MIT engineers create living circuit boards built by printing and connecting bacterial transistors We apply principles from pass transistor logic (PTL) to design bacteria that can be easily reconfigured to perform computations on a solid surface. Strains of Pantoea agglomerans were built to encode two transistors (N-type and P-type) whose inputs and outputs are small molecules. They are connected by three relay strains that convert molecular diffusion to unidirectional flow. To build circuits, an acoustic liquid handler prints patterns of these five strains on a surface. By changing the pattern, not requiring any genetic changes, different operations are implemented, including multi-input multioutput logic, demultiplexor, half-adder and full-adder. This work demonstrates that only five cell types, each encoding a simple operation, can be scaled to create complex computational operations. nature.com/articles/s41589-0… The research was funded, in part, by the U.S. Defense Advanced Research Projects Agency and by the U.S. Intelligence Advanced Research Projects Activity. The researchers took a different approach: Instead of building an entire circuit into one cell, they designed cells that could act as transistors. These transistors can then be combined in different ways to create a variety of circuits. Using these cells, they made two types of transistors that can be switched on or off by a molecule called OC-6. One of the transistors is switched on by this input, and the other is switched off. Each transistor also detects the presence of a target molecule, in this case, OC-12. Depending on whether that molecule is present, and whether the switch is active, the transistors produce an output molecule known as OHC-14. The researchers also used three strains of Pantoea agglomerans to create relays, which translate the OHC-14 signal into an output that can be fed into another transistor. Using these relay strains, the researchers can “wire” the transistors together, just like an electronic circuit board. For example, they could create a bidirectional switch with two transistors that sense OC-12, and then send that information to different relay strains based on a switch input, ultimately feeding into other transistors that further process the signal. MIT researchers created their circuits by printing colonies of bacteria onto plates containing agar, a growth medium. Each colony is printed about 5 millimeters from the nearest one. This allows the signals to travel only to the nearest colony, which then relays them to the next one, so information flows only in one direction. MIT researchers demonstrated a transistor that can perform several types of logic operations depending on its location in the circuit layout, including “multi-input,” “or,” and “imply” gates. They also combined the transistors to create more complex circuits that can add up two signals, process more signals simultaneously, or function as a demultiplexer — a circuit that takes one incoming signal and sends it to one of several possible destinations, depending on a control signal. The largest of these circuits, which adds two inputs together, contains 24 bacterial colonies wired together. Circuits made from these cells take about eight hours to perform each calculation, much longer than a computer circuit. But, for biological applications, that is a reasonable amount of time, the researchers say. news.mit.edu/2026/mit-engine…

HumanOS A wearable neural interface Apple is reportedly developing a concept known as “HumanOS” — a futuristic chip designed to blur the line between the human body and digital technology. The idea suggests that instead of using a physical device, a person’s hand itself could act as an interface, displaying iPhone-like visuals and responding to natural gestures. youtu.be/b76rjrpEnTA?si=8zps… Lines up perfectly with Human Blockchain/HumanOS Human Blockchain repository github.com/humansdotai HumanID The Foundation for a Human Internet, a New York 501c3 nonprofit, manages the humanID identity, an open source authentication alternative to SSOs & CAPTCHAs. human-id.org/ HumanID whitepaper github.com/human-internet/hu… HumanID repository github.com/human-internet JSON5 — JSON for Humans json5.org/ The JSON5 Data Interchange Format The JSON5 Data Interchange Format is a proposed extension to JSON that aims to make it easier for humans to write and maintain by hand. It does this by adding some minimal syntax features directly from ECMAScript 5.1. spec.json5.org/ ECMAScript 5.1. ECMAScript is an object-oriented programming language for performing computations and manipulating computational objects within a host environment. ECMAScript as defined here is not intended to be computationally self-sufficient; indeed, there are no provisions in this specification for input of external data or output of computed results. It is expected that the computational environment of an ECMAScript program will provide not only the objects and other facilities described in this specification but also certain environment-specific host objects, whose description and behaviour are beyond the scope of this specification except to indicate that they may provide certain properties that can be accessed and certain functions that can be called from an ECMAScript program. 262.ecma-international.org/5… LAMBDA A benchmark consisting of language-conditioned long-horizon (room-to-room and floor-to-floor navigation) MoMa tasks that are paired with human-collected expert demonstrations from both simulated and real-world environments. lambdabenchmark.github.io/ 📄 λ: A Benchmark for Data-Efficiency in Long-Horizon Indoor Mobile Manipulation Robotics 🔗: arxiv.org/html/2412.05313v6 PDF: arxiv.org/pdf/2412.05313 LANMP-Dataset-HDF5 The Language, Navigation, Manipulation, Perception (LaNMP) dataset LaNMP comprises 574 trajectories across eight simulated and real-world environments for long-horizon room-to-room pick-and-place tasks specified by natural language. Dataset Overview Data Subject(s) Data about places and objects Synthetically generated data Data about systems or products and their behaviors Others (Language data provided by humans, robot movement and visual data) dropbox.com/scl/fo/c1q9s420p… GHOST (Ghost Human Operator for Spot Teleoperation) Virtual Reality Project GHOST serves as an ideal platform for generating expert data for learning from demonstration (LfD). In GHOST, teleoperators can perform tasks while receiving the same visual data that can later be used for model training, allowing seamless recording of expert demonstrations in both virtual and physical environments. h2r.github.io/GHOST/ HUGS: Human Gaussian Splats represents an animatable human together with the scene using 3D Gaussian Splatting (3DGS). Our method takes only a monocular video with a small number of (50-100) frames, and it automatically learns to disentangle the static scene and a fully animatable human avatar within 30 minutes. We utilize the SMPL body model to initialize the human Gaussians. To capture details that are not modeled by SMPL (e.g. cloth, hairs), we allow the 3D Gaussians to deviate from the human body model. arxiv.org/pdf/2311.17910

