The unfolded protein response is the mechanism by which cells control endoplasmic reticulum (ER) protein homeostasis. Under normal conditions, the UPR is not activated; however, under certain ...stresses, such as hypoxia or altered glycosylation, the UPR can be activated due to an accumulation of unfolded proteins. The activation of the UPR involves three signaling pathways, IRE1, PERK and ATF6, which all play vital roles in returning protein homeostasis to levels seen in non-stressed cells. IRE1 is the best studied of the three pathways, as it is the only pathway present in
. This pathway involves spliceosome independent splicing of
or
in yeast and mammalians cells, respectively. PERK limits protein synthesis, therefore reducing the number of new proteins requiring folding. ATF6 is translocated and proteolytically cleaved, releasing a NH
domain fragment which is transported to the nucleus and which affects gene expression. If the UPR is unsuccessful at reducing the load of unfolded proteins in the ER and the UPR signals remain activated, this can lead to programmed cell death.
The efficient production of ammonia (NH3) from dinitrogen (N2) and water (H2O) using renewable energy is an important step on the roadmap to the ammonia economy. The productivity of this conversion ...hinges on the design and development of new active catalysts. In the wide scope of materials that have been examined as catalysts for the photo- and electro-driven reduction of N2 to NH3, functional metal–organic framework (MOF) catalysts exhibit unique properties and appealing features. By elucidating their structural and spectroscopic properties and linking this to the observed activity of MOF-based catalysts, valuable information can be gathered to inspire new generations of advanced catalysts to produce green NH3. NH3 is also a surrogate for the hydrogen (H2) economy, and the potential application of MOFs for the practical and effective capture, safe storage, and transport of NH3 is also discussed. This Perspective analyzes the contribution that MOFs can make toward the ammonia economy.
Hydrogen (H2) is a promising alternative energy carrier because of its environmental benefits, high energy density, and abundance. However, development of a practical storage system to enable the ...“Hydrogen Economy” remains a huge challenge. Metal–organic frameworks (MOFs) are an important class of crystalline coordination polymers constructed by bridging metal centers with organic linkers. MOFs show promise for H2 storage owing to their high surface area and tuneable properties. In this Account, we summarize our research on novel porous materials with enhanced H2 storage properties and describe frameworks derived from 3,5-substituted dicarboxylates (isophthalates) that serve as versatile molecular building blocks for the construction of a range of interesting coordination polymers with Cu(II) ions. We synthesized a series of materials by connecting linear tetracarboxylate linkers to {Cu(II)2} paddlewheel moieties. These materials exhibit high structural stability and permanent porosity. Varying the organic linker modulates the pore size, geometry, and functionality to control the overall H2 adsorption. Our top-performing material in this series has a H2 storage capacity of 77.8 mg g–1 at 77 K, 60 bar. H2 adsorption at low, medium, and high pressures correlates with the isosteric heat of adsorption, surface area, and pore volume, respectively. Another series, using tribranched C 3-symmetric hexacarboxylate ligands with Cu(II), gives highly porous (3,24)-connected frameworks incorporating {Cu(II)2} paddlewheels. Increasing the length of the hexacarboxylate struts directly tunes the porosity of the resultant material from micro- to mesoporosity. These materials show exceptionally high H2 uptakes owing to their high surface area and pore volume. The first member of this family reported adsorbs 111 mg g–1 of H2, or 55.9 g L–1, at 77 K, 77 bar, while at 77 K, 1 bar, the material adsorbs 2.3 wt % H2. We and others have since achieved enhanced H2 adsorption in these frameworks using combinations of polyphenyl groups linked by alkynes. The maximum storage achieved for one of the enhanced materials is 164 mg g–1 at 77 K, 70 bar, but because of its low density, its volumetric capacity is only 45.7 g L–1. We attribute the significant adsorption of H2 at low pressures to the arrangement of the {Cu24(isophthalate)24} cuboctahedral cages within the polyhedral structure. Free metal coordination positions are the first binding sites for D2, and these frameworks have two types of Cu(II) centers, one with its vacant site pointing into the cuboctahedral cage and another pointing externally. D2 molecules bind first at the former position and then at the external open metal sites. Design of ligands and complexes is key for enhancing and maximizing H2 storage, and although current materials operate at 77 K, research continues to explore routes to high capacity H2 storage materials that can function at higher temperatures.
•Analysis of meritocratic vs. non-meritocratic effects in getting a tenured professorship in German sociology.•Longitudinal career data on tenure decision, academic performance, gender, symbolic and ...social capital.•Tenure is largely related to scholarly output, network size, individual reputation, and being female.•Women are promoted earlier to tenure than men. Academic awards do not matter for men, but are the strongest predictor for women in increasing their chances for tenure.
