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ABSTRACTDuring surveys of plant pathogens in the desert regions of Xinjiang Province, China, a leaf spot was observed on Cynanchum sibiricum. The suspected fungus was isolated and identified using morphology and molecular phylogeny. Morphologically, the fungus was similar to Neodidymelliopsisspecies. Phylogenetic analyzes of combined internal transcribed spacer (ITS); the large subunit of the ribosomal rDNA (LSU); RNA polymerase II the second largest subunit (rpb2), and β-tubulin gene (tub2) sequences showed that our isolates formed a distinct monophyletic lineage in Neodidymelliopsis. This new clade is identified as Neodidymelliopsis cynanchisp. nov. based on the phylogenetic and morphological evidence. In addition, N. urticaeis synonymized with N. cannabisbased on morphology and phylogeny. Furthermore, we emphasize that in-depth revisions are required to assure the taxonomic status of N. sambuci, N. tinkyukukuand N. salvia.This is the first report of Neodidymelliopsisspecies associated with C. sibiricumleaf spot. The results expand our knowledge of Didymellaceae taxa and microfungi associated with desert environments in China.

In this study, energy optimization of multiple electrical systems in off-grid mode with optimal participation of the storage systems is investigated. Multiple renewable sources, including solar cells, diesel generators, wind turbines, and backup storage systems, are utilized to feed the demand with high reliability. The load demand is divided into AC and DC loads on the consumers' sides. Then, by the mathematical nonlinear quadratic programming approach and heuristic approach, the optimal power generation in the generation side and consumers is implemented with minimum cost and the highest level of reliability. The following assesses the impact of batteries storage system on obtaining the optimal operation level. Finally, the proposed optimization algorithm is applied in MATLAB software with numerical simulation in the different operation modes of the energy systems to show the optimal energy generation.

Size plays a crucial role in chemistry and material science. Subnanometer polyoxometalate (POM) clusters have gained attention in various fields, but their use in thermoelectrics is still limited. To address this issue, we propose the POM clusters as an effective second phase to enhance the thermoelectric properties of Bi0.4Sb1.6Te3. Thanks to their subnanometer size, POM clusters improve electrical transport behavior through the superposition of atomic orbitals and the interfacial scattering effect. Furthermore, their ultrasmall size strongly reduces thermal conductivity. Consequently, the introduction of a mere 0.1 mol % of POM into the Bi0.4Sb1.6Te3matrix realizes a state-of-the-art zTvalue of 1.46 at 348 K, a 45% enhancement over Bi0.4Sb1.6Te3(1.01), along with a maximum thermoelectric-conversion efficiency of the integrated module of 6.0%. The enhancement of carrier mobility and the suppression of thermal conduction achieved by introducing the subnanometer clusters hold promise for various applications, such as electronic devices and thermal management.

Ordered L12γ′ Ni3Al intermetallics are essential strengthening components to maintain the high strength of Ni-based superalloys and recently developed high entropy alloys at elevated temperatures. Under service conditions, structural disorder is usually encountered in these intermetallics, resulting in significant loss of their strengthening functionality. Thus, retaining the degree of order of the L12intermetallics is vital for their long-term reliability and serviceability. In this study, atomistic simulations and rate equation analysis are employed to highlight a notable enhancement in the reordering ability of L12intermetallics by incorporating multiple principal elements. Specifically, we examine the effects of Co and Ti addition on the irradiation-induced disordering and kinetic reordering process of L12intermetallics. Our results reveal that the incorporation of Ti in the Al sublattice can maintain comparable disordering resistance as Ni3Al. Better yet, the introduction of Ti or Co fosters vacancy migration, which accelerates the kinetic reordering rate during the defect diffusion stage. A synergistic effect of Ti and Co in promoting kinetic reordering is also observed. Our work thus suggests a promising approach for designing irradiation-resistant multicomponent intermetallics, which can retain a high degree of structural order under irradiation with chemical disorder contributed by desirable compositions.

