#data extraction from image

[uniquesdata]

Image Processing Services for Ecommerce Business

Image processing has become vital for eCommerce businesses to bring effective results, increase sales, improve brand value, and attract customers. Learn how image processing services help eCommerce businesses to grow.

[reluconsultant]

Discover the ultimate LinkedIn scraping tool for professionals. Extract valuable insights, streamline your lead generation, and skyrocket your success today!

[manmishra]

🚀 The ChatGPT Desktop App is Changing the Game! 🤯💻 Imagine having an AI assistant that can: ✅ Reply to emails in seconds 📧⏩ ✅ Generate high-quality images with DALL-E 🎨🤩 ✅ Summarize long content instantly 📖📜 ✅ Write HTML/CSS code from screenshots 💻💡 ✅ Translate text across multiple languages 🌍🗣️ ✅ Extract text from images easily 📷📝 ✅ Analyze large datasets from Excel/CSV files 📊📈 👉 This app is designed to save your time. #ChatGPT #ChatGPTDesktopApp #AIProductivity #dalle #TechT

[coral-skeleton]

Yep, this one can be enjoyed guilt free.

I'd just also like to point out that AI used for denoising is a very different thing from modern-day generative AI. Denoising AI is, more often than not, a closed system, guided machine learning process, meaning it is trained on a subset of the dataset it is being used on, it does not scrape the internet for training sets, it does not steal from artists, more often than not it can be run completely locally on any mildly decent computer (it might just take a while if you don't have a fancy computer with the latest high end cpu and gpu specs), it does not need those massive servers and computing centers that severly worsen global warming to work.

The type of AI used for denoising is much more similar to the AI that's being used to cure cancer and improve our capabilities to do astronomical and astrophysical research than large language models and other generative AI and tbh, it's still worlds away from the curing cancer AI. It's so different from chat gpt that the same word absolutely should not be used for both of them. I would put a metaphor for trying to compare the two here, but I genuinely can't think of two things in the same category that are this dissimilar from each other in any other context.

Anyway, this type of "AI" is actually very normal mathematics and computer algorithms, and not the evil type of AI

AI in Unification?

If you, like me, couldn't fully enjoy Unification because there was a horrible feeling in your gut the whole time of "is this AI? Did Shatner really let them use AI? That seems like a thing he'd do, because he's kind of awful" then you've come to the right place.

I did a deep dive of the technologies used for Unification and while this isn't a 100% comprehensive guide here's what I've learned:

According to Trekmovie.com's article about the film, the production team used a "team of artists and animators, who combined digital and physical prosthetics with live-action location photography, virtual production, and CG set extensions" and used "OTOY’s “Octane” rendering software and the “Render Network” decentralized GPU rendering platform. Characters and props were digitized using OTOY’s Academy-Award winning “LightStage” scanning system."

So what are all these proprietary names / jargon, and are any of them AI?

LightStage: A scanning tech that allows for digital capture of a human face (probably used to capture the stand-ins faces and superimpose older footage of Spock / Kirk like they would for a video game motion capture or something) = Not AI

OctaneRender: "Fastest unbiased, spectrally correct GPU render engine" (Probably used for sets based on the example I'm seeing on OTOY's website. It DOES use AI for "denoising and lighting" but this is a feature of the program and not the only thing the program does, so it is unclear if this is something they would have employed for the shot film. If they did, this would not be used for character work / deep fakes, and given what little information is written about this tech I'm almost curious if it is even a full AI system at all or just an automatic denoiser that they've dubbed as AI to look impressive. So I'd say results inconclusive here at best.)

The Render Network: "The network connects node operators looking to monetize their idle GPU compute power with artists looking to scale intensive 3D-rendering work and with machine learning developers looking to train and tune AI models. Through a decentralized peer-to-peer network, the Render Network achieves unprecedented levels of scale, speed, and economic efficiency. " (This basically means people can use the platform FOR AI but means nothing in the context of whether AI was used for this project.)

TL;DR: AI is an umbrella term for a lot of technology and it seems if anything, there may have been some AI used in the background rendering process but nothing generative AI / deep fakes. In my cynical opinion, if they HAD used AI in general for this, I feel like they'd be shouting it from the rooftops right now since people who love AI won't shut up about it. I'm tentatively saying this was 99% made with traditional CGI and artist work as is stated in the Trekmovie.com article, but I wouldn't be surprised if that opinion changes as the day goes on and more information is released.

[fionnaskyborn]

i have a favorite genre of image, by the way, and it's this. if anyone's got any more of the like to contribute to my collection i would be more than happy to provide those jpegs a home.

