Lifeboat Foundation

Artemisia annua plants grown from a cultivated seed line in Kentucky, USA, were extracted using either absolute ethanol or distilled water at 50 °C for 200 min and analyzed, as described in “Materials and methods” and Supplementary Information (Figures S1 and S2). Solids were removed by filtration and the solvents were evaporated. The extracted materials were dissolved in dimethylsulfoxide (DMSO) (ethanol extract) or a DMSO: water mixture (3:1 for aqueous extract) and filtered (see supporting information for details). Artemisinin (Fig. 1, (1)) was synthesized and purified following a published procedure or purchased¹⁷, while artesunate (Fig. 1, (2)) and artemether (Fig. 1, (3)) were only obtained from commercial sources.

Initial experiments were carried out at FU Berlin, Germany. To initially screen whether extracts and pure artemisinin were active against SARS-CoV-2, their antiviral activity was tested by pretreating VeroE6 cells at different time points during 120 min with selected concentrations of the extracts or compounds prior to infection with the first European SARS-CoV-2 isolated in München (SARS-CoV-2/human/Germany/BavPat 1/2020). The virus-drug mixture was then removed and cells were overlaid with medium containing 1.3% carboxymethylcellulose to prevent virus release into the medium. DMSO was used as a negative control. Plaque numbers were determined either by indirect immunofluorescence using a mixture of antibodies to SARS-CoV N protein¹⁸ or by staining with crystal violet¹⁹. The addition of either ethanolic or aqueous A. annua extracts prior to virus addition resulted in reduced plaque formation in a concentration dependent manner, while artemisinin exhibited little antiviral activity (Supplemental Tables S1 – S8).

Concentration–response experiments were carried out at Copenhagen University Hospital-Hvidovre. In these experiments the Danish SARS-CoV-2 isolate SARS-CoV-2/human/Denmark/DK-AHH1/2020 was used employing a 96-well plate based concentration–response antiviral treatment assay, allowing for multiple replicates per concentration, as described in “Materials and methods” and Supplementary Information (Figures S3 and S4)^20,21. Seven replicates were measured at each concentration and a range of concentrations was evaluated to increase data accuracy when compared to the plaque-reduction assay, which was carried out in duplicates. Extracts or compounds were added to VeroE6 cells either 1.5 h prior to (pretreatment (pt)) or 1 h post infection (treatment (t)), respectively, followed by a 2-day incubation of virus with extracts or compounds. Both protocols yielded similar results, with slightly lower median effective concentration (EC₅₀) values observed for treatment assays.

Researchers at La Jolla Institute for Immunology (LJI) have demonstrated how cancer-fighting T cells can be engineered to clear tumors without succumbing to T cell exhaustion, a phenomenon that results in T cells giving up the antitumor fight after prolonged periods of overactivity. The newly reported findings demonstrated the key role of the transcription factor BATF in the cellular pathway that triggers T cell exhaustion, and showed how increasing levels of BATF in these cells boosts their cancer-fighting ability, with the help of another transcription factor IRF4. The results suggest a potential approach to increasing the effectiveness of CAR T cell-based immunotherapy against tumors, which is important because T cell exhaustion plagues even the most cutting-edge cancer immunotherapies.

“The idea is to give the cells a little bit of armor against the exhaustion program,” said LJI professor Patrick Hogan, PhD. “The cells can go into the tumor to do their job, and then they can stick around as memory cells.” The LJI team, headed by Hogan and LJI professor Anjana Rao, PhD, report on their studies in Nature Immunology, in a paper titled, “BATF and IRF4 cooperate to counter exhaustion in tumor-infiltrating CAR T cells.”

Fighting a tumor is a marathon, not a sprint. For cancer-fighting CD8+ T cells, the race is sometimes just too long, and they quite fighting. “CD8+ T cells that encounter antigen together with effective costimulatory signals mount strong effector responses that are able to clear pathogen-infected cells and tumor cells,” the team wrote. “In contrast, CD8+ T cells that infiltrate solid tumors and are exposed to prolonged antigen stimulation in the absence of adequate costimulation enter a hyporesponsive (exhausted or dysfunctional) state in which they do not effectively destroy tumor cells.”

The future of fighter jets is coming, seemingly with more international power and disruptive technologies than predicted. As the US forges ahead to become 1st nation field sixth-generation fighter jet, other major air forces fear falling behind in the competitive race. The US, Europe, Japan, and China have made unbelievable investments looking for unique next-level capabilities like stealth, robust avionics, and navigation systems to present the most technologically advanced fighter jet. But one particular trend is crucial for all 6th gen prototypes. Artificial Intelligence is about to begin a new era of air combat.

Arms20210720.

This morning Mural, a startup that builds digital collaboration software with a focus on visual presentation, announced that it has closed a $50 million Series C. The new capital, co-led by prior investors Insight Partners and Tiger Global, values the startup at more than $2 billion.

Previously, Mural was valued at around $500 million when it closed a $118 million round last August. Mural also raised a $23 million Series A at the start of 2020.

Mural’s product focuses around a visual collaboration space, akin to a digital whiteboard. Given its product focus, it’s not hard to see why the startup had a good COVID cycle; the world’s companies moved to remote work en masse, leaving offices empty and physical whiteboards un-scribbled. Services like Mural helped fill that, and similar voids. TechCrunch caught up with Mural CEO Mariano Suarez-Battan and Insight managing director Nikhil Sachdev to learn more about deal mechanics.

Nvidia today announced the release of TensorRT 8, the latest version of its software development kit (SDK) designed for AI and machine learning inference. Built for deploying AI models that can power search engines, ad recommendations, chatbots, and more, Nvidia claims that TensorRT 8 cuts inference time in half for language queries compared with the previous release of TensorRT.

Models are growing increasingly complex, and demand is on the rise for real-time deep learning applications. According to a recent O’Reilly survey, 86.7% of organizations are now considering, evaluating, or putting into production AI products. And Deloitte reports that 53% of enterprises adopting AI spent more than $20 million in 2019 and 2020 on technology and talent.

TensorRT essentially dials a model’s mathematical coordinates to a balance of the smallest model size with the highest accuracy for the system it’ll run on. Nvidia claims that TensorRT-based apps perform up to 40 times faster than CPU-only platforms during inference, and that TensorRT 8-specific optimizations allow BERT-Large — one of the most popular Transformer-based models — to run in 1.2 milliseconds.

A new drone flyover of Tesla Gigafactory Texas is showing some great progress, and it’s even starting to look like the render of the finished building.

The start of production at Gigafactory Texas and Gigafactory Berlin is the most important thing for Tesla’s growth this year.

Continue reading “Tesla Gigafactory Texas is taking shape in new drone flyover video” »

A veritable wafer sandwich is now possible.

A team of researchers with the Institute of Microelectronics have developed technology that enables up to four layers of semiconductor circuitry to be stacked.

It was not immediately clear whether adapters would be needed.

Tesla stock TSLA, +2.21% edged lower in the extended session Tuesday after ending the regular trading day up 2.2%.