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Translation of “Vilna” chapter from Pinkas Hakehillot Polin

Published by Yad Vashem

Published in Jerusalem

Our sincere appreciation to Yad Vashem for permission to put this material on the JewishGen web site.

This is a translation from: Pinkas Hakehillot: Encyclopedia of Jewish Communities, Poland, Volume VIII, pages 30-92, published by Yad Vashem, Jerusalem

JewishGen, Inc. makes no representations regarding the accuracy of the translation. The reader may wish to refer to the original material for verification. JewishGen is not responsible for inaccuracies or omissions in the original work and cannot rewrite or edit the text to correct inaccuracies and/or omissions. Our mission is to produce a translation of the original work and we cannot verify the accuracy of statements or alter facts cited.

[Page 30]

In Lithuanian Vilnius, in Polish Wilno

District capital, Capital of Lithuania

Part 2 written by Aaron Weis

Translated by Shimon Joffe

Vilna lies on a number of hills and the valley below them at the confluence of the rivers Wilija and Wilejka. Thanks to these two rivers and at the end of the 19 th century, to the junction of the main railway lines which met at the city, as well as roads connecting it to the Baltic countries and to Russia, it became a commercial center of first order. Other important sectors in the economy were forestry, raising cattle and sheep and later also the creation of agricultural industries, timber based industry, tanning, production of agricultural machinery and other equipment, clothing, footwear etc. Vilna has a university and many cultural institutions. During the 11 th and the 12 th centuries Vilna was the seat of the prince Połock. In 1323 the Lithuanian prince Gediminas moved his capital from Trokai to Vilna and built a fortress next to it. In 1387 Vilna was granted the Magdeburg Privileges. During the second half of the 14 th and the beginning of the 15 th centuries the city suffered many incursions by the Teutonic knights coming from eastern Prussia. The city, built of wood, burnt down a number of times and had to be rebuilt each time. In the 16 th century it was the capital of the Lithuanian prince and the seat of government, the prince's mint and many public institutions. After the Polish Lithuanian union in 1569 (The Lublin Union), it was recognized officially as the capital of the Grand Duchy of Lithuania within the United Kingdom. The Polish kings, who also bore the title of Grand Duke of Lithuania and visited it regularly, consolidated its position as the center of government, of the economy and of Lithuanian culture. In 1579 King Stefan Batory founded the Academia et Universitas Vilnesis , and from 1581 the Lithuanian Tribunal (High Court) sat there.

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Gakhar, G., Bander, N. H., Nanus, D. M. Method to Observe E-selectin-mediated Interactions Between Prostate Circulating Tumor Cells Derived From Patients and Human Endothelial Cells. (87), e51468, doi:10.3791/51468 (2014).

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German ( PUMA Puma x Fenty by Rihanna Cleated Creeper Surf kSr9SlYX
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Click here for the english version. For other languages click here .

Metastasierung ist ein Prozess, in dem Tumorzellen zu vergießen aus dem Primärtumor intravasate Blut Gefäß-und Lymphsystem, damit, den Zugang zu extravasieren und bilden einen Sekundär Nische. Die Extravasation von Tumorzellen aus dem Blutgefäßsystem kann mit Endothelzellen (ECs) und Tumorzellen aus verschiedenen Zelllinien untersucht werden. Erste Studien wurden mit statischen Bedingungen durchgeführt, aber es ist gut dokumentiert, dass ECs anders unter physiologischen Flussbedingungen verhalten. Daher unterschiedlichen Strömungskammeranordnungen werden derzeit zum Studium Krebs-Zell-Interaktionen mit ECs verwendet. Aktuelle Durchflusskammer-Baugruppen bieten reproduzierbare Ergebnisse entweder mit verschiedenen Zelllinien oder Flüssigkeit bei unterschiedlichen Scherstressbedingungen. Jedoch zu beobachten und zu studieren Wechselwirkungen mit seltenen Zellen, wie zirkulierenden Tumorzellen (CTC), sind bestimmte Änderungen erforderlich, um das herkömmliche Strömungskammer-Anordnung hergestellt werden. CTCsind eine seltene Zellpopulation unter Millionen von Blutzellen. Folglich ist es schwierig, eine reine Population von CTC erhalten. Kontamination der CTC mit verschiedenen Arten von Zellen, die normalerweise im Blut gefundenen unvermeidlich mit vorliegenden Anreicherung oder Abreicherung Techniken. In dem vorliegenden Bericht beschreiben wir eine einzigartige Methode zur fluoreszenz Label zirkulierenden Prostatakrebszellen und studieren ihre Wechselwirkungen mit ECs in einer selbstorganisierten Flusskammer-System. Diese Technik kann weiter verwendet werden, um Wechselwirkungen zwischen Prostata-CTC und jedes Protein von Interesse zu beobachten.

