Lifeboat Foundation: Safeguarding Humanity
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Category: food

Imagine a large city recovering from a devastating hurricane. Roads are flooded, the power is down, and local authorities are overwhelmed. Emergency responders are doing their best, but the chaos is massive.

AI-controlled drones survey the damage from above, while process and data from sensors on the ground and air to identify which neighborhoods are most vulnerable.

Meanwhile, AI-equipped robots are deployed to deliver food, water and into areas that human responders can’t reach. Emergency teams, guided and coordinated by AI and the insights it produces, are able to prioritize their efforts, sending rescue squads where they’re needed most.

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Newly discovered brain cells count each bite before sending the order to cease eating a meal. Columbia scientists have found specialized neurons in the brains of mice that order the animals to stop eating.

Though many feeding circuits in the brain are known to play a role in monitoring food intake, the neurons in those circuits do not make the final decision to cease eating a meal.

The neurons identified by the Columbia scientists, a new element of these circuits, are located in the brainstem, the oldest part of the vertebrate brain. Their discovery could lead to new treatments for obesity.

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A viral video featuring a woman who credits sour sop leaves, apricot seeds, and dietary changes for overcoming stage 4 metastatic breast cancer has ignited widespread discussion on social media.

Originally shared by Super Food Mood on Instagram, the video has amassed significant traction, drawing both support and skepticism.

A Survivor’s Testimony

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Cow D lived on a dairy farm in New Zealand. The animal looked like the typical black-and-white cow farmers raise for milk, except for one thing: Researchers had outfitted Cow D with an artificial fistula—a hole offering them a way to reach the microbes inhabiting the animal’s bathtub-size stomach. But it’s what happened next that offers a porthole into the global debate over the use of genetic data.

In the spring of 2009, Samantha Noel, then a doctoral researcher at Massey University in Palmerston North, New Zealand, reached into Cow D’s rumen and plucked out a strain of Lachnospiraceae bacterium, later dubbed ND2006. Another team of geneticists sequenced the microbe’s complete set of genes, or genome, and uploaded the information, which was then shared with GenBank, a public database run by the US National Institutes of Health. If genes are the book of life, then this process was like adding a digital copy to an online library. In policy circles, these lines of code go by another name: digital sequence information, or DSI.

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UT Austin researchers have developed a biodegradable, biomass-based hydrogel that efficiently extracts drinkable water from the air, offering a scalable, sustainable solution for water access in off-grid communities, emergency relief, and agriculture.

Discarded food scraps, stray branches, seashells, and other natural materials serve as key ingredients in a new system developed by researchers at The University of Texas at Austin that can extract drinkable water from thin air.

This innovative system, called “molecularly functionalized biomass hydrogels,” transforms a wide range of natural products into sorbents—materials that absorb liquids. By pairing these sorbents with mild heat, the researchers can extract gallons of drinkable water from the atmosphere, even in arid conditions.

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Summary: Concerns over potential negative impacts of AI have dominated headlines, particularly regarding its threat to employment. However, a closer examination reveals AI’s immense potential to revolutionize equal and high quality access to necessities such as education and healthcare, particularly in regions with limited access to resources. From India’s agricultural advancements to Kenya’s educational support, AI initiatives are already transforming lives and addressing societal needs.

The latest technology panic is over artificial intelligence (AI). The media is focused on the negatives of AI, making many assumptions about how AI will doom us all. One concern is that AI tools will replace workers and cause mass unemployment. This is likely overblown—although some jobs will be lost to AI, if history is any guide, new jobs will be created. Furthermore, AI’s ability to replace skilled labor is also one of its greatest potential benefits.

Think of all the regions of the world where children lack access to education, where schoolteachers are scarce and opportunities for adult learning are scant.

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Pomelo is a large citrus fruit commonly grown in Southeast and East Asia. It has a very thick peel, which is typically discarded, resulting in a considerable amount of food waste. In a new study published in ACS Applied Materials & Interfaces, University of Illinois Urbana-Champaign researchers explore ways to utilize waste pomelo-peel biomass to develop tools that can power small electric devices and monitor biomechanical motions.

