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Crosslinking mass spectrometry has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information in the mass spectra of crosslinked peptides limits the numbers of protein–protein interactions that can be confidently identified. Here, we leverage chromatographic retention time information to aid the identification of crosslinked peptides from mass spectra. Our Siamese machine learning model xiRT achieves highly accurate retention time predictions of crosslinked peptides in a multi-dimensional separation of crosslinked E. coli lysate. Importantly, supplementing the search engine score with retention time features leads to a substantial increase in protein–protein interactions without affecting confidence. This approach is not limited to cell lysates and multi-dimensional separation but also improves considerably the analysis of crosslinked multiprotein complexes with a single chromatographic dimension. Retention times are a powerful complement to mass spectrometric information to increase the sensitivity of crosslinking mass spectrometry analyses. Predicting chromatographic retention times (RTs) has proven beneficial in proteomics but has not yet been achieved for crosslinked peptides. Here, the authors develop an RT prediction tool for crosslinked peptides and leverage predicted RTs to increase identifications in crosslinking mass spectrometry studies.

We present a concise workflow to enhance the mass spectrometric detection of crosslinked peptides by introducing sequential digestion and the crosslink identification software xiSEARCH. Sequential digestion enhances peptide detection by selective shortening of long tryptic peptides. We demonstrate our simple 12‐fraction protocol for crosslinked multi‐protein complexes and cell lysates, quantitative analysis, and high‐density crosslinking, without requiring specific crosslinker features. This overall approach reveals dynamic protein–protein interaction sites, which are accessible, have fundamental functional relevance and are therefore ideally suited for the development of small molecule inhibitors. Synopsis A new workflow combining sequential digestion and the search software Xi increases the number of identified crosslinks in a wide range of applications. By detecting dynamic protein interactions, crosslinking is synergistic with other structural approaches and is promising for drug development. Sequential digestion outperforms parallel digestion by shortening long peptides and thus facilitates crosslink identification. xiSEARCH outperforms other search algorithms. A dynamic interaction identified in the OCCM complex, and not seen before by cryoEM, is a potential target for cancer therapy. Graphical Abstract A new workflow combining sequential digestion and the search software Xi increases the number of identified crosslinks in a wide range of applications. By detecting dynamic protein interactions, crosslinking is synergistic with other structural approaches and is promising for drug development.

A genetic map of the filamentous fungus Fusarium graminearum (teleomorph: Gibberella zeae) was constructed to both validate and augment the draft whole-genome sequence assembly of strain PH-1. A mapping population was created from a cross between mutants of the sequenced strain (PH-1, NRRL 31084, originally isolated from Michigan) and a field strain from Minnesota (00-676, NRRL 34097). A total of 111 ascospore progeny were analyzed for segregation at 235 loci. Genetic markers consisted of sequence-tagged sites, primarily detected as dCAPS or CAPS (n = 131) and VNTRs (n = 31), in addition to AFLPs (n = 66) and 7 other markers. While most markers exhibited Mendelian inheritance, segregation distortion was observed for 25 predominantly clustered markers. A linkage map was generated using the Kosambi mapping function, using a LOD threshold value of 3.5. Nine linkage groups were detected, covering 1234 cM and anchoring 99.83% of the draft sequence assembly. The nine linkage groups and the 22 anchored scaffolds from the sequence assembly could be assembled into four chromosomes, leaving only five smaller scaffolds (59,630 bp total) of the nuclear DNA unanchored. A chromosome number of four was confirmed by cytological karyotyping. Further analysis of the genetic map data identified variation in recombination rate in different genomic regions that often spanned several hundred kilobases.

Mass spectrometry-based proteomics provides a holistic snapshot of the entire protein set of living cells on a molecular level. Currently, only a few deep learning approaches exist that involve peptide fragmentation spectra, which represent partial sequence information of proteins. Commonly, these approaches lack the ability to characterize less studied or even unknown patterns in spectra because of their use of explicit domain knowledge. Here, to elevate unrestricted learning from spectra, we introduce ‘ad hoc learning of fragmentation’ (AHLF), a deep learning model that is end-to-end trained on 19.2 million spectra from several phosphoproteomic datasets. AHLF is interpretable, and we show that peak-level feature importance values and pairwise interactions between peaks are in line with corresponding peptide fragments. We demonstrate our approach by detecting post-translational modifications, specifically protein phosphorylation based on only the fragmentation spectrum without a database search. AHLF increases the area under the receiver operating characteristic curve (AUC) by an average of 9.4% on recent phosphoproteomic data compared with the current state of the art on this task. Furthermore, use of AHLF in rescoring search results increases the number of phosphopeptide identifications by a margin of up to 15.1% at a constant false discovery rate. To show the broad applicability of AHLF, we use transfer learning to also detect cross-linked peptides, as used in protein structure analysis, with an AUC of up to 94%. Fragmentation of peptides leaves characteristic patterns in mass spectrometry data, which can be used to identify protein sequences, but this method is challenging for mutated or modified sequences for which limited information exist. Altenburg et al. use an ad hoc learning approach to learn relevant patterns directly from unannotated fragmentation spectra.