Mesenchymal stem cell (MSC)-based cardiac patches are envisioned to be a promising treatment option for patients with myocardial infarction. However, their therapeutic efficacy and duration are hampered due to their limited retention on the epicardium. We engineered a scaffold-free MSC sheet with an inherent ability to migrate into the infarcted myocardium, a strategy enabled by actively establishing a sustained intracellular hypoxic environment through the endocytosis of our FDA-approved ferumoxytol. This iron oxide nanoparticle stabilized hypoxia-induced factor-1α, triggering upregulation of the CXC chemokine receptor and subsequent MSC chemotaxis. Thus, MSCs integrated into 2/3 depth of the left ventricular anterior wall in a rat model of acute myocardial infarction and persisted for at least 28 days. This led to spatiotemporal delivery of paracrine factors by MSCs, enhancing cardiac regeneration and function. Ferumoxytol also facilitated the noninvasive MRI tracking of implanted MSCs. Our approach introduces a strategy for mobilizing MSC migration, holding promise for rapid clinical translation in myocardial infarction treatment.

The cyclic peptide c[d-Lys2, Asp5]-DN-9 has recently been identified as a multifunctional opioid/neuropeptide FF receptor agonist, displaying potent analgesic activity with reduced side effects. This study utilized Tyr-c[d-Lys-Gly-Phe-Asp]-d-Pro-NH2(0), a cyclic hexapeptide derived from the opioid pharmacophore of c[d-Lys2, Asp5]-DN-9, as a chemical template. We designed, synthesized, and characterized 22 analogs of 0with a single amino acid substitution to investigate its structure–activity relationship. Most of these cyclic hexapeptide analogs exhibited multifunctional activity at μ and δ opioid receptors (MOR and DOR, respectively) and produced antinociceptive effects following subcutaneous administration. The lead compound analog 15showed potent agonistic activities at the MOR, κ opioid receptor (KOR), and DOR in vitroand produced a strong and long-lasting analgesic effect through peripheral MOR and KOR in the tail-flick test. Further biological evaluation identified that analog 15did not cause significant side effects such as tolerance, withdrawal, or reward liability.

Unsignalized intersections pose a challenge for autonomous vehicles that must decide how to navigate them safely and efficiently. This paper proposes a reinforcement learning (RL) method for autonomous vehicles to navigate unsignalized intersections safely and efficiently. The method uses a semantic scene representation to handle variable numbers of vehicles and a universal reward function to facilitate stable learning. A collision risk function is designed to penalize unsafe actions and guide the agent to avoid them. A scalable policy optimization algorithm is introduced to improve data efficiency and safety for vehicle learning at intersections. The algorithm employs experience replay to overcome the on-policy limitation of proximal policy optimization and incorporates the collision risk constraint into the policy optimization problem. The proposed safe RL algorithm can balance the trade-off between vehicle traffic safety and policy learning efficiency. Simulated intersection scenarios with different traffic situations are used to test the algorithm and demonstrate its high success rates and low collision rates under different traffic conditions. The algorithm shows the potential of RL for enhancing the safety and reliability of autonomous driving systems at unsignalized intersections.

In this article, an adaptive dynamic programming (ADP) scheme utilizing a costate function is proposed for optimal control of unknown discrete-time nonlinear systems. The state–action data are obtained by interacting with the environment under the iterative scheme without any model information. In contrast with the traditional ADP scheme, the collected data in the proposed algorithm are generated with different policies, which improves data utilization in the learning process. In order to approximate the cost function more accurately and to achieve a better policy improvement direction in the case of insufficient data, a separate costate network is introduced to approximate the costate function under the actor–critic framework, and the costate is utilized as supplement information to estimate the cost function more precisely. Furthermore, convergence properties of the proposed algorithm are analyzed to demonstrate that the costate function plays a positive role in the convergence process of the cost function based on the alternate iteration mode of the costate function and cost function under a mild assumption. The uniformly ultimately bounded (UUB) property of all the variables is proven by using the Lyapunov approach. Finally, two numerical examples are presented to demonstrate the effectiveness and computation efficiency of the proposed method.

