Structure Responsible for the Superconducting State in La3Ni2O7 at High-Pressure and Low-Temperature Conditions.

Very recently, a new superconductor with Tc = 80 K has been reported in nickelate (La3Ni2O7) at around 15-40 GPa conditions (Nature, 621, 493, 2023), which is the second type of unconventional superconductor, besides cuprates, with Tc above liquid nitrogen temperature. However, the phase diagram plotted in this report was mostly based on the transport measurement under low-temperature and high-pressure conditions, and the assumed corresponding X-ray diffraction (XRD) results were carried out at room temperature. This encouraged us to carry out in situ high-pressure and low-temperature synchrotron XRD experiments to determine which phase is responsible for the high Tc state. In addition to the phase transition from the orthorhombic Amam structure to the orthorhombic Fmmm structure, a tetragonal phase with the space group of I4/mmm was discovered when the sample was compressed to around 19 GPa at 40 K where the superconductivity takes place in La3Ni2O7. The calculations based on this tetragonal structure reveal that the electronic states that approached the Fermi energy were mainly dominated by the eg orbitals (3dz2 and 3dx2-y2) of Ni atoms, which are located in the oxygen octahedral crystal field. The correlation between Tc and this structural evolution, especially Ni-O octahedra regularity and the in-plane Ni-O-Ni bonding angles, is analyzed. This work sheds new light to identify what is the most likely phase responsible for superconductivity in double-layered nickelate.

Modification of the Ni-Rich Layered Cathode Material by Hf Addition: Synergistic Microstructural Engineering and Surface Stabilization.

The rapid decline of the reversible capacity originating from microcracks and surface structural degradation during cycling is still a serious obstacle to the practical utilization of Ni-rich LiNixCoyAl1-x-yO2 (x ≥ 0.8) cathode materials. In this research, a feasible Hf-doping method is proposed to improve the electrochemical performance of LiNi0.9Co0.08Al0.02O2 (NCA90) through microstructural optimization and structural enhancement. The addition of Hf refines the primary particles of NCA90 and develops them into a short rod shape, making them densely arranged along the radial direction, which increases the secondary particle toughness and reduces their internal porosity. Moreover, Hf-doping stabilizes the layered structure and suppresses the side reactions through the introduction of robust Hf-O bonding. Multiple advantages of Hf-doping allowed significant improvement of the cycling stability of LiNi0.895Co0.08Al0.02Hf0.005O2 (NCA90-Hf0.5), with a reversible capacity retention rate of 95.3% after 100 cycles at 1 C, as compared with only 82.0% for the pristine NCA90. The proposed synergetic strategy combining microstructural engineering and crystal structure enhancement can effectively resolve the inherent capacity fading of Ni-rich layered cathodes, promoting their practical application for next-generation lithium-ion batteries.

Negative linear compressibility and strong enhancement of emission in Eu[Ag(CN)2]3·3H2O under pressure.

The framework material Eu[Ag(CN)2]3·3H2O exhibits a negative linear compressibility (NLC) of -4.2(1) TPa-1 over the largest pressure range yet observed (0-8.2 GPa). High-pressure single-crystal X-ray diffraction data show that the rapid contraction of the Kagome silver layers under compression causes the wine-rack lattice to expand along the c-axis. The hydrogen bonds between the water molecules and the main frameworks constrain the structural deformation under pressure and eventually a weak NLC effect generated. Furthermore, we found that the pressure-induced emission intensity increases almost 800-fold at 4.0 GPa, followed by a gradual decrease and disappearance at 8.1 GPa. Under compression, high pressure significantly tunes the triplet level positions near the Eu3+ ions, and horizontal displacement between a quenching excited state and the excited levels of Eu3+ facilitates the energy transfer process to the 5D0 excited state and limits the nonradiative corssover at elevated pressures, thus increasing the emission intensity. In addition, we observe a gradual band gap reduction with increasing pressure, and the sample could not be returned to the initial state after the pressure was completely released. By controlling the structural flexibility, we observe a coupled NLC and pressure-induced strong enhancement of the emission properties of Eu[Ag(CN)2]3·3H2O, which provides a new route for the design of new optical devices with intriguing luminescence properties under extreme environments.