Prior studies that try to explain who gets tenure and why remain inconclusive, especially on whether non-meritocratic factors influence who becomes a professor. Based on career and publication data of virtually all sociologists working in German sociology departments, we test how meritocratic factors (academic productivity) as well as non-meritocratic factors (ascription, symbolic and social capital) influence the chances of getting a permanent professorship in sociology. Our findings show that getting tenure in sociology is strongly related to scholarly output, as previous studies have shown. Improving on existing studies, however, we show specifically that each refereed journal article and each monograph increases a sociologist's chance for tenure by 10 to 15 percent, while other publications affect odds for tenure only marginally and in some cases even negatively. Regarding non-meritocratic factors, we show that network size, individual reputation, and gender matters. Women get their first permanent position as university professor with on average 23 to 44 percent fewer publications than men; all else being equal, they are about 1.4 times more likely to get tenure than men. The article generally contributes to a better understanding of the role of meritocratic and non-meritocratic factors in achieving scarce and highly competitive job positions in academia.
Conspectus Metal–organic frameworks (MOFs) are a class of hybrid porous materials characterized by their periodic assembly using metal ions and organic ligands through coordination bonds. Their high ...crystallinity, extensive surface area, and adjustable pore sizes make them promising candidates for a wide array of applications. These include gas adsorption and separation, substrate binding, and catalysis, of relevance to tackling pressing global issues such as climate change, energy challenges, and pollution. In comparison to traditional porous materials such as zeolites and activated carbons, the design flexibility of organic ligands in MOFs, coupled with their orderly arrangement with associated metal centers, allows for the precise engineering of uniform pore environments. This unique feature enables a rich variety of interactions between the MOF host and adsorbed gas molecules, which are fundamental to understanding the observed uptake capacity and selectivity for target gas molecules and thus the overall performance of the material. In this Account, a data set for three-dimensional MOFs has been constructed based upon the structural analysis of host–guest interactions using the largest experimental database, the Cambridge Structural Database (CSD). A full screening was performed on structures with guest molecules of H2, C2H2, CO2, and SO2, and the relationship between the primary binding site, the isosteric heats of adsorption (Q st), and the adsorption uptake was extracted and established. We review the methodologies to refine host–guest interactions based primarily on our studies on the host–guest chemistry of MOFs. The methods include ligand functionalization, variation of metal centers, formation of defects, addition of single atom sites, and control of pore size and structure. In situ structural and dynamic investigations using diffraction and spectroscopic techniques are powerful tools to visualize the details of host–guest interactions upon the above modifications, affording key insights into functional performance at a molecular level. Finally, we give an outlook of future research priorities in the study of host–guest chemistry in MOF materials. We hope this Account will encourage the rational development and improvement of future MOF-based sorbents for applications in challenging gas adsorption, separations, and catalysis.
In this work, we present MorphoCluster, a software tool for data-driven, fast, and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, ...volume and complexity of marine data will continue to increase in the coming years. Still, this data requires interpretation. MorphoCluster augments the human ability to discover patterns and perform object classification in large amounts of data by embedding unsupervised clustering in an interactive process. By aggregating similar images into clusters, our novel approach to image annotation increases consistency, multiplies the throughput of an annotator, and allows experts to adapt the granularity of their sorting scheme to the structure in the data. By sorting a set of 1.2 M objects into 280 data-driven classes in 71 h (16 k objects per hour), with 90% of these classes having a precision of 0.889 or higher. This shows that MorphoCluster is at the same time fast, accurate, and consistent; provides a fine-grained and data-driven classification; and enables novelty detection.
Deep learning has been successfully applied to many classification problems including underwater challenges. However, a long-standing issue with deep learning is the need for large and consistently ...labeled datasets. Although current approaches in semi-supervised learning can decrease the required amount of annotated data by a factor of 10 or even more, this line of research still uses distinct classes. For underwater classification, and uncurated real-world datasets in general, clean class boundaries can often not be given due to a limited information content in the images and transitional stages of the depicted objects. This leads to different experts having different opinions and thus producing fuzzy labels which could also be considered ambiguous or divergent. We propose a novel framework for handling semi-supervised classifications of such fuzzy labels. It is based on the idea of overclustering to detect substructures in these fuzzy labels. We propose a novel loss to improve the overclustering capability of our framework and show the benefit of overclustering for fuzzy labels. We show that our framework is superior to previous state-of-the-art semi-supervised methods when applied to real-world plankton data with fuzzy labels. Moreover, we acquire 5 to 10% more consistent predictions of substructures.
Image annotation is a time-consuming and costly task. Previously, we published MorphoCluster as a novel image annotation tool to address problems of conventional, classifier-based image annotation ...approaches: their limited efficiency, training set bias and lack of novelty detection. MorphoCluster uses clustering and similarity search to enable efficient, computer-assisted image annotation. In this work, we provide a deeper analysis of this approach. We simulate the actions of a MorphoCluster user to avoid extensive manual annotation runs. This simulation is used to test supervised, unsupervised and transfer representation learning approaches. Furthermore, shrunken
-means and partially labeled
-means, two new clustering algorithms that are tailored specifically for the MorphoCluster approach, are compared to the previously used HDBSCAN*. We find that labeled training data improve the image representations, that unsupervised learning beats transfer learning and that all three clustering algorithms are viable options, depending on whether completeness, efficiency or runtime is the priority. The simulation results support our earlier finding that MorphoCluster is very efficient and precise. Within the simulation, more than five objects per simulated click are being annotated with 95% precision.
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