Browse Articles

In situ architecture of the intercellular organelle reservoir between epididymal epithelial cells by volume electron microscopy

A structural biology compatible file format for atomic force microscopy

Enantioselective total synthesis of lycoposerramine congeners through late-stage nitrogen deletion

Native Fold Delay and its implications for co-translational chaperone binding and protein aggregation

Abnormal beam steering with kirigami reconfigurable metasurfaces

General theory for packing icosahedral shells into multi-component aggregates

Exploring replay

Abstract Animals face uncertainty about their environments due to initial ignorance or subsequent changes. They therefore need to explore. However, the algorithmic structure of exploratory choices in the brain still remains largely elusive. Artificial agents face the same problem, and a venerable idea in reinforcement learning is that they can plan appropriate exploratory choices offline, during the equivalent of quiet wakefulness or sleep. Although offline processing in humans and other animals, in the form of hippocampal replay and preplay, has recently been the subject of highly informative modelling, existing methods only apply to known environments. Thus, they cannot predict exploratory replay choices during learning and/or behaviour in the face of uncertainty. Here, we extend an influential theory of hippocampal replay and examine its potential role in approximately optimal exploration, deriving testable predictions for the patterns of exploratory replay choices in a paradigmatic spatial navigation task. Our modelling provides a normative interpretation of the available experimental data suggestive of exploratory replay. Furthermore, we highlight the importance of sequence replay, and license a range of new experimental paradigms that should further our understanding of offline processing.

Hyperfluorescence circularly polarized OLEDs consisting of chiral TADF sensitizers and achiral multi-resonance emitters

FGF21 protects against HFpEF by improving cardiac mitochondrial bioenergetics in mice

Nucleotide-induced hyper-oligomerization inactivates transcription termination factor ρ

Abstract Bacterial RNA helicase ρ is a genome sentinel that terminates the synthesis of damaged and junk RNAs that are not translated by the ribosome. It is unclear how ρ is regulated during dormancy or stress, when translation is inefficient and RNAs are vulnerable to ρ-mediated release. We use cryogenic electron microscopy, biochemical, and genetic approaches to show that substitutions of residues in the connector between two ρ domains or ADP promote the formation of extended Escherichia coli ρ filaments. By contrast, (p)ppGpp induces the formation of transient ρ dodecamers. Our results demonstrate that ADP and (p)ppGpp nucleotides bound at subunit interfaces inhibit ρ ring closure that underpins the hexamer activation, thus favoring the assembly of inactive higher-order oligomers. Connector substitutions and antibiotics that inhibit RNA and protein syntheses trigger ρ aggregation in the cell. These and other recent data implicate aggregation as a widespread strategy to tune ρ activity.

Discovery of selective low molecular weight interleukin-36 receptor antagonists by encoded library technologies

Sox9 inhibits Activin A to promote biliary maturation and branching morphogenesis

Gene therapy with feline anti-Müllerian hormone analogs disrupts folliculogenesis and induces pregnancy loss in female domestic cats

Abstract For female domestic cats, ovariohysterectomy is the only method of inducing permanent infertility. However, hundreds-of-millions of free-roaming cats globally highlight the necessity for alternative contraceptive approaches. One strategy involves a single injection of vector delivering a fertility-inhibiting protein for lifetime contraception. Recent studies in mice and cats have identified anti-Müllerian hormone as an excellent candidate for this type of contraception. Here, we leverage our recent characterization of the molecular mechanisms underlying human anti-Müllerian hormone synthesis and activity, to generate potent feline anti-Müllerian hormone analogs. Single intramuscular delivery of these analogs to female cats using an adeno-associated viral vector leads to a greater than 1000-fold increase in feline anti-Müllerian hormone levels, which are sustained for 9 months. High serum anti-Müllerian hormone is associated with abnormal estrus cyclicity, non-follicular ovarian cyst formation, and a progressive decline in antral follicle numbers, however, the few surviving large follicles continue to ovulate. Unlike previous studies, supraphysiologic levels of anti-Müllerian hormone do not block conception, although they are incompatible with the maintenance of pregnancy. Our findings highlight the complexity of the effects of anti-Müllerian hormone on ovarian physiology but confirm that this growth factor is a candidate for fertility control in free-roaming cats.

