Servant Leadership and Its Effect on Employee Job Satisfaction and Turnover Intent

Experts expect a shortage of more than 900,000 nurses by 2022, according to the United States Bureau of Labor Statistics Employment Projections. Turnover in nursing contributes significantly to the shortage and often results from poor leadership of nurse managers. The purpose of this quantitative study was to investigate how servant leadership behaviors affected the psychological state and behavioral response of staff nurses as reflected by job satisfaction and turnover intention. Specifically, the research question addressed whether servant leadership positively contributes to the psychological states and the behaviors of staff nurses leading to greater job satisfaction. The study design was correlational, nonexperimental, and cross-sectional. Use of a questions from existing surveys combined into a single survey, from 284 staff nurses at a Pennsylvania hospital, provided the data for the research. Correlation analysis determined the strength and direction of servant leadership constructs and the dependent variables of turnover intention and job satisfaction. Multiple linear regression analysis predicted the influence of job satisfaction and turnover intention, demonstrating a strong, positive correlation linking servant leadership behaviors, the psychological state of engagement and job satisfaction. The study contributed to filling the gap in health care management by providing a picture of how servant leadership behaviors influenced job satisfaction and retention of nursing staff. Implications for positive social change may lead hospital administrators to encourage the adoption of servant leadership behaviors, by nurse managers, resulting in greater staff nurse job satisfaction, improved patient quality outcomes, sustainable organizational financial success, and expanded community health

automRm: An R Package for Fully Automatic LC-QQQ-MS Data Preprocessing Powered by Machine Learning

Preprocessing of liquid chromatography-mass spectrometry (LC-MS) raw data facilitates downstream statistical and biological data analyses. In the case of targeted LC-MS data, consistent recognition of chromatographic peaks is a main challenge, in particular, for low abundant signals. Fully automatic preprocessing is faster than manual peak review and does not depend on the individual operator. Here, we present the R package automRm for fully automatic preprocessing of LC-MS data recorded in MRM mode. Using machine learning (ML) for detection of chromatographic peaks and quality control of reported results enables the automatic recognition of complex patterns in raw data. In addition, this approach renders automRm generally applicable to a wide range of analytical methods including hydrophilic interaction liquid chromatography (HILIC), which is known for sample-to-sample variations in peak shape and retention time. We demonstrate the impact of the choice of training data set, of the applied ML algorithm, and of individual peak characteristics on automRm’s ability to correctly report chromatographic peaks. Next, we show that automRm can replicate results obtained by manual peak review on published data. Moreover, automRm outperforms alternative software solutions regarding the variation in peak integration among replicate measurements and the number of correctly reported peaks when applied to a HILIC-MS data set. The R package is freely available from gitlab (https://gitlab.gwdg.de/joerg.buescher/automrm)

Effects of first and second language on segmentation of non-native speech

Do Slovak-German bilinguals apply native Slovak phonological and lexical knowledge when segmenting German speech? When Slovaks listen to their native language, segmentation is impaired when fixed-stress cues are absent (Hanulíková, McQueen & Mitterer, 2010), and, following the Possible-Word Constraint (PWC; Norris, McQueen, Cutler & Butterfield, 1997), lexical candidates are disfavored if segmentation leads to vowelless residues, unless those residues are existing Slovak words. In the present study, fixed-stress cues on German target words were again absent. Nevertheless, in support of the PWC, both German and Slovak listeners recognized German words (e.g., Rose "rose") faster in syllable contexts (suckrose) than in single-consonant contexts (krose, trose). But only the Slovak listeners recognized, for example, Rose faster in krose than in trose (k is a Slovak word, t is not). It appears that non-native listeners can suppress native stress segmentation procedures, but that they suffer from prevailing interference from native lexical knowledge.peer-reviewe

Allophones, not phonemes in spoken-word recognition

We thank Nadia Klijn for helping to prepare and test participants in Experiment 1 and Rosa Franzke for help with Experiments 2 and 3. The second author is funded by an Emmy-Noether grant (nr. RE 3047/1-1) from the German Research Council (DFG). This work was also supported by a University of Malta Research Grant to the first author.What are the phonological representations that listeners use to map information about the segmental content of speech onto the mental lexicon during spoken-word recognition? Recent evidence from perceptual-learning paradigms seems to support (context-dependent) allophones as the basic representational units in spoken-word recognition. But recent evidence from a selective-adaptation paradigm seems to suggest that context-independent phonemes also play a role. We present three experiments using selective adaptation that constitute strong tests of these representational hypotheses. In Experiment 1, we tested generalization of selective adaptation using different allophones of Dutch /r/ and /l/ – a case where generalization has not been found with perceptual learning. In Experiments 2 and 3, we tested generalization of selective adaptation using German back fricatives in which allophonic and phonemic identity were varied orthogonally. In all three experiments, selective adaptation was observed only if adaptors and test stimuli shared allophones. Phonemic identity, in contrast, was neither necessary nor sufficient for generalization of selective adaptation to occur. These findings and other recent data using the perceptual-learning paradigm suggest that pre-lexical processing during spoken-word recognition is based on allophones, and not on context-independent phonemes.peer-reviewe

At which processing level does extrinsic speaker information influence vowel perception?

The interpretation of vowel sounds depends on perceived characteristics of the speaker (e.g., average first formant (F1) frequency). A vowel between /I/ and /E/ is more likely to be perceived as /I/ if a precursor sentence indicates that the speaker has a relatively high average F1. Behavioral and electrophysiological experiments investigating the locus of this extrinsic vowel normalization are reported. The normalization effect with a categorization task was first replicated. More vowels on an /I/-/E/ continuum followed by a /papu/ context were categorized as /I/ with a high-F1 context than with a low-F1 context. Two experiments then examined this context effect in a 4I-oddity discrimination task. Ambiguous vowels were more difficult to distinguish from the /I/-endpoint if the context /papu/ had a high F1 than if it had a low F1 (and vice versa for discrimination of ambiguous vowels from the /E/-endpoint). Furthermore, between-category discriminations were no easier than within-category discriminations. Together, these results suggest that the normalization mechanism operates largely at an auditory processing level. The MisMatch Negativity (an automatically evoked brain potential) arising from the same stimuli is being measured, to investigate whether extrinsic normalization takes place in the absence of an explicit decision task

Constraints on the processes responsible for the extrinsic normalization of vowels

Listeners tune in to talkers’ vowels through extrinsic normalization. We asked here whether this process could be based on compensation for the long-term average spectrum (LTAS) of preceding sounds and whether the mechanisms responsible for normalization are indifferent to the nature of those sounds. If so, normalization should apply to nonspeech stimuli. Previous findings were replicated with first-formant (F1) manipulations of speech. Targets on a [pt]–[pɛt] (low–high F1) continuum were labeled as [pt] more after high-F1 than after low-F1 precursors. Spectrally rotated nonspeech versions of these materials produced similar normalization. None occurred, however, with nonspeech stimuli that were less speechlike, even though precursor–target LTAS relations were equivalent to those used earlier. Additional experiments investigated the roles of pitch movement, amplitude variation, formant location, and the stimuli's perceived similarity to speech. It appears that normalization is not restricted to speech but that the nature of the preceding sounds does matter. Extrinsic normalization of vowels is due, at least in part, to an auditory process that may require familiarity with the spectrotemporal characteristics of speech

Targeted LC-MS/MS-based metabolomics and lipidomics on limited hematopoietic stem cell numbers

Metabolism is important for the regulation of hematopoietic stem cells (HSCs) and drives cellular fate. Due to the scarcity of HSCs, it has been technically challenging to perform metabolome analyses gaining insight into HSC metabolic regulatory networks. Here, we present two targeted liquid chromatography–mass spectrometry approaches that enable the detection of metabolites after fluorescence-activated cell sorting when sample amounts are limited. One protocol covers signaling lipids and retinoids, while the second detects tricarboxylic acid cycle metabolites and amino acids. For complete details on the use and execution of this protocol, please refer to Schönberger et al. (2022)

Metabolic Dynamics of In Vitro CD8+ T Cell Activation

CD8+ T cells detect and kill infected or cancerous cells. When activated from their naïve state, T cells undergo a complex transition, including major metabolic reprogramming. Detailed resolution of metabolic dynamics is needed to advance the field of immunometabolism. Here, we outline methodologies that when utilized in parallel achieve broad coverage of the metabolome. Specifically, we used a combination of 2 flow injection analysis (FIA) and 3 liquid chromatography (LC) methods in combination with positive and negative mode high-resolution mass spectrometry (MS) to study the transition from naïve to effector T cells with fine-grained time resolution. Depending on the method, between 54% and 98% of measured metabolic features change in a time-dependent manner, with the major changes in both polar metabolites and lipids occurring in the first 48 h. The statistical analysis highlighted the remodeling of the polyamine biosynthesis pathway, with marked differences in the dynamics of precursors, intermediates, and cofactors. Moreover, phosphatidylcholines, the major class of membrane lipids, underwent a drastic shift in acyl chain composition with polyunsaturated species decreasing from 60% to 25% of the total pool and specifically depleting species containing a 20:4 fatty acid. We hope that this data set with a total of over 11,000 features recorded with multiple MS methodologies for 9 time points will be a useful resource for future work