Spectral similarity calculation is widely used in protein identification tools and mass spectra clustering algorithms while comparing theoretical or experimental spectra. The performance of the spectral similarity calculation plays an important role in these tools and algorithms especially in the analysis of large-scale datasets. Recently, deep learning methods have been proposed to improve the performance of clustering algorithms and protein identification by training the algorithms with existing data and the use of multiple spectra and identified peptide features. While the efficiency of these algorithms is still under study in comparison with traditional approaches, their application in proteomics data analysis is becoming more common. Here, we propose the use of deep learning to improve spectral similarity comparison. We assessed the performance of deep learning for spectral similarity, with GLEAMS and a newly trained embedder model (DLEAMSE), which uses high-quality spectra from PRIDE Cluster. Also, we dy calculations. The DLEAMSE GPU implementation is faster than NDP in preprocessing on the GPU server and the similarity calculation of DLEAMSE (Euclidean distance on 32-D vectors) takes about 1/3 of dot product calculations. The deep learning model (DLEAMSE) encoding and embedding steps needed to run once for each spectrum and the embedded 32-D points can be persisted in the repository for future comparison, which is faster for future comparisons and large-scale data. Based on these, we proposed a new tool mslookup that enables the researcher to find spectra previously identified in public data. The tool can be also used to generate in-house databases of previously identified spectra to share with other laboratories and consortiums.Cancer cells secrete extracellular vesicles (EVs) that contain molecular information, including proteins and RNA. Oncogenic signalling can be transferred via the cargo of EVs to recipient cells and may influence the behaviour of neighbouring cells or cells at a distance. HA130 This cargo may contain cancer drivers, such as EGFR, and also phosphorylated (activated) components of oncogenic signalling cascades. Till date, the cancer EV phosphoproteome has not been studied in great detail. In the present study, we used U87 and U87EGFRvIII cells as a model to explore EV oncogenic signalling components in comparison to the cellular profile. EVs were isolated using the VN96 ME-kit and subjected to LC-MS/MS based phosphoproteomics and dedicated bioinformatics. Expression of (phosphorylated)-EGFR was highly increased in EGFRvIII overexpressing cells and their secreted EVs. The increased phosphorylated proteins in both cells and EVs were associated with activated components of the EGFR-signalling cascade and included EGFR, AKT2, MAPK8, SMG1, MAP3K7, DYRK1A, RPS6KA3 and PAK4 kinases. In conclusion, EVs harbour oncogenic signalling networks including multiple activated kinases including EGFR, AKT and mTOR. SIGNIFICANCE Extracellular vesicles (EVs) are biomarker treasure troves and are widely studied for their biomarker content in cancer. However, little research has been done on the phosphorylated protein profile within cancer EVs. In the current study, we demonstrate that EVs that are secreted by U87-EGFRvIII mutant glioblastoma cells contain high levels of oncogenic signalling networks. These networks contain multiple activated (phosphorylated) kinases, including EGFR, MAPK, AKT and mTOR.Bone's hierarchical arrangement of collagen and mineral generates a confluence of toughening mechanisms acting at every length scale from the molecular to the macroscopic level. Molecular defects, disease, and age alter bone structure at different levels and diminish its fracture resistance. However, the inability to isolate and quantify the influence of specific features hampers our understanding and the development of new therapies. Here, we combine in situ micromechanical testing, transmission electron microscopy and phase-field modelling to quantify intrinsic deformation and toughening at the fibrillar level and unveil the critical role of fibril orientation on crack deflection. At this level dry bone is highly anisotropic, with fracture energies ranging between 5 and 30 J/m2 depending on the direction of crack propagation. These values are lower than previously calculated for dehydrated samples from large-scale tests. However, they still suggest a significant amount of energy dissipation. This approach provides a new tool to uncouple and quantify, from the bottom up, the roles played by the structural features and constituents of bone on fracture and how can they be affected by different pathologies. The methodology can be extended to support the rational development of new structural composites.Chromatin is a dynamic structure composed of DNA, RNA, and proteins, regulating storage and expression of the genetic material in the nucleus. Heterochromatin plays a crucial role in driving the three-dimensional arrangement of the interphase genome, and in preserving genome stability by maintaining a subset of the genome in a silent state. Spatial genome organization contributes to normal patterns of gene function and expression, and is therefore of broad interest. Mammalian heterochromatin, the focus of this review, mainly localizes at the nuclear periphery, forming Lamina-associated domains (LADs), and at the nucleolar periphery, forming Nucleolus-associated domains (NADs). Together, these regions comprise approximately one-half of mammalian genomes, and most but not all loci within these domains are stochastically placed at either of these two locations after exit from mitosis at each cell cycle. Excitement about the role of these heterochromatic domains in early development has recently been heightened by the discovery that LADs appear at some loci in the preimplantation mouse embryo prior to other chromosomal features like compartmental identity and topologically-associated domains (TADs). While LADs have been extensively studied and mapped during cellular differentiation and early embryonic development, NADs have been less thoroughly studied. Here, we summarize pioneering studies of NADs and LADs, more recent advances in our understanding of cis/trans-acting factors that mediate these localizations, and discuss the functional significance of these associations.