As the length of electrocardiogram (ECG) sequences increases, most current transformer models demand substantial computational resources for ECG arrhythmia detection. Additionally, conventional single-scale tokens encounter difficulties in accommodating various patterns of arrhythmia. Thus, in this study, a refined-attention transformer model for arrhythmia detection was proposed. Our model introduces two refined attention mechanisms, namely, refined diag- and gated linear (GAL) attentions, effectively alleviating computational burdens associated with unnecessary correlations between heartbeats. To address rhythmic and beat-pattern arrhythmias, we used two refined transformer models with a collaborative block, leveraging coarse- and fine-grained tokens to capture inter and intraheartbeat correlations. The collaborative block between two models facilitates the exchange of rhythm information, thereby improving the accuracy of beat detection. On the MIT-BIH dataset, our refined attentions yield over a 65% reduction in computational efforts compared with conventional self-attention. Notably, our refined transformer models achieve 96% accuracy for rhythmic detection and rank within the top two performers for all types of heartbeat detection. Moreover, the collaborative block enhances the recall by 8.8% and precision by 3.4% for atrial premature detection.

Our previous study reported the multifunctional agonist for opioid and neuropeptide FF receptors DN-9, along with its cyclic peptide analogues c[D-Cys2, Cys5]-DN-9 and c[D-Lys2, Asp5]-DN-9. These analogues demonstrated potent antinociceptive effects with reduced opioid-related side effects. To develop more stable and effective analgesics, we designed, synthesized, and evaluated seven hydrocarbon-stapled cyclic peptides based on DN-9. In vitrocalcium mobilization assays revealed that most of the stapled peptides, except 3, displayed multifunctional agonistic activities at opioid and neuropeptide FF receptors. Subcutaneous administration of all stapled peptides resulted in effective and long-lasting antinociceptive activities lasting up to 360 min. Among these stapled peptides, 1aand 1bemerged as the optimized compounds, producing potent central antinociception following subcutaneous, intracerebroventricular, and oral administrations. Additionally, subcutaneous administration of 1aand 1bcaused nontolerance antinociception, with limited occurrence of constipation and addiction. Furthermore, 1awas selected as the final optimized compound due to its wider safety window compared to 1b.

Graphite/Mo joining has been carried out using Ti/Cu or Ti/Cu/Nb/Ti/Cu foils. The joining region in the joint using Ti/Cu foils is mainly composed of Ti3Cu4and TiCu. The joining region using Ti/Cu/Nb/Ti/Cu foils contains a joining zone I, an Nb interlayer and a joining zone II. Both the joining zones I and II are made up of Ti3Cu4and TiCu. The Nb foil diffuses and dissolves into the liquid fillers during the bonding. The joint using Ti/Cu/Nb/Ti/Cu foils shows higher shear strength than that using Ti/Cu foils, implying the contribution of Nb foil to the joint stress relief and to the joint strength promotion.

Graphite/Mo joining has been carried out using Ti/Cu or Ti/Cu/Nb/Ti/Cu foils. The joining region in the joint using Ti/Cu foils is mainly composed of Ti3Cu4and TiCu. The joining region using Ti/Cu/Nb/Ti/Cu foils contains a joining zone I, an Nb interlayer and a joining zone II. Both the joining zones I and II are made up of Ti3Cu4and TiCu. The Nb foil diffuses and dissolves into the liquid fillers during the bonding. The joint using Ti/Cu/Nb/Ti/Cu foils shows higher shear strength than that using Ti/Cu foils, implying the contribution of Nb foil to the joint stress relief and to the joint strength promotion.

High-entropy electrocatalysts (HEECs) have been attracting extensive attention because of their multiple merits in heterogeneous catalysis. However, the diverse local environments and vast phase space behind HEECs make experimental and ab initioexploration unaffordable. In this work, we develop an accurate and efficient atomic graph attention (AGAT) network to accelerate the design of high-performance HEECs. The reliability of scaling relations and classical d-band theory is confirmed on HEEC surfaces on a statistical basis. Nonetheless, we prove that HEEC can effectively bypass the scaling relations by providing ample versatile local environments. We apply the model to explore the compositional space composed of Ni-Co-Fe-Pd-Pt, and high-performance compositions are recommended and validated by our experiments. The AGAT is inherently interpretable, as attention scores elegantly explain its behavior, which shows good agreement with physical principles. Through the interpretable AGAT model, this work opens an avenue for rational design and high-throughput screening of high-performance HEECs.