Non-local interactions determine local structure and lithium diffusion in solid electrolytes

Multi-point sensing organic light-emitting diode display based mobile cardiovascular monitor

Identification of Meibomian gland stem cell populations and mechanisms of aging

Abstract Meibomian glands secrete lipid-rich meibum, which prevents tear evaporation. Aging-related Meibomian gland shrinkage may result in part from stem cell exhaustion and is associated with evaporative dry eye disease, a common condition lacking effective treatment. The identities and niche of Meibomian gland stem cells and the signals controlling their activity are poorly defined. Using snRNA-seq, in vivo lineage tracing, ex vivo live imaging, and genetic studies in mice, we identify markers for stem cell populations that maintain distinct regions of the gland and uncover Hedgehog (Hh) signaling as a key regulator of stem cell proliferation. Consistent with this, we show that human Meibomian gland carcinoma exhibits increased Hh signaling. Aged glands display decreased Hh and EGF signaling, deficient innervation, and loss of collagen I in niche fibroblasts, indicating that alterations in both glandular epithelial cells and their surrounding microenvironment contribute to age-related degeneration. These findings suggest new approaches to treat aging-associated Meibomian gland loss.

Integration of electrically detected magnetic resonance on a chip (EDMRoC) with charge pumping for low-cost and sensitive defect characterization in silicon carbide metal–oxide–semiconductor field-effect transistors

Electrical detection of magnetic resonance (EDMR), a variant of electron paramagnetic resonance (EPR) in which the magnetic resonance effect is detected via changes in the electrical properties of materials or devices, is of high interest for studying electrically active defects in SiC metal–oxide–semiconductor field-effect transistors (MOSFETs), but requires dedicated instrumentation that is not widely available. Integration of microwave sources and detection circuits has led to the design of very compact EPR instruments, the so-called EPR-on-a-chip (EPRoC), which also offers opportunities for EDMR-on-a-chip (EDMRoC). Here, we report the demonstration of EDMRoC on lateral SiC MOSFETs under charge pumping (CP) conditions. The detected CP current gives direct access to microscopic information about the recombination centers within the transistor gate inversion region under the gate dielectric. Efficient and selective microwave excitation of the region of interest of the device can be obtained by only modest modifications to both the MOSFET and the EPRoC electronic board. A comparative study between EDMRoC and a traditional resonant cavity configuration reveals comparable signal-to-noise ratios for CP-detected EDMR spectra. In addition to space- and cost-efficiency, EDMRoC offers alternative detection modes with scanning and modulation of the microwave frequency, as well as potentially easier sample mounting and exchange. We end with a discussion of the advantages, limitations, and perspectives of the EDMRoC setup compared to EDMR in a conventional EPR spectrometer, offering promise for widespread integration of EDMR in semiconductor laboratories.

MEGA PROTAC, MEGA DOCK-based PROTAC mediated ternary complex formation pipeline with sequential filtering and rank aggregation