Background To make reasonable future medical decisions, medical students need to be sufficiently educated to interpret diagnostic tests. Natural frequencies are considered the gold standard for understanding single diagnostic test results. However, they may be less suitable in situations involving sequential diagnostic testing. We test whether odds and likelihood ratios (odds/LR) may serve as a viable alternative in these situations. Methods In our preregistered randomized-controlled crossover trial, we recruited 167 medical students and 162 psychology students. The proportion of correctly calculated positive predictive values of a single (PPV) and two sequential diagnostic tests (sPPV) was the primary, the subjective comprehensibility of the information the secondary outcome. Results The proportion of correct PPVs was significantly higher in the natural frequency (36.2%) compared to the odds/LR format (21.6%), OR 2.41. Conversely, the proportion of correct sPPVs was significantly higher in the odds/LR (10.6%) compared to the natural frequency format (4.9%), OR 2.73. Participants indicated a higher subjective comprehension of test statistics phrased as natural frequencies (Mdn = 19) than as odds/LR (Mdn = -15), r  = .61. Conclusion Teaching Odds/LR next to natural frequencies potentially improves medical students’ understanding of PPV and may enhance their ability to make future diagnostic decisions. Trial registration https://doi.org/10.17605/OSF.IO/F3297 .

Media bias has a substantial impact on individual and collective perception of news. Effective communication that may counteract its potential negative effects still needs to be developed. In this article, we analyze how to facilitate the detection of media bias with visual and textual aids in the form of (a) a forewarning message, (b) text annotations, and (c) political classifiers. In an online experiment, we randomized 985 participants to receive a biased liberal or conservative news article in any combination of the three aids. Meanwhile, their subjective perception of media bias in this article, attitude change, and political ideology were assessed. Both the forewarning message and the annotations increased media bias awareness, whereas the political classification showed no effect. Incongruence between an articles’ political position and individual political orientation also increased media bias awareness. Visual aids did not mitigate this effect. Likewise, attitudes remained unaltered.

The successful completion of cytokinesis requires the coordinated activities of diverse cellular components including membranes, cytoskeletal elements and chromosomes that together form partly redundant pathways, depending on the cell type. The biochemical analysis of this process is challenging due to its dynamic and rapid nature. Here, we systematically compared monopolar and bipolar cytokinesis and demonstrated that monopolar cytokinesis is a good surrogate for cytokinesis and it is a well-suited system for global biochemical analysis in mammalian cells. Based on this, we established a phosphoproteomic signature of cytokinesis. More than 10,000 phosphorylation sites were systematically monitored; around 800 of those were up-regulated during cytokinesis. Reconstructing the kinase-substrate interaction network revealed 31 potentially active kinases during cytokinesis. The kinase-substrate network connects proteins between cytoskeleton, membrane and cell cycle machinery. We also found consensus motifs of phosphorylation sites that can serve as biochemical markers specific to cytokinesis. Beyond the kinase-substrate network, our reconstructed signaling network suggests that combination of sumoylation and phosphorylation may regulate monopolar cytokinesis specific signaling pathways. Our analysis provides a systematic approach to the comparison of different cytokinesis types to reveal alternative ways and a global overview, in which conserved genes work together and organize chromatin and cytoplasm during cytokinesis.

Abstract BACKGROUND Freehand ventricular catheter placement may represent limited accuracy for the surgeon's intent to achieve primary optimal catheter position. OBJECTIVE To investigate the accuracy of a ventricular catheter guide assisted by a simple mobile health application (mhealth app) in a multicenter, randomized, controlled, simple blinded study (GAVCA study). METHODS In total, 139 eligible patients were enrolled in 9 centers. Catheter placement was evaluated by 3 different components: number of ventricular cannulation attempts, a grading scale, and the anatomical position of the catheter tip. The primary endpoint was the rate of primary cannulation of grade I catheter position in the ipsilateral ventricle. The secondary endpoints were rate of intraventricular position of the catheter's perforations, early ventricular catheter failure, and complications. RESULTS The primary endpoint was reached in 70% of the guided group vs 56.5% (freehand group; odds ratio 1.79, 95% confidence interval 0.89-3.61). The primary successful puncture rate was 100% vs 91.3% (P = .012). Catheter perforations were located completely inside the ventricle in 81.4% (guided group) and 65.2% (freehand group; odds ratio 2.34, 95% confidence interval 1.07-5.1). No differences occurred in early ventricular catheter failure, complication rate, duration of surgery, or hospital stay. CONCLUSION The guided ventricular catheter application proved to be a safe and simple method. The primary endpoint revealed a nonsignificant improvement of optimal catheter placement among the groups. Long-term follow-up is necessary in order to evaluate differences in catheter survival among shunted patients.

The Crop Science Society of America's (CSSA) position statement--Crop Adaptation to Climate Change--was researched and assembled by a working group of scientists from academia and industry. The statement (i) reviews the impacts of variable weather conditions arising from climate change on cropping systems, (ii) reports the progress to date in adapting crops and management practices to new conditions, and (iii) offers focus areas for increasing the speed at which global agricultural systems can adapt to climate change.

Workflow management aims at modeling and executing application processes in complex technical and organizational environments. Modern information systems are often based on object-oriented design techniques, for instance, the Unified Modeling Language (UML). These systems consist of application objects which collaborate to achieve a common goal. Although application objects collaborate in the context of business processes that can be supported by workflow technology, workflow modeling is typically done with proprietary workflow languages. Hence, two separate formalisms are present for modeling application objects and workflows. In this paper we try to remedy this situation by proposing the use of Object Coordination Nets (OCoN) for workflow modeling. OCoN nets provide a seamless integration with UML structure diagrams. The OCoN formalism also helps to deal with all relevant aspects of modeling complex workflow systems in a scalable and consistent manner. [PUBLICATION ABSTRACT]