Metastasierung ist ein komplexer mehrstufiger Prozess, der kaum verstanden bleibt. Der Ligand E-selectin/selectin Achse ist gezeigt worden, eine wichtige Rolle bei der Tumormetastasierung durch Förderung der Primär adhäsiven Wechselwirkungen zwischen dem vaskulären Endothel und Krebszellen spielen. Endothelzellen (E)-Selectin ist ein Transmembran-Protein von aktivierten Endothelzellen exprimiert wird, während die verschiedenen E-Selektin-Ligand (en) durch Tumorzellen ausgedrückt. Zahlreiche wurden erfolgreich eingesetzt, um E-selectin/selectin Ligand-Wechselwirkungen zwischen Tumorzellen und Endothelzellen (ECs) zu modellieren. Um diese Wechselwirkungen zu untersuchen, werden verschiedene Durchflusskammer-Systeme eingesetzt, um Blutgefäßsystem zu simulieren. Unter Flusskammer-Einheiten wird Parallelplatten-Durchflusskammer (PPFC) in Verbindung mit ECs routinemäßig als simuliert Scherstressbedingungen eingesetzt. HierinVerfahren ECs auf einem 35-mm-Schale gezüchtet und nach Erreichen einer Monoschicht werden ECs zum PPFC befestigt und Scherspannung basierend Experimente durchgeführt werden.

, PPFC und anderen aktuellen Systemen präsentieren jedoch viele Einschränkungen zum Studium adhäsive Wechselwirkungen zwischen zirkulierenden Tumorzellen (CTC) von Patienten und ECs stammen, die vorwiegend, weil CTC sind eine seltene Population von Zellen, aus dem Primärtumor Schuppen, unter den Millionen von zirkulierenden Blutzellen (1 CTC pro 10 Blutkörperchen) Daher, im Gegensatz zu unbegrenzten Vorrat an kultivierten Zelllinien, geringe CTC zählt zu sehr wenigen und seltenen CTC / EC-Interaktionen, erfordern richtige Strömung Kanalbreite, um die Interaktionen für die Wiedergabe Analyse aufzeichnen. Darüber hinaus, da Patienten abgeleitet CTC sind eine unreine Bevölkerung, also eine Identifikationsmarkierung ist erforderlich, um in bestimmten CTC verfolgen. Um dieses Problem zu lösen, ein neues Verfahren, um Prostatakrebs zu identifizieren (PCa) CT entwickelten wirCs unter Ausnutzung der Tatsache, daß praktisch alle diese CTC auszudrücken Prostata-spezifischen Membranantigen (PSMA) auf ihrer Zelloberfläche In diesem Bericht haben wir die Prostatakrebs-Zelllinie, MDA PCa2b (MDA), um den potentiellen Nutzen von unserem neuen System, um Prostatakrebs, CTC Wechselwirkungen mit ECs Studie zeigen, schließlich, um den Mechanismus der Metastasierung zu verstehen.

Unsere Methodik kann für verschiedene Scher basierte Experimente simuliert Gefäßsystem angewendet werden. Neben der Prüfung PCa CTC / EC-Interaktionen, konnte der Stromfluss-Kammer-System leicht für die Analyse von peripheren mononukleären Blutzellen oder Wechselwirkungen Tumorzellen 'mit ECs angepasst werden. Die Einfachheit der Demontage und Wiedermontage der Strömungskammer, ein Mikroobjekt III (nachstehend als Mikroobjekt bezeichnet), ermöglicht die Kultivierung ECs unter Perfusion und Stimulieren ECs mit verschiedenen Zytokinen induzieren protein Ausdruck. Außerdem kultivierten Endothelzellen, rekombinante Proteine, wie E-und P-Selektin auf der Mikroobjekt und Interaktion mit Tumorzellen, beschichtet werden können, unter laminaren Strömungsbedingungen beobachtet werden.

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FIG 3

A core microbiota exists across mice of both Peromyscus species. The relative abundance of the OTUs (A) and genera and other taxa (B) that were found in at least 90% of the mice. The limit of detection was 0.02%.