[probablyasocialecologist]

The supply chain capitalism of AI. This image partially captures the supply chain of AI as a global and complex phenomenon. Natural resources, components and materials to build AI infrastructure are extracted, shipped, manufactured and produced across the globe. For instance, NVIDIA obtains tungsten from Brazil; gold from Colombia and tantalum from Kazakhstan. Minerals are assembled to manufacture GPUs by TSMC. NVIDIA sells GPUs across data centres in the world. Given the refresh rates of these materials, data centres sent their components to recycle plants or dumps. The human labour wrapped-up in this chain includes, data labellers, logistics drivers, data scientists, miners, data centre operators and electronic waste dismantlers, who are also scattered across different geographies. Source: NVIDIA (2022) and fieldwork.

The supply chain capitalism of AI: a call to (re)think algorithmic harms and resistance through environmental lens

[vague-humanoid]

Two Harvard students recently revealed that it's possible to combine Meta smart glasses with face image search technology to "reveal anyone's personal details," including their name, address, and phone number, "just from looking at them."

In a Google document, AnhPhu Nguyen and Caine Ardayfio explained how they linked a pair of Meta Ray Bans 2 to an invasive face search engine called PimEyes to help identify strangers by cross-searching their information on various people-search databases. They then used a large language model (LLM) to rapidly combine all that data, making it possible to dox someone in a glance or surface information to scam someone in seconds—or other nefarious uses, such as "some dude could just find some girl’s home address on the train and just follow them home,” Nguyen told 404 Media.

This is all possible thanks to recent progress with LLMs, the students said.

"This synergy between LLMs and reverse face search allows for fully automatic and comprehensive data extraction that was previously not possible with traditional methods alone," their Google document said.

Where previously someone could spend substantial time conducting their own search of public databases to find information based on someone's image alone, their dystopian smart glasses do that job in a few seconds, their demo video said.

The co-creators said that they altered a pair of Meta Ray Bans 2 to create I-XRAY to raise awareness of "significant privacy concerns" online as technology rapidly advances.

[are-we-art-yet]

Is AWAY using it's own program or is this just a voluntary list of guidelines for people using programs like DALL-E? How does AWAY address the environmental concerns of how the companies making those AI programs conduct themselves (energy consumption, exploiting impoverished areas for cheap electricity, destruction of the environment to rapidly build and get the components for data centers etc.)? Are members of AWAY encouraged to contact their gov representatives about IP theft by AI apps?

What is AWAY and how does it work?

AWAY does not "use its own program" in the software sense—rather, we're a diverse collective of ~1000 members that each have their own varying workflows and approaches to art. While some members do use AI as one tool among many, most of the people in the server are actually traditional artists who don't use AI at all, yet are still interested in ethical approaches to new technologies.

Our code of ethics is a set of voluntary guidelines that members agree to follow upon joining. These emphasize ethical AI approaches, (preferably open-source models that can run locally), respecting artists who oppose AI by not training styles on their art, and refusing to use AI to undercut other artists or work for corporations that similarly exploit creative labor.

Environmental Impact in Context

It's important to place environmental concerns about AI in the context of our broader extractive, industrialized society, where there are virtually no "clean" solutions:

The water usage figures for AI data centers (200-740 million liters annually) represent roughly 0.00013% of total U.S. water usage. This is a small fraction compared to industrial agriculture or manufacturing—for example, golf course irrigation alone in the U.S. consumes approximately 2.08 billion gallons of water per day, or about 7.87 trillion liters annually. This makes AI's water usage about 0.01% of just golf course irrigation.

Looking into individual usage, the average American consumes about 26.8 kg of beef annually, which takes around 1,608 megajoules (MJ) of energy to produce. Making 10 ChatGPT queries daily for an entire year (3,650 queries) consumes just 38.1 MJ—about 42 times less energy than eating beef. In fact, a single quarter-pound beef patty takes 651 times more energy to produce than a single AI query.

Overall, power usage specific to AI represents just 4% of total data center power consumption, which itself is a small fraction of global energy usage. Current annual energy usage for data centers is roughly 9-15 TWh globally—comparable to producing a relatively small number of vehicles.

The consumer environmentalism narrative around technology often ignores how imperial exploitation pushes environmental costs onto the Global South. The rare earth minerals needed for computing hardware, the cheap labor for manufacturing, and the toxic waste from electronics disposal disproportionately burden developing nations, while the benefits flow largely to wealthy countries.

While this pattern isn't unique to AI, it is fundamental to our global economic structure. The focus on individual consumer choices (like whether or not one should use AI, for art or otherwise,) distracts from the much larger systemic issues of imperialism, extractive capitalism, and global inequality that drive environmental degradation at a massive scale.

They are not going to stop building the data centers, and they weren't going to even if AI never got invented.

Creative Tools and Environmental Impact

In actuality, all creative practices have some sort of environmental impact in an industrialized society:

Digital art software (such as Photoshop, Blender, etc) generally uses 60-300 watts per hour depending on your computer's specifications. This is typically more energy than dozens, if not hundreds, of AI image generations (maybe even thousands if you are using a particularly low-quality one).