Transcription factors NtNAC028 and NtNAC080 form heterodimers to regulate jasmonic acid biosynthesis during leaf senescence in Nicotiana tabacum.

Plant senescence, as a highly integrated developmental stage, involves functional degeneration and nutrient redistribution. NAM/ATAF1/CUC (NAC) transcription factors orchestrate various senescence-related signals and mediate the fine-tuning underlying plant senescence. Previous data revealed that knockout of either NtNAC028 or NtNAC080 leads to delayed leaf senescence in tobacco (Nicotiana tabacum), which implies that NtNAC028 and NtNAC080 play respective roles in the regulation of leaf senescence, although they share 91.87% identity with each other. However, the mechanism underlying NtNAC028- and NtNAC080-regulated leaf senescence remains obscure. Here, we determined that NtNAC028 and NtNAC080 activate a putative jasmonic acid (JA) biosynthetic gene, NtLOX3, and enhance the JA level in vivo. We found that NtNAC028 and NtNAC080 interact with each other and themselves through their NA-terminal region. Remarkably, only the dimerization between NtNAC028 and NtNAC080 stimulated the transcriptional activation activity, but not the DNA binding activity of this heterodimer on NtLOX3. Metabolome analysis indicated that overexpression of either NtNAC028 or NtNAC080 augments both biosynthesis and degradation of nicotine in the senescent stages. Thus, we conclude that NtNAC028 cooperates with NtNAC080 and forms a heterodimer to enhance NtLOX3 expression and JA biosynthesis to trigger the onset of leaf senescence and impact secondary metabolism in tobacco.

A biobank-scale test of marginal epistasis reveals genome-wide signals of polygenic epistasis.

The contribution of epistasis (interactions among genes or genetic variants) to human complex trait variation remains poorly understood. Methods that aim to explicitly identify pairs of genetic variants, usually single nucleotide polymorphisms (SNPs), associated with a trait suffer from low power due to the large number of hypotheses tested while also having to deal with the computational problem of searching over a potentially large number of candidate pairs. An alternate approach involves testing whether a single SNP modulates variation in a trait against a polygenic background. While overcoming the limitation of low power, such tests of polygenic or marginal epistasis (ME) are infeasible on Biobank-scale data where hundreds of thousands of individuals are genotyped over millions of SNPs. We present a method to test for ME of a SNP on a trait that is applicable to biobank-scale data. We performed extensive simulations to show that our method provides calibrated tests of ME. We applied our method to test for ME at SNPs that are associated with 53 quantitative traits across ≈ 300 K unrelated white British individuals in the UK Biobank (UKBB). Testing 15, 601 trait-loci associations that were significant in GWAS, we identified 16 trait-loci pairs across 12 traits that demonstrate strong evidence of ME signals (p-value p<5×10-853). We further partitioned the significant ME signals across the genome to identify 6 trait-loci pairs with evidence of local (within-chromosome) ME while 15 show evidence of distal (cross-chromosome) ME. Across the 16 trait-loci pairs, we document that the proportion of trait variance explained by ME is about 12x as large as that explained by the GWAS effects on average (range: 0.59 to 43.89). Our results show, for the first time, evidence of interaction effects between individual genetic variants and overall polygenic background modulating complex trait variation.

The genetic underpinnings of variable penetrance and expressivity of pathogenic mutations in cardiometabolic traits.

