Search for: All records

Abstract We report observations of direct evidence of energetic protons being accelerated above ∼400 keV within the reconnection exhaust of a heliospheric current sheet (HCS) crossing by NASA’s Parker Solar Probe (PSP) at a distance of ∼16.25 solar radii (R_s) from the Sun. Inside the exhaust, both the reconnection-generated plasma jet and the accelerated protons up to ∼400 keV propagated toward the Sun, unambiguously establishing their origin from HCS reconnection sites located antisunward of PSP. Within the core of the exhaust, PSP detected stably trapped energetic protons up to ∼400 keV, which is ≈1000 times greater than the available magnetic energy per particle. The differential energy spectrum of the accelerated protons behaved as a pure power law with spectral index of ∼−5. Supporting simulations using thekglobalmodel suggest that the trapping and acceleration of protons up to ∼400 keV in the reconnection exhaust are likely facilitated by merging magnetic islands with a guide field between ∼0.2 and 0.3 of the reconnecting magnetic field, consistent with the observations. These new results, enabled by PSP’s proximity to the Sun, demonstrate that magnetic reconnection in the HCS is a significant new source of energetic particles in the near-Sun solar wind. Our findings of in situ particle acceleration via magnetic reconnection at the HCS provide valuable insights into this fundamental process, which frequently converts the large magnetic field energy density in the near-Sun plasma environment and may be responsible for heating the Sun’s atmosphere, accelerating the solar wind, and energizing charged particles to extremely high energies in solar flares. 

The generalde novosolution of the crystallographic phase problem is difficult and only possible under certain conditions. This paper develops an initial pathway to a deep learning neural network approach for the phase problem in protein crystallography, based on a synthetic dataset of small fragments derived from a large well curated subset of solved structures in the Protein Data Bank (PDB). In particular, electron-density estimates of simple artificial systems are produced directly from corresponding Patterson maps using a convolutional neural network architecture as a proof of concept. 

Abstract The fast solar wind that fills the heliosphere originates from deep within regions of open magnetic field on the Sun called ‘coronal holes’. The energy source responsible for accelerating the plasma is widely debated; however, there is evidence that it is ultimately magnetic in nature, with candidate mechanisms including wave heating 1,2 and interchange reconnection 3–5 . The coronal magnetic field near the solar surface is structured on scales associated with ‘supergranulation’ convection cells, whereby descending flows create intense fields. The energy density in these ‘network’ magnetic field bundles is a candidate energy source for the wind. Here we report measurements of fast solar wind streams from the Parker Solar Probe (PSP) spacecraft 6 that provide strong evidence for the interchange reconnection mechanism. We show that the supergranulation structure at the coronal base remains imprinted in the near-Sun solar wind, resulting in asymmetric patches of magnetic ‘switchbacks’ 7,8 and bursty wind streams with power-law-like energetic ion spectra to beyond 100 keV. Computer simulations of interchange reconnection support key features of the observations, including the ion spectra. Important characteristics of interchange reconnection in the low corona are inferred from the data, including that the reconnection is collisionless and that the energy release rate is sufficient to power the fast wind. In this scenario, magnetic reconnection is continuous and the wind is driven by both the resulting plasma pressure and the radial Alfvénic flow bursts. 

Abstract Genetic code expansion technology allows for the use of noncanonical amino acids (ncAAs) to create semisynthetic organisms for both biochemical and biomedical applications. However, exogenous feeding of chemically synthesized ncAAs at high concentrations is required to compensate for the inefficient cellular uptake and incorporation of these components into proteins, especially in the case of eukaryotic cells and multicellular organisms. To generate organisms capable of autonomously biosynthesizing an ncAA and incorporating it into proteins, we have engineered a metabolic pathway for the synthesis ofO‐methyltyrosine (OMeY). Specifically, we endowed organisms with a marformycins biosynthetic pathway‐derived methyltransferase that efficiently converts tyrosine to OMeY in the presence of the co‐factorS‐adenosylmethionine. The resulting cells can produce and site‐specifically incorporate OMeY into proteins at much higher levels than cells exogenously fed OMeY. To understand the structural basis for the substrate selectivity of the transferase, we solved the X‐ray crystal structures of the ligand‐free and tyrosine‐bound enzymes. Most importantly, we have extended this OMeY biosynthetic system to both mammalian cells and the zebrafish model to enhance the utility of genetic code expansion. The creation of autonomous eukaryotes using a 21st amino acid will make genetic code expansion technology more applicable to multicellular organisms, providing valuable vertebrate models for biological and biomedical research. 

