Revealing excited states of rotational Bose-Einstein condensates

Rotational Bose-Einstein condensates can exhibit quantized vortices as topological excitations.In this study,the ground and excited states of the rotational Bose-Einstein condensates are systematically studied by calculating the stationary points of the Gross-Pitaevskii energy functional.Various excited states and their connections at different rotational frequencies are revealed in solution landscapes constructed with the constrained high-index saddle dynamics method.Four excitation mechanisms are identified:vortex addition,rearrangement,merging,and splitting.We demonstrate changes in the ground state with increasing rotational frequencies and decipher the evolution of the stability of ground states.

Cell-free synthesis of stable isotope-labeled internal standards for targeted quantitative proteomics

High-sensitivity mass spectrometry approaches using selected reaction monitoring(SRM)or multiple reaction monitoring(MRM)methods are powerful tools for targeted quantitative proteomics-based investigation of dynamics in specific biological systems.Both high-sensitivity detection of lowabundance proteins and their quantification using this technique employ stable isotope-labeled peptide internal standards.Currently,there are various ways for preparing standards,including chemical peptide synthesis,cellular protein expression,and cell-free protein or peptide synthesis.Cell-free protein synthesis(CFPS)or in vitro translation(IVT)systems in particular provide high-throughput and low-cost preparation methods,and various cell types and reconstituted forms are now commercially available.Herein,we review the use of such systems for precise and reliable protein quantification.

Distributed Analytics on Sensitive Medical Data:The Personal Health Train

In recent years,as newer technologies have evolved around the healthcare ecosystem,more and more data have been generated.Advanced analytics could power the data collected from numerous sources,both from healthcare institutions,or generated by individuals themselves via apps and devices,and lead to innovations in treatment and diagnosis of diseases;improve the care given to the patient;and empower citizens to participate in the decision-making process regarding their own health and well-being.However,the sensitive nature of the health data prohibits healthcare organizations from sharing the data.The Personal Health Train(PHT)is a novel approach,aiming to establish a distributed data analytics infrastructure enabling the(re)use of distributed healthcare data,while data owners stay in control of their own data.The main principle of the PHT is that data remain in their original location,and analytical tasks visit data sources and execute the tasks.The PHT provides a distributed,flexible approach to use data in a network of participants,incorporating the FAIR principles.It facilitates the responsible use of sensitive and/or personal data by adopting international principles and regulations.This paper presents the concepts and main components of the PHT and demonstrates how it complies with FAIR principles.

Modeling and measurement of curing properties of photocurable polymer containing magnetic particles and microcapsules

In microstereolithography,three-dimensional microstructures are created by scanning an ultraviolet laser on a photocurable resin and stacking several such layers to form the desired structure.By mixing different types of particles in the resin,the formed microstructures exhibit various physical properties.For example,the magnetism and density of the microstructure can be controlled by adding magnetic particles and microcapsules to the resin.This method has been used to fabricate magnetic micromachines.Although such functional resins are useful,the incorporated magnetic particles and microcapsules can affect the fabrication resolution,making it difficult to fabricate microstructures with high precision.Thus,it is necessary to understand the effects of such microparticles and microcapsules on the fabrication process.In this study,we propose a simple model of curing resins containing magnetic particles and microcapsules to explain the effects of the magnetic particles and microcapsules.The proposed model can explain the observed curing characteristics of a resin that contains particles for all concentrations as well as for different types of magnetic particles and microcapsules.Finally,using the proposed model,we discuss how to improve the characteristics of resins containing microparticles to realize the high-resolution fabrication of three-dimensional microstructures with desirable material properties.

Improving conditional random field model for prediction of protein-RNA residue-base contacts

Background： For understanding biological cellular systems, it is important to analyze interactions between protein residues and RNA bases. A method based on conditional random fields （CRFs） was developed for predicting contacts between residues and bases, which receives multiple sequence alignments for given protein and RNA sequences, respectively, and learns the model with many parameters involved in relationships between neighboring residue-base pairs by maximizing the pseudo likelihood function. Methods： In this paper, we proposed a novel CRF-based model with more complicated dependency relationships between random variables than the previous model, but which takes less parameters for the sake of avoidance of overfitting to training data. Results： We performed cross-validation experiments for evaluating the proposed model, and took the average of AUC （area under receiver operating characteristic curve） scores. The result suggests that the proposed CRF-based model without using Ll-norm regularization （lasso） outperforms the existing model with and without the lasso under several input observations to CRFs. Conclusions： We proposed a novel stochastic model for predicting protein-RNA residue-base contacts, and improved the prediction accuracy in terms of the AUC score. It implies that more dependency relationships in a CRF could be controlled by less parameters.

Interdisciplinary management of central nervous system metastasis and neoplastic meningitis:recent developments and future perspectives

The incidence of metastatic disease in the central nervous system(CNS)is rising.According to current estimates,up to a third of adult cancer patients will suffer from CNS metastasis.Clinical evidence-based data from prospective randomized trials are rare,however,because CNS metastasis patients were often excluded from clinical trial participation.The management of CNS metastasis patients is therefore rather ill-defined and an interdisciplinary challenge.Recent basic and translational science data have begun contributing to a more profound understanding of the molecular mechanisms leading to invasion of tumor cells into the CNS.This report reviews advances,challenges,and perspectives in this field.