as therapeutic targets to treat acne need to be assessed, he said.
According to Rentera, the estimation of individual genetic acne risk scores relative to the general population will be perhaps the most immediate application of the current study findings.
We demonstrated that it is possible to use a persons genetic information to estimate their genetic risk of developing acne based purely on which genetic variants they inherited from their parents, Rentera said. In the future, we will be able to identify those individuals who are at high-risk of acne many years before they even notice the first signs.
Finding genes implicated in acne is the first step toward patient stratification and personalized medicine. Although continued research has been inching closer towards personalized medicine on the genetic level, this fulfillment is likely a few years away. A clinicians treatment and management choices in the future could be informed by the genetic profile of each individual patient, Rentera said, however there is still much work to be done regarding characterizing the genetic basis of different acne types and treatment response across different life stages.
This research enables a much better understanding of the genetic basis of acne, and investigation of these variants further illustrates the shared biology processes with other skin and hair traits as well as a shared genetic etiology with other common diseases, Rentera said. However, identifying the relevant genes is not the end but rather the beginning of a journey of discovery.
This article was originally published by sister publication Dermatology Times.
Reference
Mitchell BL, Saklatvala JR, Dand N, et al.Genome-wide association meta-analysis identifies 29 new acne susceptibility loci.NatCommun. 2022 Feb 7;13(1):702. doi: 10.1038/s41467-022-28252-5.
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Paper Reporting Results from Helixmith’s Phase 3 Gene Therapy Trial for Painful Diabetic Neuropathy was One of the Top-10 Most-Downloaded Articles in…
Posted: at 2:20 am
According to Alethea Gerding, Managing Editor, ASCPT, "The article has been downloaded more than 3,000 times"
These results have important clinical implications as more than 4.2 million people in the US are known to suffer from painful DPN and nearly 1.3 million patients are considered to be refractory, meaning currently available medications do not work for them (Painful Diabetic Neuropathy, GlobalData 2018).
Helixmith launched a second phase 3 trial for DPN, REGAiN-1A (VMDN-003-2), in the US and are targeting release of top line results by the end of 2022. The company is planning to start a third phase 3 for DPN in the second half of 2022.
Key points of the CTS paper
About Diabetic Peripheral Neuropathy
Painful DPN is a common and debilitating complication of diabetes mellitus that has a profound negative impact on quality of life, sleep, and mood. Current therapies are palliative and do not target the mechanisms underlying painful DPN. Moreover, symptomatic relief is often limited, and many patients with painful DPN still use opioids.
About Helixmith Co., Ltd.
Helixmith is a clinical-stage gene therapy companybased in Seoul and San Diego, developing new and innovative biopharmaceuticals to tackle previously untreated diseases. The company has an extensive gene therapypipeline, including a CAR-T program targeting several different types of solid cancers and an AAV vector program targeting neuromuscular diseases. Engensis (VM202), the most advanced pipeline candidate, is a plasmid DNA therapy being studied for diabetic peripheral neuropathy, diabetic foot ulcers, claudication, amyotrophic lateral sclerosis, coronary artery disease, and Charcot-Marie-Tooth disease. The company is listed on KOSDAQ.
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Alltrna Announces Formation of Scientific Advisory Board with World-Leading RNA Experts – Yahoo Finance
Posted: at 2:19 am
CAMBRIDGE, Mass., March 16, 2022 /PRNewswire/ -- Alltrna, a Flagship Pioneering company unlocking transfer RNA (tRNA) biology and pioneering tRNA therapeutics to regulate the protein universe and resolve disease, today announced the formation of Alltrna's Scientific Advisory Board (SAB) with leading experts in RNA biology and therapeutics. The SAB will work closely with Alltrna's leadership team as they map tRNA biology to systematically design tRNA medicines and encode a completely new, unifying approach to treating both rare and common human diseases driven by shared genetic mutations.
To watch the full video, click here.
"We are honored to have these remarkable and accomplished scientific leaders join Alltrna's Scientific Advisory Board," said Lovisa Afzelius, Ph.D., Origination Partner at Flagship Pioneering and Founding CEO of Alltrna. "Each person has made significant contributions and pioneered breakthroughs in RNA research and therapeutics, and together, they will be a powerhouse of expertise and experience for Alltrna, as we leverage our deep understanding of tRNA biology and its diverse combinatorial modifications to systematically design, program, and deliver tRNAs to correct disease."
"I've been working closely with Alltrna over the past couple years as the team has built a truly remarkable platform to unlock the entire tRNA biology space with an unprecedented therapeutic opportunity to help millions of patients with both rare and common diseases," said Rachel Green, Ph.D., Chair of Alltrna's SAB. "I'm delighted that these world leaders in RNA biology and therapeutic development have joined Alltrna's SAB at this pivotal time in the company's growth and look forward to working together to help Alltrna realize the enormous potential of tRNA biology as a novel framework and source for new programmable medicines."
Alltrna Scientific Advisory Board Members
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Rachel Green, Ph.D., Chair, is an investigator at the Howard Hughes Medical Institute (HHMI) and a Bloomberg Distinguished Professor in the Department of Molecular Biology and Genetics at the Johns Hopkins University School of Medicine. During her doctoral research at Harvard Medical School, Dr. Green developed in vitro selection approaches that are broadly used for the analysis of functional RNAs in many systems. Her current research focuses on mechanisms of mRNA translation and its regulation. Dr. Green is an elected member of the American Academy of Arts and Sciences (AAAS) and the National Academy of Sciences (NAS). In addition to Alltrna, she serves on the SAB for Moderna and Initial Therapeutics.
Tracy Johnson, Ph.D., is a HHMI Professor and Dean of Life Sciences and Senior Associate Dean at the David Geffen School of Medicine at UCLA. She has more than 25 years of experience studying the biochemistry of RNA. Her laboratory utilizes a combination of molecular genetics, bioinformatic, and biochemical approaches to understand mechanisms of gene regulation, particularly RNA splicing and chromatin modification, and the intersection between these reactions. Dr. Johnson has received numerous awards, including the National Science Foundation's CAREER and PECASE awards and the 2022 Ruth Kirschstein Diversity in Science Award from the American Society for Biochemistry and Molecular Biology.
Anastasia Khvorova, Ph.D., is a Professor in the RNA Therapeutics Institute and Program in Molecular Medicine at the University of Massachusetts (UMass) Chan Medical School, where her lab develops novel approaches and solutions to understanding natural and therapeutic RNA trafficking and delivery. She founded the UMass Nucleic Acid Chemistry Core, the only nonprofit facility in North America capable of gram-scale synthesis of modified oligonucleotides. Prior to UMass, Dr. Khvorova served as Chief Scientific Officer at Dharmacon, ThermoFisher, and RXi Pharmaceuticals. She is inventor on more than 150 patents and 200 patent applications and has authored more than 50 peer-reviewed publications, defining the field of RNAi drug design and development.