Traditional art supplies rely on similar if not worse scales of resource extraction, chemical processing, and global supply chains, all of which come with their own environmental impact.

Paint production requires roughly thirteen gallons of water to manufacture one gallon of paint.

Many oil paints contain toxic heavy metals and solvents, which have the potential to contaminate ground water.

Synthetic brushes are made from petroleum-based plastics that take centuries to decompose.

That being said, the point of this section isn't to deflect criticism of AI by criticizing other art forms. Rather, it's important to recognize that we live in a society where virtually all artistic avenues have environmental costs. Focusing exclusively on the newest technologies while ignoring the environmental costs of pre-existing tools and practices doesn't help to solve any of the issues with our current or future waste.

The largest environmental problems come not from individual creative choices, but rather from industrial-scale systems, such as:

Industrial manufacturing (responsible for roughly 22% of global emissions)

Industrial agriculture (responsible for roughly 24% of global emissions)

Transportation and logistics networks (responsible for roughly 14% of global emissions)

Making changes on an individual scale, while meaningful on a personal level, can't address systemic issues without broader policy changes and overall restructuring of global economic systems.

Intellectual Property Considerations

AWAY doesn't encourage members to contact government representatives about "IP theft" for multiple reasons:

We acknowledge that copyright law overwhelmingly serves corporate interests rather than individual creators

Creating new "learning rights" or "style rights" would further empower large corporations while harming individual artists and fan creators

Many AWAY members live outside the United States, many of which having been directly damaged by the US, and thus understand that intellectual property regimes are often tools of imperial control that benefit wealthy nations

Instead, we emphasize respect for artists who are protective of their work and style. Our guidelines explicitly prohibit imitating the style of artists who have voiced their distaste for AI, working on an opt-in model that encourages traditional artists to give and subsequently revoke permissions if they see fit. This approach is about respect, not legal enforcement. We are not a pro-copyright group.

In Conclusion

AWAY aims to cultivate thoughtful, ethical engagement with new technologies, while also holding respect for creative communities outside of itself. As a collective, we recognize that real environmental solutions require addressing concepts such as imperial exploitation, extractive capitalism, and corporate power—not just focusing on individual consumer choices, which do little to change the current state of the world we live in.

When discussing environmental impacts, it's important to keep perspective on a relative scale, and to avoid ignoring major issues in favor of smaller ones. We promote balanced discussions based in concrete fact, with the belief that they can lead to meaningful solutions, rather than misplaced outrage that ultimately serves to maintain the status quo.

If this resonates with you, please feel free to join our discord. :)

Works Cited:

USGS Water Use Data: https://www.usgs.gov/mission-areas/water-resources/science/water-use-united-states

Golf Course Superintendents Association of America water usage report: https://www.gcsaa.org/resources/research/golf-course-environmental-profile

Equinix data center water sustainability report: https://www.equinix.com/resources/infopapers/corporate-sustainability-report

Environmental Working Group's Meat Eater's Guide (beef energy calculations): https://www.ewg.org/meateatersguide/

Hugging Face AI energy consumption study: https://huggingface.co/blog/carbon-footprint

International Energy Agency report on data centers: https://www.iea.org/reports/data-centres-and-data-transmission-networks

Goldman Sachs "Generational Growth" report on AI power demand: https://www.goldmansachs.com/intelligence/pages/gs-research/generational-growth-ai-data-centers-and-the-coming-us-power-surge/report.pdf

Artists Network's guide to eco-friendly art practices: https://www.artistsnetwork.com/art-business/how-to-be-an-eco-friendly-artist/

The Earth Chronicles' analysis of art materials: https://earthchronicles.org/artists-ironically-paint-nature-with-harmful-materials/

Natural Earth Paint's environmental impact report: https://naturalearthpaint.com/pages/environmental-impact

Our World in Data's global emissions by sector: https://ourworldindata.org/emissions-by-sector

"The High Cost of High Tech" report on electronics manufacturing: https://goodelectronics.org/the-high-cost-of-high-tech/

"Unearthing the Dirty Secrets of the Clean Energy Transition" (on rare earth mineral mining): https://www.theguardian.com/environment/2023/apr/18/clean-energy-dirty-mining-indigenous-communities-climate-crisis

Electronic Frontier Foundation's position paper on AI and copyright: https://www.eff.org/wp/ai-and-copyright

Creative Commons research on enabling better sharing: https://creativecommons.org/2023/04/24/ai-and-creativity/

[roksik-dnd]

For everyone who asked: a dialogue parser for BG3 alongside with the parsed dialogue for the newest patch. The parser is not mine, but its creator a) is amazing, b) wished to stay anonymous, and c) uploaded the parser to github - any future versions will be uploaded there first!