Over three percent of people carry a dominant pathogenic mutation, yet only a fraction of carriers develop disease (incomplete penetrance), and phenotypes from mutations in the same gene range from mild to severe (variable expressivity). Here, we investigate underlying mechanisms for this heterogeneity: variable variant effect sizes, carrier polygenic backgrounds, and modulation of carrier effect by genetic background (epistasis). We leveraged exomes and clinical phenotypes from the UK Biobank and the Mt. Sinai Bio Me Biobank to identify carriers of pathogenic variants affecting cardiometabolic traits. We employed recently developed methods to study these cohorts, observing strong statistical support and clinical translational potential for all three mechanisms of variable penetrance and expressivity. For example, scores from our recent model of variant pathogenicity were tightly correlated with phenotype amongst clinical variant carriers, they predicted effects of variants of unknown significance, and they distinguished gain- from loss-of-function variants. We also found that polygenic scores predicted phenotypes amongst pathogenic carriers and that epistatic effects can exceed main carrier effects by an order of magnitude.

Fast kernel-based association testing of non-linear genetic effects for biobank-scale data.

Our knowledge of non-linear genetic effects on complex traits remains limited, in part, due to the modest power to detect such effects. While kernel-based tests offer a versatile approach to test for non-linear relationships between sets of genetic variants and traits, current approaches cannot be applied to Biobank-scale datasets containing hundreds of thousands of individuals. We propose, FastKAST, a kernel-based approach that can test for non-linear effects of a set of variants on a quantitative trait. FastKAST provides calibrated hypothesis tests while enabling analysis of Biobank-scale datasets with hundreds of thousands of unrelated individuals from a homogeneous population. We apply FastKAST to 53 quantitative traits measured across ≈ 300 K unrelated white British individuals in the UK Biobank to detect sets of variants with non-linear effects at genome-wide significance.

Sweet syndrome induced by SARS-CoV-2 vaccines: A systematic review of patient-report studies.

Since COVID-19 became a global pandemic in 2020, the development and application of SARS-CoV-2 vaccines has become an important task to prevent the spread of the epidemic. In addition to the safety and efficacy of COVID-19 vaccines, the adverse reactions caused by vaccines in a small number of people also deserve our attention. We aimed to discuss and analyze the possible causes of Sweet syndrome caused by the COVID-19 vaccine by integrating the effective information from 16 patients and combining it with the latest views on the innate immune mechanism. We searched the PubMed and Embase databases for published patient reports on the occurrence or recurrence of Sweet syndrome after COVID-19 vaccination. We summarized the basic information of the patients, the type of vaccination, the presence of underlying diseases, and the clinical manifestations, clinical treatment and prognosis of the patients. The results were reported in narrative methods and were sorted into tables. We initially identified 53 studies. 16 articles were included through full-text screening. Based on the table we compiled, we generally concluded that the first dose of any type of COVID-19 vaccine was more likely to cause Sweet syndrome than subsequent doses. Sweet syndrome may occur after COVID-19 vaccination. Clinicians should consider Sweet syndrome in addition to common adverse reactions such as anaphylaxis and infection when a patient presents with acute fever accompanied by nodular erythema, pustules, and edematous plaques after COVID-19 vaccination.

Leveraging family data to design Mendelian randomization that is provably robust to population stratification.

Mendelian randomization (MR) has emerged as a powerful approach to leverage genetic instruments to infer causality between pairs of traits in observational studies. However, the results of such studies are susceptible to biases owing to weak instruments, as well as the confounding effects of population stratification and horizontal pleiotropy. Here, we show that family data can be leveraged to design MR tests that are provably robust to confounding from population stratification, assortative mating, and dynastic effects. We show in simulations that our approach, MR-Twin, is robust to confounding from population stratification and is not affected by weak instrument bias, whereas standard MR methods yield inflated false positive rates. We then conduct an exploratory analysis of MR-Twin and other MR methods applied to 121 trait pairs in the UK Biobank data set. Our results suggest that confounding from population stratification can lead to false positives for existing MR methods, whereas MR-Twin is immune to this type of confounding, and that MR-Twin can help assess whether traditional approaches may be inflated owing to confounding from population stratification.