Effects of host physiology on diversity and richness. We next tested how differences in sex, reproductive condition, and age affected the gut microbiota of the Peromyscus sp. animals. We were unable to detect a statistically significant sex-based difference in the richness, Shannon diversity, phylogenetic diversity, or community structure of the Peromyscus sp. microbiota. To investigate possible sex-based differences further, we focused on sexually mature adult males and females, which had descended testes and emerged nipples, respectively; however, we were unable to identify a sex-based effect on the richness, Shannon diversity, phylogenetic diversity, or community structure. Furthermore, none of the OTUs that appeared in at least half of the samples were differentially enriched in either sex. Stratification of the mice into the age categories of juvenile ( n = 30), adolescent ( n = 30), and adult ( n = 51) did reveal small but significant differences in the richness and diversity between the cohorts; the observed differences in their phylogenetic diversity was not statistically significant ( Fig. 4A ). The overall trend was for older mice to have a richer and more complex community. This was paralleled by a modest correlation between the animals' weight and their Shannon diversity (Spearman's ρ = 0.31; Fig. 4B ). Although there did appear to be some effect of age on the richness and diversity of the communities, it was difficult to ascribe much biological significance to these differences, considering that the differences in overall community structure were not significantly different from each other and there were no OTUs that differed in abundance among the three age categories. Overall, by the parameters we were able to measure, we were unable to detect a robust effect of host physiology on their microbiota.

View larger version:
FIG 4

Effects of age (A) and weight (B) on richness and diversity.

Location of sampling was not associated with variation in microbiota. Next, we hypothesized that animals collected from the same environment would be more likely to have similar microbiotas than animals collected from different environments. For this analysis, we used the sampling coordinates to define the animals' environment ( Born Karava KYUkF
). When we performed a Mantel test to examine the association between the distance between the sampling points and the β diversity of the gut microbiotas, we failed to detect a significant association (Mantel correlation coefficient for θ distances [] = −0.03; Mantel correlation coefficient for weighted UniFrac distances [] = ∼−0.03). To test for a more localized habitat effect, we compared the distances collected at the same site to those collected at different sites and again failed to detect a statistically significant effect. These results suggest the lack of a geographic or habitat-based effect on the Peromyscus sp. microbiota.

Diet was not associated with variation in microbiota. To test the role of diet in the shaping of the Peromyscus species gut microbiota, we characterized the dietary contents of each P. leucopus and . gracilis sample by 18S rRNA sequencing. The diets of P. leucopus and . gracilis consisted of plant material (e.g., seeds, nuts, fruits, and green vegetation), arthropods, and fungi, which was consistent with previous results ( Dolce Vita Taya wqeHIJWgdh
). We removed sequences outside these broad taxonomic groups to minimize the contributions of microeukaryotic members of the gut microbiota. First, we tested for a difference in diet composition based on the two species of Peromyscus . We failed to find a significant difference between the structures of the diets when using AMOVA with correction for repeated measures. Second, using the Mantel test, we failed to identify a significant association between the distances between samples based on their bacterial community structure and the distances based on the structure of their diet ( = 0.07, = 0.11). Both analyses suggest the lack of an association between the structures of the diet and microbiota.

To further characterize the relationship between diet and the microbiota, we attempted to correlate the relative abundance of OTUs that were found in more than half of the samples with the relative abundance of dietary components that appeared in more than half of the samples. We were unable to detect any statistically significant Spearman correlation coefficients. Next, we used DMM models to identify clusters within the diet data. This approach identified two clusters, a cluster of samples dominated by plant material ( = 51) and a cluster of samples with a more diverse mixture of plants, arthropods, and fungi ( = 36). When we used these clusters to test for an association within the community structure distance matrix between the microbiotas of the mice, we did not observe a significant effect of diet on community structure. We then used the random-forest algorithm to identify features in the microbiota that could distinguish the two dietary groups; however, the out-of-bag error rate was 37.9%, which indicates that it was unable to correctly classify the samples. As these analyses demonstrate, we were unable to find associations between specific members of the microbiota and the dietary contents of each sample. This confirms our observation that the weak association between the distances between the microbiota and diet structures was unlikely to be biologically relevant.

A transient microbiota. A striking characteristic of the microbiotas characterized in this study was the high intra- and interindividual variation. Studies of other animals have observed less variation in microbiota structure within an individual over time than between individuals. We sought to determine whether nondomesticated animals also harbor personalized microbiotas. To investigate this in Peromyscus spp., we took advantage of the ability to catch and release the same animal multiple times. By the OTU-based approach, the median intraindividual distance was significantly less than the median interindividual distance ( Fig. 5A ); however, by the weighted UniFrac-based approach, the difference was not significant. To investigate this further, we calculated the number of days that elapsed between recapturing events and created five time windows such that each window had approximately the same number of mice that were recaptured within that time period (i.e., 1 to 3 [ n = 30], 4 to 11 [ n = 22], 12 to 14 [ n = 24], 15 to 19 [ n = 21], and ≥20 [ n = 24] days apart). We then compared the intra-animal distances within these time periods to the interanimal distances of different mice captured within the same intervals. Surprisingly, only the mice captured between 1 and 3 days apart were more similar to themselves than to other mice ( Merrell Dassie Mary Jane Flat R6dEgVtS
). In none of the other time windows was there a significant difference between the samples collected from the same animal and the samples collected from different animals. These results suggest that any individualization of an animal's microbiota is quickly lost.

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