Beating the break-even point with a discrete-variable-encoded logical qubit.

Quantum error correction (QEC) aims to protect logical qubits from noises by using the redundancy of a large Hilbert space, which allows errors to be detected and corrected in real time1. In most QEC codes2-8, a logical qubit is encoded in some discrete variables, for example photon numbers, so that the encoded quantum information can be unambiguously extracted after processing. Over the past decade, repetitive QEC has been demonstrated with various discrete-variable-encoded scenarios9-17. However, extending the lifetimes of thus-encoded logical qubits beyond the best available physical qubit still remains elusive, which represents a break-even point for judging the practical usefulness of QEC. Here we demonstrate a QEC procedure in a circuit quantum electrodynamics architecture18, where the logical qubit is binomially encoded in photon-number states of a microwave cavity8, dispersively coupled to an auxiliary superconducting qubit. By applying a pulse featuring a tailored frequency comb to the auxiliary qubit, we can repetitively extract the error syndrome with high fidelity and perform error correction with feedback control accordingly, thereby exceeding the break-even point by about 16% lifetime enhancement. Our work illustrates the potential of hardware-efficient discrete-variable encodings for fault-tolerant quantum computation19.

Neural mechanisms underlying placebo and nocebo effects in tonic muscle pain.

Pain is a highly subjective and multidimensional experience, significantly influenced by various psychological factors. Placebo analgesia and nocebo hyperalgesia exemplify this influence, where inert treatments result in pain relief or exacerbation, respectively. While extensive research has elucidated the psychological and neural mechanisms behind these effects, most studies have focused on transient pain stimuli. To explore these mechanisms in the context of tonic pain, we conducted a study using a 15-minute tonic muscle pain induction procedure, where hypertonic saline was infused into the left masseter of healthy participants. We collected real-time Visual Analogue Scale (VAS) scores and functional magnetic resonance imaging (fMRI) data during the induction of placebo analgesia and nocebo hyperalgesia via conditioned learning. Our findings revealed that placebo analgesia was more pronounced and lasted longer than nocebo hyperalgesia. Real-time pain ratings correlated significantly with neural activity in several brain regions. Notably, the putamen was implicated in both effects, while the caudate and other regions were differentially involved in placebo and nocebo effects. These findings confirm that the tonic muscle pain paradigm can be used to investigate the mechanisms of placebo and nocebo effects and indicate that placebo analgesia and nocebo hyperalgesia may have more distinct than common neural bases.

Superconductivity in a Layered Cobalt Oxychalcogenide Na2CoSe2O with a Triangular Lattice.

Unconventional superconductivity in bulk materials under ambient pressure is extremely rare among the 3d transition metal compounds outside the layered cuprates and iron-based family. It is predominantly linked to highly anisotropic electronic properties and quasi-two-dimensional (2D) Fermi surfaces. To date, the only known example of a Co-based exotic superconductor is the hydrated layered cobaltate, NaxCoO2·yH2O, and its superconductivity is realized in the vicinity of a spin-1/2 Mott state. However, the nature of the superconductivity in these materials is still a subject of intense debate, and therefore, finding a new class of superconductors will help unravel the mysteries of their unconventional superconductivity. Here, we report the discovery of superconductivity at ∼6.3 K in our newly synthesized layered compound Na2CoSe2O, in which the edge-shared CoSe6 octahedra form [CoSe2] layers with a perfect triangular lattice of Co ions. It is the first 3d transition metal oxychalcogenide superconductor with distinct structural and chemical characteristics. Despite its relatively low TC, this material exhibits very high superconducting upper critical fields, µ0HC2(0), which far exceeds the Pauli paramagnetic limit by a factor of 3-4. First-principles calculations show that Na2CoSe2O is a rare example of a negative charge transfer superconductor. This cobalt oxychalcogenide with a geometrical frustration among Co spins shows great potential as a highly appealing candidate for the realization of unconventional and/or high-TC superconductivity beyond the well-established Cu- and Fe-based superconductor families and opens a new field in the physics and chemistry of low-dimensional superconductors.

UV-B photoreceptor UVR8 interacts with MYB73/MYB77 to regulate auxin responses and lateral root development.