Tin selenide(SnSe) material exhibits very excellent thermoelectric properties in both p- and n-type single crystals, benefiting from advantageous electronic structure and ultra-low lattice thermal conductivity. However, poor mechanical properties and time-consuming production limit its practical application, and polycrystalline SnSe has attracted great attention in recent years. Herein, the SnSe microplates are prepared by a facile solvothermal method and compacted by spark plasma sintering (SPS). With enhanced electrical conductivity(σ) and optimized Seebeck coefficient(S) benefited from the Sn vacancies and increased density of states effective mass by the incorporating of heavy rare earth element Gd, an enhanced power factor(PF) value of about 0.86 mW/(m·K2) is realized in the p-type polycrystalline Sn0.985Gd0.015Se sample. Combined with the medium total thermal conductivity ktotand lattice thermal conductivity klatof 0.7 and 0.56 W/(m·K), respectively, an enhanced peak dimensionless figure of merit(ZT) of ca.1.1 was achieved at 873 K along vertical to the SPS pressing direction. The average ZTwas also improved as compared to that of the pristine SnSe sample.

ABSTRACTThis study aims to investigate how exercise-induced myocardial hypertrophy preconditioning affects cardiac fibroblasts in the context of myocardial fibrosis, a chronic disease that can cause cardiac arrhythmia and heart failure. Heart failure was induced in male C57BL/6 mice via Transverse aortic constriction, and some mice were given swimming exercise before surgery to test the effects of exercise-induced myocardial hypertrophy preconditioning on myocardial fibrosis. Myocardial tissue was evaluated for fibrosis, senescent cells, and apoptotic cells. Myocardial fibroblasts from rats were cultured and treated with norepinephrine to induce fibrosis which were then treated with si-Nrf2 and analyzed for markers of fibrosis, senescence, apoptosis, and cell proliferation. Exercise-induced myocardial hypertrophy preconditioning reduced myocardial fibrosis in mice, as shown by decreased mRNA expression levels of fibrosis-related indicators and increased cell senescence. In vitro data indicated that norepinephrine (NE) treatment increased fibrosis-related markers and reduced apoptotic and senescent cells, and this effect was reversed by pre-conditioning in PRE+NE group. Preconditioning activated Nrf2 and downstream signaling genes, promoting premature senescence in cardiac fibroblasts and tissues isolated from preconditioned mice. Moreover, Nrf2 knockdown reversed proapoptotic effects, restored cell proliferation, reduced senescence-related protein expression, and increased oxidative stress markers and fibrosis-related genes, indicating Nrf2‘s crucial role in regulating oxidative stress response of cardiac fibroblasts. Exercise-induced myocardial hypertrophy preconditioning improves myocardial fibrosis which is Nrf2-dependent, indicating the protective effect of hypertrophy preconditioning. These findings may contribute to the development of therapeutic interventions to prevent or treat myocardial fibrosis.

Ligand-assisted wet chemical synthesis is a versatile methodology to produce controllable nanocrystals (NCs). The post-treatment of ligands is significant for the performance of functional devices. Herein, a method that retains ligands of colloidal-synthesized nanomaterials to produce thermoelectric nanomaterials is proposed, which differs from the conventional methods that strip ligands using multistep cumbersome processes. The ligand-retention method can control the size and dispersity of nanocrystals during the consolidation of the NCs into dense pellets, in which retained ligands are transformed into organic carbon within the inorganic matrices, establishing clear organic–inorganic interfaces. Characterizations of the nonstripped and stripped samples confirm that this strategy can affect electric transport slightly but reduce the thermal conductivity largely. As a result, the materials (e.g., SnSe, Cu2–xS, AgBiSe2, and Cu2ZnSnSe4) with ligands retained achieve higher peak zTand better mechanical properties. This method can be applied to other colloidal thermoelectric NCs and functional materials.

Devices capable of deterministically manipulating photonic entanglement are of paramount importance, because photons are ideal messengers for quantum information. However, due to the non-interacting nature of photons, many photonic quantum operations have only been demonstrated using probabilistic linear-optical approaches, leading to an overwhelming resource overhead and poor scalability. Here we report a novel entanglement filter that transmits the desired photonic entangled state and blocks unwanted ones. In contrast to previous probabilistic approaches, our experiment exploits the strong and controllable photon–photon interaction enabled by Rydberg atoms, so the filtering of undesired states succeeds in every experimental trial. Photonic entanglement with near-unity fidelity can be extracted from an input state with an arbitrarily low initial fidelity. The protocol is inherently robust, and succeeds both in the Rydberg blockade regime and in the interaction-induced dissipation regime. Such an entanglement filter opens new routes towards achieving scalable photonic quantum information processing with multiple ensembles of Rydberg atoms.