Melissa Moore, Ph.D., is the Chief Scientific Officer, Scientific Affairs at Moderna, where she leads mRNA biology, delivery, and computation science research. Previously, she was a Professor of Biochemistry & Molecular Pharmacology and a HHMI Investigator at the UMass Chan Medical School, where she was instrumental in creating a faculty-led program to facilitate the translation of discoveries into drugs, products, technologies, and companies. Her 23-year career in academic research focused on the roles of RNA and RNA-protein complexes in regulating gene expression, and her research touched on many human diseases. Dr. Moore is an elected member of AAAS and NAS, and she received the RNA Society Lifetime Achievement Award in 2021.
About Alltrna
Alltrna unlocks tRNA biology to correct disease. The company's platform incorporates AI/ML tools to learn the tRNA language and deliver diverse programmable molecules with broad therapeutic potential. Alltrna has an unprecedented opportunity to advance a single tRNA medicine to unify treatment across a wide range of diseases with the same underlying genetic mutation. Alltrna was founded in 2018 by Flagship Pioneering. For more info, visit http://www.alltrna.com.
About Flagship Pioneering
Flagship Pioneering conceives, creates, resources, and develops first-in-category bioplatform companies to transform human health and sustainability. Since its launch in 2000, the firm has, through its Flagship Labs unit, applied its unique hypothesis-driven innovation process to originate and foster more than 100 scientific ventures, resulting in more than $140 billion in aggregate value. To date, Flagship has deployed over $2.6 billion in capital toward the founding and growth of its pioneering companies alongside more than $19 billion of follow-on investments from other institutions. The current Flagship ecosystem comprises 42 transformative companies, including Axcella Health (Nasdaq: AXLA), Codiak BioSciences (Nasdaq: CDAK), Denali Therapeutics (Nasdaq: DNLI), Evelo Biosciences (Nasdaq: EVLO), Foghorn Therapeutics (Nasdaq: FHTX), Indigo Ag, Kaleido Biosciences (Nasdaq: KLDO), Moderna (Nasdaq: MRNA), Omega Therapeutics (Nasdaq: OMGA), Rubius Therapeutics (Nasdaq: RUBY), Sana Biotechnology (Nasdaq: SANA), Seres Therapeutics (Nasdaq: MCRB), and Sigilon Therapeutics (Nasdaq: SGTX).
Media Contact for AlltrnaJessica Yingling, Ph.D., Little Dog Communications Inc., jessica@litldog.com, +1.858.344.8091
(PRNewsfoto/Alltrna)
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2 years of COVID-19: Origins, variants, and the future – Medical News Today
Posted: at 2:19 am
Before March 2020, many people saw pandemics as a thing of the past. Then came COVID-19. Scientists still do not know exactly where the virus that caused it SARS-CoV-2 came from, but it soon reached almost every country worldwide. Over 2 years, the virus has evolved, producing several variants. In this Special Feature, we look at the evolution of SARS-CoV-2 and ask what lessons scientists have learned.
In late 2019, there was a sudden increase in pneumonia cases in central China. By January 7, scientists had identified and isolated a previously unknown coronavirus, now designated SARS-CoV-2.
On March 11, 2020, the World Health Organization (WHO) declared COVID-19 a pandemic.
Now, 2 years on, authorities have recorded more than 458 million cases of COVID-19, the disease resulting from SARS-CoV-2. The disease has also played a role in the deaths of more than 6 million people.
However, the actual death toll may well be far higher than 6 million. According to a recent paper in The Lancet, the actual death toll may be at least three times that.
On December 29, 2019, experts linked four cases of pneumonia of unknown etiology to the Huanan Seafood Wholesale Market in Wuhan, central China.
On January 7, 2020, researchers isolated the causative agent, SARS-CoV-2, and on January 10, they sequenced its genome.
By January 2, 2020, doctors had confirmed that 41 people in a Wuhan hospital with severe respiratory illness had a SARS-CoV-2 infection. Of these individuals, 27 had had exposure to the seafood market.
Many coronaviruses exist, affecting both animals and people. Most cause infections with mild to moderate symptoms in the upper respiratory tract, such as colds.
In recent years, two coronaviruses SARS-CoV and MERS-CoV have caused more severe disease. SARS-CoV, which scientists identified in November 2002, was responsible for severe acute respiratory syndrome (SARS), which emerged in Asia. The Centers for Disease Control and Prevention (CDC) note that of the 8,096 people with a known SARS infection, 774 died. There have been no reported cases since 2004.
Scientists first identified Middle East Respiratory Syndrome (MERS), the disease that MERS-CoV-2 causes, in 2012 in Saudi Arabia. The mortality rate for MERS is high of every 10 people with the infection, three or four die. There continue to be occasional, localized outbreaks of this disease.
Both of these coronaviruses caused diseases with high fatality rates, but it was possible to contain the spread before they reached pandemic levels. So, were we ready for the next coronavirus?
Experts believe that SARS came from bats and that MERS crossed over to people from camels. However, for SARS-CoV-2, researchers have not all agreed on any of the many existing theories.
At first, people thought that SARS-CoV-2 might have come directly from bats. Scientists discounted that theory, though, as the spike protein on SARS-CoV-2 is very different from that on the coronaviruses present in bats.
Now, researchers think it is likely that the virus originated in bats but had an intermediate host between bats and people. A recent study which has not yet undergone peer review suggests that live mammals for sale at the Huanan Seafood Wholesale Market in Wuhan, the epicenter of early cases, might have been the intermediate host.
Another recent study also yet to undergo peer review that analyzed the evolution of SARS-CoV-2 suggests that SARS-CoV-2 emergence likely resulted from multiple zoonotic events. The researchers do not suggest what the intermediate animal hosts might be.
Alternatively, did SARS-CoV-2 escape from a laboratory in Wuhan, as some media outlets have suggested? The WHO has dismissed this theory as extremely unlikely.
So, there is still uncertainty about the origins of SARS-CoV-2. And this may be due, in some measure, to a lack of international cooperation, as Prof. Jonathan Stoye, a virologist at the Francis Crick Institute in London, United Kingdom, told Medical News Today.
In his opinion, one mistake was to start pointing fingers at China and blaming them for the origin of this virus. I think that, naturally, led to pushback from the Chinese [authorities].
He added: I absolutely believe in natural origins [of SARS-CoV-2], but the Chinese [authorities] could have made things easier if theyd opened up their books straightaway. They werent going to do that when they were being accused of being responsible [for the virus].