UPD: The parser was updated!! Now all the lines are parsed, AND there are new features like audio and dialogue tree visualisation. See below!

Patch 7 dialogue is uploaded!

If you don't want to touch the parser and just want the dialogues, make sure to download the whole "BG3 ... (1.6)" folder and keep the "styles" folder within: it is needed for the html files functionality (hide/show certain types of information as per the menu at the top, jumps when you click on [jump], color for better readability, etc). See the image below for what it should look like. The formatting was borrowed from TORcommunity with their blessing.

If you want to run the parser yourself instead of downloading my parsed files, it's easy:

run bg3dialogreader.exe, OPEN any .pak file inside of your game's '\steamapps\common\Baldurs Gate 3\Data' folder,

select your language

press ‘LOAD’, it'll create a database file with all the tags, flags, etc.

Once that is done, press ‘EXPORT all dialogs to html’, and give it a minute or two to finish.

Find the parser dialogue in ‘Dialogs’ folder. If you move the folder elsewhere, move the ‘styles’ folder as well! It contains the styles you need for the color coding and functionality to keep working!

New features:

Once you've created the database (after step three above), you can also preview the dialogue trees inside of the parser and extract only what you need:

You can also listen to the correspinding audio files by clicking the line in the right window. But to do that, as the parser tells you, you need to download and put the filed from vgmstream-win64.zip inside of the parser's main folder (restart the parser after).

You can CONVERT the bg3 dialogue to the format that the Divinity Original Sin 2's Editor understands. That way, you can view the dialogues as trees! Unlike the html files, the trees don't show ALL the relevant information, but it's much easier to orient yourself in.

To get that, you DO need to have bought and installed Larian's previous game, Divinity Original Sin 2. It comes with a tool called 'The Divinity Engine 2'. Here you can read about how to unstall and lauch it. Once you have it, you need to load/create a project. We're trying to get to the point where the tool allows you to open the Dialog Editor. Then you can Open any bg3 dialogue file you want. And in case you want it, here's an in-depth Dialog Editor tutorial. But if you simply want to know how to open the Editor, here's the gist:

Update: In order to see the names of the speakers (up to ten), you can put the _merged.lsf file inside of the "\Divinity Original Sin 2\DefEd\Data\Public\[your project's name here]\RootTemplates\_merged.lsf" file path.

Feel free to ask if you have any questions! Please let me know if you modify the parser, I'd be curious to know what you added, and will possibly add it to the google drive.

[smallmariofindings]

During the cup award cutscene in Mario Kart Arcade GP 2, there are some characters shown watching the racers from the space between the podium and the camera, appearing blurry due to presumably being out of focus.

However, extracting their textures from the files reveals that they are neither blurry due to being out of focus as part of a camera rendering functionality, nor being blurred in-engine with a graphical effect. Instead, they are simply saved as already blurry images in the data.

Main Blog | Twitter | Patreon | Small Findings | Source

[uniquesdata]

How Image Processing and Data Entry Support Various Sectors

Economy is vast, consisting of a variety of sectors that require image processing and data management services for optimum management and utilization of data. Uniquesdata is proficient in offering data entry services for various sectors. Continue to gain more insights.

[reluconsultant]

LinkedIn Job Scrapper

LinkedIn Job Scraper: A Comprehensive Guide to Extracting Job Listings and Data

LinkedIn has become an indispensable platform for professionals seeking new career opportunities and for recruiters looking to find the best talent. It's a treasure trove of job listings and valuable data. However, manually searching for jobs and extracting data can be time-consuming. That's where LinkedIn Job Scraper comes into play. In this comprehensive guide, we will explore what LinkedIn Job Scraper is, why you might want to use it, its legality and ethics, and how to effectively build and use a scraper.

What is a LinkedIn Job Scraper?

LinkedIn Job Scraper refers to a software tool or program that is designed to extract job listings and related data from LinkedIn, a popular professional networking platform. This tool automates the process of collecting information about job opportunities, such as job titles, company details, job descriptions, and other relevant data, from LinkedIn's job postings.

LinkedIn Job Scrapers are typically used by job seekers, recruiters, and data analysts

Why Use LinkedIn Job Scraper?

LinkedIn Job Scraper offers several advantages for job seekers, recruiters, and businesses:

Efficient Job Searching: For job seekers, it automates the process of searching for job listings that match their criteria, saving time compared to manual searching. It allows users to access a large number of job postings quickly and efficiently, increasing their chances of finding the right opportunity .

Streamlined Recruitment: Recruiters can use LinkedIn scrapers to gather relevant candidate profiles, helping them streamline the recruitment process. This tool automates the collection of candidate data, making it easier to identify potential hires.