The UV-B photoreceptor UVR8 mediates multiple UV-B responses in plants, but the function of UVR8 in regulating root development has not previously been investigated. Here, we show that UV-B light inhibits Arabidopsis lateral root growth in a UVR8-dependent manner. Monomeric UVR8 inhibits auxin responses in a tissue-autonomous manner and thereby regulates lateral root growth. Genome-wide gene expression analysis demonstrated that auxin and UV-B irradiation antagonistically regulate auxin-regulated gene expression. We further show that UVR8 physically interacts with MYB73/MYB77 (MYB DOMAIN PROTEIN 73/77) in a UV-B-dependent manner. UVR8 inhibits lateral root development via regulation of MYB73/MYB77. When activated by UV-B light, UVR8 localizes to the nucleus and inhibits the DNA-binding activities of MYB73/MYB77 and directly represses the transcription of their target auxin-responsive genes. Our results demonstrate that UV-B light and UVR8 are critical for both shoot morphogenesis and root development. The UV-B-dependent interaction of UVR8 and MYB73/MYB77 serves as an important module that integrates light and auxin signaling and represents a new UVR8 signaling mechanism in plants.

Autonomous Stabilization of Fock States in an Oscillator against Multiphoton Losses.

Fock states with a well-defined number of photons in an oscillator have shown a wide range of applications in quantum information science. Nonetheless, their usefulness has been marred by single and multiphoton losses due to unavoidable environment-induced dissipation. Though several dissipation engineering methods have been developed to counteract the leading single-photon-loss error, averting multiple-photon losses remains elusive. Here, we experimentally demonstrate a dissipation engineering method that autonomously stabilizes multiphoton Fock states against losses of multiple photons using a cascaded selective photon-addition operation in a superconducting quantum circuit. Through measuring the photon-number populations and Wigner tomography of the oscillator states, we observe a prolonged preservation of nonclassical Wigner negativities for the stabilized Fock states |N⟩ with N=1, 2, 3 for a duration of about 10 ms. Furthermore, the dissipation engineering method demonstrated here also facilitates the implementation of a nonunitary operation for resetting a binomially encoded logical qubit. These results highlight potential applications in error-correctable quantum information processing against multiple-photon-loss errors.

Robust Quantum Gates against Correlated Noise in Integrated Quantum Chips.

As quantum circuits become more integrated and complex, additional error sources that were previously insignificant start to emerge. Consequently, the fidelity of quantum gates benchmarked under pristine conditions falls short of predicting their performance in realistic circuits. To overcome this problem, we must improve their robustness against pertinent error models besides isolated fidelity. Here, we report the experimental realization of robust quantum gates in superconducting quantum circuits based on a geometric framework for diagnosing and correcting various gate errors. Using quantum process tomography and randomized benchmarking, we demonstrate robust single-qubit gates against quasistatic noise and spatially correlated noise in a broad range of strengths, which are common sources of coherent errors in large-scale quantum circuits. We also apply our method to nonstatic noises and to realize robust two-qubit gates. Our Letter provides a versatile toolbox for achieving noise-resilient complex quantum circuits.

Demonstrating Path-Independent Anyonic Braiding on a Modular Superconducting Quantum Processor.

Anyons, exotic quasiparticles in two-dimensional space exhibiting nontrivial exchange statistics, play a crucial role in universal topological quantum computing. One notable proposal to manifest the fractional statistics of anyons is the toric code model; however, scaling up its size through quantum simulation poses a serious challenge because of its highly entangled ground state. In this Letter, we demonstrate that a modular superconducting quantum processor enables hardware-pragmatic implementation of the toric code model. Through in-parallel control across separate modules, we generate a 10-qubit toric code ground state in four steps and realize six distinct braiding paths to benchmark the performance of anyonic statistics. The path independence of the anyonic braiding statistics is verified by correlation measurements in an efficient and scalable fashion. Our modular approach, serving as a hardware embodiment of the toric code model, offers a promising avenue toward scalable simulation of topological phases, paving the way for quantum simulation in a distributed fashion.

Development and validation of an enzyme-linked immunosorbent assay for the quantification of a recombinant humanized anti-IL-4Rα monoclonal antibody CM310 in serum and its application to pharmacokinetic study in Sprague-Dawley Rats.