For almost a year, the original Wuhan variant of SARS-CoV-2 moved across the globe. Then, in late 2020, the number of COVID-19 cases increased rapidly in South East England, in the United Kingdom.
Researchers discovered that a new variant, which was 50% more transmissible than the original and had 17 unique mutations, was responsible. In December 2020, the WHO designated it B.1.1.7, or the Alpha variant.
Scientists have since identified many other variants, but the WHO has only designated five as variants of concern (VOC). The VOCs and the location of their initial identification are:
Each variant has different features. Some variants are more transmissible than others, and some are more virulent. It is these features that have caused the multiple waves of COVID-19.
The regular and rapid emergence of new variants in the past 2 years have made the course of the pandemic very unpredictable.
Dr. Arturo Casadevall, distinguished professor and chair of molecular microbiology and immunology and infectious diseases at the Johns Hopkins Bloomberg School of Public Health in Baltimore
Viruses mutate all the time. Each time they replicate, which they do frequently, their genetic material is copied. A mutation happens when part of the genetic material is copied incorrectly.
In a coronavirus, the genetic material is ribonucleic acid (RNA). An enzyme called RNA polymerase controls the replication, and it often makes errors. Most mutations create a virus that cannot replicate and spread among people. However, some mutations lead to a virus that can replicate: a variant.
A mutation might give the virus a selective advantage, such as better transmissibility or greater virulence. If it is more transmissible, the variant may spread faster and outcompete previous variants. This is what happened with the Alpha, Delta, and Omicron variants of the coronavirus.
Some situations give viruses more opportunities to mutate, as Dr. Christopher Coleman, assistant professor of infection immunology at the University of Nottingham, U.K., explained to MNT:
Viruses naturally mutate as they replicate, so in an immunocompromised host where the virus replicates more easily, there will be a correspondingly increased number of mutations.
Omicron has more than 50 mutations, of which some 30 are in the spike protein that the virus uses to gain entry to host cells. One theory suggests that it may have evolved in people with HIV, a virus that suppresses the immune system.
Moving between host species also increases the mutation rate. Dr. Coleman added that the [i]nfection of animals by humans will mean the virus then adapts to a new host, which involves mutations.
Domestic animals, such as cats, dogs, and ferrets, have had SARS-CoV-2 infections. The CDC notes that on one mink farm in Michigan, several animals contracted the virus, which then passed back to workers. On testing, the viral samples from the workers contained several mink-related mutations.
You are getting evolution occurring from different starting points. Whether they are occurring through immunosuppressed or immunocompromised patients, or whether they are happening through animals, or how, I dont know that we know, and I dont know that we will ever really know.
Prof. Jonathan Stoye
Decades of research into coronaviruses led to the rapid development of vaccines, many of them using new technologies. These have been incredibly effective in reducing the impact of COVID-19 and allowing society to regain some measure of normality.
But, as Prof. Stoye explained, [i]n retrospect, we have been lucky that it has been possible to make a vaccine against this particular virus, whereas for things like HIV [], we still dont have vaccines.
However, vaccines designed against one variant might be ineffective against another.
The evolution of SARS-CoV-2 variants upended many optimistic predictions made when the vaccines were rolled out in 2020.
Dr. Arturo Casadevall
Despite the evolution of variants, vaccines still guard against severe COVID-19, particularly in those who have received multiple vaccinations.
Despite suggestions that vaccines might even drive the evolution of new vaccine-resistant variants, this does not seem to be the case, as a recent report states: Given the emergence of immunity-evading variants even before vaccines were broadly deployed, it is hard to implicate vaccines or vaccine deployment strategies as the major drivers of immune evasion.
Prof. Stoye feels that vaccines will continue to be important. I suspect we will have to have yearly boosters of the vaccine, at least for the foreseeable future, he said.
And he expressed a hope that research might create more powerful vaccines:
It would be very nice if scientists could establish a pan-coronavirus vaccine that worked against multiple viruses. That has to be one of the hopes of the future that you will have a method of vaccination that will protect you against various viruses.
After 2 years, people are becoming tired of restrictions, feeling that the pandemic should surely be over. However, Prof. Stoye is one of many experts expressing concern that governments are removing COVID-19 testing and control measures too early.
One of the things Im frightened about is that we will, in fact, lose our ability to follow these processes as we stop testing and sequencing so much. [] As we test less, as we sequence less, we will lose that ability to recognize new variants in real time, he told us.
These things will come again. We need to realize that, and we need to have a response ready quickly. I think we need to be able to recognize very rapidly the appearance of new diseases this comes back to geopolitics.
Prof. Jonathan Stoye
This is not the first pandemic, and it is unlikely to be the last. Some aspects have been well-handled, while others have not, and the geopolitical debates will continue for years. At least vaccines are continuing to protect against severe illness and death from all variants.
Possibly the most important lesson is that it is crucial to address future disease outbreaks globally. Although people in high income countries have had ready access to vaccines and boosters, many African countries have yet to vaccinate even 10% of their population due to inequitable vaccine distribution.
The lack of widespread vaccination can also contribute to the development of new variants.
Prof. Stoye stressed the importance of global cooperation in countering pandemics:
The global aspects of this are the interesting and important ones. Whether those lessons will be learned, I dont know. [] I would hate to think that, suppose in 2 or 3 years down the track, we are living comfortably with this virus, and SARS-3 comes along or HIV5, and we have forgotten all the lessons we have learned. Its trying to retain that memory that is the important lesson.
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2 years of COVID-19: Origins, variants, and the future - Medical News Today
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What is Ki-67 in Breast Cancer? It Can Spell the Difference in Treatment Options – URMC
Posted: at 2:19 am
At first glance, two 50-something women with breast cancer appear to have similar cases. But an article by Wilmot Cancer Institute experts points out that technically, only one woman is eligible for a newer, potentially lifesaving treatment due to slight differences in tumor markers.
The article raises questions about rules around using the latest therapies and illustrates how nuances in tumor biology can have a major influence on treatment choices. It also speaks to the critical need for consistent, high standards for pathology testing across the U.S.
Ruth O'Regan, MD
Co-author Ruth ORegan, M.D., a national thought-leader in breast cancer and executive at Wilmot, would like to see the U.S. follow Europe and loosen the limitations for the breast cancer treatment in question, known as CDK inhibitors.
We believe that restricting the use of these drugs is controversial and deserves more discussion, she said.
ORegan and David Hicks, M.D., a professor of Pathology and Laboratory Medicine who has devoted his career to innovations in breast tumor analysis, described two cases of breast cancer in postmenopausal women in a Grand Rounds article in the Journal of Clinical Oncology.
Both women were diagnosed with stage 2 breast cancer, which had begun to spread to the lymph nodes. Each woman faced a high risk of the cancer recurring after initial treatment was completed. Testing also showed that the hormones estrogen and progesterone were largely fueling each persons disease.
A key fact separated these Wilmot patients, however: Only one waseligible for CDK therapy, which has a proven survival advantage.
The reason? Her tumor had a higher level of a cancer-related gene known as Ki-67, indicating rapid reproduction of cancer cells.
A mixed bag of clinical trial data has shown that certain levels of Ki-67 in breast tumors support better outcomes for patients who are treated with CDK inhibitors. The FDA approved the use of CDK inhibitors for individuals whose tumors have Ki-67 levels of 20 percent or higher.
But, ORegan said, this leaves out women whose tumors have Ki-67 markers at 15-to-19 percent, for example, even though other factors in their cases may indicate that treatment with the drug would help.
One clinical trial in the U.S. does, in fact, support the use of the newer drug for tumors with lower levels of Ki-67, and Europes equivalent of the FDA approved the CDK treatment without limitations, ORegan noted.
David Hicks, MD
The lynchpin in the U.S. is that measurement of Ki-67 is notoriously inconsistent across hospital systems and clinical laboratories. Many institutions do not routinely check for this tumor marker although all breast cancer patients at Wilmot receive Ki-67 testing in optimal, standardized laboratory conditions, ORegan said.
It will take time to overcome the testing hurdle nationwide, Hicks and ORegan wrote, due to variability among practices and materials available.
Meanwhile, ORegan continues to assess cases on an individual basis, often in collaboration with Wilmots tumor board, and has achieved insurance approval for CDK therapy for a few patients that fell outside of current FDA eligibility requirements, she said.
CDK inhibitors are also available to women with stage 4 breast cancer; limitations only exist for earlier-stage disease. Breast cancer patients should ask their doctors about this newer form of treatment, O'Regan said.
ORegan is the Charles A. Dewey Professor and Chair of Medicine at the University of Rochester Medical Center, as well as the Associate Director of Wilmots education and mentoring program. Hicks has studied tumor markers in breast cancer for several years at URMC and helped to develop national guidelines on estrogen-receptor and HER2 gene testing.
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Hope for breast cancer patients, but with a cruel caveat – Harvard Gazette
Posted: at 2:19 am
The clinical trial of an old antibiotic for a new purpose is offering hope to thousands of patients with drug-resistant breast cancer whose early remissions have given way to resurgent tumors.
Novobiocin was once used in humans but today is largely limited to animal applications, such as treating mastitis in dairy cows. Its trial as a cancer drug is expected to begin this spring at the Dana-Farber Cancer Institute. If it proves effective, the fact that its still manufactured and already approved in people should allow it to become rapidly available to patients, trial organizers say.
While the discovery of a potentially powerful anti-cancer agent in a veterinary niche may seem serendipitous, it sits at the end of a long chain of discovery. That chain has already deepened our understanding of a group of well-known cancers breast, ovarian, pancreatic, and prostate that together afflict more than 600,000 people and kill about 140,000 each year. Research in recent decades has revealed that half of ovarian cancers, 15 percent of breast and prostate, and 10 percent of pancreatic cancers share a flaw in their DNA repair mechanism that make them susceptible to drugs like novobiocin. The work has also shown that they are related to a rare, often fatal childhood disease called Fanconi anemia.
In fact, the discoveries trace back to the suffering of Fanconi anemia families, hinging on a key moment two decades ago at a Maine summer camp where children suffering Fanconi anemia offered their blood for science. The distressing irony is that the resulting treatments, which now offer hope to thousands of cancer sufferers, are not only ineffective for Fanconi children, theyre potentially fatal.
This is a terrible disease, said Alan DAndrea, who heads Dana-Farbers Susan F. Smith Center for Womens Cancers and who has worked on Fanconi anemia since the early 1990s. The children have birth defects. They have a strong disposition to developing anemia and then leukemia. And their cells are super-sensitive to DNA damaging agents.
The condition is rare and genetic. As a recessive disease, a child must inherit two copies of a Fanconi gene one from each parent for the condition to develop. It affects just one in 130,000 U.S. births each year, which translates to about 28 children, many of whom are afflicted with short stature, microcephaly, abnormal facial features, or other birth defects. Anemia tends to emerge around age 7, often followed by acute myeloid leukemia and eventually bone marrow failure. Many patients dont live to adulthood, and the average age of death in 2000 was 30.
Sometimes the most important discoveries in science are obvious when you hit them.
DAndrea, who also directs Dana-Farbers Center for DNA Damage and Repair, became interested in Fanconi anemia in a roundabout fashion. While he was an undergraduate in Quincy House at Harvard College in the late 1970s, his mother developed breast cancer. She recovered, but the episode sparked an interest that led DAndrea to the lab of William Haseltine at Dana-Farber, then called the Sidney Farber Cancer Institute. Haseltine was studying DNA repair, a subject that grabbed DAndreas interest. The pair pioneered using new tools of gene sequencing to investigate DNA damage and repair. Later, while studying at Harvard Medical School, DAndrea became interested in leukemia and then, as an assistant professor at the Medical School and Dana-Farber in the early 1990s, in Fanconi anemia. DAndrea thought that a better understanding of the condition might not only help those afflicted with it, but also produce insights broadly applicable to leukemias, which affect 61,000 Americans and kill 24,000 per year.
DAndreas efforts were met with enthusiasm. Families of Fanconi sufferers often struggle alone, trying to manage a condition frequently unrecognized by physicians and ignored by researchers. The Fanconi Anemia Research Fund was just a year old when DAndrea got involved in 1990, but it was already beginning to support research on the condition.
I first met Alan in the year 1990; our daughter Katie died in 1991, said Lynn Frohnmayer, one of the funds founders. We were advised to consult with a DNA expert at Harvard about her condition, so we talked to him for a long time. Its hard to remember a time when he hasnt been part of what we were doing.
Community was no less important than research to Fanconi families, who gathered annually at a summer camp on Maines Sebago Lake.
Id go to this camp every summer and teach the families what we knew about the disease, said DAndrea, the Alvan T. and Viola D. Fuller American Cancer Society Professor of Radiation Oncology. At the same time, we would collect blood samples from the children or the parents, and sometimes skin biopsies. We assumed at this point that if we could clone the genes that were involved for Fanconi anemia, we might discover something very fundamental about why these children get leukemia, and also discover some kind of DNA repair pathway.
DAndreas method identifying defective Fanconi genes and using them to understand the function of the normal gene took time, but slowly revealed the diseases genetic underpinnings. As Fanconi genes were discovered scientists have so far identified 23 DAndreas lab demonstrated how the proteins they encode work together in a biochemical pathway required for DNA repair.