Data-Driven Insights: Businesses can benefit from scraping job data to gain insights into job market trends, analyze companies' hiring practices, and gather data for various studies. This data-driven approach can inform strategic decisions related to hiring and workforce planning .

Competitive Analysis: Businesses can use job scraping to monitor job postings by competitors, helping them understand their hiring strategies and stay competitive in the talent market. This insight can be invaluable for staying ahead in the industry .

Consolidation of Job Posts: LinkedIn Job Scrapers can consolidate job posts from various sources into a single database. This feature is particularly useful for aggregating job listings from multiple job sites, making it easier for users to access a wide range of opportunities without the need to visit multiple platforms .

Cost-Effective Solutions: Some LinkedIn  scraping tools offer cost-effective solutions for scraping and extracting data. This can be an affordable way to gather information from LinkedIn without the need for extensive manual labor.

Utilizing Pre-built Tools

Utilizing pre-built tools for web scraping, such as those designed for LinkedIn scraping, offers several advantages, including ease of use and time savings. Here are some pre-built tools that can help you in your web scraping endeavors:

Phantombuster: Phantombuster is a comprehensive web scraping tool that supports various platforms, including LinkedIn. It allows users to automate data extraction from LinkedIn profiles, posts, and more.

Captain Data: Captain Data is another versatile data scraping tool that can be used for LinkedIn scraping. It offers a user-friendly interface and supports data extraction from various sources.

La Growth Machine: This tool specializes in LinkedIn automation and data scraping. It can be useful for extracting data related to job postings, candidate profiles, and more.

Waalaxy: Waalaxy is a LinkedIn automation platform that includes scraping features. It can help automate tasks like connection requests, messaging, and profile scraping.

Dux-Soup: Dux-Soup is a LinkedIn automation tool that also supports scraping. It offers features for extracting data from profiles and automating various LinkedIn activities.

lemlist: lemlist provides automation and scraping capabilities for LinkedIn. It focuses on personalized outreach but can also be used for data extraction.

Evaboot: Evaboot is a LinkedIn automation and scraping tool designed to streamline outreach and data collection on the platform.

TexAu: TexAu is an all-in-one automation platform that includes LinkedIn scraping capabilities. It supports various LinkedIn-related tasks.

Linked Helper: Linked Helper is a LinkedIn automation tool with scraping features. It can automate connections, messaging, and data extraction.

Surfe (ex-Leadjet): Surfe is a LinkedIn scraping tool that enables users to collect data from LinkedIn profiles, including job-related information.

Real-World Applications

LinkedIn Job Scraper, or tools designed for scraping job postings and related data from LinkedIn, has real-world applications across various sectors:

Job Seekers:

Efficient Job Search: Job seekers can use LinkedIn scrapers to efficiently search for job listings that match their qualifications and preferences. These tools can help in quickly identifying relevant job opportunities.

Data Analysis: Scrapped job data can be analyzed to identify trends in job markets, helping job seekers make informed decisions about their career paths.

Recruiters:

Candidate Sourcing: Recruiters can use LinkedIn scrapers to gather candidate profiles and resumes that match specific job requirements. This speeds up the candidate sourcing process.

Talent Pool Management: LinkedIn scrapers enable recruiters to build and manage talent pools for future job openings. They can store candidate information for future reference.

Businesses:

Competitor Analysis: Businesses can monitor job postings by competitors on LinkedIn to gain insights into their hiring strategies and the skills they are seeking.

Market Research: Scraped job data can be used for market research to understand hiring trends, skill demand, and the overall job market health.

HR Analytics: HR departments can use LinkedIn scraping to analyze their own job postings and applicant data to make data-driven HR decisions.

Academic and Research: Researchers and academics can use LinkedIn scraping tools to collect data for studies related to employment trends, skills demand, and labor market analysis.

Career Counselors and Coaches: Professionals in career guidance can utilize scraped job data to provide informed advice to clients about job prospects and industry trends.

Government and Workforce Development: Government agencies and organizations responsible for workforce development can use scraped data to understand regional job markets and create strategies for employment growth.

Freelancers and Gig Workers: Freelancers can use scrapers to find short-term gigs and contract work on LinkedIn, making it easier to identify relevant projects.

Data Analysis and AI: Data scientists and AI developers can use scraped job data to develop algorithms and models for predicting job trends, skill demand, and salary expectations.

These real-world applications demonstrate the versatility of LinkedIn scrapers in supporting job-related activities, whether it's job searching, recruitment, business strategy, research, or career development. However, it's crucial to use these tools responsibly and in compliance with legal and ethical considerations.

Conclusion

LinkedIn scraping has become a crucial aspect of data acquisition in various fields, including business, research, and recruitment. The key takeaways regarding LinkedIn scraping and its future are as follows:

Growing Importance: Data scraping, especially from LinkedIn, is gaining popularity due to its significance in understanding market trends, making informed business decisions, and sourcing talent efficiently. It has become an essential tool for many professionals and organizations.

