Neuromodulation guide for the non-neuromodulator clinician: What it is and how it can benefit patients?

Neuromodulation is being utilized across a variety of medical subspecialties to treat both painful and non-painful medical conditions. However, publications on neuromodulation topics infrequently occur in journals targeting generalists and medical specialties outside of pain medicine and neurosurgery. This study reviewed implantable neuromodulation devices, their respective Food and Drug Administration-approved indications for use, as well as off-label usage, and the associated potential risks and benefits for each device. PubMed and Medline databases were queried for systematic reviews with or without meta-analyses and randomized controlled trials of implantable neuromodulation devices. The literature review resulted in 106 studies eligible for inclusion, and 67 were included in the final review. In conclusion, as the clinical volume of neuromodulation continues to grow, supporting and educating medical professionals who care for patients that receive implanted neuromodulation devices is paramount. It is likely the use of neuromodulation will continue to expand across all medical subspecialties, and as such, every clinician should have a baseline understanding of this treatment.

Unveiling the Interfacial Species Synergy in Promoting CO2 Tandem Electrocatalysis in Near-Neutral Electrolyte.

The interfacial species-built local environments on Cu surfaces impact the CO2 electroreduction process significantly in producing value-added multicarbon (C2+) products. However, intricate interfacial dynamics leads to a challenge in understanding how these species affect the process. Herein, with ab initio molecular dynamics (AIMD) and finite element method (FEM) simulations, we reveal that the highly concentrated interfacial species, including the *CO, hydroxide, and K+, could synergistically promote the C-C coupling on the one-dimensional (1D) porous hollow structure regulated interfacial environment. The Cu-Ag tandem catalyst was then synthesized with the as-designed structure, exhibiting a high C2+ Faradaic efficiency of 76.0% with a partial current density of 380.0 mA cm-2 in near-neutral electrolytes. Furthermore, in situ Raman spectra validate that the 1D porous structure regulates the concentration of interfacial CO intermediates and ions to increase *CO coverage, local pH value, and ionic field, promoting the CO2-to-C2+ activity. These results provide insights into the design of practical ECR electrocatalysts by regulating interfacial species-induced local environments.

Machine Learning Accelerates Precise Excited-State Potential Energy Surface Calculations on a Quantum Computer.

Electronically excited-state problems represent a crucial research field in quantum chemistry, closely related to numerous practical applications in photophysics and photochemistry. The emerging of quantum computing provides a promising computational paradigm to solve the Schrödinger equation for predicting potential energy surfaces (PESs). Here, we present a deep neural network model to predict parameters of the quantum circuits within the framework of variational quantum deflation and subspace search variational quantum eigensolver, which are two popular excited-state algorithms to implement on a quantum computer. The new machine learning-assisted algorithm is employed to study the excited-state PESs of small molecules, achieving highly accurate predictions. We then apply this algorithm to study the excited-state properties of the ArF system, which is essential to a gas laser. Through this study, we believe that with future advancements in hardware capabilities, quantum computing could be harnessed to solve excited-state problems for a broad range of systems.

From bulk effective mass to 2D carrier mobility accurate prediction via adversarial transfer learning.

Data scarcity is one of the critical bottlenecks to utilizing machine learning in material discovery. Transfer learning can use existing big data to assist property prediction on small data sets, but the premise is that there must be a strong correlation between large and small data sets. To extend its applicability in scenarios with different properties and materials, here we develop a hybrid framework combining adversarial transfer learning and expert knowledge, which enables the direct prediction of carrier mobility of two-dimensional (2D) materials using the knowledge learned from bulk effective mass. Specifically, adversarial training ensures that only common knowledge between bulk and 2D materials is extracted while expert knowledge is incorporated to further improve the prediction accuracy and generalizability. Successfully, 2D carrier mobilities are predicted with the accuracy over 90% from only crystal structure, and 21 2D semiconductors with carrier mobilities far exceeding silicon and suitable bandgap are successfully screened out. This work enables transfer learning in simultaneous cross-property and cross-material scenarios, providing an effective tool to predict intricate material properties with limited data.

