The promises of large language models for protein design and modeling.

The recent breakthroughs of Large Language Models (LLMs) in the context of natural language processing have opened the way to significant advances in protein research. Indeed, the relationships between human natural language and the language of proteins invite the application and adaptation of LLMs to protein modelling and design. Considering the impressive results of GPT-4 and other recently developed LLMs in processing, generating and translating human languages, we anticipate analogous results with the language of proteins. Indeed, protein language models have been already trained to accurately predict protein properties, generate novel functionally characterized proteins, achieving state-of-the-art results. In this paper we discuss the promises and the open challenges raised by this novel and exciting research area, and we propose our perspective on how LLMs will affect protein modeling and design

The promises of large language models for protein design and modeling

The recent breakthroughs of Large Language Models (LLMs) in the context of natural language processing have opened the way to significant advances in protein research. Indeed, the relationships between human natural language and the “language of proteins” invite the application and adaptation of LLMs to protein modelling and design. Considering the impressive results of GPT-4 and other recently developed LLMs in processing, generating and translating human languages, we anticipate analogous results with the language of proteins. Indeed, protein language models have been already trained to accurately predict protein properties, generate novel functionally characterized proteins, achieving state-of-the-art results. In this paper we discuss the promises and the open challenges raised by this novel and exciting research area, and we propose our perspective on how LLMs will affect protein modeling and design

Implementing an XML-RPC client in Mathematica

XML-RPC is a protocol used to remotely execute a program independently of the particular hardware and operating system used on both ends of the communication channel, in that all the conveyed information consists in text containing XML encodings coupled with HTTP headers. A sagacious mix of J/Link, XML, and RegularExpression technologies available within Mathematica allows to easily implement a client exploiting this protocol

Drawing attention to the dangerous

ICANN/ICONIP 2003,Istanbul,TurkeyIn this paper we present an architecture of attention-based control for artificial agents. The agent is responsible for monitoring adaptively the user in order to detect context switches in his state. Assuming a successful detection appropriate action will be taken. Simulation results based on a simple scenario show that Attention is an appropriate mechanism for implementing context switch detector systems

Peri-implant conditions around sintered porous-surfaced (SPS) implants. A 36-month prospective cohort study

Objectives: The specific aim of this study was to assess sintered porous-surfaced (SPS) implant system from a biological point of view, through a prospective study of the health status and the evolution of the peri-implant tissues over time and analysis of the changes observed in the various peri-implant parameters. Material and Methods: Hundred and fifty-one patients were treated consecutively from 2005 to 2007 using 280 SPS implants, which were restored with a single crown or a partial fixed denture. To accurately monitor the health and biological evolution of peri-implant soft and hard tissues, a number of clinical parameters were adopted, such as the modified Plaque Index (mPI), the modified sulcus Bleeding Index (mBI), Peri-implant Probing Depth (PPD), and Crestal Bone Level (CBL). Clinical and radiographic examinations were scheduled over a 36-month follow-up of functional loading according to a well-established protocol generally applied to determine implant success rates and Peri-implant Bone Loss (PBL). Statistical analysis was used to determine any significant differences or correlations (P = 0.05). Results: A total of 259 SPS implants in 136 patients were followed up for 36 months. According to Buser's success criteria, the overall implant-based success rate was 98.1% and the mean PBL was 0.48 \ub1 0.29 mm. MBI and mPI mean values showed statistically significant differences between baseline and follow-up analyses (P < 0.001). No statistically significant differences in mean PPD values were found between baseline and control analyses (P = 0.060). Conclusion: This prospective cohort study revealed that the biological behavior of SPS implant system was characterized by high tissue stability during the observation period, both as regards soft and hard tissues. In particular, the crestal bone remodeling pattern was very similar to that reported in other studies, confirming that the bone loss around SPS implants, at least at 36 months, seems to be predictable