Automation: Automation is a significant trend in LinkedIn scraping. Businesses are increasingly using automated scraping tools to streamline data collection processes and free up time for other critical tasks. Automation is expected to continue evolving and becoming more accessible.

Business Impact: LinkedIn scraping has a significant impact on investment decisions and business strategies. By extracting data on startups' growth signals and market trends, it enables businesses to make informed choices, which, in turn, affect their bottom line positively.

Regulation and Compliance: As data scraping continues to grow, it is likely that there will be increased attention on data privacy and regulation. Scrappers need to stay updated with legal requirements and best practices to maintain the ethical and legal use of scraped data.

User-Friendly Tools: LinkedIn scraping tools are becoming more user-friendly and accessible, allowing individuals and organizations to harness the power of web scraping without extensive technical expertise. The future of LinkedIn scraping appears promising, with a focus on simplicity, efficiency, and responsible use. As technology evolves, scraping tools are expected to become even more sophisticated, offering greater insights into LinkedIn data and its applications. However, ethical considerations and compliance with data privacy regulations will remain essential to ensure the responsible use of scraped information.

[blueiscoool]

A photo of the body casts of two adults and two children who died in what's now called the house of the golden bracelet in Pompeii. A new DNA analysis shows that these four people are not genetically related to one another. (Image credit: Archeological Park of Pompeii).

DNA Analysis Rewrites The Stories of People Buried in Pompeii

An ancient-DNA analysis of victims in Pompeii who died in Mount Vesuvius' eruption reveals some unusual relations between the people who died together.

Ancient DNA taken from the Pompeii victims of Mount Vesuvius' eruption nearly 2,000 years ago reveals that some people's relationships were not what they seemed, according to a new study.

For instance, an adult who was wearing a golden bracelet and holding a child on their lap was long thought to be a mother with her child. But the new DNA analysis revealed that, in reality, the duo were "an unrelated adult male and child," study co-author David Reich, a professor of genetics at Harvard Medical School, said in a statement.

In another example, a couple who died in an embrace and were "thought to be sisters, or mother and daughter, were found to include at least one genetic male," Reich said. "These findings challenge traditional gender and familial assumptions."

In the study, published Thursday (Nov. 7) in the journal Current Biology, Reich and an international team of researchers looked at the genetics of five individuals who died during the A.D. 79 eruption that killed around 2,000 people.

When Mount Vesuvius erupted, it covered the surrounding area in a deadly layer of volcanic ash, pumice and pyroclastic flow, burying people alive and preserving the shapes of many bodies beneath the calcified layers of ash. The remains of the city were rediscovered only in the 1700s. In the following century, archaeologist Giuseppe Fiorelli perfected his plaster technique, in which he filled in the human-shaped holes left after the bodies had decomposed to create casts of the victims.

The casts of two people who died about 2,000 years ago in the house of the cryptoporticus in Pompeii. A new DNA analysis found that one individual was biologically male, but the sex of the other could not be determined. (Image credit: Archeological Park of Pompeii).

The casts allowed scholars to study the victims in their last moments and make hypotheses about their identities based on details such as their locations, positions and apparel. The problem with this approach, however, was that their interpretations were influenced by modern-day assumptions — for instance, that the four people at the house with the golden bracelet, which included the adult holding the child, were two parents with their children, when in reality none of them were genetically related, the researchers wrote in the study.

For their research, the team analyzed 14 casts and extracted DNA from fragmented skeletal remains in five of them. By analyzing this genetic material, the scientists determined the individuals' genetic relationships, sex and ancestry. The team concluded that the victims had a "diverse genomic background," primarily descending from recent eastern Mediterranean immigrants, per the statement, confirming the Roman Empire's multiethnic reality.

The cast of a person who died in the villa of the mysteries in Pompeii in A.D. 79. (Image credit: Archeological Park of Pompeii).

Our findings have significant implications for the interpretation of archaeological data and the understanding of ancient societies," study co-author Alissa Mittnik, an archaeogeneticist at Harvard Medical School and the Max Planck Institute for Evolutionary Anthropology in Germany, said in the statement. "They highlight the importance of integrating genetic data with archaeological and historical information to avoid misinterpretations based on modern assumptions."

It's possible that past misconceptions led to the "exploitation of the casts as vehicles for storytelling," meaning that curators may have manipulated the victims' "poses and relative positioning" for exhibits, the team wrote in the study.

Sex misassignment is "not uncommon" in archaeology, Carles Lalueza-Fox, a biologist at the Institute of Evolutionary Biology (CSIC-UPF) in Barcelona who specializes in the study of ancient DNA but was not involved with the study, said in an email.