Coupled Dynamic Characteristics of Mobile Ions in Halide Perovskites.

Mixed perovskites show immense promise for diverse applications owing to their exceptional compositional flexibility and outstanding optoelectronic performance. Nevertheless, a significant hurdle in their widespread use is their susceptibility to compositional instability. Some mixed perovskites exhibit a tendency to segregate into regions with varying halide content, negatively impacting the material's electronic properties and impeding its overall advancement. This study focuses on investigating the lattice and A-site cation dynamics in mixed-halide perovskites as well as the relationship between the stability and dynamic properties of mixed-halide perovskites. Our findings reveal an intrinsic link between the kinetics of organic molecules and halogen ion migration. The stability of halide ions is linearly positively correlated with the radius, number of H atoms, and moment of inertia of the organic molecules. Organic molecules with lower rotational kinetics effectively suppress the overall cationic kinetic activity, enhancing lattice dynamic stability in mixed perovskite systems. This inhibition further impedes the migration of halogen ions and hinders the halide segregation process. The presence of dominant I/MA vacancies in perovskites accelerates the rotation of MA and the migration of halogen ions. The coupled dynamic behavior of varying vacancy concentrations in A-site cations/X-site anions within the inorganic framework significantly impacts the photovoltaic performance of these halide perovskites.

Heavily Doped Carbon Nitride Nanocrystal Promotes Visible-Near-Infrared Photosynthesis of Hydrogen Peroxide with Near-Unit Photon Utilization.

Direct photosynthesis of hydrogen peroxide (H2O2) from water and oxygen represents an intriguing alternative to the current indirect process involving the reduction and oxidation of quinones. However, limited light utilization and sluggish charge transfer largely impede overall photocatalytic efficiency. Herein, we present a heavily doped carbon nitride (CNKLi) nanocrystal for efficient and selective photoproduction of H2O2 via a two-electron oxygen reduction reaction (ORR) pathway. CNKLi induces metal-to-ligand charge transfer (MLCT) and electron trapping, which broadens the light absorption to the visible-near-infrared (vis-NIR) spectrum and prolongs the photoelectron lifetime to the microsecond time scale with an exceptional charge diffusion length of ∼1200 nm. Near-unit photoutilization with an apparent quantum yield (AQY) of 100% for H2O2 generation is achieved below 420 nm. Impressively, CNKLi exhibits an appreciable AQY of 16% at 700 nm, which reaches the absorption capacity (∼16%), thus suggesting a near-unit photon utilization <700 nm. In situ characterization and theoretical calculations reveal the facilitated charge transfer from K+ to the heptazine ring skeleton. These findings provide an approach to improve the photosynthetic efficiency of direct H2O2 preparation in the vis-NIR region and expand applications for driving kinetically slow and technologically desirable oxidations or high-value chemical generation.

Hybridization State Transition under Working Conditions: Activity Origin of Single-Atom Catalysts.

Single-atom catalysts (SACs) have been widely investigated and have emerged as a transformative approach in electrocatalysis. Despite their clear structure, the origin of their exceptional activity remains elusive. Herein, we elucidate a common phenomenon of the hybridization state transition of metal centers, which is responsible for the activity origin across various SACs for different reactions. Focusing on N-doped carbon-supported Ni SAC (NiN4 SAC) for CO2 reduction reaction (CO2RR), our comprehensive computations successfully clarify the hybridization state transition under working conditions and its relation with the activity. This transition, triggered by the reaction intermediates and applied potential, converts the Ni center from the inert dsp2 hybridization state to the active d2sp3 hybridization state. Importantly, the calculated activity and selectivity of the CO2RR over the d2sp3-hybridized Ni center are consistent with the experimental results, offering strong support for the proposed hypothesis. This work suggests a universal principle of electronic structure evolution in SACs that could revolutionize catalyst design, which also introduces a new paradigm for manipulating electronic states to enhance catalytic performance, with implications for various reactions and catalyst platforms.

Multiplex-genome-editing based rapid directional improvement of complex traits in rice.

