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Lack of support, maintenance and further development is common with scientific research software. In particular, development by a single researcher can easily result in orphaned software packages, especially if combined with poor documentation or lack of adherence to open software development standards. The RforMassSpectrometry initiative aims to develop an efficient, thoroughly documented and stable infrastructure for mass spectrometry (MS) data analysis. As part of this initiative, a growing ecosystem of R software packages was and is being developed covering different aspects of metabolomics and proteomics data analysis. To avoid the aforementioned problems open shared development, documentation, support and stability are emphasized. At the heart of the package ecosystem is the Spectra package, that provides the core infrastructure to handle MS data. Core functionality, which can be easily re-used by other R software packages, is provided by the MsCoreUtils and MetaboCoreUtils packages. Version 4 of the xcms package for LC-MS data pre-processing is now based mainly on this new infrastructure hence gaining support for additional data types, better data handling and support for ion mobility data. Integration of the xcms package into the package ecosystem simplifies complete analysis workflows which can include the MsFeatures package for feature grouping, and the MetaboAnnotation package for annotation of untargeted metabolomics data. Seamless integration of publicly available annotation resources is possible through packages such as MsBackendMassbank, MsBackendMsp or CompoundDb, the latter also allowing to create and manage lab-specific annotation resources. MsQuality enables rapid, efficient, and standardized quality assessment of MS data. Finally, integration of Python based functionality, such as provided by the matchms package, is possible through the SpectriPy package, and the SpectraQL adds support for the MassQL common query language to R/Spectra., contact: johannes.rainer@eurac.edu, {"references":["Naake, Thomas et al (2023). MsQuality – an interoperable open-source package for the calculation of standardized quality metrics of mass spectrometry data. bioRxiv https://doi.org/10.1101/2023.05.12.540477","Rainer, Johannes et al. (2022). A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R. Metabolites. https://doi.org/10.3390/metabo12020173","Kockmann, Thomas et al. (2021). The rawrr R Package: Direct Access to Orbitrap Data and Beyond. Journal of Proteome Research. https://doi.org/10.1021/acs.jproteome.0c00866","Huber, Florian et al. (2020). matchms – processing and similarity evaluation of mass spectrometry data. JOSS https://doi.org/10.21105/joss.02411","Jarmusch, Alan K et al. (2022). A Universal Language for Finding Mass Spectrometry Data Patterns. bioRxiv https://doi.org/10.1101/2022.08.06.503000"]}

The steady-state localisation of proteins provides vital insight into their function. These localisations are context speci c with proteins translocating between di erent subcellular niches upon perturbation of the subcellular environment. Di erential localisation, that is a change in the steady-state subcellular location of a protein, provides a step towards mechanistic in- sight of subcellular protein dynamics. High-accuracy high-throughput mass spectrometry-based methods now exist to map the steady-state localisation and re-localisation of proteins. Here, we describe a principled Bayesian approach, BANDLE, that uses these data to compute the probability that a protein di erentially localises upon cellular perturbation. Extensive simula- tion studies demonstrate that BANDLE reduces the number of both type I and type II errors compared to existing approaches. Application of BANDLE to several datasets recovers well- studied translocations. In an application to cytomegalovirus infection, we obtain insights into the rewiring of the host proteome. Integration of other high-throughput datasets allows us to provide the functional context of these data.

Additional file 1: Supplementary methods. Supplementary tables.

Additional file 2: Supplementary Figures.

Current markers of β-cell function poorly reflect glucose homeostasis after diabetes onset, mainly because of the lack of integration of parameters of insulin sensitivity and β-cell responsiveness to glucose. Recently, the widespread use of continuous glucose monitoring (CGM) helped stratify glucose control in patients with long-term diabetes in new clinically relevant glucotypes. In our DIATAG study, we investigated how CGM metrics may help to segregate patients with new-onset type 1 diabetes (T1D) entering or not in partial remission (PR). We collected data from 66 pediatric patients with T1D (10.4±4.7 yo) during the first year after diabetes onset (Δ). Clinical parameters (i.e. A1C, insulin daily dose, insulin-dose adjusted A1c [IDAA1C]) and CGM data were collected at Δ+3, +6, +9, +12 months (n=168, 57% remitters [IDAA1C 180 mg/dL], 20±9%) especially during the day while target in range (TIR [70-180 mg/dL]) was nearly equivalent to Cluster 1 (80±8%). Notably, CV remained low (32±5%) with little time spent below range (TBR [

On February 1, 2015, the global information and analytics corporation Elsevier and the National Autonomous University of Mexico (UNAM) established the agreement UNAM-Elsevier contract DGAJ-DPI-39-081114- 241, which saw the transfer from UNAM to Elsevier for the “production and hosting, advertising and support” of 44 Mexican open access academic journals published by UNAM. This article documents said contract and describes a Freedom of Information Request enquiring the total cost of the contract and its corresponding response. It also shares a series of considerations that, based on this case, can be helpful to other institutions that may face similar circumstances in the future. We conclude scholarly publishing and academic self-determination are interdependent and a crucial point of future debate for the future of University presses and Open Access worldwide.

OBJECTIVE To evaluate whether indexes of glycemic variability may overcome residual β-cell secretion estimates in the longitudinal evaluation of partial remission in a cohort of pediatric patients with new-onset type 1 diabetes. RESEARCH DESIGN AND METHODS Values of residual β-cell secretion estimates, clinical parameters (e.g., HbA1c or insulin daily dose), and continuous glucose monitoring (CGM) from 78 pediatric patients with new-onset type 1 diabetes were longitudinally collected during 1 year and cross-sectionally compared. Circadian patterns of CGM metrics were characterized and correlated to remission status using an adjusted mixed-effects model. Patients were clustered based on 46 CGM metrics and clinical parameters and compared using nonparametric ANOVA. RESULTS Study participants had a mean (± SD) age of 10.4 (± 3.6) years at diabetes onset, and 65% underwent partial remission at 3 months. β-Cell residual secretion estimates demonstrated weak-to-moderate correlations with clinical parameters and CGM metrics (r2 = 0.05–0.25; P < 0.05). However, CGM metrics strongly correlated with clinical parameters (r2 >0.52; P < 0.05) and were sufficient to distinguish remitters from nonremitters. Also, CGM metrics from remitters displayed specific early morning circadian patterns characterized by increased glycemic stability across days (within 63–140 mg/dL range) and decreased rate of grade II hypoglycemia (P < 0.0001) compared with nonremitters. Thorough CGM analysis allowed the identification of four novel glucotypes (P < 0.001) that segregate patients into subgroups and mirror the evolution of remission after diabetes onset. CONCLUSIONS In our pediatric cohort, combination of CGM metrics and clinical parameters unraveled key clinical milestones of glucose homeostasis and remission status during the first year of type 1 diabetes.