The Mitochondrial Genome of the Lycophyte Huperzia squarrosa: The Most Archaic Form in Vascular Plants

Mitochondrial genomes have maintained some bacterial features despite their residence within eukaryotic cells for approximately two billion years. One of these features is the frequent presence of polycistronic operons. In land plants, however, it has been shown that all sequenced vascular plant chondromes lack large polycistronic operons while bryophyte chondromes have many of them. In this study, we provide the completely sequenced mitochondrial genome of a lycophyte, from Huperzia squarrosa, which is a member of the sister group to all other vascular plants. The genome, at a size of 413,530 base pairs, contains 66 genes and 32 group II introns. In addition, it has 69 pseudogene fragments for 24 of the 40 protein- and rRNA-coding genes. It represents the most archaic form of mitochondrial genomes of all vascular plants. In particular, it has one large conserved gene cluster containing up to 10 ribosomal protein genes, which likely represents a polycistronic operon but has been disrupted and greatly reduced in the chondromes of other vascular plants. It also has the least rearranged gene order in comparison to the chondromes of other vascular plants. The genome is ancestral in vascular plants in several other aspects: the gene content resembling those of charophytes and most bryophytes, all introns being cis-spliced, a low level of RNA editing, and lack of foreign DNA of chloroplast or nuclear origin

NT2 Derived Neuronal and Astrocytic Network Signalling

A major focus of stem cell research is the generation of neurons that may then be implanted to treat neurodegenerative diseases. However, a picture is emerging where astrocytes are partners to neurons in sustaining and modulating brain function. We therefore investigated the functional properties of NT2 derived astrocytes and neurons using electrophysiological and calcium imaging approaches. NT2 neurons (NT2Ns) expressed sodium dependent action potentials, as well as responses to depolarisation and the neurotransmitter glutamate. NT2Ns exhibited spontaneous and coordinated calcium elevations in clusters and in extended processes, indicating local and long distance signalling. Tetrodotoxin sensitive network activity could also be evoked by electrical stimulation. Similarly, NT2 astrocytes (NT2As) exhibited morphology and functional properties consistent with this glial cell type. NT2As responded to neuronal activity and to exogenously applied neurotransmitters with calcium elevations, and in contrast to neurons, also exhibited spontaneous rhythmic calcium oscillations. NT2As also generated propagating calcium waves that were gap junction and purinergic signalling dependent. Our results show that NT2 derived astrocytes exhibit appropriate functionality and that NT2N networks interact with NT2A networks in co-culture. These findings underline the utility of such cultures to investigate human brain cell type signalling under controlled conditions. Furthermore, since stem cell derived neuron function and survival is of great importance therapeutically, our findings suggest that the presence of complementary astrocytes may be valuable in supporting stem cell derived neuronal networks. Indeed, this also supports the intriguing possibility of selective therapeutic replacement of astrocytes in diseases where these cells are either lost or lose functionality

Mesoporous Ternary Nitrides of Earth-Abundant Metals as Oxygen Evolution Electrocatalyst

As sustainable energy becomes a major concern for modern society, renewable and clean energy systems need highly active, stable, and low-cost catalysts for the oxygen evolution reaction (OER). Mesoporous materials offer an attractive route for generating efficient electrocatalysts with high mass transport capabilities. Herein, we report an efficient hard templating pathway to design and synthesize three-dimensional (3-D) mesoporous ternary nickel iron nitride (Ni3FeN). The as-synthesized electrocatalyst shows good OER performance in an alkaline solution with low overpotential (259 mV) and a small Tafel slope (54 mV dec(−1)), giving superior performance to IrO(2) and RuO(2) catalysts. The highly active contact area, the hierarchical porosity, and the synergistic effect of bimetal atoms contributed to the improved electrocatalytic performance toward OER. In a practical rechargeable Zn–air battery, mesoporous Ni(3)FeN is also shown to deliver a lower charging voltage and longer lifetime than RuO(2). This work opens up a new promising approach to synthesize active OER electrocatalysts for energy-related devices. [Image: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s40820-020-0412-8) contains supplementary material, which is available to authorized users

Selective Regulation of NR2B by Protein Phosphatase-1 for the Control of the NMDA Receptor in Neuroprotection

An imbalance between pro-survival and pro-death pathways in brain cells can lead to neuronal cell death and neurodegeneration. While such imbalance is known to be associated with alterations in glutamatergic and Ca2+ signaling, the underlying mechanisms remain undefined. We identified the protein Ser/Thr phosphatase protein phosphatase-1 (PP1), an enzyme associated with glutamate receptors, as a key trigger of survival pathways that can prevent neuronal death and neurodegeneration in the adult hippocampus. We show that PP1α overexpression in hippocampal neurons limits NMDA receptor overactivation and Ca2+ overload during an excitotoxic event, while PP1 inhibition favors Ca2+ overload and cell death. The protective effect of PP1 is associated with a selective dephosphorylation on a residue phosphorylated by CaMKIIα on the NMDA receptor subunit NR2B, which promotes pro-survival pathways and associated transcriptional programs. These results reveal a novel contributor to the mechanisms of neuroprotection and underscore the importance of PP1-dependent dephosphorylation in these mechanisms. They provide a new target for the development of potential therapeutic treatment of neurodegeneration

Signal extraction from movies of honeybee brain activity: the ImageBee plugin for KNIME