Dynemicin is an enediyne natural product fromMicromonospora chersinaATCC53710. Access to the biosynthetic gene cluster of dynemicin has enabled thein vitrostudy of gene products within the cluster to decipher their roles in assembling this unique molecule. This paper reports the crystal structure of DynF, the gene product of one of the genes within the biosynthetic gene cluster of dynemicin. DynF is revealed to be a dimeric eight-stranded β-barrel structure with palmitic acid bound within a cavity. The presence of palmitic acid suggests that DynF may be involved in binding the precursor polyene heptaene, which is central to the synthesis of the ten-membered ring of the enediyne core. 

In this paper, we introduce two new methods of mitigating decoder error propagation for low-latency sliding window decoding (SWD) of spatially coupled low-density parity-check (SC-LDPC) codes. Building on the recently introduced idea of check node (CN) doping of regular SC-LDPC codes, here we employ variable node (VN) doping to fix (set to a known value) a subset of variable nodes in the coupling chain. Both of these doping methods have the effect of allowing SWD to recover from error propagation, at a cost of a slight rate loss. Experimental results show that, similar to CN doping, VN doping improves performance by up to two orders of magnitude compared to un-doped SC-LDPC codes in the typical signal-to-noise ratio operating range. Further, compared to CN doping, VN doping has the advantage of not requiring any changes to the decoding process. In addition, a log-likelihood-ratio based window extension algorithm is proposed to reduce the effect of error propagation. Using this approach, we show that decoding latency can be reduced by up to a significant fraction without suffering any loss in performance. 

Abstract Observations show predictive skill of the minimum sea ice extent (Min SIE) from late winter anomalous offshore ice drift along the Eurasian coastline, leading to local ice thickness anomalies at the onset of the melt season—a signal then amplified by the ice–albedo feedback. We assess whether the observed seasonal predictability of September sea ice extent (Sept SIE) from Fram Strait Ice Area Export (FSIAE; a proxy for Eurasian coastal divergence) is present in global climate model (GCM) large ensembles, namely the CESM2-LE, GISS-E2.1-G, FLOR-LE, CNRM-CM6-1, and CanESM5. All models show distinct periods where winter FSIAE anomalies are negatively correlated with the May sea ice thickness (May SIT) anomalies along the Eurasian coastline, and the following Sept Arctic SIE, as in observations. Counterintuitively, several models show occasional periods where winter FSIAE anomalies are positively correlated with the following Sept SIE anomalies when the mean ice thickness is large, or late in the simulation when the sea ice is thin, and/or when internal variability increases. More important, periods with weak correlation between winter FSIAE and the following Sept SIE dominate, suggesting that summer melt processes generally dominate over late-winter preconditioning and May SIT anomalies. In general, we find that the coupling between the winter FSIAE and ice thickness anomalies along the Eurasian coastline at the onset of the melt season is a ubiquitous feature of GCMs and that the relationship with the following Sept SIE is dependent on the mean Arctic sea ice thickness. 

In this paper, we introduce two new methods of mitigating decoder error propagation for low-latency sliding window decoding (SWD) of spatially coupled low density parity check (SC-LDPC) codes. Building on the recently introduced idea of check node (CN) doping of regular SC-LDPC codes, here we employ variable node (VN) doping to fix (set to a known value) a subset of variable nodes in the coupling chain. Both of these doping methods have the effect of allowing SWD to recover from error propagation, at a cost of a slight rate loss. Experimental results show that, similar to CN doping, VN doping improves performance by up to two orders of magnitude compared to undoped SC-LDPC codes in the typical signal-to-noise ratio operating range. Further, compared to CN doping, VN doping has the advantage of not requiring any changes to the decoding process.In addition, a log-likelihood-ratio based window extension algorithm is proposed to reduce the effect of error propagation. Using this approach, we show that decoding latency can be reduced by up to a significant fraction without suffering any loss in performance