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The simultaneous detection of gravitational waves and light from the binary neutron star merger GW170817 led to independent measurements of distance and redshift, providing a direct estimate of the Hubble constant H-0 that does not rely on a cosmic distance ladder, nor assumes a specific cosmological model.
By using gravitational waves as "standard sirens", this approach holds promise to arbitrate the existing tension between the H-0 value inferred from the cosmic microwave background and those obtained from local measurements.
However, the known degeneracy in the gravitational-wave analysis between distance and inclination of the source led to a H-0 value from GW170817 that was not precise enough to resolve the existing tension.
In this review, we summarize recent works exploiting the viewing-angle dependence of the electromagnetic signal, namely the associated short gamma-ray burst and kilonova, to constrain the system inclination and improve on H-0.
We outline the key ingredients of the different methods, summarize the results obtained in the aftermath of GW170817 and discuss the possible systematics introduced by each of these methods.
Physically interacting proteins form macromolecule complexes that drive diverse cellular processes. Advances in experimental techniques that capture interactions between proteins provide us with protein-protein interaction (PPI) networks from several model organisms. These datasets have enabled the prediction and other computational analyses of protein complexes. Here we provide a systematic review of the state-of-the-art algorithms for protein complex prediction from PPI networks proposed in the past two decades. The existing approaches that solve this problem are categorized into three groups, including: cluster-quality-based, node affinity-based, and network embedding-based approaches, and we compare and contrast the advantages and disadvantages. We further include a comparative analysis by computing the performance of eighteen methods based on twelve well-established performance measures on four widely used benchmark protein-protein interaction networks. Finally, the limitations and drawbacks of both, current data and approaches, along with the potential solutions in this field are discussed, with emphasis on the points that pave the way for future research efforts in this field. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).
Electrochemical methods offer the simple characterization of the synthesis of molecularly imprinted polymers (MIPs) and the readouts of target binding. The binding of electroinactive analytes can be detected indirectly by their modulating effect on the diffusional permeability of a redox marker through thin MIP films. However, this process generates an overall signal, which may include nonspecific interactions with the nonimprinted surface and adsorption at the electrode surface in addition to (specific) binding to the cavities. Redox-active low-molecular-weight targets and metalloproteins enable a more specific direct quantification of their binding to MIPs by measuring the faradaic current. The in situ characterization of enzymes, MIP-based mimics of redox enzymes or enzyme-labeled targets, is based on the indication of an electroactive product. This approach allows the determination of both the activity of the bio(mimetic) catalyst and of the substrate concentration.
The cryosphere in mountain regions is rapidly declining, a trend that is expected to accelerate over the next several decades due to anthropogenic climate change. A cascade of effects will result, extending from mountains to lowlands with associated impacts on human livelihood, economy, and ecosystems. With rising air temperatures and increased radiative forcing, glaciers will become smaller and, in some cases, disappear, the area of frozen ground will diminish, the ratio of snow to rainfall will decrease, and the timing and magnitude of both maximum and minimum streamflow will change. These changes will affect erosion rates, sediment, and nutrient flux, and the biogeochemistry of rivers and proglacial lakes, all of which influence water quality, aquatic habitat, and biotic communities. Changes in the length of the growing season will allow low-elevation plants and animals to expand their ranges upward. Slope failures due to thawing alpine permafrost, and outburst floods from glacier-and moraine-dammed lakes will threaten downstream populations.Societies even well beyond the mountains depend on meltwater from glaciers and snow for drinking water supplies, irrigation, mining, hydropower, agriculture, and recreation. Here, we review and, where possible, quantify the impacts of anticipated climate change on the alpine cryosphere, hydrosphere, and biosphere, and consider the implications for adaptation to a future of mountains without permanent snow and ice.
Molecularly imprinted polymers (MIPs) have the potential to complement antibodies in bioanalysis, are more stable under harsh conditions, and are potentially cheaper to produce. However, the affinity and especially the selectivity of MIPs are in general lower than those of their biological pendants. Enzymes are useful tools for the preparation of MIPs for both low and high-molecular weight targets: As a green alternative to the well-established methods of chemical polymerization, enzyme-initiated polymerization has been introduced and the removal of protein templates by proteases has been successfully applied. Furthermore, MIPs have been coupled with enzymes in order to enhance the analytical performance of biomimetic sensors: Enzymes have been used in MIP-sensors as tracers for the generation and amplification of the measuring signal. In addition, enzymatic pretreatment of an analyte can extend the analyte spectrum and eliminate interferences.