BACKGROUND: In the antennal lobe, a dedicated olfactory center of the honeybee brain, odours are encoded as activity patterns of coding units, the so-called glomeruli. Optical imaging with calcium-sensitive dyes allows us to record these activity patterns and to gain insight into olfactory information processing in the brain. METHOD: We introduce ImageBee, a plugin for the data analysis platform KNIME. ImageBee provides a variety of tools for processing optical imaging data. The main algorithm behind ImageBee is a matrix factorisation approach. Motivated by a data-specific, non-negative mixture model, the algorithm aims to select the generating extreme vectors of a convex cone that contains the data. It approximates the movie matrix by non-negative combinations of the extreme vectors. These correspond to pure glomerular signals that are not mixed with neighbour signals. RESULTS: Evaluation shows that the proposed algorithm can identify the relevant biological signals on imaging data from the honeybee AL, as well as it can recover implanted source signals from artificial data. CONCLUSIONS: ImageBee enables automated data processing and visualisation for optical imaging data from the insect AL. The modular implementation for KNIME offers a flexible platform for data analysis projects, where modules can be rearranged or added depending on the particular application. AVAILABILITY: ImageBee can be installed via the KNIME update service. Installation instructions are available at http://tech.knime.org/imagebee-analysing-imaging-data-from-the-honeybee-brain

Performance and calibration of quark/gluon-jet taggers using 140 fb⁻¹ of pp collisions at √s=13 TeV with the ATLAS detector

The identification of jets originating from quarks and gluons, often referred to as quark/gluon tagging, plays an important role in various analyses performed at the Large Hadron Collider, as Standard Model measurements and searches for new particles decaying to quarks often rely on suppressing a large gluon-induced background. This paper describes the measurement of the efficiencies of quark/gluon taggers developed within the ATLAS Collaboration, using √s=13 TeV proton–proton collision data with an integrated luminosity of 140 fb-1 collected by the ATLAS experiment. Two taggers with high performances in rejecting jets from gluon over jets from quarks are studied: one tagger is based on requirements on the number of inner-detector tracks associated with the jet, and the other combines several jet substructure observables using a boosted decision tree. A method is established to determine the quark/gluon fraction in data, by using quark/gluon-enriched subsamples defined by the jet pseudorapidity. Differences in tagging efficiency between data and simulation are provided for jets with transverse momentum between 500 GeV and 2 TeV and for multiple tagger working points

Improving topological cluster reconstruction using calorimeter cell timing in ATLAS

Clusters of topologically connected calorimeter cells around cells with large absolute signal-to-noise ratio (topo-clusters) are the basis for calorimeter signal reconstruction in the ATLAS experiment. Topological cell clustering has proven performant in LHC Runs 1 and 2. It is, however, susceptible to out-of-time pile-up of signals from soft collisions outside the 25 ns proton-bunch-crossing window associated with the event’s hard collision. To reduce this effect, a calorimeter-cell timing criterion was added to the signal-to-noise ratio requirement in the clustering algorithm. Multiple versions of this criterion were tested by reconstructing hadronic signals in simulated events and Run 2 ATLAS data. The preferred version is found to reduce the out-of-time pile-up jet multiplicity by ∼50% for jet pT ∼ 20 GeV and by ∼80% for jet pT 50 GeV, while not disrupting the reconstruction of hadronic signals of interest, and improving the jet energy resolution by up to 5% for 20 < pT < 30 GeV. Pile-up is also suppressed for other physics objects based on topo-clusters (electrons, photons, τ -leptons), reducing the overall event size on disk by about 6% in early Run 3 pileup conditions. Offline reconstruction for Run 3 includes the timing requirement

Software Performance of the ATLAS Track Reconstruction for LHC Run 3

Charged particle reconstruction in the presence of many simultaneous proton–proton (pp) collisions in the LHC is a challenging task for the ATLAS experiment’s reconstruction software due to the combinatorial complexity. This paper describes the major changes made to adapt the software to reconstruct high-activity collisions with an average of 50 or more simultaneous pp interactions per bunch crossing (pileup) promptly using the available computing resources. The performance of the key components of the track reconstruction chain and its dependence on pile-up are evaluated, and the improvement achieved compared to the previous software version is quantified. For events with an average of 60 pp collisions per bunch crossing, the updated track reconstruction is twice as fast as the previous version, without significant reduction in reconstruction efficiency and while reducing the rate of combinatorial fake tracks by more than a factor two

Combination of searches for heavy spin-1 resonances using 139 fb−1 of proton-proton collision data at √s = 13 TeV with the ATLAS detector

A combination of searches for new heavy spin-1 resonances decaying into diferent pairings of W, Z, or Higgs bosons, as well as directly into leptons or quarks, is presented. The data sample used corresponds to 139 fb−1 of proton-proton collisions at √ s = 13 TeV collected during 2015–2018 with the ATLAS detector at the CERN Large Hadron Collider. Analyses selecting quark pairs (qq, bb, tt¯, and tb) or third-generation leptons (τν and τ τ ) are included in this kind of combination for the frst time. A simplifed model predicting a spin-1 heavy vector-boson triplet is used. Cross-section limits are set at the 95% confdence level and are compared with predictions for the benchmark model. These limits are also expressed in terms of constraints on couplings of the heavy vector-boson triplet to quarks, leptons, and the Higgs boson. The complementarity of the various analyses increases the sensitivity to new physics, and the resulting constraints are stronger than those from any individual analysis considered. The data exclude a heavy vector-boson triplet with mass below 5.8 TeV in a weakly coupled scenario, below 4.4 TeV in a strongly coupled scenario, and up to 1.5 TeV in the case of production via vector-boson fusion