CM310 is a recombinant humanized monoclonal antibody targeting Interleukin (IL)-4 receptor alpha (IL-4Rα). IL-4Rα blockade prevents IL-4 and IL-13 from binding to their receptor, thereby inhibiting downstream signaling pathways that drive Type 2 helper T-cell (Th2) inflammation. CM310 holds potential for treating Th2-related inflammatory diseases, such as asthma, atopic dermatitis and chronic sinusitis with nasal polyposis. In this study, a direct enzyme-linked immunosorbent assay (ELISA) was developed to measure the concentrations of CM310 in rat serum. Seven calibration standards (ranging from 25 to 1600 ng/mL) and three quality controls (70, 500 and 1250 ng/mL) were defined. The limit of detection (LOD), lower limit of quantification (LLOQ) and upper limit of quantification (ULOQ) were 13, 25 and 1600 ng/mL, respectively. The method exhibited excellent precision and accuracy and successfully applied to in vitro serum stability and pharmacokinetic (PK) studies. In conclusion, we have developed and validated a highly sensitive and selective method for measuring CM310 in Sprague-Dawley rats. The development and validation ELISA method met the acceptable criteria, which suggested that these can be applied to quantify CM310, as well as in PK studies.

Mechanism and Kinetic Study on Ultrasonically Enhanced Reduction Leaching of Zinc Suboxide.

The leaching process represents the primary bottleneck in achieving efficient utilization of zinc suboxide, thereby resulting in a squandering of germanium resources. In this Article, the kinetic mechanisms of conventional and ultrasonic enhanced reduction leaching of zinc suboxide were investigated while optimizing the leaching conditions. The optimized conditions for the ultrasonic enhanced reduction leaching process were found to be 358 K, FeS of 0.6% zinc suboxide mass, and 300 W of ultrasonic power. The leaching efficiency of germanium can reach 91.34% under these conditions, exhibiting an improvement of 8.51%, compared with conventional conditions. Moreover, the Fe3+ concentration in the leaching solution is consistently maintained at ∼15 mg/L, satisfying the requisite criteria for germanium precipitation. Moreover, both the conventional and ultrasonic leaching processes obey the Drozdov kinetic model and are governed by internal diffusion. The difference, however, is that, under ultrasonic conditions, the activation energy of the reaction is reduced by 2.05 kJ/mol, the self-resistance coefficient is smaller, the reaction rate is faster, and the germanium leaching efficiency is higher than under conventional conditions. Ultrasonically enhanced FeS reduction leaching disrupts the encapsulation of silica gel and lead sulfate, shattering large dust grains and reducing the surface tension and viscosity of the solution, thus reducing the energy barrier to the leaching of germanium-containing components and improving the kinetics. The present study elucidates the kinetic laws governing conventional and ultrasonic processes, thereby offering guidance and a theoretical foundation for enhancing the germanium leaching efficiency in zinc suboxide. These findings hold significant implications for maximizing the utilization of germanium resources and advancing the development of the germanium industry.

Mechanism of Germanium Adsorption by Iron Hydroxide Colloids during the Leaching Process of Secondary Zinc Oxide.

In the leaching process of secondary zinc oxide, there is a problem of germanium loss caused by the colloidal adsorption of germanium by iron hydroxide (Fe(OH)3) formed by Fe3+ hydrolysis. In response to this, this article elucidates the hydrolysis conditions of Fe3+ and the adsorption mechanism of the Fe(OH)3 colloid on germanium through theoretical analysis and simulation of the adsorption process. The coexistence of Fe3+ and H2GeO3 requires high acidity conditions (pH < 1.53 at 25 °C). The adsorption of germanium by the Fe(OH)3 colloid is a spontaneous exothermic entropy reduction process, which conforms to a pseudo-second-order kinetic model and includes three stages: fast, slow, and equilibrium. In addition, the adsorption process can be fitted by the Langmuir isotherm adsorption model, mainly consisting of monolayer and chemical adsorption. The Fe(OH)3 colloid has a great adsorption capacity for germanium at 328 K, and the equilibrium adsorption capacity is 261.15 mg/g in 40 min. During leaching, the adsorption of germanium by Fe(OH)3 colloids can be inhibited by increasing the reaction temperature, controlling the pH value of the solution system, and suppressing the formation of Fe3+ at the source. This study provides direction for how to suppress the adsorption of germanium by Fe(OH)3 colloids during the leaching process of secondary zinc oxide, which is of great significance for improving the germanium leaching efficiency and fully utilizing limited germanium resources.

Two {CuI[P4Mo6]2}-Based Coordination Polymers Incorporating In Situ Converted Tetrapyridyl Ligands for Trace Analysis of Nitrofuran Antibiotics.

