Search

ABSTRACT The search for the “Holy Grail” in clinical diagnostic microbiology—a reliable, accurate, low-cost, real-time, easy-to-use method—has brought up several methods with the potential to meet these criteria. One is Raman spectroscopy, an optical, nondestructive method based on the inelastic scattering of monochromatic light. The current study focuses on the possible use of Raman spectroscopy for identifying microbes causing severe, often life-threatening bloodstream infections. We included 305 microbial strains of 28 species acting as causative agents of bloodstream infections. Raman spectroscopy identified the strains from grown colonies, with 2.8% and 7% incorrectly identified strains using the support vector machine algorithm based on centered and uncentred principal-component analyses, respectively. We combined Raman spectroscopy with optical tweezers to speed up the process and captured and analyzed microbes directly from spiked human serum. The pilot study suggests that it is possible to capture individual microbial cells from human serum and characterize them by Raman spectroscopy with notable differences among different species. IMPORTANCE Bloodstream infections are among the most common causes of hospitalizations and are often life-threatening. To establish an effective therapy for a patient, the timely identification of the causative agent and characterization of its antimicrobial susceptibility and resistance profiles are essential. Therefore, our multidisciplinary team of microbiologists and physicists presents a method that reliably, rapidly, and inexpensively identifies pathogens causing bloodstream infections—Raman spectroscopy. We believe that it might become a valuable diagnostic tool in the future. Combined with optical trapping, it offers a new approach where the microorganisms are individually trapped in a noncontact way by optical tweezers and investigated by Raman spectroscopy directly in a liquid sample. Together with the automatic processing of measured Raman spectra and comparison with a database of microorganisms, it makes the whole identification process almost real time.

Summary: Genetic loss and gain of function in mice have typically been studied by using knockout or knockin mice that take months to years to generate. To address this problem for the nervous system, we developed NEPTUNE (NEural Plate Targeting by in Utero NanoinjEction) to rapidly and flexibly transduce the neural plate with virus prior to neurulation, and thus manipulate the future nervous system. Stable integration in >95% of cells in the brain enabled long-term overexpression, and conditional expression was achieved by using cell-type-specific MiniPromoters. Knockdown of Olig2 by using NEPTUNE recapitulated the phenotype of Olig2−/− embryos. We used NEPTUNE to investigate Sptbn2, mutations in which cause spinocerebellar ataxia type 5. Sptbn2 knockdown induced dose-dependent defects in the neural tube, embryonic turning, and abdominal wall closure, previously unreported functions for Sptbn2. NEPTUNE thus offers a rapid and cost-effective technique to test gene function in the nervous system and can reveal phenotypes incompatible with life. Motivation: Current methods used to alter gene expression in mouse embryos to study brain development lack efficacy and are time consuming. NEPTUNE provides an addition to the molecular toolbox, allowing rapid and widespread manipulation of gene expression before neurulation in vivo. This technique can be adapted to specifically target central nervous system (CNS) cell subtypes such as neurons or glia. Functional analysis can be performed within days after injection. NEPTUNE can thus reduce the financial and ethical costs of animal research, while accelerating the process from hypothesis to results in vivo.

Embryonic neural stem cells (NSCs), comprising neuroepithelial and radial glial cells, are indispensable precursors of neurons and glia in the mammalian developing brain. Since the process of neurogenesis occurs in a hypoxic environment, the question arises of how NSCs deal with low oxygen tension and whether it affects their stemness. Genes from the hypoxia-inducible factors (HIF) family are well known factors governing cellular response to hypoxic conditions. In this study, we have discovered that the endogenous stabilization of hypoxia-inducible factor 1α (Hif1α) during neural induction is critical for the normal development of the NSCs pool by preventing its premature depletion and differentiation. The knock-out of the Hif1α gene in mESC-derived neurospheres led to a decrease in self-renewal of NSCs, paralleled by an increase in neuronal differentiation. Similarly, neuroepithelial cells differentiated in hypoxia exhibited accelerated neurogenesis soon after Hif1α knock-down. In both models, the loss of Hif1α was accompanied by an immediate drop in neural repressor Hes1 levels while changes in Notch signaling were not observed. We found that active Hif1α/Arnt1 transcription complex bound to the evolutionarily conserved site in Hes1 gene promoter in both neuroepithelial cells and neural tissue of E8.5 – 9.5 embryos. Taken together, these results emphasize the novel role of Hif1α in the regulation of early NSCs population through the activation of neural repressor Hes1, independently of Notch signaling.

