Analysis of the Matrix Event Graph Replicated Data Type

Matrix is a new kind of decentralized, topic-based publish-subscribe middleware for communication and data storage that is getting particularly popular as a basis for secure instant messaging. By comparison with traditional decentralized communication systems, Matrix replaces pure message passing with a replicated data structure. This data structure, which we extract and call the Matrix Event Graph (MEG), depicts the causal history of messages. We show that this MEG represents an interesting and important replicated data type for decentralized applications that are based on causal histories of publish-subscribe events: First, we prove that the MEG is a Conflict-Free Replicated Data Type for causal histories and, thus, provides Strong Eventual Consistency (SEC). With SEC being among the best known achievable trade-offs in the scope of the well-known CAP theorem, the MEG provides a powerful consistency guarantee while being available during network partition. Second, we discuss the implications of byzantine attackers on the data type’s properties. We note that the MEG, as it does not strive for consensus or strong consistency, can cope with $n>f$ environments with $n$ participants, of which $f$ are byzantine. Furthermore, we analyze scalability: Using Markov chains, we study the number of forward extremities of the MEG over time and observe an almost optimal evolution. We conjecture that this property is inherent to the underlying spatially inhomogeneous random walk. With the properties shown, a MEG represents a promising element in the set of data structures for decentralized applications, but with distinct trade-offs compared to traditional blockchains and distributed ledger technologies

Analysis of the Matrix Event Graph Replicated Data Type

Matrix is a new kind of decentralized, topic-based publish-subscribe middleware for communication and data storage that is getting particularly popular as a basis for secure instant messaging. By comparison with traditional decentralized communication systems, Matrix replaces pure message passing with a replicated data structure. This data structure, which we extract and call the Matrix Event Graph (MEG), depicts the causal history of messages. We show that this MEG represents an interesting and important replicated data type for decentralized applications that are based on causal histories of publish-subscribe events: First, we prove that the MEG is a Conflict-Free Replicated Data Type for causal histories and, thus, provides Strong Eventual Consistency (SEC). With SEC being among the best known achievable trade-offs in the scope of the well-known CAP theorem, the MEG provides a powerful consistency guarantee while being available during network partition. Second, we discuss the implications of byzantine attackers on the data type’s properties. We note that the MEG, as it does not strive for consensus or strong consistency, can cope with $n>f$ environments with $n$ participants, of which $f$ are byzantine. Furthermore, we analyze scalability: Using Markov chains, we study the number of forward extremities of the MEG over time and observe an almost optimal evolution. We conjecture that this property is inherent to the underlying spatially inhomogeneous random walk. With the properties shown, a MEG represents a promising element in the set of data structures for decentralized applications, but with distinct trade-offs compared to traditional blockchains and distributed ledger technologies

Analysis of the Matrix Event Graph Replicated Data Type

Matrix is a new kind of decentralized, topic-based publish-subscribe middleware for communication and data storage that is getting popular particularly as a basis for secure instant messaging. In comparison to traditional decentralized communication systems, Matrix replaces pure message passing with a replicated data structure. This data structure, which we extract and call the Matrix Event Graph (MEG), depicts the causal history of messages. We show that this MEG represents an interesting and important replicated data type for general decentralized applications that are based on causal histories of publish-subscribe events: we show that a MEG possesses strong properties with respect to consistency, byzantine attackers, and scalability. First, we show that the MEG provides Strong Eventual Consistency (SEC), and that it is available under partition, by proving that the MEG is a Conflict-Free Replicated Data Type for causal histories. While strong consistency is impossible here as shown by the famous CAP theorem, SEC is among the best known achievable trade-offs. Second, we discuss the implications of byzantine attackers on the data type\u27s properties. We note that the MEG, as it does not strive for consensus, can cope with $n>f$ environments with $n$ total participants of which $f$ show byzantine faults. Furthermore, we analyze scalability: Using Markov chains we study the width of the MEG, defined as the number of forward extremities, over time and observe an almost optimal evolution. We conjecture that this property is inherent to the underlying spatially inhomogeneous random walk