Although thousands of genes have been identified or cloned in rice (Oryza sativa) in the last two decades, the majority of them have only been separately characterized in specific varieties or single-gene modified backgrounds, thus limiting their practical application. We developed an optimized multiplex genome editing (MGE) toolbox that can efficiently assemble and stably express up to twelve sgRNA targets in a single plant expression vector. In this study, we established the MGE-based Rapid Directional Improvement (MRDI) strategy for directional improvement of complex agronomic traits in one small-scale rice transformation. This approach provides a rapid and practical procedure, encompassing sgRNA assembly, transgene-free screening and the creation of promising germplasm, by combining the precision of gene editing with phenotype-based field breeding. The MRDI strategy was used to generate the full diversity of twelve main agronomic genes in rice cultivar FXZ for the directional improvement of its growth duration and plant architecture. After applying the MRDI to FXZ, ideal plants with the desired traits of early heading date reduced plant height, and more effective panicles were generated without compromising yield, blast resistance and grain quality. Furthermore, the results of whole-genome sequencing (WGS), including the analysis of structural variations (SVs) and single nucleotide variations (SNVs) in the MGE plants, confirmed the high specificity and low frequency of unwanted mutations associated with this strategy. The MRDI breeding strategy would be a robust approach for exploring and applying crucial agronomic genes, as well as for generating novel elite germplasm in the future.

Towards the selective growth of two-dimensional ordered CxNy compounds via epitaxial substrate mediation.

Two-dimensional (2D) ordered carbon-nitrogen binary compounds (CxNy) show great potential in many fields owing to their diverse structures and outstanding properties. However, the scalable and selective synthesis of 2D CxNy compounds remain a challenge due to the variable C/N stoichiometry induced coexistence of graphitic, pyridinic, and pyrrolic N species and the competitive growth of graphene. Here, this work systematically explored the mechanism of selective growth of a series of 2D ordered CxNy compounds, namely, the g-C3N4, C2N, C3N, and C5N, on various epitaxial substrates via first-principles calculations. By establishing the thermodynamic phase diagram, it is revealed that the individualized surface interaction and symmetry match between 2D CxNy compounds and substrates together enable the selective epitaxial growth of single crystal 2D CxNy compounds within distinct chemical potential windows of feedstock. The kinetics behaviors of the diffusion and attachment of the decomposed feedstock C/N atoms to the growing CxNy clusters further confirmed the feasibility of the substrate mediated selective growth of 2D CxNy compounds. Moreover, the optimal experimental conditions, including the temperature and partial pressure of feedstock, are suggested for the selective growth of targeted 2D CxNy compound on individual epitaxial substrates by carefully considering the chemical potential of carbon/nitrogen as the functional of experimental parameters based on the standard thermochemical tables. This work provides an insightful understanding on the mechanism of selective epitaxial growth of 2D ordered CxNy compounds for guiding the future experimental design.

Laser-Induced Ultrafast Spin Injection in All-Semiconductor Magnetic CrI3/WSe2 Heterobilayer.

Spin injection stands out as a crucial method employed for initializing, manipulating, and measuring the spin states of electrons, which are fundamental to the creation of qubits in quantum computing. However, ensuring efficient spin injection while maintaining compatibility with standard semiconductor processing techniques is a significant challenge. Herein, we demonstrate the capability of inducing an ultrafast spin injection into a WSe2 layer from a magnetic CrI3 layer on a femtosecond time scale, achieved through real-time time-dependent density functional theory calculations upon a laser pulse. Following the peak of the magnetic moment in the CrI3 sublayer, the magnetic moment of the WSe2 layer reaches a maximum of 0.89 µB (per unit cell containing 4 WSe2 and 1 CrI3 units). During the spin dynamics, spin-polarized excited electrons transfer from the WSe2 layer to the CrI3 layer via type-II band alignment. The large spin splitting in conduction bands and the difference in the number of spin-polarized local unoccupied states available in the CrI3 layer lead to a net spin in the WSe2 layer. Furthermore, we confirmed that the number of available states, the spin-flip process, and the laser pulse parameters play important roles during the spin injection process. This work highlights the dynamic and rapid nature of spin manipulation in layered all-semiconductor systems, offering significant implications for the development and enhancement of quantum information processing technologies.