Pressure-Induced Ferroelastic Transition Drives a Large Shape Change in a Ni(II) Complex Single Crystal.

Crystals with significant length reduction at an accessible low pressure are highly desirable for piezo-responsive devices. Here, we show a molecular crystal [Ni(en)3](ox) (en = ethylenediamine and ox = oxalate anion) that exhibits an abrupt shape change with a contraction rate of ∼4.7% along its c axis near the phase transition pressure of ∼0.2 GPa. High-pressure single-crystal X-ray diffraction and Raman spectroscopy measurements reveal that this material undergoes a first-order ferroelastic transition from high-symmetry trigonal P3̅1c to low-symmetry monoclinic P21/n at ∼0.2 GPa. The oxalate anions serve as unique components, and their disorder-order transformation and rotation of 90° through cooperative intermolecular hydrogen bonding triggered unconventional anisotropic microsize contraction under compression, which can be appreciated visually. Such a prominent directional deformation at a low pressure driven by molecular motors of oxalate anions provides insights for the design of novel molecular crystal-based piezo-responsive switches and actuators in deep-sea environments.

Leveraging family data to design Mendelian Randomization that is provably robust to population stratification.

Mendelian Randomization (MR) has emerged as a powerful approach to leverage genetic instruments to infer causality between pairs of traits in observational studies. However, the results of such studies are susceptible to biases due to weak instruments as well as the confounding effects of population stratification and horizontal pleiotropy. Here, we show that family data can be leveraged to design MR tests that are provably robust to confounding from population stratification, assortative mating, and dynastic effects. We demonstrate in simulations that our approach, MR-Twin, is robust to confounding from population stratification and is not affected by weak instrument bias, while standard MR methods yield inflated false positive rates. We applied MR-Twin to 121 trait pairs in the UK Biobank dataset and found that MR-Twin identifies likely causal trait pairs and does not identify trait pairs that are unlikely to be causal. Our results suggest that confounding from population stratification can lead to false positives for existing MR methods, while MR-Twin is immune to this type of confounding.

Multi-modal affine fusion network for social media rumor detection.

With the rapid development of the Internet, people obtain much information from social media such as Twitter and Weibo every day. However, due to the complex structure of social media, many rumors with corresponding images are mixed in factual information to be widely spread, which misleads readers and exerts adverse effects on society. Automatically detecting social media rumors has become a challenge faced by contemporary society. To overcome this challenge, we proposed the multimodal affine fusion network (MAFN) combined with entity recognition, a new end-to-end framework that fuses multimodal features to detect rumors effectively. The MAFN mainly consists of four parts: the entity recognition enhanced textual feature extractor, the visual feature extractor, the multimodal affine fuser, and the rumor detector. The entity recognition enhanced textual feature extractor is responsible for extracting textual features that enhance semantics with entity recognition from posts. The visual feature extractor extracts visual features. The multimodal affine fuser extracts the three types of modal features and fuses them by the affine method. It cooperates with the rumor detector to learn the representations for rumor detection to produce reliable fusion detection. Extensive experiments were conducted on the MAFN based on real Weibo and Twitter multimodal datasets, which verified the effectiveness of the proposed multimodal fusion neural network in rumor detection.

Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data.

In recent years, methods were proposed for assigning feature importance scores to measure the contribution of individual features. While in some cases the goal is to understand a specific model, in many cases the goal is to understand the contribution of certain properties (features) to a real-world phenomenon. Thus, a distinction has been made between feature importance scores that explain a model and scores that explain the data. When explaining the data, machine learning models are used as proxies in settings where conducting many real-world experiments is expensive or prohibited. While existing feature importance scores show great success in explaining models, we demonstrate their limitations when explaining the data, especially in the presence of correlations between features. Therefore, we develop a set of axioms to capture properties expected from a feature importance score when explaining data and prove that there exists only one score that satisfies all of them, the Marginal Contribution Feature Importance (MCI). We analyze the theoretical properties of this score function and demonstrate its merits empirically.