Nitrofurans are important synthetic broad-spectrum antibacterial drugs with the basic structure of 5-nitrofuran. Due to their toxicity, it is essential to develop a sensitive sensor with strong anti-interference capabilities for their detection. In this work, two {P4Mo6O31}12--based compounds, [H4(HPTTP)]2{CuI[Mo12O24(OH)6(PO4)3(HPO4)(H2PO4)4]}·xH2O (x = 13 for (1), 7 for (2); HPTTP = 4,4',4″,4‴-(1H-pyrrole-2,3,4,5-tetrayl)tetrapyridine), exhibiting similar coordination but distinct stacking modes. Both compounds were synthesized and used for the electrochemical detection of nitrofuran antibiotics. The tetrapyridine-based ligand was generated in situ during assembly, and its potential mechanism was discussed. Composite electrode materials, formed by mixing graphite powder with compounds 1-2 and physically grinding them, proved to be highly effective in the electrochemical trace detection of furazolidone (FZD) and furaltadone hydrochloride (FTD·HCl) under optimal conditions. Besides, the possible electrochemical detection mechanisms of two nitro-antibiotics were studied.

Predicting who has delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage using machine learning approach: a multicenter, retrospective cohort study.

BACKGROUND: Early prediction of delayed cerebral ischemia (DCI) is critical to improving the prognosis of aneurysmal subarachnoid hemorrhage (aSAH). Machine learning (ML) algorithms can learn from intricate information unbiasedly and facilitate the early identification of clinical outcomes. This study aimed to construct and compare the ability of different ML models to predict DCI after aSAH. Then, we identified and analyzed the essential risk of DCI occurrence by preoperative clinical scores and postoperative laboratory test results. METHODS: This was a multicenter, retrospective cohort study. A total of 1039 post-operation patients with aSAH were finally included from three hospitals in China. The training group contained 919 patients, and the test group comprised 120 patients. We used five popular machine-learning algorithms to construct the models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, and f1 score were used to evaluate and compare the five models. Finally, we performed a Shapley Additive exPlanations analysis for the model with the best performance and significance analysis for each feature. RESULTS: A total of 239 patients with aSAH (23.003%) developed DCI after the operation. Our results showed that in the test cohort, Random Forest (RF) had an AUC of 0.79, which was better than other models. The five most important features for predicting DCI in the RF model were the admitted modified Rankin Scale, D-Dimer, intracranial parenchymal hematoma, neutrophil/lymphocyte ratio, and Fisher score. Interestingly, clamping or embolization for the aneurysm treatment was the fourth button-down risk factor in the ML model. CONCLUSIONS: In this multicenter study, we compared five ML methods, among which RF performed the best in DCI prediction. In addition, the essential risks were identified to help clinicians monitor the patients at high risk for DCI more precisely and facilitate timely intervention.

Genetic variations in MdSAUR36 participate in the negative regulation of mesocarp cell division and fruit size in Malus species.

Final fruit size of apple (Malus domestica) cultivars is related to both mesocarp cell division and cell expansion during fruit growth, but it is unclear whether the cell division and/or cell enlargement determine most of the differences in fruit size between Malus species. In this study, by using an interspecific hybrid population between Malus asiatica "Zisai Pearl" and Malus domestica cultivar "Red Fuji," we found that the mesocarp cell number was the main causal factor of diversity in fruit size between Malus species. Rapid increase in mesocarp cell number occurred prior to 28 days after anthesis (DAA), while cell size increased gradually after 28 DAA until fruit ripening. Six candidate genes related to auxin signaling or cell cycle were predicted by combining the RNA-seq data and previous QTL data for fruit weight. Two InDels and 10 SNPs in the promoter of a small auxin upregulated RNA gene MdSAUR36 in Zisai Pearl led to a lower promoter activity than that of Red Fuji. One non-synonymous SNP G/T at 379 bp downstream of the ATG codon of MdSAUR36, which was heterozygous in Zisai Pearl, exerted significant genotype effects on fruit weight, length, and width. Transgenic apple calli by over-expressing or RNAi MdSAUR36 confirmed that MdSAUR36 participated in the negative regulation of mesocarp cell division and thus apple fruit size. These results could provide new insights in the molecular mechanism of small fruit size in Malus accession and be potentially used in molecular assisted breeding via interspecific hybridization. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-024-01441-4.

Size effect on the pyrolysis of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) nanoparticles: a ReaxFF molecular dynamics study.