Analysis of the Matrix Event Graph Replicated Data Type

Matrix is a new kind of decentralized, topic-based publish-subscribe middleware for communication and data storage that is getting particularly popular as a basis for secure instant messaging. By comparison with traditional decentralized communication systems, Matrix replaces pure message passing with a replicated data structure. This data structure, which we extract and call the Matrix Event Graph (MEG), depicts the causal history of messages. We show that this MEG represents an interesting and important replicated data type for decentralized applications that are based on causal histories of publish-subscribe events: First, we prove that the MEG is a Conflict-Free Replicated Data Type for causal histories and, thus, provides Strong Eventual Consistency (SEC). With SEC being among the best known achievable trade-offs in the scope of the well-known CAP theorem, the MEG provides a powerful consistency guarantee while being available during network partition. Second, we discuss the implications of byzantine attackers on the data type’s properties. We note that the MEG, as it does not strive for consensus or strong consistency, can cope with $n>f$ environments with $n$ participants, of which $f$ are byzantine. Furthermore, we analyze scalability: Using Markov chains, we study the number of forward extremities of the MEG over time and observe an almost optimal evolution. We conjecture that this property is inherent to the underlying spatially inhomogeneous random walk. With the properties shown, a MEG represents a promising element in the set of data structures for decentralized applications, but with distinct trade-offs compared to traditional blockchains and distributed ledger technologies

Dental and Maxillofacial Cone Beam CT-High Number of Incidental Findings and Their Impact on Follow-Up and Therapy Management.

Cone beam computed tomography (CBCT) is increasingly used for dental and maxillofacial imaging. The occurrence of incidental findings has been reported, but clinical implications of these findings remain unclear. The study's aim was to identify the frequency and clinical impact of incidental findings in CBCT. A total of 374 consecutive CBCT examinations of a 3 year period were retrospectively evaluated for the presence, kind, and clinical relevance of incidental findings. In a subgroup of 54 patients, therapeutic consequences of CBCT incidental findings were queried from the referring physicians. A total of 974 incidental findings were detected, involving 78.6% of all CBCT, hence 2.6 incidental findings per CBCT. Of these, 38.6% were classified to require treatment, with an additional 25.2% requiring follow-up. Incidental findings included dental pathologies in 55.3%, pathologies of the paranasal sinuses and airways in 29.2%, osseous pathologies in 14.9% of all CBCT, and findings in the soft tissue or TMJ in few cases. Clinically relevant dental incidental findings were detected significantly more frequently in CBCT for implant planning compared to other indications (60.7% vs. 43.2%, p < 0.01), and in CBCT with an FOV ≥ 100 mm compared to an FOV < 100 mm (54.7% vs. 40.0%, p < 0.01). Similar results were obtained for paranasal incidental findings. In a subgroup analysis, 29 of 54 patients showed incidental findings which were previously unknown, and the findings changed therapeutical management in 19 patients (35%). The results of our study highlighted the importance of a meticulous analysis of the entire FOV of CBCT for incidental findings, which showed clinical relevance in more than one in three patients. Due to a high number of clinically relevant incidental findings especially in CBCT for implant planning, an FOV of 100 × 100 mm covering both the mandible and the maxilla was concluded to be recommendable for this indication

Germanii din România. Migraţie şi patrimoniu cultural după 1945

Vorträge gehalten bei einer Tagung der Rumänischen Akademie der Wissenschaften mit dem Titel "Die Deutschen aus Rumänien. Migration und kulturelles Erbe", Sibiu 2019

Tissue-specific and interpretable sub-segmentation of whole tumour burden on CT images by unsupervised fuzzy clustering.