Virtual Human Twins initiative for health and care digital-strategy.ec.europa.e…

IoT cloud-based environments for the harvesting of one's biological metadata. The human being has been turned into an application. They are playing “The Sims” out here no pun intended! biothings.io humanode.io Open Stack, a technology that allows the exchange of information with IoT devices regardless of their location, hardware, software and network configuration. Open Stack works only on Linux operating systems and has been released under the Apache license, which means that it is an open source project. SmartMe engineers are able to design cloud environments that provide extreme versatility in cross-platform use and flexibility in data access (time-series and NoSql databases) and control of individual system nodes. smartme.io/en/services/#serv… 📃 From Sensors to Data Intelligence: Leveraging IoT, Cloud, and Edge Computing with AI At the heart of IoT are sensor networks, which form the foundation for real-time data acquisition. These networks consist of interconnected devices that monitor environmental, physical, and even biological parameters, producing a continuous stream of valuable data. With the proliferation of connected devices, IoT has enabled many applications, from smart homes and healthcare systems to industrial automation and urban management. One of the most significant advancements in IoT has been the evolution of smart sensors. These devices are no longer passive data collectors; they now have built-in processing capabilities, often directly incorporating AI or machine learning (ML) algorithms on the sensor. This on-device processing reduces the need for extensive data transmission to remote servers, thus decreasing latency and conserving bandwidth. Another breakthrough in IoT is the development of sophisticated communication protocols that ensure efficient data transmission. As IoT networks grow, the demand for faster and more reliable communication has led to adopting technologies such as 5G, which provides ultra-low latency and high bandwidth capabilities. These advancements are crucial for applications like autonomous vehicles, where real-time communication between vehicles, infrastructure, and the cloud is essential for safety and decision-making. Additionally, IoT systems increasingly rely on specialized networks like Low-Power Wide-Area Networks (LPWANs), which are designed for long-range communications with minimal energy consumption. LPWAN technologies like LoRaWAN have become vital in applications such as environmental monitoring, where sensors can operate over vast distances while requiring little power. Energy efficiency is another critical consideration for IoT and sensor networks, particularly for devices deployed in remote or difficult-to-access locations. The development of energy-harvesting technologies (such as solar-powered sensors or vibration-based energy systems) has extended the operational lifespan of these devices, reducing maintenance costs and the need for battery replacements. The integration of IoT, cloud computing, edge computing, and AI offers a robust pathway to achieve this transformation, enabling real-time decision-making and predictive insights. This paper explores innovative approaches to combine these technologies, emphasizing their role in enabling real-time decision-making, predictive analytics, and low-latency data processing. This work analyzes several integration approaches among IoT, cloud/edge computing, and AI through examples and applications, highlighting challenges and approaches to seamlessly integrate these techniques to achieve pervasive environmental intelligence. mdpi.com/1424-8220/25/6/1763 Oparaca (Object Paradigm on Serverless Cloud Abstraction) Object-as-a-Service (OaaS) paradigm github.com/hpcclab/OaaS Functions Development Guide github.com/hpcclab/OaaS/blob… OaaS: Single Node Deployment This guide is for deploying the Oparac system on the local Kubernetes (ex. kind, k3d). github.com/hpcclab/OaaS/tree…