"Of course we look at the past with the cultural eyes of the present and this view is sometimes distorted; for me the discovery of a man with a golden bracelet trying to save an unrelated child is more interesting and culturally complex than assuming it was a mother and her child," Lalueza-Fox said.

By Margherita Bassi.

[addysadness]

Mclaren consistently excuse themselves and their lack of strategy and sense as harbouring "Two number one drivers" and that due to this "papaya rules" are needed and this excuses their lack of backing for Lando last year.

The most ironic thing about this narrative they are pushing, is that that couldn't be further from the truth.

Piastri and Norris cannot be equal or both number one drivers because Mclaren refuses to treat Lando as one.

How is it equal that the car its not neutral between them? Andrea Stella admitted the car suits Piastri (and is quite obviously tailored around him), this means Lando is having to fight against a car that goes against how he extracts time.

Piastri's Strategist is also the Head of Strategy overall. The Conflict of interest is massive! Along with Mclaren Pitwall constantly changing Lando's strategy to appease Piastri (see Jeddah 25').

Piastri uses Lando's setups, he doesn't use his own therefore he already has a leg up. (This is often why after changing to Lando's setup and looking at Lando's data Piastri often suddenly improves).

They receive completely different atmospheric support and narratives around them both from within and outside of the team . Oscar is constantly praised and hailed by Mclaren management (especially Andrea Stella) yet Lando is often belitled emotionally manipulated and abused and subject from ableist comments and remarks...from his own team. They also categorically refuse to stand up or address the horrific online hate and abuse he has been subjected to - despite proving them can as they have for other leagues - possibly because its their own management and driver that is often contributing to this mindless hate by regurgitating ableist mentality comments again and again. And their most coddled driver's whole PR persona and public image being based on toxic masculinity and spite.

Even on things like social media, their are always graphics and post for Piastri's achievements and none for Lando's along with merch for Piastri's win in Miami (where Lando won his inaugrul grand prix) and none for Lando when he won the Monaco Grand Prix along with breaking a new track record. And instances like the Miami posts where a post with Lando blocked out by Piastri was uploaded, made their Twitter header and on Instagram Lando wasn't even tagged. Despite this Lando is often made to do a lot of PR heavy lifting (along with breaking his back in all other aspects for the team, (ironic due to the back pain this regs of cars has given him, and how porpoising came back recently... in 2025 after another of Mclaren's setup changes that make him drop behind his teamate) go to events that don't even get covered by Mclaren on Social media (despite a Mclaren photographer being there).

So despite Mclaren excusing their appalling treatment of the driver who has been with them since he was a teenager and been very important for over 7yrs, no Lando is not on equal footing to Piastri.

Mclaren's consistent pedalling of the Number 1 driver gimmick is also genuine as the Papaya family i.e complete and utter bullshit.

[dandelionsresilience]

Dandelion News - March 8-14

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1. Caribbean reef sharks rebound in Belize with shark fishers’ help

“Caribbean reef shark populations have rebounded beyond previous levels, more than tripling at both Turneffe and Lighthouse atolls[…. The recovery] arose from a remarkable synergy among shark fishers, marine scientists and management authorities[….]”

2. Landmark Ruling on Uncontacted Indigenous Peoples’ Rights Strikes at Oil Industry

“[T]he Ecuadorian government [must] ensure any future expansion or renewal of oil operations does not impact Indigenous peoples living in voluntary isolation. [… E]ffective measures must be adopted to prevent serious or irreversible damage, which in this case would be the contact of these isolated populations,” said the opinion[….]”

3. America's clean-energy industry is growing despite Trump's attacks. At least for now

“The buildout of big solar and battery plants is expected to hit an all-time high in 2025, accounting for 81% of new power generation[….] The industry overall has boomed thanks to falling technology costs, federal tax incentives and state renewable-energy mandates.”

4. Study says endangered Asian elephant population in Cambodia is more robust than previously thought

“A genetic study of Asian elephants […] reveals a larger and more robust population than previously thought, raising hopes the endangered species could slowly recover. […] “With sufficient suitable habitat remaining in the region, the population has the potential to grow if properly protected,” the report concludes.”

5. Scientists are engineering a sense of touch for people who are paralyzed

“[Engineers are] testing a system that can restore both movement and sensation in a paralyzed hand. [… A]fter more than a year of therapy and spinal stimulation, [… h]is increased strength and mobility allow him to do things like pet his dog. And when he does, he says, "I can feel a little bit of the fur."“

6. Florida is now a solar superpower. Here’s how it happened.

“In a first, Florida vaulted past California last year in terms of new utility-scale solar capacity plugged into its grid. It built 3 gigawatts of large-scale solar in 2024, making it second only to Texas. And in the residential solar sector, Florida continued its longtime leadership streak.”

