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"With each interaction, Alexa is training to hear better, to interpret more precisely, to trigger actions that map to the user's commands more accurately, and to build a more complete model of their preferences, habits and desires. What is required to make this possible? Put simply: each small moment of convenience - be it answering a question, turning on a light, or playing a song - requires a vast planetary network, fueled by the extraction of non-renewable materials, labor, and data. The scale of resources required is many magnitudes greater than the energy and labor it would take a human to operate a household appliance or flick a switch. A full accounting for these costs is almost impossible, but it is increasingly important that we grasp the scale and scope if we are to understand and govern the technical infrastructures that thread through our lives. III The Salar, the world's largest flat surface, is located in southwest Bolivia at an altitude of 3,656 meters above sea level. It is a high plateau, covered by a few meters of salt crust which are exceptionally rich in lithium, containing 50% to 70% of the world's lithium reserves. 4 The Salar, alongside the neighboring Atacama regions in Chile and Argentina, are major sites for lithium extraction. This soft, silvery metal is currently used to power mobile connected devices, as a crucial material used for the production of lithium-Ion batteries. It is known as 'grey gold.' Smartphone batteries, for example, usually have less than eight grams of this material. 5 Each Tesla car needs approximately seven kilograms of lithium for its battery pack. 6 All these batteries have a limited lifespan, and once consumed they are thrown away as waste. Amazon reminds users that they cannot open up and repair their Echo, because this will void the warranty. The Amazon Echo is wall-powered, and also has a mobile battery base. This also has a limited lifespan and then must be thrown away as waste. According to the Ay

A sweeping effort to map a hospital's microorganisms has found that infectious pathogens hide in a place that's all about cleaning: the sink. Niranjan Nagarajan at the Genome Institute of Singapore and his colleagues sampled bacteria from bed rails, sinks and other sites in a Singapore hospital. Microbes that tend to grow in slimy 'biofilms' and cause hospital-acquired infections were prevalent on sink traps and faucet aerators, whereas skin-dwelling bacteria were abundant on objects, such as door knobs and bed rails, that are often touched. Frequently touched sites harboured multidrug-resistant microbes such as methicillin-resistant Staphylococcus aureus, which might persist in the hospital environment for more than eight years, the team suggested.

Analysis by WWF found that just 29 sites across South America, Africa and South East Asia were responsible for more than half of the global forest loss.

Analysis by WWF found that just 29 sites across South America, Africa and South East Asia were responsible for more than half of the global forest loss.

Land use change-for example, the conversion of natural habitats to agricultural or urban ecosystems-is widely recognized to influence the risk and emergence of zoonotic disease in humans1,2. However, whether such changes in risk are underpinned by predictable ecological changes remains unclear. It has been suggested that habitat disturbance might cause predictable changes in the local diversity and taxonomic composition of potential reservoir hosts, owing to systematic, trait-mediated differences in species resilience to human pressures3,4. Here we analyse 6,801 ecological assemblages and 376 host species worldwide, controlling for research effort, and show that land use has global and systematic effects on local zoonotic host communities. Known wildlife hosts of human-shared pathogens and parasites overall comprise a greater proportion of local species richness (18-72% higher) and total abundance (21-144% higher) in sites under substantial human use (secondary, agricultural and urban ecosystems) compared with nearby undisturbed habitats. The magnitude of this effect varies taxonomically and is strongest for rodent, bat and passerine bird zoonotic host species, which may be one factor that underpins the global importance of these taxa as zoonotic reservoirs. We further show that mammal species that harbour more pathogens overall (either human-shared or non-human-shared) are more likely to occur in human-managed ecosystems, suggesting that these trends may be mediated by ecological or life-history traits that influence both host status and tolerance to human disturbance5,6. Our results suggest that global changes in the mode and the intensity of land use are creating expanding hazardous interfaces between people, livestock and wildlife reservoirs of zoonotic disease.

In the flagship article, The ENCODE Project Consortium et al.5 provide a bird's-eye view of the updated encyclopedia, which contains newly added data sets from 6,000 experiments, performed on around 1,300 samples. By integrating these data sets, the consortium has created an online registry of candidate CREs. Most are classified as promoters or enhancers - CREs respectively located at or some distance from the genomic site at which transcription of a gene begins. The consortium tracked the activity of each candidate CRE, along with the proteins that bind to it in many different samples from various tissues. They used chromatin-looping data to link enhancers to genes that they might regulate. This online registry marks a true milestone, turning an overwhelming amount of genomic information into a searchable, filterable and retrievable encyclopedia of DNA elements, which is freely accessible at https://screen.encodeproject.org.

Though it may not be common knowledge, AI systems are currently being used in a wide variety of commercial applications, including article selection on news and social media sites, which movies get made,and maps that appear on our phones-AI systems have become trusted tools by big business. But their use has not always been without controversy. In recent years, researchers have found that AI apps used to approve mortgage and other loan applications are biased, for example, in favor of white males. This, researchers found, was because the dataset used to train the system mostly comprised white male profiles. In this new effort, the researchers wondered if the same might be true for AI systems used to assist doctors in diagnosing patients.