The nanoscale form of the typical insensitive energetic material 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) exhibits the capability to improve the energy performances while maintaining low sensitivity compared to raw TATB. Investigating the particle size effect on the intrinsic pyrolysis mechanisms facilitates the selection of the TATB particle size in applications to ensure efficient energy release and high safety levels. However, the intrinsic mechanism of this effect remains unclear. This study focuses on pyrolysis as a prerequisite behavior for energy release, employing reactive molecular dynamics simulations to investigate the pyrolysis of TATB nanoparticles with different sizes, aiming to explore the qualitative changes in thermal properties at the atomic level. Results demonstrate that with increasing particle size, the decomposition rate of TATB decreases. Smaller particles exhibit a propensity towards dehydrogenation and C-NO2 bond cleavage reactions. However, larger nano-TATB particles demonstrate a preference for dimerization, which results in the formation of clusters with greater polymerization and increased stability. The highly polymerized clusters are stable under thermal stimulation, inhibiting further decomposition of TATB. These insights reveal the mechanism underlying the qualitative change in the energy performance of TATB nanoparticles at the atomic level.

Predicting major adverse cardiovascular events within 3 years by optimization of radiomics model derived from pericoronary adipose tissue on coronary computed tomography angiography: a case-control study.

BACKGROUND: Coronary inflammation induces changes in pericoronary adipose tissue (PCAT) can be detected by coronary computed tomography angiography (CCTA). Our aim was to investigate whether different PCAT radiomics model based on CCTA could improve the prediction of major adverse cardiovascular events (MACE) within 3 years. METHODS: This retrospective study included 141 consecutive patients with MACE and matched to patients with non-MACE (n = 141). Patients were randomly assigned into training and test datasets at a ratio of 8:2. After the robust radiomics features were selected by using the Spearman correlation analysis and the least absolute shrinkage and selection operator, radiomics models were built based on different machine learning algorithms. The clinical model was then calculated according to independent clinical risk factors. Finally, an overall model was established using the radiomics features and the clinical factors. Performance of the models was evaluated for discrimination degree, calibration degree, and clinical usefulness. RESULTS: The diagnostic performance of the PCAT model was superior to that of the RCA-model, LAD-model, and LCX-model alone, with AUCs of 0.723, 0.675, 0.664, and 0.623, respectively. The overall model showed superior diagnostic performance than that of the PCAT-model and Cli-model, with AUCs of 0.797, 0.723, and 0.706, respectively. Calibration curve showed good fitness of the overall model, and decision curve analyze demonstrated that the model provides greater clinical benefit. CONCLUSION: The CCTA-based PCAT radiomics features of three major coronary arteries have the potential to be used as a predictor for MACE. The overall model incorporating the radiomics features and clinical factors offered significantly higher discrimination ability for MACE than using radiomics or clinical factors alone.

Preparation and evaluation of germanium concentrate by ultrasonic purification of tannin germanium residue.

Germanium (Ge) is a dispersed metal primarily recovered from secondary Ge-containing resources. The traditional treatment method is hindered by incomplete impurity removal, resulting in a low grade of tannin germanium residue (TGR) and Ge concentrate, high production costs, and significant hazardous waste. This study proposes a new technology involving ultrasonic pre-purification of TGR to enhance the quality of Ge concentrate prepared by roasting. Under optimal conditions (ultrasonic power 225 W, liquid-solid ratio 7:1, H2SO4 concentration 20 g/L, reaction time 30 min, and reaction temperature 40 °C), the removal efficiencies of impurities Zn, Mg, Fe, As, and S from purified tannin germanium residue (PTGR) increased by 4.2%, 4.2%, 17.4%, 8.7%, and 2.9% respectively. Moreover, the Ge content in PTGR increased from 2.9% to 4.1%. The mechanism of ultrasonic action indicated the ultrasonic energy reduced the particle size of the reactants from 67.698 µm to 31.768 µm, thereby accelerating impurity removal. Roasting ultrasonic-purified tannin germanium residue (U-PTGR) at 650 °C with 40 L/h air flow for 120 min produced Ge concentrate with a Ge grade of 33.26%, which is 6.11% higher than the regular method. Analysis using XRD and HRTEM, combined with crystallite size calculation, revealed that the Ge concentrate prepared by U-PTGR exhibited low sintering degree, good crystal properties, and high crystallinity. Implementing this technology could save enterprises approximately $57,412 annually in production costs. Additionally, it holds significant practical importance in reducing hazardous waste emissions and promoting the high-quality development of the Ge industry.

Coronary artery segmentation in CCTA images based on multi-scale feature learning.

