From specialized motors to the use of machine-learning algorithms, Turkey's billion-dollar hair-transplant industry is the result of a constant process of innovation. The astounding growth of the hair-transplant industry in Turkey is not just a medical tourism success story; it's also a tale of "hacked" medical equipment and algorithmic craftsmanship. From a biological and evolutionary perspective, human hair is often viewed as an unremarkable mass of keratin that still plays some important functions--protecting our scalps from the sun's harmful ultraviolet rays and regulating our body temperatures--but, for the most part, is no longer essential to our survival. Yet, since ancient times, our subconscious perceptions of whether another person is healthy, young, or fertile have been based on visual cues such as skin radiance, the integrity of teeth, and hair density. Deep within our perceptions, hair has become one of the most powerful representations of our identity and self-confidence. Today, the global hair-transplant and restoration industry, which has evolved around this deep psychological and evolutionary need, has grown into a massive, multibillion-dollar industry. Various research firms have estimated the total size of the global hair-transplant market as sitting somewhere between $7.33 billion and $11.61 billion in 2024. And those figures don't include the underground economy.

World Health Organization Director-General Tedros Adhanom Ghebreyesus washes his hands as he arrives at Bunia National Airport in Congo on May 30. BUNIA, Congo - The World Health Organization chief traveled on Saturday to the Congolese province hardest hit by an Ebola outbreak, urging residents to seek treatment and practice safe burials as officials scramble to contain the fatal disease. The outbreak -- the 17th in Congo and the third-largest since Ebola was discovered half a century ago -- is outpacing the global response, something WHO Director-General Tedros Adhanom Ghebreyesus acknowledged this week before traveling to Kinshasa on Thursday. His visit came as Brazil said on Saturday it was investigating a suspected Ebola case in Sao Paulo state involving a man who recently visited Congo. Authorities said the patient was in isolation at a specialist hospital. After meeting Prime Minister Judith Suminwa Tuluka on Friday, Tedros flew on Saturday to Bunia, capital of Ituri province, where the first cases were confirmed earlier this month.

Plus: Anthropic is now valued higher than OpenAI. How a new extraction process could unlock the world's lithium A new method for extracting lithium could cut costs and emissions from one of the world's most important materials for EVs and energy storage. The technique uses a weak acid to dissolve silicate minerals. That frees not only the lithium but also other useful materials, including alumina and silica. "At scale, we believe this will be the lowest-cost way of sourcing lithium in the world," says Yet-Ming Chiang, an MIT professor who co-authored a study of the process published yesterday in . Startup Rock Zero is already working to commercialize the research.

Pigeons use their livers to sense Earth's magnetic field Special immune cells may be one piece of their internal compass. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The homing pigeons in this study were trained to fly 12.4 miles back to their aviary. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

But don't throw away the bug spray just yet. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. DEET has helped repel mosquitoes for 80 years. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

The Centers for Research in Emerging Infectious Diseases were launched during the Covid-19 pandemic. The group lost its funding under Trump in part due to conspiracy theories. As the world struggles to contain the rapidly growing Ebola outbreak in the Democratic Republic of Congo's Ituri province, a vital network of research centers has been unable to help on the ground. The reason: The Trump administration slashed its funding last year, in part due to conspiracy theories about the origins of Covid-19. Established in 2020 by the National Institutes of Health, the Centers for Research in Emerging Infectious Diseases (CREID) Network was conducting research into viruses that emerge from wildlife and spill over to people, including the family of viruses that Ebola belongs to.

Predicting the effect of interventions with many possible variations, e.g., therapeutic content that affects mental health outcomes or an earnings call transcript that drives movement in share price, is useful across several domains. However, classical causal estimators tend to assume that all possible interventions are observed, which is infeasible when interventions vary widely, for instance, in the space of all text strings. We adapt a well-known approach of recasting causal inference as a learning problem, to address high-dimensional treatment spaces. Specifically, under standard assumptions like no unobserved confounding, we show that causal error decomposes into a series of moment-balancing errors of increasing order, and design objectives that directly improve causal estimation. We also show how to project the effect of a high-dimensional treatment onto lower-dimensional treatment attributes, which allows a single model to answer several causal questions without additional attribute-specific training. We empirically evaluate our estimators in settings with high-dimensional continuous, discrete, and text treatments, the last of which used a semi-synthetic dataset of Amazon Reviews. Our experiments demonstrate the benefit of higher-order balance error optimization and competitive performance of projected causal estimates with attribute-specific estimators.

Reinforcement learning algorithms are generally designed to maximize the expected return across a population. However, a policy that is optimal on average may be suboptimal for certain individuals, leading to potential safety concerns. To address this, we first formalize the notion of individual harm from a counterfactual perspective and define harm as the event in which a chosen action results in a strictly worse outcome than a baseline alternative. We then propose a general two-stage procedure for learning policies that maximize the expected return while accounting for individual harm. We further establish the finite-sample properties of the learned policy, derive an upper bound on its sub-optimality gap, and show that the harm rate remains well-controlled. Numerical experiments on both simulated and real-world datasets demonstrate the effectiveness of the proposed approach.

Bayesian optimal experimental design (BOED) selects experiments to maximize information gain about model parameters. However, in decision-critical settings, reducing parameter uncertainty does not necessarily improve downstream decisions, as only specific parameter directions relevant to the objective truly matter. We propose GoBOED, a goal-driven BOED framework that directly optimizes experimental designs for a specified decision-making objective. GoBOED combines an amortized variational posterior surrogate with a differentiable convex decision layer, enabling gradient-based design optimization that is fully decision-focused. We theoretically show that GoBOED gradients are insensitive to parameter directions irrelevant to the decision objective, providing a formal justification for why goal-driven design achieves equivalent decision quality over a wider set of experimental designs than information-gain maximization. Empirically, across source localization, epidemic management, and pharmacokinetic control, GoBOED identifies designs that better align with downstream decision objectives and reveals that near-optimal design windows are substantially wider than those predicted by goal-agnostic BOED approaches.

These Robots Are Making Meals for a Nonprofit in San Francisco's Tenderloin A nonprofit in the city's most troubled district has turned to robotic meal prep tech to make up for a dearth of human volunteers. Project Open Hand, a nonprofit founded in 1985 by local grandmother and HIV-awareness advocate Ruth Brinker, prepares and packages meals to meet the diverse nutritional requirements of people who need them. The effort began in response to the AIDS crisis, but the nonprofit has since expanded the meals it makes for people with conditions such as heart disease, diabetes, or chronic kidney disease. But it takes many people to make these meals, and Project Open Hand has struggled to entice volunteers to help fill the meal kits. The organization is housed in a four-story building in San Francisco's Tenderloin district.