Abstract In this work, we proposed a new data acquisition approach that significantly improves the repetition rates of Laser-Induced Breakdown Spectroscopy (LIBS) experiments, where high-end echelle spectrometers and intensified detectors are commonly used. The moderate repetition rates of recent LIBS systems are caused by the utilization of intensified detectors and their slow full frame (i.e. echellogram) readout speeds with consequent necessity for echellogram-to-1D spectrum conversion (intensity vs. wavelength). Therefore, we investigated a new methodology where only the most effective pixels of the echellogram were selected and directly used in the LIBS experiments. Such data processing resulted in significant variable down-selection (more than four orders of magnitude). Samples of 50 sedimentary ores samples (distributed in 13 ore types) were analyzed by LIBS system and then classified by linear and non-linear Multivariate Data Analysis algorithms. The utilization of selected pixels from an echellogram yielded increased classification accuracy compared to the utilization of common 1D spectra.

Dependency trees have proven to be a very successful model to represent the syntactic structure of sentences of human languages. In these structures, vertices are words and edges connect syntactically-dependent words. The tendency of these dependencies to be short has been demonstrated using random baselines for the sum of the lengths of the edges or their variants. A ubiquitous baseline is the expected sum in projective orderings (wherein edges do not cross and the root word of the sentence is not covered by any edge), that can be computed in time O(n). Here we focus on a weaker formal constraint, namely planarity. In the theoretical domain, we present a characterization of planarity that, given a sentence, yields either the number of planar permutations or an efficient algorithm to generate uniformly random planar permutations of the words. We also show the relationship between the expected sum in planar arrangements and the expected sum in projective arrangements. In the domain of applications, we derive a O(n)-time algorithm to calculate the expected value of the sum of edge lengths. We also apply this research to a parallel corpus and find that the gap between actual dependency distance and the random baseline reduces as the strength of the formal constraint on dependency structures increases, suggesting that formal constraints absorb part of the dependency distance minimization effect. Our research paves the way for replicating past research on dependency distance minimization using random planar linearizations as random baseline. [ABSTRACT FROM AUTHOR]

Grain prices in early modern Europe reflected the effects of weather and climate on crop yields and a complex array of societal and socio-economic factors. This study presents a newly developed series of grain prices for Sušice (southwestern Bohemia, Czech Republic) for the period 1725–1824 CE, based on various archival sources. It aims to analyse their relationships with weather and climate, represented by temperature, precipitation, and drought (self-calibrated Palmer Drought Severity Index, scPDSI) reconstructions, as well as particular weather extremes and anomalies reported in documentary evidence. Wheat, rye, barley, and oats series in Sušice showed high mutual correlations. The mean highest prices during the year typically occurred from May to July before the harvest, while prices usually declined afterwards. Wheat, rye, and barley prices were significantly negatively correlated with spring temperatures and positively correlated with scPDSI from winter to summer. This indicates that wetter winters, cooler and wetter springs, and wetter summers contributed to higher prices. The extremely high grain prices in the years 1746, 1771–1772, 1802–1806, and 1816–1817 were separately analysed with respect to weather and climate patterns and other socio-economic and political factors. The results obtained were discussed in relation to data uncertainty, factors influencing grain prices, and the broader European context. [ABSTRACT FROM AUTHOR]

To what extent can neural network models learn generalizations about language structure, and how do we find out what they have learned? We explore these questions by training neural models for a range of natural language processing tasks on a massively multilingual dataset of Bible translations in 1,295 languages. The learned language representations are then compared to existing typological databases as well as to a novel set of quantitative syntactic and morphological features obtained through annotation projection. We conclude that some generalizations are surprisingly close to traditional features from linguistic typology, but that most of our models, as well as those of previous work, do not appear to have made linguistically meaningful generalizations. Careful attention to details in the evaluation turns out to be essential to avoid false positives. Furthermore, to encourage continued work in this field, we release several resources covering most or all of the languages in our data: (1) multiple sets of language representations, (2) multilingual word embeddings, (3) projected and predicted syntactic and morphological features, (4) software to provide linguistically sound evaluations of language representations. [ABSTRACT FROM AUTHOR]