ML241 Antagonizes ERK 1/2 Activation and Inhibits Rotavirus Proliferation.

Rotavirus (RV) is the main pathogen that causes severe diarrhea in infants and children under 5 years of age. No specific antiviral therapies or licensed anti-rotavirus drugs are available. It is crucial to develop effective and low-toxicity anti-rotavirus small-molecule drugs that act on novel host targets. In this study, a new anti-rotavirus compound was selected by ELISA, and cell activity was detected from 453 small-molecule compounds. The anti-RV effects and underlying mechanisms of the screened compounds were explored. In vitro experimental results showed that the small-molecule compound ML241 has a good effect on inhibiting rotavirus proliferation and has low cytotoxicity during the virus adsorption, cell entry, and replication stages. In addition to its in vitro effects, ML241 also exerted anti-RV effects in a suckling mouse model. Transcriptome sequencing was performed after adding ML241 to cells infected with RV. The results showed that ML241 inhibited the phosphorylation of ERK1/2 in the MAPK signaling pathway, thereby inhibiting IκBα, activating the NF-κB signaling pathway, and playing an anti-RV role. These results provide an experimental basis for specific anti-RV small-molecule compounds or compound combinations, which is beneficial for the development of anti-RV drugs.

Forage plants in grasslands with different topographies affect yak foraging preferences on the eastern Tibetan plateau.

Diet selection, a core problem of foraging behavior, is a nutritional adaptation strategy formed in the long-term natural selection process by grazing herbivores and is significant for the sustainable management of grassland. Studies have mainly focused on the impacts of the individual and whole community spatial characteristics and herbivore body status on herbivore foraging behavior; thus, the response and mechanism of forage plants in different terrains to the diet selection of grazing herbivores remains unclear. Therefore, in this study, forage plants (gramineae, cyperaceae, legume, forbs, edible shrubs, and community) in different topographies (terrace, riparian zones, shady slope, half shady slope, half sunny slope, sunny slope) on the eastern Tibetan plateau were selected to study changes in nutrient and mineral content characteristics of forage plants, as well as the difference in feeding bias of yaks for forage plants in different terrains by using an indoor cafeteria trial. A structural equation model was used to illustrate the impact of the forage plants in different terrains on the feeding bias of yak. The multi-criterion decision model TOPSIS showed that the nutritional value of gramineae was highest for the shaded slope, and that of cyperaceae and leguminosae was the highest for the terraces. The nutrient value of forbs and the whole community was highest for the sunny slope. Dry matter intake by yaks of leguminosae, forbs, and the whole plant community was significantly higher for terraces than for grasslands with other topographies, and all were significantly lower in riparian zones. Yak forage preference of leguminosae, forbs, and the whole community was the highest for the terrace and the lowest for the riparian zones. Structural equation modeling showed that for functional groups, the interactions between topography and functional groups were the drivers influencing yak forage preferences. Our study highlights the propensity of yaks to forage for plants in areas with different topographies. These results have provided a scientific basis for understanding the relationship between herbivores and plants in grasslands and for formulating scientific grazing management strategies, which are of considerable importance for sustainable grassland livestock husbandry.

Mechanism of surface oxygen-containing species promoted electrocatalytic CO2 reduction.

