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Autophagy is a highly conserved catabolic process cells use to maintain their homeostasis by degrading misfolded, damaged and excessive proteins, nonfunctional organelles, foreign pathogens and other cellular components. Hence, autophagy can be nonselective, where bulky portions of the cytoplasm are degraded upon stress, or a highly selective process, where preselected cellular components are degraded. To distinguish between different cellular components, autophagy employs selective autophagy receptors, which will link the cargo to the autophagy machinery, thereby sequestering it in the autophagosome for its subsequent degradation in the lysosome. Autophagy receptors undergo post-translational and structural modifications to fulfil their role in autophagy, or upon executing their role, for their own degradation. We highlight the four most prominent protein modifications – phosphorylation, ubiquitination, acetylation and oligomerisation – that are essential for autophagy receptor recruitment, function and turnover. Understanding the regulation of selective autophagy receptors will provide deeper insights into the pathway and open up potential therapeutic avenues.
Autophagy is a highly conserved catabolic process cells use to maintain their homeostasis by degrading misfolded, damaged and excessive proteins, nonfunctional organelles, foreign pathogens and other cellular components. Hence, autophagy can be nonselective, where bulky portions of the cytoplasm are degraded upon stress, or a highly selective process, where preselected cellular components are degraded. To distinguish between different cellular components, autophagy employs selective autophagy receptors, which will link the cargo to the autophagy machinery, thereby sequestering it in the autophagosome for its subsequent degradation in the lysosome. Autophagy receptors undergo post-translational and structural modifications to fulfil their role in autophagy, or upon executing their role, for their own degradation. We highlight the four most prominent protein modifications – phosphorylation, ubiquitination, acetylation and oligomerisation – that are essential for autophagy receptor recruitment, function and turnover. Understanding the regulation of selective autophagy receptors will provide deeper insights into the pathway and open up potential therapeutic avenues.
The use of phylogenies in ecology is increasingly common and has broadened our understanding of biological diversity. Ecological sub-disciplines, particularly conservation, community ecology and macroecology, all recognize the value of evolutionary relationships but the resulting development of phylogenetic approaches has led to a proliferation of phylogenetic diversity metrics. The use of many metrics across the sub-disciplines hampers potential meta-analyses, syntheses, and generalizations of existing results. Further, there is no guide for selecting the appropriate metric for a given question, and different metrics are frequently used to address similar questions. To improve the choice, application, and interpretation of phylo-diversity metrics, we organize existing metrics by expanding on a unifying framework for phylogenetic information.
Generally, questions about phylogenetic relationships within or between assemblages tend to ask three types of question: how much; how different; or how regular? We show that these questions reflect three dimensions of a phylogenetic tree: richness, divergence, and regularity. We classify 70 existing phylo-diversity metrics based on their mathematical form within these three dimensions and identify ‘anchor’ representatives: for α-diversity metrics these are PD (Faith's phylogenetic diversity), MPD (mean pairwise distance), and VPD (variation of pairwise distances). By analysing mathematical formulae and using simulations, we use this framework to identify metrics that mix dimensions, and we provide a guide to choosing and using the most appropriate metrics. We show that metric choice requires connecting the research question with the correct dimension of the framework and that there are logical approaches to selecting and interpreting metrics. The guide outlined herein will help researchers navigate the current jungle of indices.
Justification: In Mexico, the number of unidentified bodies has been steadily rising for years. By now, more than 50,000 bodies are considered unidentified. Forensic laboratories that could perform comparative molecular genetic investigation are often overburdened and examinations can take months. Therefore, pragmatic approaches that can help to identify more unknown bodies must be sought. The increased use of distinctive physical features might be one, and the high rate of tattooed people in Mexico points towards a great potential of tattoos as a tool for identification. The prerequisite for a comparison of antemortem (missing persons) and postmortem (unknown bodies) data is an objective description of the particularities, e.g., of the tattoos. The aim of this study was to establish an objective classification for tattoo motives, taking into consideration local preferences.
Methods: In the database of the medicolegal services of the Instituto Jaliscience de Ciencias Forenses (IJCF) in Guadalajara, postmortem data of 1000 tattooed bodies from 2019 were evaluated. According to sex and age, the tattooed body localization and the tattoo motives were categorized.