BACKGROUND: Coronary artery segmentation is a prerequisite in computer-aided diagnosis of Coronary Artery Disease (CAD). However, segmentation of coronary arteries in Coronary Computed Tomography Angiography (CCTA) images faces several challenges. The current segmentation approaches are unable to effectively address these challenges and existing problems such as the need for manual interaction or low segmentation accuracy. OBJECTIVE: A Multi-scale Feature Learning and Rectification (MFLR) network is proposed to tackle the challenges and achieve automatic and accurate segmentation of coronary arteries. METHODS: The MFLR network introduces a multi-scale feature extraction module in the encoder to effectively capture contextual information under different receptive fields. In the decoder, a feature correction and fusion module is proposed, which employs high-level features containing multi-scale information to correct and guide low-level features, achieving fusion between the two-level features to further improve segmentation performance. RESULTS: The MFLR network achieved the best performance on the dice similarity coefficient, Jaccard index, Recall, F1-score, and 95% Hausdorff distance, for both in-house and public datasets. CONCLUSION: Experimental results demonstrate the superiority and good generalization ability of the MFLR approach. This study contributes to the accurate diagnosis and treatment of CAD, and it also informs other segmentation applications in medicine.

The effect of background liked music on acute pain perception and its neural correlates.

Music shows tremendous promise in pain relief, especially when considering its non-pharmacological nature. However, our understanding of the precise mechanisms behind music-induced analgesia (MIA) remains poor. The positive emotional state induced by music is one of the key components explaining MIA. To test this possibility and reveal its neural correlates, the present study applied nociceptive laser stimuli to 28 healthy participants when their liked or disliked songs were played as background music, or when they were resting in silence. Differences among conditions were quantified by self-reports of pain intensity and unpleasantness, as well as brain activations in response to acute laser stimuli. As expected, liked music significantly lowered pain ratings to acute painful stimuli compared to disliked music and no music. Consistent with this observation, brain activations in response to acute painful stimuli were deceased within brain areas encoding sensory components of pain, such as the right precentral and postcentral gyri (PreCG/PoCG), brain areas related to affective components of pain, such as the anterior cingulate cortex and bilateral putamen, and brain areas associated with motor control and avoidance reactions to pain, such as the left cerebellum, when liked music was played in the background in comparison to disliked music. Importantly, the relationship between music listening and differences in pain ratings of two music conditions was mediated by the magnitude of right PreCG/PoCG and left cerebellum activations. These findings deepened our understanding of the analgesic benefits of background liked music, a property relevant to clinical applications.

Ultrasensitive Self-Driven Terahertz Photodetectors Based on Low-Energy Type-II Dirac Fermions and Related Van der Waals Heterojunctions.

The exotic electronic properties of topological semimetals (TSs) have opened new pathways for innovative photonic and optoelectronic devices, especially in the highly pursuit terahertz (THz) band. However, in most cases Dirac fermions lay far above or below the Fermi level, thus hindering their successful exploitation for the low-energy photonics. Here, low-energy type-II Dirac fermions in kitkaite (NiTeSe) for ultrasensitive THz detection through metal-topological semimetal-metal heterostructures are exploited. Furthermore, a heterostructure combining two Dirac materials, namely, graphene and NiTeSe, is implemented for a novel photodetector exhibiting a responsivity as high as 1.22 A W-1 , with a response time of 0.6 µs, a noise-equivalent power of 18 pW Hz-0.5 , with outstanding stability in the ambient conditions. This work brings to fruition of Dirac fermiology in THz technology, enabling self-powered, low-power, room-temperature, and ultrafast THz detection.

Therapeutic targeting of hepatic ACSL4 ameliorates NASH in mice.

BACKGROUND AND AIMS: Globally, NAFLD is one of the most common liver disorders, with an estimated prevalence rate of more than 30% in men and 15% in women and an even higher prevalence in people with type 2 diabetes mellitus. Optimal pharmacologic therapeutic approaches for NAFLD are an urgent necessity. APPROACH AND RESULTS: In this study, we showed that compared with healthy controls, hepatic ACSL4 levels in patients with NAFLD were found to be elevated. Suppression of ACSL4 expression promoted mitochondrial respiration, thereby enhancing the capacity of hepatocytes to mediate ß-oxidation of fatty acids and to minimize lipid accumulation by up-regulating peroxisome proliferator-activated receptor coactivator-1 alpha. Moreover, we found that abemaciclib is a potent and selective ACSL4 inhibitor, and low dose of abemaciclib significantly ameliorated most of the NAFLD symptoms in multiple NAFLD mice models. CONCLUSIONS: Therefore, inhibition of ACSL4 is a potential alternative therapeutic approach for NAFLD.