Lifeboat Foundation

It’s estimated that almost half of the world’s population — about 3.7 billion people under the age of 50 — are infected with (HSV-1), which can cause oral herpes. About half a billion people between the ages of 15 and 49 are infected with herpes simplex virus-2 (HSV-2), the cause of genital herpes. There are therapeutics that can eliminate some symptoms of herpes, like blisters, but there is no cure for the infection, and those who are infected can spread the virus to others. Studies have suggested that HSV-1 may increase the risk of dementia, and HSV-2 raises the risk of HIV infection.

Scientists have now developed a gene therapy that can eliminate as much as 90 percent of oral herpes and 97 percent of genital herpes infections in pre-clinical mouse models. The gene therapy also reduced the level of virus that was released from an individual in a mouse model of the infection. These reductions took about one month to be completed, and more of the virus seemed to be eliminated over time. The work has been reported in Nature Communications.

How thinking machines implement one of the most important functions of cognition.

Nanoscale materials offer remarkable chemical and physical properties that transform theoretical applications, like single-molecule sensing and minimally invasive photothermal therapy, into practical realities.

The unparalleled features of nanoparticles make them promising for various research and industrial uses. However, effectively using these materials is challenging due to the absence of a rapid and consistent method to transfer a uniform monolayer of nanoparticles, a crucial step in device manufacturing.

One potential solution to this challenge lies in electrostatic assembly processes, where oppositely charged nanoparticles adhere to a surface, forming a monolayer that repels other similarly charged particles from attaching further. While effective, this process is often slow. Nature provides an innovative model to address this limitation through underwater adhesion strategies, which have evolved to circumvent similar problems.

Scaling back treatment for three kinds of cancer can make life easier for patients without compromising outcomes, doctors reported at the world’s largest cancer conference.

It’s part of a long-term trend toward studying whether doing less — less surgery, less chemotherapy or less radiation — can help patients live longer and feel better. The latest studies involved ovarian and esophageal cancer and Hodgkin lymphoma.

Thirty years ago, cancer research was about doing more, not less. In one sobering example, women with advanced breast cancer were pushed to the brink of death with massive doses of chemotherapy and bone marrow transplants. The approach didn’t work any better than chemotherapy and patients suffered.

Industry and academia in Japan and the United States are collaborating on research to pioneer quantum-centric supercomputing.

Learn the basics of Convolutional Neural Networks (CNNs) in this beginner-friendly guide. Discover how they work, their applications in image recognition, and how they’re changing the world of AI.

Conductive aerogels have gained significant research interests due to their ultralight characteristics, adjustable mechanical properties, and outstanding electrical performance^1,2,3,4,5,6. These attributes make them desirable for a range of applications, spanning from pressure sensors^7,8,9,10 to electromagnetic interference shielding^11,12,13, thermal insulation^14,15,16, and wearable heaters^17,18,19. Conventional methods for the fabrication of conductive aerogels involve the preparation of aqueous mixtures of various building blocks, followed by a freeze-drying process^20,21,22,23. Key building blocks include conductive nanomaterials like carbon nanotubes, graphene, Ti₃C₂T_x MXene nanosheets^{24,25,26,27,28,29,30}, functional fillers like cellulose nanofibers (CNFs), silk nanofibrils, and chitosan^{29,31,32,33,34}, polymeric binders like gelatin^25,26, and crosslinking agents that include glutaraldehyde (GA) and metal ions^30,35,36,37. By adjusting the proportions of these building blocks, one can fine-tune the end properties of the conductive aerogels, such as electrical conductivities and compression resilience^38,39,40,41. However, the correlations between compositions, structures, and properties within conductive aerogels are complex and remain largely unexplored^{42,43,44,45,46,47}. Therefore, to produce a conductive aerogel with user-designated mechanical and electrical properties, labor-intensive and iterative optimization experiments are often required to identify the optimal set of fabrication parameters. Creating a predictive model that can automatically recommend the ideal parameter set for a conductive aerogel with programmable properties would greatly expedite the development process⁴⁸.

Machine learning (ML) is a subset of artificial intelligence (AI) that builds models for predictions or recommendations^49,50,51. AI/ML methodologies serve as an effective toolbox to unravel intricate correlations within the parameter space with multiple degrees of freedom (DOFs)^50,52,53. The AI/ML adoption in materials science research has surged, particularly in the fields with available simulation programs and high-throughput analytical tools that generate vast amounts of data in shared and open databases⁵⁴, including gene editing^55,56, battery electrolyte optimization^57,58, and catalyst discovery^59,60. However, building a prediction model for conductive aerogels encounters significant challenges, primarily due to the lack of high-quality data points. One major root cause is the lack of standardized fabrication protocols for conductive aerogels, and different research laboratories adopt various building blocks^35,40,46. Additionally, recent studies on conductive aerogels focus on optimizing a single property, such as electrical conductivity or compressive strength, and the complex correlations between these attributes are often neglected to understand^{37,42,61,62,63,64}. Moreover, as the fabrication of conductive aerogels is labor-intensive and time-consuming, the acquisition rate of training data points is highly limited, posing difficulties in constructing an accurate prediction model capable of predicting multiple characteristics.

Herein, we developed an integrated platform that combines the capabilities of collaborative robots with AI/ML predictions to accelerate the design of conductive aerogels with programmable mechanical and electrical properties (see Supplementary Fig. 1 for the robot–human teaming workflow). Based on specific property requirements, the robots/ML-integrated platform was able to automatically suggest a tailored parameter set for the fabrication of conductive aerogels, without the need for conducting iterative optimization experiments. To produce various conductive aerogels, four building blocks were selected, including MXene nanosheets, CNFs, gelatin, and GA crosslinker (see Supplementary Note 1 and Supplementary Fig. 2 for the selection rationale and model expansion strategy). Initially, an automated pipetting robot (i.e., OT-2 robot) was operated to prepare 264 mixtures with varying MXene/CNF/gelatin ratios and mixture loadings (i.e.

In the general formulation of quantum information, quantum states are represented by a special class of matrices called density matrices. This lesson describes the basics of how density matrices work and explains how they relate to quantum state vectors. It also introduces the Bloch sphere, which provides a useful geometric representation of qubit states, and discusses different types of correlations that can be described using density matrices.

0:00 — Introduction.
1:46 — Overview.
2:55 — Motivation.
4:40 — Definition of density matrices.
9:55 — Examples.
12:58 — Interpretation.
15:37 — Connection to state vectors.
20:13 — Probabilistic selections.
25:23 — Completely mixed state.
28:41 — Probabilistic states.
32:03 — Spectral theorem.
37:36 — Bloch sphere (introduction)
38:36 — Qubit quantum state vectors.
41:30 — Pure states of a qubit.
43:52 — Bloch sphere.
47:38 — Bloch sphere examples.
51:36 — Bloch ball.
55:40 — Multiple systems.
56:46 — Independence and correlation.
1:00:55 — Reduced states for an e-bit.
1:04:16 — Reduced states in general.
1:08:53 — The partial trace.
1:12:23 — Conclusion.

Continue reading “Lesson 09: Density Matrices | Understanding Quantum Information & Computation” »

Brain-inspired spiking neural networks have shown their capability for effective learning, however current models may not consider realistic heterogeneities present in the brain. The authors propose a neuron model with temporal dendritic heterogeneity for improved neuromorphic computing applications.