There are two main parts of the pomelo peel—a thin outer layer and a thick, white inner layer. The white part is soft and feels like a sponge when you push on it. Some people have used pomelo peels to extract compounds for essential oils or pectin, but we wanted to take advantage of the natural porous, spongy structure of the peel.

If we can upcycle the peel to higher-value products instead of simply throwing it away, we can not only reduce waste from pomelo production, consumption, and juice making, but also create more value from food and agricultural waste, said study co-author Yi-Cheng Wang, an assistant professor in the Department of Food Science and Human Nutrition, part of the College of Agricultural, Consumer and Environmental Sciences at Illinois.

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Satellite-based optical remote sensing from missions such as ESA’s Sentinel-2 (S2) have emerged as valuable tools for continuously monitoring the Earth’s surface, thus making them particularly useful for quantifying key cropland traits in the context of sustainable agriculture [1]. Upcoming operational imaging spectroscopy satellite missions will have an improved capability to routinely acquire spectral data over vast cultivated regions, thereby providing an entire suite of products for agricultural system management [2]. The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) [3] will complement the multispectral Copernicus S2 mission, thus providing enhanced services for sustainable agriculture [4, 5]. To use satellite spectral data for quantifying vegetation traits, it is crucial to mitigate the absorption and scattering effects caused by molecules and aerosols in the atmosphere from the measured satellite data. This data processing step, known as atmospheric correction, converts top-of-atmosphere (TOA) radiance data into bottom-of-atmosphere (BOA) reflectance, and it is one of the most challenging satellite data processing steps e.g., [6, 7, 8]. Atmospheric correction relies on the inversion of an atmospheric radiative transfer model (RTM) leading to the obtaining of surface reflectance, typically through the interpolation of large precomputed lookup tables (LUTs) [9, 10]. The LUT interpolation errors, the intrinsic uncertainties from the atmospheric RTMs, and the ill posedness of the inversion of atmospheric characteristics generate uncertainties in atmospheric correction [11]. Also, usually topographic, adjacency, and bidirectional surface reflectance corrections are applied sequentially in processing chains, which can potentially accumulate errors in the BOA reflectance data [6]. Thus, despite its importance, the inversion of surface reflectance data unavoidably introduces uncertainties that can affect downstream analyses and impact the accuracy and reliability of subsequent products and algorithms, such as vegetation trait retrieval [12]. To put it another way, owing to the critical role of atmospheric correction in remote sensing, the accuracy of vegetation trait retrievals is prone to uncertainty when atmospheric correction is not properly performed [13].

Although advanced atmospheric correction schemes became an integral part of the operational processing of satellite missions e.g., [9,14,15], standardised exhaustive atmospheric correction schemes in drone, airborne, or scientific satellite missions remain less prevalent e.g., [16,17]. The complexity of atmospheric correction further increases when moving from multispectral to hyperspectral data, where rigorous atmospheric correction needs to be applied to hundreds of narrow contiguous spectral bands e.g., [6,8,18]. For this reason, and to bypass these challenges, several studies have instead proposed to infer vegetation traits directly from radiance data at the top of the atmosphere [12,19,20,21,22,23,24,25,26].

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Humanity came close to extinction 800,000 years ago. Only 1,280 of our ancestors survived.

A recent study published in Science suggests that a catastrophic “ancestral bottleneck” reduced the global population to just 1,280 breeding individuals, wiping out 98.7% of the early human lineage.

This population crash, lasting about 117,000 years, likely resulted from extreme climate shifts, prolonged droughts, and dwindling food sources.

Using a groundbreaking genetic analysis method called FitCoal, researchers analyzed modern human genomes to trace this dramatic decline, potentially explaining a gap in the African and Eurasian fossil record.

Despite the near-extinction, this bottleneck may have played a crucial role in shaping modern humans. Scientists believe it contributed to a key evolutionary event—chromosome fusion—which may have set Homo sapiens apart from earlier hominin species, including Neanderthals and Denisovans. The study raises intriguing questions about how this small population survived, possibly through early fire use and adaptive intelligence. Understanding this ancient crisis helps scientists piece together the story of human evolution and the resilience that allowed our species to thrive against all odds.

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