Structural Variability of 3C 111 on Parsec Scales

We discuss the parsec-scale structural variability of the extragalactic jet 3C 111 related to a major radio flux density outburst in 2007, The data analyzed were taken within the scope of the MOJAVE, UMRAO, and F-GAMMA programs, which monitor a large sample of the radio brightest compact extragalactic jets with the VLBA, the University of Michigan 26 m, the Effelsberg 100 m, and the IRAM 30 m radio telescopes. The analysis of the VLBA data is performed by fitting Gaussian model components in the visibility domain, We associate the ejection of bright features in the radio jet with a major flux-density outburst in 2007, The evolution of these features suggests the formation of a leading component and multiple trailing component

The eROSITA X-ray telescope on SRG

eROSITA (extended ROentgen Survey with an Imaging Telescope Array) is the primary instrument on the Spectrum-Roentgen-Gamma (SRG) mission, which was successfully launched on July 13, 2019, from the Baikonour cosmodrome. After the commissioning of the instrument and a subsequent calibration and performance verification phase, eROSITA started a survey of the entire sky on December 13, 2019. By the end of 2023, eight complete scans of the celestial sphere will have been performed, each lasting six months. At the end of this program, the eROSITA all-sky survey in the soft X-ray band (0.2-2.3 keV) will be about 25 times more sensitive than the ROSAT All-Sky Survey, while in the hard band (2.3-8 keV) it will provide the first ever true imaging survey of the sky. The eROSITA design driving science is the detection of large samples of galaxy clusters up to redshifts z > 1 in order to study the large-scale structure of the universe and test cosmological models including Dark Energy. In addition, eROSITA is expected to yield a sample of a few million AGNs, including obscured objects, revolutionizing our view of the evolution of supermassive black holes. The survey will also provide new insights into a wide range of astrophysical phenomena, including X-ray binaries, active stars, and diffuse emission within the Galaxy. Results from early observations, some of which are presented here, confirm that the performance of the instrument is able to fulfil its scientific promise. With this paper, we aim to give a concise description of the instrument, its performance as measured on ground, its operation in space, and also the first results from in-orbit measurements

Genomic view of the evolution of the complement system

The recent accumulation of genomic information of many representative animals has made it possible to trace the evolution of the complement system based on the presence or absence of each complement gene in the analyzed genomes. Genome information from a few mammals, chicken, clawed frog, a few bony fish, sea squirt, fruit fly, nematoda and sea anemone indicate that bony fish and higher vertebrates share practically the same set of complement genes. This suggests that most of the gene duplications that played an essential role in establishing the mammalian complement system had occurred by the time of the teleost/mammalian divergence around 500 million years ago (MYA). Members of most complement gene families are also present in ascidians, although they do not show a one-to-one correspondence to their counterparts in higher vertebrates, indicating that the gene duplications of each gene family occurred independently in vertebrates and ascidians. The C3 and factor B genes, but probably not the other complement genes, are present in the genome of the cnidaria and some protostomes, indicating that the origin of the central part of the complement system was established more than 1,000 MYA

Pierwiastki rzadkie w infrastrukturze kolejowej – potencjał dla systemu informacyjnego jako narzędzie dla operatorów i innych zainteresowanych stron

Rare earth elements are used for specific products and components in the railway infrastructure. The application of rare earths in this sector includes fields such as command and control technology, power supply and telecommunication. The demand for these items continues to rise, whereas the natural supply remains limited. This can lead to escalation of prices and can advance to natural resource conflicts. In addition, the degradation of these elements is often carried out under conditions which can be life threatening or harmful. This contribution presents a new systematic approach for analysis of rare earths in the field of railway infrastructure. This fills the gap of studies in the sector of railway infrastructure regarding identification, substitution, recycling and innovation to replace these elements, which are well performed in other fields of technology and industry. This paper first provides the basics of rare earths and analyses their relevance in the sector. Furthermore, proposes a basic conceptual design of an information system for evaluation and analysis of these elements. This was done by contextual analysis where the relevant stakeholders such as infrastructure operators or industry, were identified. The analysis included the demand of the stakeholder for such an information system. Thus, the design and the architecture of an information system was specified. The system should provide a tool for operators of rail infrastructures to get a systematic overview on the use of rare earths elements and for strategic decisions, e.g. substitution by other elements, cooperation with suppliers for alternatives, including consideration of the Life Cycle Cost.Pierwiastki rzadkie są wykorzystywane do konkretnych produktów i komponentów infrastruktury kolejowej. Zastosowanie pierwiastków rzadkich w tym sektorze zawiera się w obszarach, takich jak: dowodzenie i techniki sterowania, zasilanie oraz telekomunikacja. Popyt na pierwiastki rzadkie nadal rośnie, podczas gdy naturalna podaż pozostaje ograniczona. Może to prowadzić do eskalacji cen, i przekształcić się w konflikty o zasoby naturalne. Ponadto, degradacja takich elementów często przeprowadzana jest w warunkach, które mogą stanowić zagrożenie dla życia. Artykuł pokazuje nowe podejście do analizy systematycznej pierwiastków rzadkich w obszarze infrastruktury kolejowej. Analiza wypełnia lukę w badaniach w sektorze infrastruktury kolejowej w zakresie identyfikacji, substytucji, recyklingu i innowacji elementów, które 112 F. Michelberger, H. Grossberger, P. Judmaier są dobrze opisane w innych dziedzinach techniki i przemysłu. Praca jako pierwsza dostarcza podstaw wiedzy obejmującej pierwiastki rzadkie i analizuje ich znaczenie w sektorze kolejowym. Ponadto, proponuje podstawowy projekt koncepcyjny systemu informacji do oceny i analizy tych elementów. Został on opracowany na podstawie analizy kontekstowej, w którym brały udział zainteresowane strony, takie jak operatorzy infrastruktury i przemysłu. W analizie uwzględniono zapotrzebowanie udziałowcy dla takiego systemu informatycznego. Zarówno projektowanie, jak i architektura systemu informacyjnego zostały określone. System powinien być narzędziem dla operatorów infrastruktury kolejowej tak, aby móc uzyskiwać systematyczny przegląd wykorzystania pierwiastków rzadkich i podejmować strategiczne decyzje, przykładowo dotyczące zastąpienia ich przez inne elementy, współpracy z dostawcami dla alternatyw, uwzględniania kosztu cyklu życia

Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure

<div><p>Temporally ordered multi-neuron patterns likely encode information in the brain. We introduce an unsupervised method, SPOTDisClust (Spike Pattern Optimal Transport Dissimilarity Clustering), for their detection from high-dimensional neural ensembles. SPOTDisClust measures similarity between two ensemble spike patterns by determining the minimum transport cost of transforming their corresponding normalized cross-correlation matrices into each other (SPOTDis). Then, it performs density-based clustering based on the resulting inter-pattern dissimilarity matrix. SPOTDisClust does not require binning and can detect complex patterns (beyond sequential activation) even when high levels of out-of-pattern “noise” spiking are present. Our method handles efficiently the additional information from increasingly large neuronal ensembles and can detect a number of patterns that far exceeds the number of recorded neurons. In an application to neural ensemble data from macaque monkey V1 cortex, SPOTDisClust can identify different moving stimulus directions on the sole basis of temporal spiking patterns.</p></div

More patterns can be detected than the number of neurons with SPOTDisClust.

<p>Left: Two realizations of two different patterns, for 50 neurons. Simulation parameters were λ_<i>in</i> = 0.35 spks/sample, λ_<i>out</i> = 0.05 spks/sample, <i>T</i>_<i>epoch</i> = 300 samples, <i>T</i>_<i>pulse</i> = 30 samples. For each pattern and each neuron, a random position was chosen for the activation pulse. Right: For 500 patterns, 30 realizations per pattern were generated, and 15000 noise epochs were added. t-SNE projection with HDBSCAN labels shows that our clustering method can retrieve all patterns from the data.</p

SPOTDisClust can detect temporal patterns expressed in ensembles of sparsely firing neurons.

<p>Example of 5 patterns with sparse firing. Simulation parameters were λ_<i>in</i> = 0.015 spks/sample, λ_<i>out</i> = 0.0001 spks/sample, <i>T</i>_<i>epoch</i> = 300 samples, <i>T</i>_<i>pulse</i> = 30 samples. For each pattern, two spike realizations are shown. Bottom panels show sorted dissimilarity matrix and t-SNE with ground-truth cluster labels (left) and HDBSCAN cluster labels (right).</p

Application to neuronal data.

<p>Top: shown are the average peri-stimulus histograms for four moving bar stimuli spanning the four cardinal directions. Middle: multi-unit spike train realizations for each of the four conditions. Bottom: sorted dissimilarity matrix, t-SNE with ground-truth labels, and t-SNE with HDBSCAN cluster labels.</p

Detection of other types of complex patterns using SPOTDisClust.

<p>(A) Realizations of four different patterns, in which two patterns (1-2 and 3-4) have the same coarse structure, but a finer is structure embedded inside each coarse pattern. Simulation parameters were λ_{<i>in</i>,<i>coarse</i>} = 0.2 spks/sample, λ_{<i>in</i>,<i>fine</i>} = 0.8 spks/sample, λ_<i>out</i> = 0.05 spks/sample <i>T</i>_<i>epoch</i> = 300, <i>T</i>_{<i>pulse</i>,<i>coarse</i>} = 90 samples, <i>T</i>_{<i>pulse</i>,<i>fine</i>} = 30 samples. Panels on bottom show sorted dissimilarity matrix and t-SNE for simulations with patterned noise (left) and homogeneous noise (right). (B) Realizations of multiple patterns, in which different random subsets of neurons become simultaneously active, leading to a synchronous firing without temporal order. Simulation parameters were λ_<i>in</i> = 0.4 spks/sample, λ_<i>out</i> = 0.05 spks/sample, <i>T</i>_<i>epoch</i> = 300, <i>T</i>_<i>pulse</i> = 50 samples.</p