The soybean aphid, Aphis glycinesMatsumura (Hemiptera: Aphididae), is one of the greatest threats to soybean production, and both trend analysis and periodic analysis of its population dynamics are important for integrated pest management (IPM). Based on systematically investigating soybean aphid populations in the field from 2018 to 2020, this study adopted the inverse logistic model for the first time, and combined it with the classical logistic model to describe the changes in seasonal population abundance from colonization to extinction in the field. Then, the increasing and decreasing phases of the population fluctuation were divided by calculating the inflection points of the models, which exhibited distinct seasonal trends of the soybean aphid populations in each year. In addition, multifactor logistic models were then established for the first time, in which the abundance of soybean aphids in the field changed with time and relevant environmental conditions. This model enabled the prediction of instantaneous aphid abundance at a given time based on relevant meteorological data. Taken as a whole, the successful approaches implemented in this study could be used to build a theoretical framework for practical IPM strategies for controlling soybean aphids.

A new green synergistic extraction agent (di-2-ethylhexyl phosphoric acid (D2EHPA)—decanol) was developed to recycle Ni(II) from nickel plating effluent. The results show that the D2EHPA—decanol mixture carrier provided a significant synergistic effect in the process. When sulfonated kerosene or sunflower seed oil was used as a diluent, the single extraction efficiency of Ni(II) reached 97.8% and 97.1%, respectively. After three-times extraction, the Ni(II) concentration in the nickel plating effluent decreased to below 1 mg/L. Additionally, in the stripping stage, the stripping efficiency of Ni(II) in the D2EHPA—decanol mixed carrier was larger than that of D2EHPA. Mechanism studies indicate that the polar groups (—OH) from decanol initially break the hydrogen bonds in the D2EHPA dimer structure. Afterwards, van der Waals forces shorten the bond length of P—O to form electron-rich centers of O atoms. Finally, a new P—O—Ni structure is formed through interactions between P—O bond and nickel ions under electrostatic forces.

The emerging connected automated vehicle (CAV) technique brings a new opportunity for the development of cooperative collision avoidance systems. This paper proposes a distributed coordinated brake control (DCBC) algorithm for longitudinal collision avoidance of multiple CAVs. Firstly, the studied scenario is defined, within which the communication topology and vehicle dynamics are modeled for a vehicle string and the control objective is designed to minimize the total relative kinetic energy. Next, the control problem is formulated into a centralized model predictive control problem, which is then transformed into a quadratic programming (QP) problem. By separating the QP problem into several sub-problems assigned to each CAV, distributed optimization is introduced to solve the QP problem in a distributed manner. Elaborate numerical simulation comparisons are conducted to demonstrate the advantage of the proposed DCBC algorithm.

To balance the unexpected power disturbances, an independent system operator (ISO) should assign the dynamic power regulation commands to all the regulation resources via an automatic generation control (AGC) dispatch. It can be described as a bi-objective Pareto optimization by considering the minimizations of total power deviation and total regulation mileage payment. In this work, a novel dropout deep neural network assisted transfer learning (DDNN-TL) is proposed to rapidly approximate the high-quality Pareto optimal solutions for AGC dispatch. Firstly, the training data is generated from the Pareto optimal solutions and fronts obtained by various multi-objective optimization algorithms according to the anticipated total power regulation commands. Secondly, the network parameters of DDNN will be updated via an off-line training with these data at each frequency regulation service period. Finally, based on an efficient transfer learning with a correction of infeasible solutions, DDNN-TL can directly approximate the high-quality Pareto optimal solutions for on-line decision of AGC dispatch. Case studies of DDNN-TL for bi-objective Pareto AGC dispatch are carried out on a two-area load frequency control model and Hainan power grid of China Southern Power Grid (CSG), which demonstrates its superior performance on optimization speed and stability compared with other multi-objective optimization algorithms.