Results: The 1000 tattooed deceased showed tattoos on 2342 body localizations. The motives were grouped and linked to the following 11 keywords (with decreasing frequency): letters/numbers, human, symbol (other), plant, symbol (religious), animal, object, fantasy/demon/comic, tribal/ornament/geometry, other, unrecognizable.
Conclusion: Using the proposed classification, tattoo motives can be described objectively and classified in a practical way. If used for antemortem (missing persons) and postmortem (unknown bodies) documentation, motives can be searched and compared efficiently—helping to identify unknown bodies.
Animal tracking and biologging devices record large amounts of data on individual movement behaviors in natural environments. In these data, movement ecologists often view unexplained variation around the mean as “noise” when studying patterns at the population level. In the field of behavioral ecology, however, focus has shifted from population means to the biological underpinnings of variation around means. Specifically, behavioral ecologists use repeated measures of individual behavior to partition behavioral variability into intrinsic among-individual variation and reversible behavioral plasticity and to quantify: a) individual variation in behavioral types (i.e. different average behavioral expression), b) individual variation in behavioral plasticity (i.e. different responsiveness of individuals to environmental gradients), c) individual variation in behavioral predictability (i.e. different residual within-individual variability of behavior around the mean), and d) correlations among these components and correlations in suites of behaviors, called ‘behavioral syndromes’. We here suggest that partitioning behavioral variability in animal movements will further the integration of movement ecology with other fields of behavioral ecology. We provide a literature review illustrating that individual differences in movement behaviors are insightful for wildlife and conservation studies and give recommendations regarding the data required for addressing such questions. In the accompanying R tutorial we provide a guide to the statistical approaches quantifying the different aspects of among-individual variation. We use movement data from 35 African elephants and show that elephants differ in a) their average behavior for three common movement behaviors, b) the rate at which they adjusted movement over a temporal gradient, and c) their behavioral predictability (ranging from more to less predictable individuals). Finally, two of the three movement behaviors were correlated into a behavioral syndrome (d), with farther moving individuals having shorter mean residence times. Though not explicitly tested here, individual differences in movement and predictability can affect an individual’s risk to be hunted or poached and could therefore open new avenues for conservation biologists to assess population viability. We hope that this review, tutorial, and worked example will encourage movement ecologists to examine the biology of individual variation in animal movements hidden behind the population mean.
As part of the Next Generation EU (NGEU) program, the European Commission has pledged to issue up to EUR 250 billion of the NGEU bonds as green bonds, in order to confirm their commitment to sustainable finance and to support the transition towards a greener Europe. Thereby, the EU is not only entering the green bond market, but also set to become one of the biggest green bond issuers. Consequently, financial market participants are eager to know what to expect from the EU as a new green bond issuer and whether a negative green bond premium, a so-called Greenium, can be expected for the NGEU green bonds. This research paper formulates an expectation in regards to a potential Greenium for the NGEU green bonds, by conducting an interview with 15 sustainable finance experts and analyzing the public green bond market from September 2014 until June 2021, with respect to a potential green bond premium and its underlying drivers. The regression results confirm the existence of a significant Greenium (-0.7 bps) in the public green bond market and that the Greenium increases for supranational issuers with AAA rating, such as the EU. Moreover, the green bond premium is influenced by issuer sector and credit rating, but issue size and modified duration have no significant effect. Overall, the evaluated expert interviews and regression analysis lead to an expected Greenium for the NGEU green bonds of up to -4 bps, with the potential to further increase in the secondary market.
Ferroptosis is an iron-dependent form of cell death, which is triggered by disturbed membrane integrity due to an overproduction of lipid peroxides. Induction of ferroptosis comprises several alterations, i.e. altered iron metabolism, response to oxidative stress, or lipid peroxide production. At the physiological level transcription, translation, and microRNAs add to the appearance and/or activity of building blocks that negatively or positively balance ferroptosis. Ferroptosis contributes to tissue damage in the case of, e.g., brain and heart injury but may be desirable to overcome chemotherapy resistance. For a more complete picture, it is crucial to also consider the cellular microenvironment, which during inflammation and in the tumor context is dominated by hypoxia. This graphical review visualizes basic mechanisms of ferroptosis, categorizes general inducers and inhibitors of ferroptosis, and puts a focus on microRNAs, iron homeostasis, and hypoxia as regulatory components.