To cope with the already large, and ever increasing, amount of information stored in organizational memory, "forgetting," as an important human memory process, might be transferred to the organizational context. Especially in intentionally planned change processes (e.g., change management), forgetting is an important precondition to impede the recall of obsolete routines and adapt to new strategic objectives accompanied by new organizational routines. We first comprehensively review the literature on the need for organizational forgetting and particularly on accidental vs. intentional forgetting. We discuss the current state of the art of theory and empirical evidence on forgetting from cognitive psychology in order to infer mechanisms applicable to the organizational context. In this respect, we emphasize retrieval theories and the relevance of retrieval cues important for forgetting. Subsequently, we transfer the empirical evidence that the elimination of retrieval cues leads to faster forgetting to the forgetting of organizational routines, as routines are part of organizational memory. We then propose a classification of cues (context, sensory, business process-related cues) that are relevant in the forgetting of routines, and discuss a meta-cue called the "situational strength" cue, which is relevant if cues of an old and a new routine are present simultaneously. Based on the classification as business process-related cues (information, team, task, object cues), we propose mechanisms to accelerate forgetting by eliminating specific cues based on the empirical and theoretical state of the art. We conclude that in intentional organizational change processes, the elimination of cues to accelerate forgetting should be used in change management practices.
Two decades ago, sphingosine 1-phosphate (S1P) was discovered as a novel bioactive molecule that regulates a variety of cellular functions. The plethora of S1P-mediated effects is due to the fact that the sphingolipid not only modulates intracellular functions but also acts as a ligand of G protein-coupled receptors after secretion into the extracellular environment. In the plasma, S1P is found in high concentrations, modulating immune cell trafficking and vascular endothelial integrity. The liver is engaged in modulating the plasma S1P content, as it produces apolipoprotein M, which is a chaperone for the S1P transport. Moreover, the liver plays a substantial role in glucose and lipid homeostasis. A dysfunction of glucose and lipid metabolism is connected with the development of liver diseases such as hepatic insulin resistance, non-alcoholic fatty liver disease, or liver fibrosis. Recent studies indicate that S1P is involved in liver pathophysiology and contributes to the development of liver diseases. In this review, the current state of knowledge about S1P and its signaling in the liver is summarized with a specific focus on the dysregulation of S1P signaling in obesity-mediated liver diseases. Thus, the modulation of S1P signaling can be considered as a potential therapeutic target for the treatment of hepatic diseases.
Moving Beyond ERP Components
(2018)
Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or "components" derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.
Introduction:
We aim to highlight the utility of this model in the analysis of the psycho-behavioral implications of family cancer, presenting the scientific literature that used Leventhal’s model as the theoretical framework of approach.
Material and methods:
A systematic search was performed in six databases (EBSCO, ScienceDirect, PubMed Central, ProQuest, Scopus, and Web of Science) with empirical studies published between 2006 and 2015 in English with regard to the Common Sense Model of Self-Regulation (CSMR) and familial/hereditary cancer. The key words used were: illness representations, common sense model, self regulatory model, familial/hereditary/genetic cancer, genetic cancer counseling. The selection of studies followed the PRISMA-P guidelines (Moher et al., 2009; Shamseer et al., 2015), which suggest a three-stage procedure.
Results:
Individuals create their own cognitive and emotional representation of the disease when their health is threatened, being influenced by the presence of a family history of cancer, causing them to adopt or not a salutogenetic behavior. Disease representations, particularly the cognitive ones, can be predictors of responses to health threats that determine different health behaviors. Age, family history of cancer, and worrying about the disease are factors associated with undergoing screening. No consensus has been reached as to which factors act as predictors of compliance with cancer screening programs.
Conclusions:
This model can generate interventions that are conceptually clear as well as useful in regulating the individuals’ behaviors by reducing the risk of developing the disease and by managing as favorably as possible health and/or disease.