Dependency-based approaches to syntactic analysis assume that syntactic structure can be analyzed in terms of binary asymmetric dependency relations holding between elementary syntactic units. Computational models for dependency parsing almost universally assume that an elementary syntactic unit is a word, while the influential theory of Lucien Tesnière instead posits a more abstract notion of nucleus, which may be realized as one or more words. In this article, we investigate the effect of enriching computational parsing models with a concept of nucleus inspired by Tesnière. We begin by reviewing how the concept of nucleus can be defined in the framework of Universal Dependencies, which has become the de facto standard for training and evaluating supervised dependency parsers, and explaining how composition functions can be used to make neural transition-based dependency parsers aware of the nuclei thus defined. We then perform an extensive experimental study, using data from 20 languages to assess the impact of nucleus composition across languages with different typological characteristics, and utilizing a variety of analytical tools including ablation, linear mixed-effects models, diagnostic classifiers, and dimensionality reduction. The analysis reveals that nucleus composition gives small but consistent improvements in parsing accuracy for most languages, and that the improvement mainly concerns the analysis of main predicates, nominal dependents, clausal dependents, and coordination structures. Significant factors explaining the rate of improvement across languages include entropy in coordination structures and frequency of certain function words, in particular determiners. Analysis using dimensionality reduction and diagnostic classifiers suggests that nucleus composition increases the similarity of vectors representing nuclei of the same syntactic type. [ABSTRACT FROM AUTHOR]

Recent advances in multilingual language modeling have brought the idea of a truly universal parser closer to reality. However, such models are still not immune to the "curse of multilinguality": Cross-language interference and restrained model capacity remain major obstacles. To address this, we propose a novel language adaptation approach by introducing contextual language adapters to a multilingual parser. Contextual language adapters make it possible to learn adapters via language embeddings while sharing model parameters across languages based on contextual parameter generation. Moreover, our method allows for an easy but effective integration of existing linguistic typology features into the parsing model. Because not all typological features are available for every language, we further combine typological feature prediction with parsing in a multi-task model that achieves very competitive parsing performance without the need for an external prediction system for missing features. The resulting parser, UDapter, can be used for dependency parsing as well as sequence labeling tasks such as POS tagging, morphological tagging, and NER. In dependency parsing, it outperforms strong monolingual and multilingual baselines on the majority of both high-resource and low-resource (zero-shot) languages, showing the success of the proposed adaptation approach. In sequence labeling tasks, our parser surpasses the baseline on high resource languages, and performs very competitively in a zero-shot setting. Our in-depth analyses show that adapter generation via typological features of languages is key to this success. [ABSTRACT FROM AUTHOR]

This paper is a loose sequel to our 2017 essay “The Hussite Era in the First Edition of Daniel Adam of Veleslavín’s Historical Calendar”, in which we argued that any researcher of an Early Modern Czech historiographic text should thoroughly compare its factual content with the sources it creatively paraphrases, mainly with Václav Hájek of Libočany’s Czech Cronicle. The present article introduces eight Czech manuscripts that emerged in the years 1741–1835 containing passages devoted to the Hussite era and retelling the story of the late 14th and 15th century, each with its own particular angle and emphasis. Since at least six authors are Catholic, their reception of Jan Hus and the militant Utraquist movement is predictably negative; however, our most interesting outputs concern the way historiographers pursued an intertextual discussion with their Humanist predecessors in the first three decades of the 15th century, while paying little or no attention to events that took place after the ratification of the Basel Compacts in 1436. [ABSTRACT FROM AUTHOR]

The abundance of semiconductors in our smartphones, computers, fiber optic junctions, cars, light sources, photovoltaic and thermoelectric cells results from the possibilities of controlling their properties through doping, lighting, and applying various fields. This paper, a part of the volume celebrating 100 years of the Polish Physical Society, presents a biased selection of worthwhile results obtained by researchers at the Institute of Physics, Polish Academy of Sciences relevant, as seen today, to topological matter and spintronics. Comprehensive studies, combining materials development, experimental investigations, and theoretical description of narrow-gap and dilute-magnetic semiconductors have been especially significant in this context. This survey also emphasizes, in an autobiographical tone, a half of a century of the author's intellectual emotions accompanying the rise of ideas and quantitative theories, allowing identifying the physics behind ongoing and future observations. [ABSTRACT FROM AUTHOR]