As an organic pollutant difficult to be degraded in water bodies, phenol is toxic even at low concentrations. Therefore, studying the degradation technology of phenol is of great significance. In order to investigate the effect of transition metal doping on the performance of oxidative degradation of phenol by the persulfate activated by metal-organic frameworks, the different doping levels of Ce were tested based on the NH2-MIL-101(Fe). Then, NH2-MIL-101(Fe,Ce) with different Fe/Ce ratios was successfully prepared by solvothermal method. They were characterized by means of XRD, SEM, TEM, XPS and FT-IR. Selecting phenol as the target pollutant, the NH2-MIL-101(Fe,Ce)/PMS system was constructed to study the degradation rate. The factors of the different temperature, initial pH, anions, Ce doping ratios, catalyst dosage and PMS concentration were investigated. The mechanism of degradation of phenol for the above system was analyzed through free radical burst test, and the metal ion dissolution of catalytic materials in simulated wastewater treatment process was monitored by ICP-OES instrument. Finally, the intermediate products of phenol in the degradation process were inferred using LC-MS method. The results showed that when the molar ratio of Ce/(Ce+Fe) was 15 %, under the same reaction conditions, NH2-MIL-101(Fe,Ce)-15 % (referred to as NM-15 %) had the highest degradation rate for phenol. After the 50 min, phenol could be almostly completely removed, with K value of 0.059 min−1. This system could achieve the efficient degradation of phenol under the range of pH values (pH=3–9). The results of the free radical burst test and the free radical trapping test showed that the dominant oxidising groups were SO4•−, •OH, •O2−and non-free radical 1O2.

Modulating electrical and thermal transport through zero-dimensional point defects and one-dimensional dislocations are extensively investigated. However, the systematic study of exploring single-atom-layer vacancies for improving thermoelectric (TE) performance remains rare. In this study, we modulate the number density of single-atom-layer two-dimensional (2D) Te vacancies in lead telluride (PbTe) for the first time through bismuth and indium doping. DFT calculations reveal (Bi, In) co-doping lowers the formation energy of such defects, confirming the feasibility of this method. 2D Te vacancies simultaneously enhance phonon scattering and evoke bond softening, significantly lowering the lattice thermal conductivity. Meanwhile, indium doping generates resonant level in the energy band, which increases the carrier concentration without deteriorating the Seebeck coefficient drastically. As a result, a peak TE figure of merit (zT) of ∼1.6 and an average zT(zTavg) of 1.17 are achieved in the (Bi, In) co-doped PbTe. Furthermore, a high conversion efficiency of 6.5 % is obtained for the fabricated 2-pair module at a temperature difference of 500 K.

Metastatic skin cutaneous melanoma (MSCM) is the most rapidly progressing/invasive skin-based malignancy, with median survival rates of about 12 months. It appears that metabolic disorders accelerate disease progression. However, correlations between metabolism-linked genes (MRGs) and prognosis in MSCM are unclear, and potential mechanisms explaining the correlation are unknown. The Cancer Genome Atlas (TCGA) was utilized as a training set to develop a genomic signature based on the differentially expressed MRGs (DE-MRGs) between primary skin cutaneous melanoma (PSCM) and MSCM. The Gene Expression Omnibus (GEO) was utilized as a validation set to verify the effectiveness of genomic signature. In addition, a nomogram was established to predict overall survival based on genomic signature and other clinic-based characteristics. Moreover, this study investigated the correlations between genomic signature and tumor micro-environment (TME). This study established a genomic signature consisting of 3 genes (CD38, DHRS3, and TYRP1) and classified MSCM patients into low and high-risk cohorts based on the median risk scores of MSCM cases. It was discovered that cases in the high-risk cohort had significantly lower survival than cases in the low-risk cohort across all sets. Furthermore, a nomogram containing this genomic signature and clinic-based parameters was developed and demonstrated high efficiency in predicting MSCM case survival times. Interestingly, Gene Set Variation Analysis results indicated that the genomic signature was involved in immune-related physiological processes. In addition, this study discovered that risk scoring was negatively correlated with immune-based cellular infiltrations in the TME and critical immune-based checkpoint expression profiles, indicating that favorable prognosis may be influenced in part by immunologically protective micro-environments. A novel 3-genomic signature was found to be reliable for predicting MSCM outcomes and may facilitate personalized immunotherapy.