Reducing Staphylococcus aureus biofilm formation on stainless steel 316L using functionalized self-assembled monolayers

Stainless steel 316L (SS316L) is a common material used in orthopedic implants. Bacterial colonization of the surface and subsequent biofilm development can lead to refractory infection of the implant. Since the greatest risk of infection occurs perioperatively, strategies that reduce bacterial adhesion during this time are important. As a strategy to limit bacterial adhesion and biofilm formation on SS316L, self-assembled monolayers (SAMs) were used to modify the SS316L surface. SAMs with long alkyl chains terminated with hydrophobic (- CH3) or hydrophilic (oligoethylene glycol) tail groups were used to form coatings and in an orthogonal approach, SAMs were used to immobilize gentamicin or vancomycin on SS316L for the first time to form an active antimicrobial coating to inhibit early biofilm development. Modified SS316L surfaces were characterized using surface infrared spectroscopy, contact angles, MALDI-TOF mass spectrometry and atomic force microscopy. The ability of SAM-modified SS316L to retard biofilm development by Staphylococcus aureus was functionally tested using confocal scanning laser microscopy with COMSTAT image analysis, scanning electron microscopy and colony forming unit analysis. Neither hydrophobic nor hydrophilic SAMs reduced biofilm development. However, gentamicin-linked and vancomycin-linked SAMs significantly reduced S. aureus biofilm formation for up to 24 and 48 h, respectively. © 2013 Elsevier B.V

Reducing Staphylococcus aureus biofilm formation on stainless steel 316L using functionalized self-assembled monolayers

Stainless steel 316L (SS316L) is a common material used in orthopedic implants. Bacterial colonization of the surface and subsequent biofilm development can lead to refractory infection of the implant. Since the greatest risk of infection occurs perioperatively, strategies that reduce bacterial adhesion during this time are important. As a strategy to limit bacterial adhesion and biofilm formation on SS316L, self-assembled monolayers (SAMs) were used to modify the SS316L surface. SAMs with long alkyl chains terminated with hydrophobic (-CH(3)) or hydrophilic (oligoethylene glycol) tail groups were used to form coatings and in an orthogonal approach, SAMs were used to immobilize gentamicin or vancomycin on SS316L for the first time to form an “active” antimicrobial coating to inhibit early biofilm development. Modified SS316L surfaces were characterized using surface infrared spectroscopy, contact angles, MALDI-TOF mass spectrometry and atomic force microscopy. The ability of SAM-modified SS316L to retard biofilm development by Staphylococcus aureus was functionally tested using confocal scanning laser microscopy with COMSTAT image analysis, scanning electron microscopy and colony forming unit analysis. Neither hydrophobic nor hydrophilic SAMs reduced biofilm development. However, gentamicin-linked and vancomycin-linked SAMs significantly reduced S. aureus biofilm formation for up to 24 and 48 hours, respectively

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc