The relative contribution of climate to changes in lesser prairie-chicken abundance

Citation: Ross, B. E., Haukos, D., Hagen, C., & Pitman, J. (2016). The relative contribution of climate to changes in lesser prairie-chicken abundance. Ecosphere, 7(6), 11. doi:10.1002/ecs2.1323Managing for species using current weather patterns fails to incorporate the uncertainty associated with future climatic conditions; without incorporating potential changes in climate into conservation strategies, management and conservation efforts may fall short or waste valuable resources. Understanding the effects of climate change on species in the Great Plains of North America is especially important, as this region is projected to experience an increased magnitude of climate change. Of particular ecological and conservation interest is the lesser prairie-chicken (Tympanuchus pallidicinctus), which was listed as "threatened" under the U.S. Endangered Species Act in May 2014. We used Bayesian hierarchical models to quantify the effects of extreme climatic events (extreme values of the Palmer Drought Severity Index [PDSI]) relative to intermediate (changes in El Nino Southern Oscillation) and long-term climate variability (changes in the Pacific Decadal Oscillation) on trends in lesser prairie-chicken abundance from 1981 to 2014. Our results indicate that lesser prairie-chicken abundance on leks responded to environmental conditions of the year previous by positively responding to wet springs (high PDSI) and negatively to years with hot, dry summers (low PDSI), but had little response to variation in the El Nino Southern Oscillation and the Pacific Decadal Oscillation. Additionally, greater variation in abundance on leks was explained by variation in site relative to broad-scale climatic indices. Consequently, lesser prairie-chicken abundance on leks in Kansas is more strongly influenced by extreme drought events during summer than other climatic conditions, which may have negative consequences for the population as drought conditions intensify throughout the Great Plains

Structural Analysis to Determine the Core of Hypoxia Response Network

The advent of sophisticated molecular biology techniques allows to deduce the structure of complex biological networks. However, networks tend to be huge and impose computational challenges on traditional mathematical analysis due to their high dimension and lack of reliable kinetic data. To overcome this problem, complex biological networks are decomposed into modules that are assumed to capture essential aspects of the full network's dynamics. The question that begs for an answer is how to identify the core that is representative of a network's dynamics, its function and robustness. One of the powerful methods to probe into the structure of a network is Petri net analysis. Petri nets support network visualization and execution. They are also equipped with sound mathematical and formal reasoning based on which a network can be decomposed into modules. The structural analysis provides insight into the robustness and facilitates the identification of fragile nodes. The application of these techniques to a previously proposed hypoxia control network reveals three functional modules responsible for degrading the hypoxia-inducible factor (HIF). Interestingly, the structural analysis identifies superfluous network parts and suggests that the reversibility of the reactions are not important for the essential functionality. The core network is determined to be the union of the three reduced individual modules. The structural analysis results are confirmed by numerical integration of the differential equations induced by the individual modules as well as their composition. The structural analysis leads also to a coarse network structure highlighting the structural principles inherent in the three functional modules. Importantly, our analysis identifies the fragile node in this robust network without which the switch-like behavior is shown to be completely absent

Holding blame at bay? ‘Gene talk’ in family members’ accounts of schizophrenia aetiology

We provide the first detailed analysis of how, for what purposes and with what consequences people related to someone with a diagnosis of schizophrenia use ‘gene talk'. The article analyses findings from a qualitative interview study conducted in London and involving 19 participants (mostly women). We transcribed the interviews verbatim and analysed them using grounded theory methods. We analyse how and for what purposes participants mobilized ‘gene talk' in their affectively freighted encounter with an unknown interviewer. Gene talk served to (re)position blame and guilt, and was simultaneously used imaginatively to forge family history narratives. Family members used ‘gene talk' to recruit forebears with no psychiatric diagnosis into a family history of mental illness, and presented the origins of the diagnosed family member's schizophrenia as lying temporally before, and hence beyond the agency of the immediate family. Gene talk was also used in attempts to dislodge the distressing figure of the schizophrenia-inducing mother. ‘Gene talk', however, ultimately displaced, rather than resolved, the (self-)blame of many family members, particularly mothers. Our article challenges the commonly expressed view that genetic accounts will absolve family members' sense of (self-)blame in relation to their relative's/relatives' diagnosis

Transmission of Carbapenem-Resistant Klebsiella pneumoniae in US Hospitals

Background: Carbapenem-resistant Klebsiella pneumoniae (CRKp) is the most prevalent carbapenem-resistant Enterobacterales in the United States. We evaluated CRKp clustering in patients in US hospitals. Methods: From April 2016 to August 2017, 350 patients with clonal group 258 CRKp were enrolled in the Consortium on Resistance Against Carbapenems in Klebsiella and other Enterobacteriaceae, a prospective, multicenter, cohort study. A maximum likelihood tree was constructed using RAxML. Static clusters shared ≤21 single-nucleotide polymorphisms (SNP) and a most recent common ancestor. Dynamic clusters incorporated SNP distance, culture timing, and rates of SNP accumulation and transmission using the R program TransCluster. Results: Most patients were admitted from home (n = 150, 43%) or long-term care facilities (n = 115, 33%). Urine (n = 149, 43%) was the most common isolation site. Overall, 55 static and 47 dynamics clusters were identified involving 210 of 350 (60%) and 194 of 350 (55%) patients, respectively. Approximately half of static clusters were identical to dynamic clusters. Static clusters consisted of 33 (60%) intrasystem and 22 (40%) intersystem clusters. Dynamic clusters consisted of 32 (68%) intrasystem and 15 (32%) intersystem clusters and had fewer SNP differences than static clusters (8 vs 9; P =. 045; 95% confidence interval [CI]: -4 to 0). Dynamic intersystem clusters contained more patients than dynamic intrasystem clusters (median [interquartile range], 4 [2, 7] vs 2 [2, 2]; P =. 007; 95% CI: -3 to 0). Conclusions: Widespread intrasystem and intersystem transmission of CRKp was identified in hospitalized US patients. Use of different methods for assessing genetic similarity resulted in only minor differences in interpretation

Helping hands: A cluster randomised trial to evaluate the effectiveness of two different strategies for promoting hand hygiene in hospital nurses

Background: hand hygiene prescriptions are the most important measure in the prevention of hospital-acquired infections. Yet, compliance rates are generally below 50% of all opportunities for hand hygiene. This study aims at evaluating the short- and long-term effects of two different strategies for promoting hand hygiene in hospital nurses.Methods/design: this study is a cluster randomised controlled trial with inpatient wards as the unit of randomisation. Guidelines for hand hygiene will be implemented in this study. Two strategies will be used to improve the adherence to guidelines for hand hygiene. The state-of-the-art strategy is derived from the literature and includes education, reminders, feedback, and targeting adequate products and facilities. The extended strategy also contains activities aimed at influencing social influence in groups and enhancing leadership. The unique contribution of the extended strategy is built upon relevant behavioural science theories. The extended strategy includes all elements of the state-of-the-art strategy supplemented with gaining active commitment and initiative of ward management, modelling by informal leaders at the ward, and setting norms and targets within the team. Data will be collected at four points in time, with six-month intervals. An average of 3,000 opportunities for hand hygiene in approximately 900 nurses will be observed at each time point.Discussion: performing and evaluating an implementation strategy that also targets the social context of teams may considerably add to the general body of knowledge in this field. Results from our study will allow us to draw conclusions on the effects of different strategies for the implementation of hand hygiene guidelines, and based on these results we will be able to define a preferred implementation strategy for hospital based nursing.Trial registration: the study is registered as a Clinical Trial in ClinicalTrials.gov, dossier number: NCT0054801

Modularization of biochemical networks based on classification of Petri net t-invariants

<p>Abstract</p> <p>Background</p> <p>Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior.</p> <p>With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system.</p> <p>Methods</p> <p>Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied.</p> <p>Results</p> <p>We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in <it>Saccharomyces cerevisiae</it>) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability.</p> <p>Conclusion</p> <p>We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis.</p

Belle II Vertex Detector Performance

The Belle II experiment at the SuperKEKB accelerator (KEK, Tsukuba, Japan) collected its first e+e− collision data in the spring 2019. The aim of accumulating a 50 times larger data sample than Belle at KEKB, a first generation B-Factory, presents substantial challenges to both the collider and the detector, requiring not only state-of-the-art hardware, but also modern software algorithms for tracking and alignment. The broad physics program requires excellent performance of the vertex detector, which is composed of two layers of DEPFET pixels and four layers of double sided-strip sensors. In this contribution, an overview of the vertex detector of Belle II and our methods to ensure its optimal performance, are described, and the first results and experiences from the first physics run are presented