SchNetPack 2.0: A neural network toolbox for atomistic machine learning

SchNetPack is a versatile neural networks toolbox that addresses both the requirements of method development and application of atomistic machine learning. Version 2.0 comes with an improved data pipeline, modules for equivariant neural networks as well as a PyTorch implementation of molecular dynamics. An optional integration with PyTorch Lightning and the Hydra configuration framework powers a flexible command-line interface. This makes SchNetPack 2.0 easily extendable with custom code and ready for complex training task such as generation of 3d molecular structures

Multidisciplinary Late Effects Clinics for Childhood Cancer Survivors in Germany - a Two-Center Study

Background: Childhood cancer survivors are at risk for therapy-related sequelae and, therefore, require long-term follow-up. At 2 university hospitals in Germany collaborative multidisciplinary late effects clinics were installed to provide specialized care and to evaluate the current health status of these patients in a clinical setting. Patients andMethods: Every patient who visited the late effects clinics at the university hospital in Lübeck and Erlangen over a period of 3 years and met the inclusion criteria was included in the study. Patients' characteristics as well as cancer diagnosis, treatment related factors and the prevalence of chronic health conditions were assessed. Results: 220 patients attended the late effects clinics during the observation period. The median follow-up period was 16 years (range 5-45 years). In total over 64% of the patients were affected by at least 1 chronic health condition, including endocrine disruptions in 19.1% of the patients. Moreover, secondary neoplasms occurred in 9.1% of the study participants. Conclusion: German childhood cancer survivors are affected by multiple therapy-related sequelae. A comprehensive network of late effects clinics should be established to ensure specialized and risk-adapted care for every childhood cancer survivor in Germany

Induction treatment in high-grade B-cell lymphoma with a concurrent MYC and BCL2 and/or BCL6 rearrangement: a systematic review and meta-analysis

Background and aimHigh-grade B cell lymphomas with concomitant MYC and BCL2 and/or BCL6 rearrangements (HGBCL-DH/TH) have a poor prognosis when treated with the standard R-CHOP-like chemoimmunotherapy protocol. Whether this can be improved using intensified regimens is still under debate. However, due to the rarity of HGBCL-DH/TH there are no prospective, randomized controlled trials (RCT) available. Thus, with this systematic review and meta-analysis we attempted to compare survival in HGBCL-DH/TH patients receiving intensified vs. R-CHOP(-like) regimens.MethodsThe PubMed and Web of Science databases were searched for original studies reporting on first-line treatment in HGBCL-DH/TH patients from 08/2014 until 04/2022. Studies with only localized stage disease, ≤10 patients, single-arm, non-full peer-reviewed publications, and preclinical studies were excluded. The quality of literature and the risk of bias was assessed using the Methodological Index for Non-Randomized Studies (MINORS) and National Heart, Lung, and Blood Institute (NHLBI) Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Random-effect models were used to compare R-CHOP-(like) and intensified regimens regarding 2-year overall survival (2y-OS) and 2-year progression-free survival (2y-PFS).ResultsAltogether, 11 retrospective studies, but no RCT, with 891 patients were included. Only four studies were of good quality based on aforementioned criteria. Intensified treatment could improve 2y-OS (hazard ratio [HR]=0.78 [95% confidence interval [CI] 0.63-0.96]; p=0.02) as well as 2y-PFS (HR=0.66 [95% CI 0.44-0.99]; p=0.045).ConclusionsThis meta-analysis indicates that intensified regimens could possibly improve 2y-OS and 2y-PFS in HGBCL-DH/TH patients. However, the significance of these results is mainly limited by data quality, data robustness, and its retrospective nature. There is still a need for innovative controlled clinical trials in this difficult to treat patient population.Systematic review registrationhttps://www.crd.york.ac.uk/prospero, identifier CRD42022313234

Governing and accelerating transformative entrepreneurship: exploring the potential for small business innovation on urban sustainability transitions

The alluring yet nebulous concept of transformative change is increasingly gaining traction in conversations about pathways to more sustainable futures. As such, new conceptual tools are needed to illuminate variety of actors, interests, and capacities at play in potentially radical experiments. This paper draws upon multi-level governance theory, sustainability transitions scholarship, and sustainability entrepreneurship literature, to interrogate the transformative potential of small and medium-sized enterprises (SMEs). We (1) identify characteristics of SMEs that might make them relatively more able to produce radical innovations, (2) explore dimensions of the broader socio-political context that influence the likelihood of this potential to be translated into action in urban spaces, and (3) discuss implications of these dynamics for transformative sustainability governance

Governing and accelerating transformative entrepreneurship: exploring the potential for small business innovation on urban sustainability transitions

The alluring yet nebulous concept of transformative change is increasingly gaining traction in conversations about pathways to more sustainable futures. As such, new conceptual tools are needed to illuminate variety of actors, interests, and capacities at play in potentially radical experiments. This paper draws upon multi-level governance theory, sustainability transitions scholarship, and sustainability entrepreneurship literature, to interrogate the transformative potential of small and medium-sized enterprises (SMEs). We (1) identify characteristics of SMEs that might make them relatively more able to produce radical innovations, (2) explore dimensions of the broader socio-political context that influence the likelihood of this potential to be translated into action in urban spaces, and (3) discuss implications of these dynamics for transformative sustainability governance

Pretrained models: "Inverse design of 3d molecular structures with conditional generative neural networks"

The rational design of molecules with desired properties is a long-standing challenge in chemistry. Generative neural networks have emerged as a powerful approach to sample novel molecules from a learned distribution. Here, we propose a conditional generative neural network for 3d molecular structures with specified chemical and structural properties. This approach is agnostic to chemical bonding and enables targeted sampling of novel molecules from conditional distributions, even in domains where reference calculations are sparse. We demonstrate the utility of our method for inverse design by generating molecules with specified motifs or composition, discovering particularly stable molecules, and jointly targeting multiple electronic properties beyond the training regime.BMBF, 01IS18037A, Verbundprojekt BIFOLD-BZML: Berlin Institute for the Foundations of Learning and DataBASLEARN – TU Berlin/BASF Joint Lab for Machine Learnin

Generated molecules: "Inverse design of 3d molecular structures with conditional generative neural networks"

The rational design of molecules with desired properties is a long-standing challenge in chemistry. Generative neural networks have emerged as a powerful approach to sample novel molecules from a learned distribution. Here, we propose a conditional generative neural network for 3d molecular structures with specified chemical and structural properties. This approach is agnostic to chemical bonding and enables targeted sampling of novel molecules from conditional distributions, even in domains where reference calculations are sparse. We demonstrate the utility of our method for inverse design by generating molecules with specified motifs or composition, discovering particularly stable molecules, and jointly targeting multiple electronic properties beyond the training regime.BMBF, 01IS18037A, Verbundprojekt BIFOLD-BZML: Berlin Institute for the Foundations of Learning and DataBASLEARN – TU Berlin/BASF Joint Lab for Machine Learnin