Abstract Proteolysis-targeting chimaeras (PROTACs), which induce proteolysis by recruiting an E3 ligase to dock into a target protein, are acquiring popularity as a novel pharmacological modality because of the unique features of PROTAC, including high potency, low dosage, and effective on undruggable targets. While PROTACs are promising prospects as chemical probes and therapeutic agents, their discovery usually necessitates the synthesis of numerous analogues to explore variations on the chemical linker structure exhaustively. Without extensive trial and error, it is unknown how to link the two protein-recruiting moieties to facilitate the formation of a productive ternary complex. Although molecular docking-based and optimization pipelines have been designed to predict ternary complexes, guiding rational PROTAC design, they have suffered from limited predictive performance in the quality of the ternary structure and their ranks. Here, MEGA PROTAC has been designed to enhance the performance in quality and ranking of ternary structures. MEGA PROTAC employs MEGADOCK to execute docking for protein-protein complexes (PPCs). The docking establishes an initial exploration area for PPCs. A sequential filtration strategy combined with rank aggregation is employed to choose a subset of PPCs for grid search. Once candidate PPCs are selected, a grid search method is used separately for translation and rotation. The remaining proteins have been grouped into clusters, and MEGA PROTAC further filters these clusters based on the energy score of the proteins within each cluster. MEGA PROTAC utilises rank aggregation to choose the best clusters and then employs MEGADOCK to dock PROTAC into the selected PPCs, forming a ternary structure. Finally, MEGA PROTAC was tested on 22 cases to compare with the state-of-the-art method, Bayesian optimisation for ternary complex prediction (BOTCP). MEGA PROTAC outperformed BOTCP on 16 test cases out of 22 cases, achieving a higher maximum DockQ score with an 18% higher mean and 35% higher median. Also, MEGA PROTAC exhibited 75% superior ranks and a reduced cluster number for maximum DockQ score compared to BOTCP. Also, MEGA PROTAC outperforms BOTCP by achieving a twofold improvement in locating the first acceptable DockQ scores, with a more significant proportion of near-native structures within the detected cluster.

Reactive rate coefficients and machine learning predictions for O(3P) + C2(X1Σg+) collisions on an accurate PIP-NN potential energy surface

A full-dimensional potential energy surface (PES) for the 3A″ state of the [CCO] system has been constructed using neural networks (NNs) with permutationally invariant polynomials. This global analytical PES was accurately fitted from 9293 ab initio energies at the MRCI + Q/aug-cc-pVTZ level of theory. Based on the newly developed surfaces, the microscopic chemical reaction mechanisms of the O(3P) + C2(X1Σg+) → CO(X1Σ+) + C(3P) reactive collision were investigated using the quasi-classical trajectory (QCT) method. The reaction cross sections and rate coefficients obtained from QCT calculations are in good agreement with available theoretical and experimental data reported in the literature. Rate coefficient calculations indicate that for O + C2 collisions, the results for the reactive channel are significantly higher than those for the inelastic channel across a wide temperature range of 1000–20 000 K. Finally, to reduce computational demands, we also established an NN-based model to predict cross section by combining QCT with NNs. The developed model accurately reproduces the original QCT results.

Parameter-free multiscale analysis of hydrogen solubility in Pd nanofilms under hydrogen gas using density functional theory

A parameter-free multiscale analysis of hydrogen solubility, hydrogen coverage, and hydrogen bulk concentration for face-centered-cubic Pd nanofilms with a (111) surface under hydrogen gas conditions is proposed using density functional theory and a simple kinetic model. The calculated solubility is quantitatively comparable to that obtained via experimental observations. Although the Pd surface is fully covered by hydrogen in a short time (microseconds to milliseconds) under exposure to 10–10000 ppm hydrogen gas pressure (1.0 atm), the hydrogen concentration in the subsurface or bulk changes significantly on the experimental time scale depending on gas pressure. We confirmed that the hydrogen concentration in the bulk or subsurface of Pd nanofilms (not the Pd surface) plays a role in the resistance change of Pd through comparison between the calculated hydrogen concentration and experimental observations of an electric resistance change caused by exposure to hydrogen gas. A hydrogen sensor requires a 0.1% change in the hydrogen concentration in the bulk to observe a significant change in the electric resistance. Furthermore, we calculated the time-dependent diffusion coefficient of hydrogen in a Pd nanofilm and compared it with the experimental observed one. We also investigated temperature dependency of the solubility and confirmed that the hydrogen gas pressure determines hydrogen solubility in Pd nanofilms at the equilibrium state, whereas the temperature controls the speed to reach the equilibrium state.