We figured out that these genes probably work together in some kind of a genetic DNA repair pathway and that was exciting, DAndrea said. But it was a backwater field of cancer research. I would give talks on Fanconi anemia at big meetings and thered be 12 people in the audience, and theyd be reading the newspaper, not paying attention.
In the early 2000s, DAndrea was at the camp drawing blood from an 11-year-old girl who had recently developed leukemia. He was talking with her mother, who was in her 30s, and noticed that her arm was in a sling. When she said shed had a mastectomy after a breast cancer diagnosis, his interest was piqued.
In the mid-1990s, researchers had identified mutations in two genes, BRCA1 and BRCA2, that increase the risk of early breast cancer. The BRCA genes are tumor suppressors that play a role in DNA repair. In most women, healthy BRCA genes prevent tumors by keeping DNA functioning properly. In women who inherit mutated genes, faulty DNA repair opens the door to tumor growth.
Suddenly, this rare disease, Fanconi anemia, and this rare pathway that we have been studying crashed into breast cancer and ovarian cancer research.
DAndreas work over the prior decade had pointed to faulty DNA repair as a cause of Fanconi anemia, so when he heard the young mothers story, something clicked. He tracked down the girls father and asked about his family history. The man said he had been healthy, but that his mother had died of ovarian cancer.
After DAndrea raced back to the lab, he and colleagues examined DNA from the girls samples. They found that she had two copies of a faulty breast cancer gene BRCA2 one inherited from each parent.
Suddenly, this rare disease, Fanconi anemia, and this rare pathway that we have been studying crashed into breast cancer and ovarian cancer research, DAndrea said. And not only those cancers in the general population, but BRCA2 and, subsequently, BRCA1, extremely important cancer-susceptibility genes. We call it today the Fanconi anemia/BRCA pathway.
In hindsight, the connection seems obvious, DAndrea said.
We had been studying all these other Fanconi anemia genes and we knew that process had something to do with DNA repair. It made sense. Sometimes the most important discoveries in science are obvious when you hit them. You realize this child with Fanconi anemia had mutations in the BRCA gene thats why this child got cancer. When you get cancer as a child, you get leukemia, you dont get breast cancer, you dont get ovarian cancer. Leukemia that was a very severe form of BRCA deficiency.
Subsequent research found that the Fanconi anemia/BRCA pathway was disrupted not only in some breast and ovarian cancers, but also in a significant number of prostate and pancreatic cancers.
Not only did these children have Fanconi anemia, but their parents and grandparents have other cancers: breast, ovarian, and we now know prostate cancer and pancreatic cancer, DAndrea said. These poor families, they have children with Fanconi anemia, a fatal childhood disease, and their older siblings, parents, older family members who have a mutation in one of the genes, they get breast, ovarian, prostate, pancreatic cancer.
The discovery of a common DNA repair pathway linking Fanconi anemia to deadly cancers brought immediate attention to the condition and to DNA repair as a common feature of some cancers. It also provided a new way to treat them. Subsequent research showed that cancers caused by BRCA mutations become more dependent on other DNA repair pathways. Drugs called PARP inhibitors were developed to attack a key protein in a backup DNA repair pathway used by BRCA-deficient tumors. PARP inhibitors rapidly disrupt tumor growth, leading to dramatic remissions, but only for a time.
A year to 18 months after PARP treatment begins, tumors often recur as the cancer mutates to use a third DNA repair pathway, which relies on a protein called polymerase theta. To counter that move, DAndrea turned to modern drug-screening techniques, examining thousands of compounds effectiveness against polymerase theta. Novobiocin rose to the top. Subsequent trials in mouse models confirmed its effectiveness, which led to plans for the spring trial.
Should novobiocin prove an effective tool, researchers will shift to examining how tumors respond over time and whether they can eventually evade the drugs effects by using one of the bodys other DNA repair pathways, according to Geoffrey Shapiro, Dana-Farbers senior vice president for developmental therapeutics and a professor of medicine who is collaborating with DAndrea on the novobiocin trial. Having an additional drug will also let researchers explore combination therapies that might be harder for tumors to overcome. Such therapies are already extending the lives of many patients and, in some cases, reducing cancer to a chronic disease.
Ultimately, we will be combining all these DNA repair inhibitors to try to maximize response up front if its safe enough to do that, Shapiro said. This is our next decade of work.
Fanconi anemia remains a target for DAndrea and other researchers. Thanks to recent advances, including improved survival rates for bone-marrow transplants, more patients are living into their 30s or later. This is good news for families, but the threat of cancer is ever-present, and comes with a cruel twist. While treatments such as PARP and novobiocin grew out of Fanconi-related science, the key difference between Fanconi patients and others with cancer makes those treatments not only useless but potentially deadly for people with the condition.
For most cancer patients, the DNA repair defect that is vulnerable to PARP inhibitors and, potentially, novobiocin is in their tumor cells, which creates targets for treatment. In Fanconi patients, the defect is present throughout their bodies, meaning that the inhibitors would attack all their cells, not just cancerous ones. Frohnmayer said chemotherapy and radiation therapy are also dangerous for Fanconi patients, whichlimits cancer-fighting optionstoa heavy emphasis on early detection and surgery while the search for other treatments continues.
The first gene was discovered in 1992, so we were in the dark. All we had were these horrible statistics, Frohnmayer said. Today its much more hopeful. People know that getting through the bone-marrow-failure part of the problem is at least a possibility, maybe even a likelihood. Were working really hard on the cancer problem. And you can at least have the hope that, by the time your child is in danger, theres going to be a better answer than we have today.
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Artificial intelligence is everywhere now. This report shows how we got here. – Popular Science
Posted: at 2:18 am
Artificial intelligence is getting cheaper, better at the tasks we assign it, and more widespreadbut concerns over bias, ethics, and regulatory oversight still remain. At a time when AI is becoming accessible to everyone, Stanford University put together a sweeping 2022 report analyzing the ins and outs of the growing field. Here are some of the highlights.
The number of publications alone on the topic tell a story: They doubled in the last decade, from 162,444 in 2010 to 334,497 in 2021. The most popular AI categories that researchers and others published on were pattern recognition, machine learning, and algorithms.
Whats more, the number of patent filings related to AI innovations in 2021 is 30 times greater than the filings in 2015. In 2021, the majority of filed patents were from China, but the majority of patents actually granted were from the US.
The number of users participating in open-source AI software libraries on GitHub also rose from 2015 to 2021. These libraries house collections of computer codes that are used for applications and products. One called TensorFlow remains the most popular, followed by OpenCV, Keras and PyTorch (which Meta AI uses).
Specifically, out of the various tasks that AI can perform, last year, the research community was focused on applying AI to computer vision, a subfield that teaches machines to understand images and videos in order to get good at classifying images, recognizing objects, mapping the position and movement of human body joints, and detecting faces (with and without masks).