7. Rare frog rediscovered after 130 years

“The researchers discovered two populations of the frog[….] "The rediscovery of A. vittatus allowed us to obtain, more than a century after its description, the first biological and ecological data on the species.” [… S]hedding light on where and how they live is the first step in protecting them.”

8. Community composting programs show promise in reducing household food waste

“The program [increased awareness and reduced household waste, and] also addressed common barriers to home composting, including pest concerns and technical challenges that had previously discouraged participants from composting independently.”

9. Pioneering Australian company marks new milestone on “mission” to upcycle end-of-life solar panels

“[…] SolarCrete – a pre-mixed concrete made using glass recovered from used solar panels – will form part of the feasibility study[….] A second stage would then focus on the extraction of high value materials[…] for re-use in PV and battery grade silicon, [… and] electrical appliances[….]”

10. Beavers Just Saved The Czech Government Big Bucks

“The aim was to build a dam to prevent sediment and acidic water from two nearby ponds from spilling over, but the project was delayed for years due to negotiations over land use[….] Not only did the industrious rodents complete the work faster than the humans had intended, they also doubled the size of the wetland area that was initially planned.”

March 1-7 news here | (all credit for images and written material can be found at the source linked; I don’t claim credit for anything but curating.)

[wrizard]

wrizard's super basic guide to y-chromosome-based identification!!

for those interested, on this fitzcovery day:

a dear friend asked me to explain why i felt completely insane about the phrase "genetic distance of one" and, as usual, i got overexcited and wrote an entire thing about it complete with goofy images! it's on twt HERE, but i figured it would also be nice to pop it up here also. SO. with the caveat that it has been many years since my last bio class and this is VERY OVERSIMPLIFIED. here's

Human DNA is grouped into chromosomes. We generally have TWO of each chromosome: 22 pairs (numbered 1-22), plus one pair of sex chromosome (typically either two X-chromosomes (XX), or one X-chromosome and one Y-chromosome (XY)). That's 23 pairs, or 46 chromosomes, in total.

When producing sex cells, matching chromosome pairs will RECOMBINE (swap bits of information) - eg. one Chromosome 4 will remix itself with the other Chromosome 4, making TWO UNIQUE C4s. When the cell splits into two sex cells, each sex cell will carry ONE unique C4.

That's sexual reproduction! Every new offspring is genetically unique - new combinations of traits pop up quickly, and if they improve reproductive fitness, can be passed on to future offspring. This allows for rapid adaptation and changes in a species over time.

But what about Y-chromosomes, which don’t have pairs? They can't recombine in the way paired chromosomes can - which means Y-chromosomes pretty much only change via mutation (errors in copying DNA). Mutation is VERY slow, especially compared to recombination.

This means that when an XY parent passes down their Y-chromosome to a child, chances are high that chromosome will have few, if any, changes – as opposed to X-chromosomes, which recombine in both XX parents and children, shuffling genetic information all over the place.

Due to this slow rate of change, Y-chromosomes can be more easily tracked through the generations than other human chromosomes. A Y-chromosome might be passed down nearly unchanged for hundreds of years from genetic father to genetic son.

GENETIC DISTANCE refers to the measurement of difference between two sets of DNA. The lower the genetic distance, the more closely related the two samples are likely to be. A genetic distance of 1 means the samples are close to identical.

Because we know how slowly Y-chromosomes change over time, we know that if the Y-chromosomes of two people have a low genetic distance, this implies that those people are paternally related – even if the two people live/lived hundreds of years apart.

In the case of Captain James Fitzjames, genetic data was extracted from a set of unidentified remains (a molar from a disarticulated mandible). 17 genetic markers from the molar’s Y-chromosome were compared to the Y-chromosome of a confirmed paternal relative of the Captain.

Those 17 markers were the same in both samples, giving the two Y-chromosomes a genetic distance of one – meaning, with the genetic information available, the living relative and the unidentified decedent are more than 2000 TIMES more likely to be paternally related than not.

EDIT: DOIP I MISREAD THE CHART 16 of 17 match, not all 17!!

Along with all the information we have from the historical record, the context of the remains, and this new comparative genetic analysis, we can safely conclude that this particular set of remains belong to Captain Fitzjames.

160 years isn't long in the grand scheme. Every identified set of remains is another reminder that these were people, not just a distant curiosity. It's humbling to remember not just that we have identified Cpt. Fitzjames, but that still, today, we have a genetic distance of one.

Photos and Y-chromosome comparison chart taken from Stephen, Fratpietro, and Park's paper "Identification of a senior officer from Sir John Franklin’s Northwest Passage expedition" from the Journal of Archaeological Science: https://www.sciencedirect.com/science/article/pii/S2352409X24003766?via%3Dihub

hope my nonsense is helpful and/or informative and/or at least made you smile!! if you like this sort of thing :) cheers doves