Knowledge of the mechanical and primarily fracture parameters of composites with a brittle matrix is essential for the quantification of their resistance to crack initiation and growth, and also for the specification of material model parameters employed for the simulation of the quasi-brittle behavior of structures made from this type of composite. Therefore, the main target of this paper is to quantify the mechanical fracture parameters of alkali-activated slag composites with steel microfibers and the contribution of the matrix to their fracture response. The first alkali-activated slag composite was a reference version without fibers; the others incorporated steel microfibers amounting to 5, 10, 15 and 20% by weight of the slag. Prism specimens with an initial central edge notch were used to perform the three-point bending fracture tests. Load vs. displacement (deflection at midspan) and load vs. crack mouth opening displacement diagrams were recorded during the fracture tests. The obtained diagrams were employed as inputs for parameter identification, the aim of which was to transfer the fracture test response data to the desired material parameters. Values were also determined for fracture parameters using the effective crack model, work-of-fracture method and double-K fracture model. All investigated mechanical fracture parameters were improved by the addition of steel microfibers to the alkali-activated matrix. Based on the obtained results, the addition of 10 to 15% of microfibers by weight is optimal from the point of view of the enhancement of the fracture parameters of alkali-activated slag composite. [ABSTRACT FROM AUTHOR]

Notch signaling regulates a vast array of crucial developmental processes. It is therefore not surprising that mutations in genes encoding Notch receptors or ligands lead to a variety of congenital disorders in humans. For example, loss of function of Notch results in Adams-Oliver syndrome, Alagille syndrome, spondylocostal dysostosis and congenital heart disorders, while Notch gain of function results in Hajdu-Cheney syndrome, serpentine fibula polycystic kidney syndrome, infantile myofibromatosis and lateral meningocele syndrome. Furthermore, structure-abrogating mutations in NOTCH3 result in CADASIL. Here, we discuss these human congenital disorders in the context of known roles for Notch signaling during development. Drawing on recent analyses by the exome aggregation consortium (EXAC) and on recent studies of Notch signaling in model organisms, we further highlight additional Notch receptors or ligands that are likely to be involved in human genetic diseases. [ABSTRACT FROM AUTHOR]

The topic of the presented paper is a promising approach to achieve optimal Design of Experiment (DoE), i.e. spreading of points within a design domain, using a simulation of a discrete dynamical system of interacting particles within an n-dimensional design space. The system of mutually repelling particles represents a physical analogy of the Audze-Eglājs (AE) optimization criterion and its periodical modification (PAE), respectively. The paper compares the performance of two approaches to implementation: a single-thread process using the JAVA language environment and a massively parallel solution employing the nVidia CUDA platform. [ABSTRACT FROM AUTHOR]

The eruption of Mount Tambora in Indonesia in 1815 was one of the most powerful of its kind in recorded history. This contribution addresses climatic responses to it, the post-eruption weather, and its impacts on human life in the Czech Lands. The climatic effects are evaluated in terms of air temperature and precipitation on the basis of long-term homogenised series from the Prague-Klementinum and Brno meteorological stations, and mean Czech series in the short term (1810-1820) and long term (1800-2010). This analysis is complemented by other climatic and environmental data derived from rich documentary evidence. Czech documentary sources make no direct mention of the Tambora eruption, neither do they relate any particular weather phenomena to it, but they record an extremely wet summer for 1815 and an extremely cold summer for 1816 (the "Year Without a Summer") that contributed to bad grain harvests and widespread grain price increases in 1817. Possible reasons for the cold summers in the first decade of the 19th century reflected in the contemporary press included comets, sunspot activity, long-term cooling and finally - as late as 1817 - earthquakes with volcanic eruptions. [ABSTRACT FROM AUTHOR]

Copyright of Studia Historica Brunensia is the property of Masaryk University, Faculty of Arts and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)