[Related: MIT scientists taught robots how to sabotage each other]
For image classification, the most popular database used to train AI models is called ImageNet. Some researchers pre-train their models on additional datasets before exposing them to ImageNet. But models still make mistakes, on average mis-identifying 1 out of 10 images. The model that performs the best is from the Google Brain Team. In addition to identifying images and faces, AI can also generate fake images that are nearly indistinguishable from real ones, and to combat this, researchers have been working on deepfake detection algorithms that are based on datasets like FaceForensics++.
[Related: This new AI tool from Google could change the way we search online]
Natural language processing, a subfield that has been actively explored since the 1950s, is slowly making progress in English language understanding, summarizing, inferring reasonable outcomes, identifying emotional context, speech recognition and transcription, and translation. For basic reading comprehension, AI can perform better than humans, but when language tasks get more complicated, like when interpreting context clues is necessary, humans still have an edge. On the other hand, AI ethicists are worried that bias could affect large language models that draw from a mixed bag of training data.
Tech companies like Amazon, Netflix, Spotify, and YouTube have been improving the AI used in recommendation systems. The same is true for AIs role in reinforcement learning, which has enabled it to react and perform well in virtual games such as chess and Go. Reinforcement learning can also be used to teach autonomous vehicles tasks like changing lanes, or help data models predict future events.
As AI appears to have become better at doing what we want it to do, the cost to train it has come down as well, dropping by over 60 percent since 2018. Meanwhile, a system that wouldve taken 6 minutes to train in 2018 would now only take a little over 13 seconds. Accounting for hardware costs, in 2021, an image classification system would take less than $5 to train, whereas that cost wouldve been over $1,000 in 2017.
More AI applications across industries means more demand for AI education and jobs. Across the US in 2021, California, Texas, New York, and Virginia had the highest demand for AI-related occupations. In the last decade, the most popular specialties among PhD computer science students were artificial intelligence and machine learning.
Private investment in AI is at an all-time high, totalling $93.5 billion in 2021 (double the amount from 2020). AI companies that were skilled in data management, processing, and cloud, according to the report, got the most funding in 2021, followed by companies dedicated to medical and healthcare and financial technology (fintech for short).
In fiscal year 2021, US government agencies spent $1.53 billion on AI research and development for non-defense purposes, which was 2.7 times the amount spent in fiscal year 2018. For defense purposes, the Department of Defense allocated $9.26 billion across 500 AI research and development programs in 2021, which was about 6 percent more than what it spent in the year before. The top two uses of AI were for prototyping technologies and in programs countering weapons of mass destruction.
Last, the report looked at global, federal, and state regulations related to AI (looking for keywords like artificial intelligence, machine learning, autonomous vehicle or algorithmic bias). The report examined 25 countries around the world, and found that they have collectively passed 55 AI-related bills to law from 2016 to 2021. Last year, Spain, the UK and the US each had three AI-related bills that became law.
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What’s Next in Artificial Intelligence? Three Key Directions – Stanford HAI
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After a long winter, the artificial intelligence field has seen a resurgence in the past 15 years as computer power increased and a lot of digital data became available. In the past few years alone, giant language models advanced so quickly to outpace benchmarks, computer vision capabilities took self-driving cars from the lab to the street, and generative models tested democracies during major elections.
But parallel to this technologys rapid rise is its potential for massive harm; technologists, activists, and academics alike began calling for better regulation and understanding of its impact.
This spring, Stanford Institute for Human-Centered AI (HAI) will address three of the most critical areas of artificial intelligence during a one-day conference free and open to all:
Stanford HAI Associate Director and linguistics and computer science professor Christopher Manning, who will cohost the event with HAI Denning Co-director and computer science professor Fei-Fei Li, explains what this conference will cover and who should attend.
This conference will look at key advances in AI. Why are we focusing on foundation models, accountable AI, and embodied AI? What makes these the areas where you expect major growth?
An enormous amount of work is going on in AI in many directions. For a one-day event, we wanted to focus in on a small number of areas that we felt were key to where the most important and exciting research might appear this decade. We ended up focusing on three areas. First, there has been enormous excitement and investment around the development of large pre-trained language models and their generalization to including multiple data modalities that we have named foundation models. Second, there has been an exciting resurgence of work linking AI and robotics, often enabled by the use of simulated worlds, which allow the exploration of embodied AI and grounding. Finally, the increasing concerns about understanding AI decisions and maintaining data privacy in part demand societal and regulatory solutions, but they are also an opportunity for technical AI advances as to how you can produce interpretable AI systems or systems that still work effectively on data that is obscured for privacy reasons.
Who are you excited to hear from?
Ilya Sutskever has been one of the central people at the heart of the resurgence of deep learning-based AI, starting from his breakthrough work on the computer vision system AlexNet with Geoff Hinton in 2012. His impact has grown since he became the chief scientist of Open AI, which among other things has led in the development of foundation models. Im looking forward to hearing more about their latest models such as InstructGPT and what he sees lying ahead.
The recent successes in AI just would not have been possible without the amazing breakthroughs in parallel computing largely led by NVIDIA. Bill Dally is a leader in computer architecture, and, for the last decade, he has been the chief scientist at NVIDIA. He can give us powerful insights into the recent and future advances in parallel computing via GPUs but also insights into the broader range of vision, virtual reality, and other AI research going on at NVIDIA.
And Hima Lakkaraju is a trailblazing Harvard professor developing new strands of work in trustworthy and interpretable machine learning. When AI models are used in high-stakes settings, most times people would like accurate and reliable explanations of why the systems make certain decisions. One exciting direction in Himas work is in developing formal Bayesian models that can give reliable explanations.
Who should attend this conference?
Through a combination of short talks and panel discussions, were trying to achieve a balance between technical depth and accessibility. So on the one hand this conference should be of interest to anyone working in AI as a student, researcher, or developer, but beyond that we hope to be able to convey some of the excitement, results, and progress in these areas to anybody with an interest in AI, whether as a scientist, decision maker, or concerned citizen.
What do you hope your audience will take away from this experience?
I hope the audience will get a deeper understanding of how AI has been able to advance so quickly in the last 15 years, where it might go next, and what we should and shouldnt worry about. I hope people will take away the awesome powers of the huge new foundation models that are being built. But equally they will see why building a model from mountains of digital data is not sufficient, and we want to explore embodied AI models in a physical or simulated world that can learn more as babies learn. And finally, we will see something about how there is now a lot of exciting technical work underway to address the worries and downsides of AI that have been very prominently covered in the media in recent years.
Interested in attending the 2022 HAI Spring Conference? Learn more or register.
