Interactive locomotion: Investigation and modeling of physically-paired humans while walking

In spite of extensive studies on human walking, less research has been conducted on human walking gait adaptation during interaction with another human. In this paper, we study a particular case of interactive locomotion where two humans carry a rigid object together. Experimental data from two persons walking together, one in front of the other, while carrying a stretcher-like object is presented, and the adaptation of their walking gaits and coordination of the foot-fall patterns are analyzed. It is observed that in more than 70% of the experiments the subjects synchronize their walking gaits; it is shown that these walking gaits can be associated to quadrupedal gaits. Moreover, in order to understand the extent by which the passive dynamics can explain this synchronization behaviour, a simple 2D model, made of two-coupled spring-loaded inverted pendulums, is developed, and a comparison between the experiments and simulations with this model is presented, showing that with this simple model we are able to reproduce some aspects of human walking behaviour when paired with another human

Viral load decrease in SARS-CoV-2 BA.1 and BA.2 Omicron sublineages infection after treatment with monoclonal antibodies and direct antiviral agents

BACKGROUND: The efficacy on the Omicron variant of the approved early- coronavirus disease 2019 (COVID-19) therapies, especially monoclonal antibodies, has been challenged by in vitro neutralization data, while data on in vivo antiviral activity are lacking. MATERIALS AND METHODS: We assessed potential decrease from day1 to day7 viral load (VL) in nasopharyngeal swabs of outpatients receiving Sotrovimab, Molnupiravir, Remdesivir, or Nirmatrelvir/ritonavir for mild-to-moderate COVID-19 due to sublineages BA.1 or BA.2, and average treatment effect (ATE) by weighted marginal linear regression models. RESULTS: A total of 521 patients [378 BA.1 (73%),143 (27%) BA.2] received treatments (Sotrovimab 202, Molnupiravir 117, Nirmatrelvir/ritonavir 84, and Remdesivir 118): median age 66 years, 90% vaccinated, median time from symptoms onset 3 days. Day1 mean viral load was 4.12 log2 (4.16 for BA.1 and 4.01 for BA.2). The adjusted analysis showed that Nirmatrelvir/ritonavir significantly reduced VL compared to all the other drugs, except vs. Molnupiravir in BA.2. Molnupiravir was superior to Remdesivir in both BA.1 and BA.2, and to Sotrovimab in BA.2. Sotrovimab had better activity than Remdesivir only against BA.1. CONCLUSIONS: Nirmatrelvir/ritonavir showed the greatest antiviral activity against Omicron variant, comparable to Molnupiravir only in the BA.2 subgroup. VL decrease could be a valuable surrogate of drug activity in the context of the high prevalence of vaccinated people and low probability of hospital admission. This article is protected by copyright. All rights reserved

Human Intention Detection as a Multiclass Classification Problem: Application in Physical Human–Robot Interaction While Walking

Lanini J, Razavi H, Urain J, Ijspeert A. Human Intention Detection as a Multiclass Classification Problem: Application in Physical Human–Robot Interaction While Walking. IEEE Robotics and Automation Letters. 2018;3(4):4171-4178

SIMPD: an algorithm for generating simulated time splits for validating machine learning approaches.

Time-split cross-validation is broadly recognized as the gold standard for validating predictive models intended for use in medicinal chemistry projects. Unfortunately this type of data is not broadly available outside of large pharmaceutical research organizations. Here we introduce the SIMPD (simulated medicinal chemistry project data) algorithm to split public data sets into training and test sets that mimic the differences observed in real-world medicinal chemistry project data sets. SIMPD uses a multi-objective genetic algorithm with objectives derived from an extensive analysis of the differences between early and late compounds in more than 130 lead-optimization projects run within the Novartis Institutes for BioMedical Research. Applying SIMPD to the real-world data sets produced training/test splits which more accurately reflect the differences in properties and machine-learning performance observed for temporal splits than other standard approaches like random or neighbor splits. We applied the SIMPD algorithm to bioactivity data extracted from ChEMBL and created 99 public data sets which can be used for validating machine-learning models intended for use in the setting of a medicinal chemistry project. The SIMPD code and simulated data sets are available under open-source/open-data licenses at github.com/rinikerlab/molecular_time_series

SIMPD: an Algorithm for Generating Simulated Time Splits for Validating Machine Learning Approaches

Time-split cross-validation is broadly recognized as the gold standard for validating predictive models intended for use in medicinal chemistry projects. Unfortunately this type of data is not broadly available outside of large pharmaceutical research organizations. Here we introduce the SIMPD (simulated medicinal chemistry project data) algorithm to split public data sets into training and test sets that mimic the differences observed in real-world medicinal chemistry project data sets. SIMPD uses a multi-objective genetic algorithm with objectives derived from an extensive analysis of the differences between early and late compounds in more than 130 lead-optimization projects run within the Novartis Institutes for BioMedical Research. Applying SIMPD to the real-world data sets produced training/test splits which more accurately reflect the differences in properties and machine-learning performance observed for temporal splits than other standard approaches like random or neighbor splits. We applied the SIMPD algorithm to bioactivity data extracted from ChEMBL and created 56 public data sets which can be used for validating machine-learning models intended for use in the setting of a medicinal chemistry project. The SIMPD code and simulated data sets are available under open-source/open-data licenses at github.com/rinikerlab/molecular_time_series

Machine learning for small molecule drug discovery in academia and industry: ML for small molecules drug discovery

Academic and pharmaceutical industry research are both key for progresses in the field of molecular machine learning. Despite common open research questions and long-term goals, the nature and scope of investigations typically differ between academia and industry. Herein, we highlight the opportunities that machine learning models offer to accelerate and improve compound selection. All parts of the model life cycle are discussed, including data preparation, model building, validation, and deployment. Main challenges in molecular machine learning as well as differences between academia and industry are highlighted. Furthermore, application aspects in the design-make-test-analyze cycle are discussed. We close with strategies that could improve collaboration between academic and industrial institutions and will advance the field even further.ISSN:2667-318

Machine learning for small molecule drug discovery in academia and industry

Academic and pharmaceutical industry research are both key for progresses in the field of molecular machine learning. Despite common open research questions and long-term goals, the nature and scope of investigations typically differ between academia and industry. Herein, we highlight the opportunities that machine learning models offer to accelerate and improve compound selection. All parts of the model life cycle are discussed, including data preparation, model building, validation, and deployment. Main challenges in molecular machine learning as well as differences between academia and industry are highlighted. Furthermore, application aspects in the design-make-test-analyze cycle are discussed. We close with strategies that could improve collaboration between academic and industrial institutions and will advance the field even further

Machine Learning for Small Molecule Drug Discovery in Academia and Industry

Academic and pharmaceutical industry research are both key for progresses in the field of molecular machine learning. Despite common open research questions and long-term goals, the nature and scope of investigations typ- ically differ between academia and industry. Herein, we highlight the op- portunities that machine learning models offer to accelerate and improve compound selection. All parts of the model life cycle are discussed, including data preparation, model building, validation, and deployment. Main chal- lenges in molecular machine learning as well as differences between academia and industry are highlighted. Furthermore, application aspects in the design- make-test-analyze cycle are discussed. We close with strategies to potentially improve collaboration between academic and industrial institutions