Network graphs have become a popular tool to represent complex systems composed of many interacting subunits; especially in neuroscience, network graphs are increasingly used to represent and analyze functional interactions between multiple neural sources. Interactions are often reconstructed using pairwise bivariate analyses, overlooking the multivariate nature of interactions: it is neglected that investigating the effect of one source on a target necessitates to take all other sources as potential nuisance variables into account; also combinations of sources may act jointly on a given target. Bivariate analyses produce networks that may contain spurious interactions, which reduce the interpretability of the network and its graph metrics. A truly multivariate reconstruction, however, is computationally intractable because of the combinatorial explosion in the number of potential interactions. Thus, we have to resort to approximative methods to handle the intractability of multivariate interaction reconstruction, and thereby enable the use of networks in neuroscience. Here, we suggest such an approximative approach in the form of an algorithm that extends fast bivariate interaction reconstruction by identifying potentially spurious interactions post-hoc: the algorithm uses interaction delays reconstructed for directed bivariate interactions to tag potentially spurious edges on the basis of their timing signatures in the context of the surrounding network. Such tagged interactions may then be pruned, which produces a statistically conservative network approximation that is guaranteed to contain non-spurious interactions only. We describe the algorithm and present a reference implementation in MATLAB to test the algorithm’s performance on simulated networks as well as networks derived from magnetoencephalographic data. We discuss the algorithm in relation to other approximative multivariate methods and highlight suitable application scenarios. Our approach is a tractable and data-efficient way of reconstructing approximative networks of multivariate interactions. It is preferable if available data are limited or if fully multivariate approaches are computationally infeasible.
The membrane proximal external region (MPER) of the HIV-1 glycoprotein gp41 is targeted by the broadly neutralizing antibodies 2F5 and 4E10. To date, no immunization regimen in animals or humans has produced HIV-1 neutralizing MPER-specific antibodies. We immunized llamas with gp41-MPER proteoliposomes and selected a MPER-specific single chain antibody (VHH), 2H10, whose epitope overlaps with that of mAb 2F5. Bi-2H10, a bivalent form of 2H10, which displayed an approximately 20-fold increased affinity compared to the monovalent 2H10, neutralized various sensitive and resistant HIV-1 strains, as well as SHIV strains in TZM-bl cells. X-ray and NMR analyses combined with mutagenesis and modeling revealed that 2H10 recognizes its gp41 epitope in a helical conformation. Notably, tryptophan 100 at the tip of the long CDR3 is not required for gp41 interaction but essential for neutralization. Thus bi-2H10 is an anti-MPER antibody generated by immunization that requires hydrophobic CDR3 determinants in addition to epitope recognition for neutralization similar to the mode of neutralization employed by mAbs 2F5 and 4E10.
Wetlands such as bogs, swamps, or freshwater marshes are hotspots of biodiversity. For 5.1 million km2 of inland wetlands, the dynamics of area and water storage, which strongly impact biodiversity and ecosystem services, were simulated using the global hydrological model WaterGAP. For the first time, the impacts of both human water use and man‐made reservoirs (WUR) and future climate change (CC) on wetlands around the globe were quantified. WUR impacts are concentrated in arid/semiarid regions, where WUR decreased mean wetland water storage by more than 5% on 8.2% of the mean wetland area during 1986–2005 (Am), with highest decreases in groundwater depletion area. Using output of three climate models, CC impacts on wetlands were quantified, distinguishing unavoidable impacts [i.e., at 2 °C global warming (GW)] from avoidable impacts (difference between 3 °C and 2 °C impacts). Even unavoidable CC impacts are projected to be much larger than WUR impacts, also in arid/semiarid regions. On most wetland area with reliable estimates, avoidable CC impacts are more than twice as large as unavoidable impacts. In case of 2 °C GW, half of Am is estimated to be unaffected by mean storage changes of more than 5%, but only one third in case of 3 °C GW. Temporal variability of water storage will increase for most wetlands. Wetlands in dry regions will be affected the most, particularly by water storage decreases in the dry season. Different from wealthier countries, low‐income countries will dominantly suffer from a decrease in wetland water storage due to CC.