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What's Next in Artificial Intelligence? Three Key Directions - Stanford HAI
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The Vulnerability of AI Systems May Explain Why Russia Isn’t Using Them Extensively in Ukraine – Forbes
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Output of an Artificial Intelligence system from Google Vision, performing Facial Recognition on a ... [+] photograph of a man in San Ramon, California, November 22, 2019. (Photo by Smith Collection/Gado/Getty Images)
The news that Ukraine is using facial recognition software to uncover Russian assailants and identify Ukrainians killed in the ongoing war is noteworthy largely because its one of few documented uses of artificial intelligence in the conflict. A Georgetown University think tank is trying to figure out why while advising U.S. policymakers of the risks of AI.
The CEO of the controversial American facial recognition company Clearview AI told Reuters that Ukraines defense ministry began using its imaging software Saturday after Clearview offered it for free. The reportedly powerful recognition tool relies on artificial intelligence algorithms and a massive quantity of image training data scraped from social media and the internet.
But aside from Russian influence campaigns with their much-discussed deep fakes and misinformation-spreading bots, the lack of known tactical use (at least publicly) of AI by the Russian military has surprised many observers. Andrew Lohn isnt one of them.
Lohn, a senior fellow with Georgetown Universitys Center for Security and Emerging Technology, works on its Cyber-AI Project, which is seeking to draw policymakers attention to the growing body of academic research showing that AI and machine-learning (ML) algorithms can be attacked in a variety of basic, readily exploitable ways.
We have perhaps the most aggressive cyber actor in the world in Russia who has twice turned off the power to Ukraine and used cyber-attacks in Georgia more than a decade ago. Most of us expected the digital domain to play a much larger role. Its been small so far, Lohn says.
We have a whole bunch of hypotheses [for limited AI use] but we dont have answers. Our program is trying to collect all the information we can from this encounter to figure out which are most likely.
They range from the potential effectiveness of Ukrainian cyber and counter-information operations, to an unexpected shortfall in Russian preparedness for digital warfare in Ukraine, to Russias need to preserve or simplify the digital operating environment for its own tactical reasons.
All probably play some role, Lohn believes, but just as crucial may be a dawning recognition of the limits and vulnerability of AI/ML. The willingness to deploy AI tools in combat is a confidence game.
Junk In, Junk Out
Artificial intelligence and machine learning require vast amounts of data, both for training and to interpret for alerts, insights or action. Even when AI/ML have access to an unimpeded base of data, they are only as good as the information and assumptions which underlie them. If for no other reason than natural variability, both can be significantly flawed. Whether AI/ML systems work as advertised is a huge question, Lohn acknowledges.
The tech community refers to unanticipated information as Out of Distribution data. AI/ML may perform at what is deemed to be an acceptable level in a laboratory or in otherwise controlled conditions, Lohn explains. Then when you throw it into the real world, some of what it experiences is different in some way. You dont know how well it will perform in those circumstances.
In circumstances where life, death and military objectives are at stake, having confidence in the performance of artificial intelligence in the face of disrupted, deceptive, often random data is a tough ask.
Lohn recently wrote a paper assessing the performance of AI/ML when such systems scoop in out of distribution data. While their performance doesnt fall off quite as quickly as he anticipated, he says that if they operate in an environment where theres a lot of conflicting data, theyre garbage.
He also points out that the accuracy rate of AI/ML is impressively high but compared to low expectations. For example, image classifiers can work at 94%, 98% or 99.9% accuracy. The numbers are striking until one considers that safety-critical systems like cars/airplanes/healthcare devices/weapons are typically certified out to 5 or 6 decimal points (99.999999%) accuracy.
Lohn says AI/ML systems may still be better than humans at some tasks but the AI/ML community has yet to figure out what accuracy standards to put in place for system components. Testing for AI systems is very challenging, he adds.
For a start, the artificial intelligence development community lacks a test culture similar to what has become so familiar for military aerospace, land, maritime, space or weapons systems; a kind of test-safety regime that holistically assesses the systems-of-systems that make up the above.
The absence of such a back end combined with specific conditions in Ukraine may go some distance to explain the limited application of AI/ML on the battlefield. Alongside it lies the very real vulnerability of AI/ML to the compromised information and active manipulation that adversaries already to seek to feed and to twist it.
Bad Data, Spoofed Data & Classical Hacks
Attacking AI/ML systems isnt hard. It doesnt even require access to their software or databases. Age-old deceptions like camouflage, subtle visual environment changes or randomized data can be enough to throw off artificial intelligence.
As a recent article in the Armed Forces Communications and Electronics Associations (AFCEA) magazine noted, researchers from Chinese e-commerce giant Tencent managed to get a Tesla sedans autopilot (self-driving) feature to switch lanes into oncoming traffic simply by using inconspicuous stickers on the roadway. McAfee Security researchers used similarly discreet stickers on speed limit signs to get a Tesla to speed up to 85 miles per hour in a 35 mile-an-hour zone.
An Israeli soldier is seen during a military exercise in the Israeli Arab village of Abu Gosh on ... [+] October 20, 2013 in Abu Gosh, Israel. (Photo by Lior Mizrahi/Getty Images)
Such deceptions have probably already been examined and used by militaries and other threat actors Lohn says but the AI/ML community is reluctant to openly discuss exploits that can warp its technology. The quirk of digital AI/ML systems is that their ability to sift quickly through vast data sets - from images to electromagnetic signals - is a feature that can be used against them.
Its like coming up with an optical illusion that tricks a human except with a machine you get to try it a million times within a second and then determine whats the best way to effect this optical trick, Lohn says.
The fact that AI/ML systems tend to be optimized to zero in on certain data to bolster their accuracy may also be problematic.
Were finding that [AI/ML] systems may be performing so well because theyre looking for features that are not resilient, Lohn explains. Humans have learned to not pay attention to things that arent reliable. Machines see something in the corner that gives them high accuracy, something humans miss or have chosen not to see. But its easy to trick.
The ability to spoof AI/ML from outside joins with the ability to attack its deployment pipeline. The supply chain databases on which AI/ML rely are often open public databases of images or software information libraries like GitHub.
Anyone can contribute to these big public databases in many instances, Lohn says. So there are avenues [to mislead AI] without even having to infiltrate.
The National Security Agency has recognized the potential of such data poisoning. In January, Neal Ziring, director of NSAs Cybersecurity Directorate, explained during a Billington CyberSecurity webinar that research into detecting data poisoning or other cyber attacks is not mature. Some attacks work by simply seeding specially crafted images into AI/ML training sets, which have been harvested from social media or other platforms.
According to Ziring, a doctored image can be indistinguishable to human eyes from a genuine image. Poisoned images typically contain data that can train the AI/ML to misidentify whole categories of items.