Oxygen-containing species have been demonstrated to play a key role in facilitating electrocatalytic CO2 reduction (CO2RR), particularly in enhancing the selectivity towards multi-carbon (C2+) products. However, the underlying promotion mechanism is still under debate, which greatly limits the rational optimization of the catalytic performance of CO2RR. Herein, taking CO2 and O2 co-electrolysis over Cu as the prototype, we successfully clarified how O2 boosts CO2RR from a new perspective by employing comprehensive theoretical simulations. Our results demonstrated that O2 in feed gas can be rapidly reduced into *OH, leading to the partial oxidation of Cu surface under reduction conditions. Surface *OH accelerates the formation of quasi-specifically adsorbed K+ due to the electrostatic interaction between *OH and K+ ions, which significantly increases the concentration of K+ near the Cu surface. These quasi-specifically adsorbed K+ ions can not only lower the C-C coupling barriers but also promote the hydrogenation of CO2 to improve the CO yield rate, which are responsible for the remarkably enhanced efficiency of C2+ products. During the whole process, O2 co-electrolysis plays an indispensable role in stabilizing surface *OH. This mechanism can be also adopted to understand the effect of high pH of electrolyte and residual O in oxide-derived Cu (OD-Cu) on the catalytic efficiency towards C2+ products. Therefore, our work provides new insights into strategies for improving C2+ products on the Cu-based catalysts, i.e., maintaining partial oxidation of surface under reduction conditions.

Safety, Immunogenicity, and Mechanism of a Rotavirus mRNA-LNP Vaccine in Mice.

Rotaviruses (RVs) are a major cause of diarrhea in young children worldwide. The currently available and licensed vaccines contain live attenuated RVs. Optimization of live attenuated RV vaccines or developing non-replicating RV (e.g., mRNA) vaccines is crucial for reducing the morbidity and mortality from RV infections. Herein, a nucleoside-modified mRNA vaccine encapsulated in lipid nanoparticles (LNP) and encoding the VP7 protein from the G1 type of RV was developed. The 5' untranslated region of an isolated human RV was utilized for the mRNA vaccine. After undergoing quality inspection, the VP7-mRNA vaccine was injected by subcutaneous or intramuscular routes into mice. Mice received three injections in 21 d intervals. IgG antibodies, neutralizing antibodies, cellular immunity, and gene expression from peripheral blood mononuclear cells were evaluated. Significant differences in levels of IgG antibodies were not observed in groups with adjuvant but were observed in groups without adjuvant. The vaccine without adjuvant induced the highest antibody titers after intramuscular injection. The vaccine elicited a potent antiviral immune response characterized by antiviral clusters of differentiation CD8+ T cells. VP7-mRNA induced interferon-γ secretion to mediate cellular immune responses. Chemokine-mediated signaling pathways and immune response were activated by VP7-mRNA vaccine injection. The mRNA LNP vaccine will require testing for protective efficacy, and it is an option for preventing rotavirus infection.

Cascade Dual Sites Modulate Local CO Coverage and Hydrogen-Binding Strength to Boost CO2 Electroreduction to Ethylene.

Rationally modulating the binding strength of reaction intermediates on surface sites of copper-based catalysts could facilitate C-C coupling to generate multicarbon products in an electrochemical CO2 reduction reaction. Herein, theoretical calculations reveal that cascade Ag-Cu dual sites could synergistically increase local CO coverage and lower the kinetic barrier for CO protonation, leading to enhanced asymmetric C-C coupling to generate C2H4. As a proof of concept, the Cu3N-Ag nanocubes (NCs) with Ag located in partial Cu sites and a Cu3N unit center are successfully synthesized. The Faraday efficiency and partial current density of C2H4 over Cu3N-Ag NCs are 7.8 and 9.0 times those of Cu3N NCs, respectively. In situ spectroscopies combined with theoretical calculations confirm that Ag sites produce CO and Cu sites promote asymmetric C-C coupling to *COCHO, significantly enhancing the generation of C2H4. Our work provides new insights into the cascade catalysis strategy at the atomic scale for boosting CO2 to multicarbon products.

Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory.

The past decade has witnessed the significant efforts in novel material discovery in the use of data-driven techniques, in particular, machine learning (ML). However, since it needs to consider the precursors, experimental conditions, and availability of reactants, material synthesis is generally much more complex than property and structure prediction, and very few computational predictions are experimentally realized. To solve these challenges, a universal framework that integrates high-throughput experiments, a priori knowledge of chemistry, and ML techniques such as subgroup discovery and support vector machine is proposed to guide the experimental synthesis of materials, which is capable of disclosing structure-property relationship hidden in high-throughput experiments and rapidly screening out materials with high synthesis feasibility from vast chemical space. Through application of our approach to challenging and consequential synthesis problem of 2D silver/bismuth organic-inorganic hybrid perovskites, we have increased the success rate of the synthesis feasibility by a factor of four relative to traditional approaches. This study provides a practical route for solving multidimensional chemical acceleration problems with small dataset from typical laboratory with limited experimental resources available.