BACKGROUND: Cancer typically exhibits genotypic and phenotypic heterogeneity, which can have prognostic significance and influence therapy response. Computed Tomography (CT)-based radiomic approaches calculate quantitative features of tumour heterogeneity at a mesoscopic level, regardless of macroscopic areas of hypo-dense (i.e., cystic/necrotic), hyper-dense (i.e., calcified), or intermediately dense (i.e., soft tissue) portions. METHOD: With the goal of achieving the automated sub-segmentation of these three tissue types, we present here a two-stage computational framework based on unsupervised Fuzzy C-Means Clustering (FCM) techniques. No existing approach has specifically addressed this task so far. Our tissue-specific image sub-segmentation was tested on ovarian cancer (pelvic/ovarian and omental disease) and renal cell carcinoma CT datasets using both overlap-based and distance-based metrics for evaluation. RESULTS: On all tested sub-segmentation tasks, our two-stage segmentation approach outperformed conventional segmentation techniques: fixed multi-thresholding, the Otsu method, and automatic cluster number selection heuristics for the K-means clustering algorithm. In addition, experiments showed that the integration of the spatial information into the FCM algorithm generally achieves more accurate segmentation results, whilst the kernelised FCM versions are not beneficial. The best spatial FCM configuration achieved average Dice similarity coefficient values starting from 81.94±4.76 and 83.43±3.81 for hyper-dense and hypo-dense components, respectively, for the investigated sub-segmentation tasks. CONCLUSIONS: The proposed intelligent framework could be readily integrated into clinical research environments and provides robust tools for future radiomic biomarker validation

Dietary α-linolenic acid diminishes experimental atherogenesis and restricts T cell-driven inflammation

Aims Epidemiological studies report an inverse association between plant-derived dietary α-linolenic acid (ALA) and cardiovascular events. However, little is known about the mechanism of this protection. We assessed the cellular and molecular mechanisms of dietary ALA (flaxseed) on atherosclerosis in a mouse model. Methods and results Eight-week-old male apolipoprotein E knockout (ApoE−/−) mice were fed a 0.21 % (w/w) cholesterol diet for 16 weeks containing either a high ALA [7.3 % (w/w); n = 10] or low ALA content [0.03 % (w/w); n = 10]. Bioavailability, chain elongation, and fatty acid metabolism were measured by gas chromatography of tissue lysates and urine. Plaques were assessed using immunohistochemistry. T cell proliferation was investigated in primary murine CD3-positive lymphocytes. T cell differentiation and activation was assessed by expression analyses of interferon-γ, interleukin-4, and tumour necrosis factor α (TNFα) using quantitative PCR and ELISA. Dietary ALA increased aortic tissue levels of ALA as well as of the n−3 long chain fatty acids (LC n−3 FA) eicosapentaenoic acid, docosapentaenoic acid, and docosahexaenoic acid. The high ALA diet reduced plaque area by 50% and decreased plaque T cell content as well as expression of vascular cell adhesion molecule-1 and TNFα. Both dietary ALA and direct ALA exposure restricted T cell proliferation, differentiation, and inflammatory activity. Dietary ALA shifted prostaglandin and isoprostane formation towards 3-series compounds, potentially contributing to the atheroprotective effects of ALA. Conclusion Dietary ALA diminishes experimental atherogenesis and restricts T cell-driven inflammation, thus providing the proof-of-principle that plant-derived ALA may provide a valuable alternative to marine LC n−3 F

Cosmogenic radionuclides reveal an extreme solar particle storm near a solar minimum 9125 years BP

During solar storms, the Sun expels large amounts of energetic particles (SEP) that can react with the Earth’s atmospheric constituents and produce cosmogenic radionuclides such as 14C, 10Be and 36Cl. Here we present 10Be and 36Cl data measured in ice cores from Greenland and Antarctica. The data consistently show one of the largest 10Be and 36Cl production peaks detected so far, most likely produced by an extreme SEP event that hit Earth 9125 years BP (before present, i.e., before 1950 CE), i.e., 7176 BCE. Using the 36Cl/10Be ratio, we demonstrate that this event was characterized by a very hard energy spectrum and was possibly up to two orders of magnitude larger than any SEP event during the instrumental period. Furthermore, we provide 10Be-based evidence that, contrary to expectations, the SEP event occurred near a solar minimum