The mathematics of these systems, depending on what type of model youre using, can be very susceptible to shifts in the way recognition or classification is done, based on even a small number of training items, he explained.
Stanford cryptography professor Dan Boneh told AFCEA that one technique for crafting poisoned images is known as the fast gradient sign method (FGSM). The method identifies key data points in training images, leading an attacker to make targeted pixel-level changes called perturbations in an image. The modifications turn the image into an adversarial example, providing data inputs that make the AI/ML misidentify it by fooling the model being used. A single corrupt image in a training set can be enough to poison an algorithm, causing misidentification of thousands of images.
FGSM attacks are white box attacks, where the attacker has access to the source code of the AI/ML. They can be conducted on open-source AI/ML for which there are several publicly accessible repositories.
You typically want to try the AI a bunch of times and tweak your inputs so they yield the maximum wrong answer, Lohn says. Its easier to do if you have the AI itself and can [query] it. Thats a white box attack.
If you dont have that, you can design your own AI that does the same [task] and you can query that a million times. Youll still be pretty effective at [inducing] the wrong answers. Thats a black box attack. Its surprisingly effective.
Black box attacks where the attacker only has access to the AI/ML inputs, training data and outputs make it harder to generate a desired wrong answer. But theyre effective at producing random misinterpretation, creating chaos Lohn explains.
DARPA has taken up the problem of increasingly complex attacks on AI/ML that dont require inside access/knowledge of the systems being threatened. It recently launched a program called Guaranteeing AI Robustness against Deception (GARD), aimed at the development of theoretical foundations for defensible ML and the creation and testing of defensible systems.
More classical exploits wherein attackers seek to penetrate and manipulate the software and networks that AI/ML run on remain a concern. The tech firms and defense contractors crafting artificial intelligence systems for the military have themselves been targets of active hacking and espionage for years. While Lohn says there has been less reporting of algorithm and software manipulation, that would be potentially be doable as well.
It may be harder for an adversary to get in and change things without being noticed if the defender is careful but its still possible.
Since 2018, the Army Research Laboratory (ARL) along with research partners in the Internet of Battlefield Things Collaborative Research Alliance, looked at methods to harden the Armys machine learning algorithms and make them less susceptible to adversarial machine learning techniques. The collaborative developed a tool it calls Attribution-Based Confidence Metric for Deep Neural Networks in 2019 to provide a sort of quality assurance for applied AI/ML.
Despite the work, ARL scientist Brian Jalaian told its public affairs office that, While we had some success, we did not have an approach to detect the strongest state-of-the-art attacks such as [adversarial] patches that add noise to imagery, such that they lead to incorrect predictions.
If the U.S. AI/ML community is facing such problems, the Russians probably are too. Andrew Lohn acknowledges that there are few standards for AI/ML development, testing and performance, certainly nothing like the Cybersecurity Maturity Model Certification (CMMC) that DoD and others adopted nearly a decade ago.
Lohn and CSET are trying to communicate these issues to U.S. policymakers not to dissuade the deployment of AI/ML systems, Lohn stresses, but to make them aware of the limitations and operational risks (including ethical considerations) of employing artificial intelligence.
Thus far he says, policymakers are difficult to paint with a broad brush. Some of those Ive talked with are gung-ho, others are very reticent. I think theyre beginning to become more aware of the risks and concerns.
He also points out that the progress weve made in AI/ML over the last couple of decades may be slowing. In another recent paper he concluded that advances in the formulation of new algorithms have been overshadowed by advances in computational power which has been the driving force in AI/ML development.
Weve figured out how to string together more computers to do a [computational] run. For a variety of reasons, it looks like were basically at the edge of our ability to do that. We may already be experiencing a breakdown in progress.
Policymakers looking at Ukraine and at the world before Russias invasion were already asking about the reliability of AI/ML for defense applications, trying to gauge the level of confidence they should place in it. Lohn says hes basically been telling them the following;
Self driving cars can do some things that are pretty impressive. They also have giant limitations. A battlefield is different. If youre in a permissive environment with an application similar to existing commercial applications that have proven successful, then youre probably going to have good odds. If youre in a non-permissive environment, youre accepting a lot of risk.
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Picsart Launches AI-Generated Fonts, Paving the Way for Asset Creation by Artificial Intelligence – Business Wire
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MIAMI--(BUSINESS WIRE)--Picsart, the worlds leading digital creation platform and a top 20 most downloaded app worldwide, today announced its first ever end-to-end solution for fonts generated by artificial intelligence.
This project began as part of a Picsart hackathon last year and developed into a full fledged AI-generated font solution. Creators on the Picsart platform can already access and apply over 30 of these unique fonts as part of Picsart Gold, with additional fonts added monthly.
In the last decade, weve seen a shift in written communication becoming increasingly visual, said Anush Ghambaryan, Director of AI and Machine Learning at Picsart. As the demand for visual communication increases, so does the need for new and unlimited options for creation. Fonts are one of the most popular features in our entire content library, and this new technology opens the door for infinite font creation.
Picsart AI Research (PAIR) develops new fonts with AI by training models with a large dataset of selected fonts, allowing the models to create glyphs - letters, symbols and numbers - from the provided input, like a font related keyword or tag. The technology creates thousands of glyphs, and then converts the glyphs to a vectorized image, after passing additional checks such as quality control, and creates a font file in the most common font file types: .TTF or .OTF format.
At PAIR, were on an exciting mission to innovate and develop the best AI tools and products to empower creative communication for everyone, said Humphrey Shi, Chief Scientist at Picsart and PAIR Founder and Lead. And releasing this new font generation solution is a great start. The future applications for designers, creators and businesses to use this technology is exciting as it will reduce time and cost, and increase the possibilities for design and communication.
Picsart launched PAIR last year with Shi to accelerate AI research, development and products at this new innovation hub, which has already released industry leading research and tools - including a one-tap Background Remove tool.
This news comes just after the company made its world-class creative tools available to businesses through the opening of its API. The API offering includes AI and image tools that make photos and videos stand out, and processes them faster. Picsart also has plans to integrate AI fonts into its API offering. To apply these fonts to your own creations, download the app or visit picsart.com.
About Picsart
Picsart is the worlds largest digital creation platform and a top 20 most downloaded app. Every month, the Picsart community creates, remixes, and shares billions of visual stories using the companys powerful and easy-to-use editing tools. Picsart has amassed one of the largest open-source content collections in the world, including free-to-edit photos, stickers, backgrounds, templates, and more. Picsart is available in 30 languages for free and as a subscription on iOS, Android, Windows devices and on the Web. Headquartered in Miami, with offices around the world, Picsart is backed by SoftBank, Sequoia Capital, DCM Ventures, Insight Partners, and others. Download the app or visit picsart.com for more information.
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