TIM22 and TIM29 inhibit HBV replication by up-regulating SRSF1 expression.

Hepatitis B virus (HBV) infection is a serious global health problem. After the viruses infect the human body, the host can respond to the virus infection by coordinating various cellular responses, in which mitochondria play an important role. Evidence has shown that mitochondrial proteins are involved in host antiviral responses. In this study, we found that the overexpression of TIM22 and TIM29, the members of the inner membrane translocase TIM22 complex, significantly reduced the level of intracellular HBV DNA and RNA and secreted HBV surface antigens and E antigen. The effects of TIM22 and TIM29 on HBV replication and transcription is attributed to the reduction of core promoter activity mediated by the increased expression of SRSF1 which acts as a suppressor of HBV replication. This study provides new evidence for the critical role of mitochondria in the resistance of HBV infection and new targets for the development of treatment against HBV infection.

Tandem Synergistic Effect of Cu-In Dual Sites Confined on the Edge of Monolayer CuInP2 S6 toward Selective Photoreduction of CO2 into Multi-Carbon Solar Fuels.

One-unit-cell, single-crystal, hexagonal CuInP2 S6 atomically thin sheets of≈0.81 nm in thickness was successfully synthesized for photocatalytic reduction of CO2 . Exciting ethene (C2 H4 ) as the main product was dominantly generated with the yield-based selectivity reaching ≈56.4 %, and the electron-based selectivity as high as ≈74.6 %. The tandem synergistic effect of charge-enriched Cu-In dual sites confined on the lateral edge of the CuInP2 S6 monolayer (ML) is mainly responsible for efficient conversion and high selectivity of the C2 H4 product as the basal surface site of the ML, exposing S atoms, can not derive the CO2 photoreduction due to the high energy barrier for the proton-coupled electron transfer of CO2 into *COOH. The marginal In site of the ML preeminently targets CO2 conversion to *CO under light illumination, and the *CO then migrates to the neighbor Cu sites for the subsequent C-C coupling reaction into C2 H4 with thermodynamic and kinetic feasibility. Moreover, ultrathin structure of the ML also allows to shorten the transfer distance of charge carriers from the interior onto the surface, thus inhibiting electron-hole recombination and enabling more electrons to survive and accumulate on the exposed active sites for CO2 reduction.

Mechanism-Guided Design of Photocatalysts for CO2 Reduction toward Multicarbon Products.

Photocatalytic reduction of CO2 to high value-added multicarbon (C2+) products is an important way to achieve sustainable production of green energy but limited by the low efficiency of catalysts. One fundamental issue lies in the high complexity of catalyst structure and reaction process, making the rational catalyst design and targeted performance optimization a grand challenge. Herein, we performed a mechanism-guided design of photocatalysts for CO2 reduction by using the experimentally reported Cu doped TiO2 (Cu-TiO2) with high C3H8 selectivity and well-defined structure as the prototype. Our mechanistic study highlights three key factors for C3H8 formation, i.e., formation of double O vacancies (Vdi-O) for selectivity, C-C coupling for activity, and Vdi-O recovery for durability. More importantly, Vdi-O formation/recovery and C-C coupling are negatively correlated, indicating that ideal candidates should achieve a balance between oxygen vacancy (VO) formation and C-C coupling. On this basis, TiO2 with the doping of two adjacent Cu atoms (Cu-Cu-TiO2) was designed with enhanced performance for CO2 photoreduction toward C3H8. Furthermore, a simple descriptor (Nµ, "effective d electron number") based on inherent atomic properties was constructed to uncover the underlying causes of the performance variation of different systems. These results provide new insights into the "structure-performance" relation of metal oxide-based photocatalysts, thus offering useful strategies for the rational design of excellent catalysts for CO2 photoreduction.