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Angioleiomyoma (ALM) is a rare benign perivascular (pericytic) tumor primarily composed of well-differentiated smooth muscle and vascular components. Its clinical and radiological features lack specificity, making diagnosis challenging and prone to misdiagnosis. This report summarizes the clinical data of a patient treated at our hospital who was preoperatively misdiagnosed with rectal cancer but was subsequently found to have perirectal ALM. Additionally, a review of the relevant literature is provided. [ABSTRACT FROM AUTHOR]

This study investigated the mediating role of perceived stress in long-term development of posttraumatic stress symptoms (PTSS) and emotional distress among local government officials over seven years after the Wenchuan earthquake in China. Moreover, how dysfunctional coping would impact the relationship between perceived stress and PTSS or emotional distress was examined. Data were collected one year (T1) and seven years (T2) after the earthquake. Trauma experience, PTSS, and emotional distress were measured at one year. PTSS and emotional distress were re-assessed at seven years together with perceived stress and dysfunctional coping (i.e., behavioral disengagement, denial, self-distraction, self-blame, substance use, and venting). Mediation analyses were conducted to examine the mediating role of perceived stress. Moderated mediation analyses were performed to test the moderation effect of dysfunctional coping. Perceived stress fully mediated the association of T1 emotional distress with its' T2 scores (indirect effect: B = 0.10, SE = 0.04, β = 0.11, 95% CI [0.02, 0.18]), while did not mediate the association of T1 PTSS with T2 PTSS (indirect effect: B = 0.04, SE = 0.03, β = 0.04, 95% CI [−0.02, 0.10]). Compared to a low frequency use of dysfunctional coping strategies (ps = 0.148–0.326), the detrimental effects of perceived stress on PTSS and emotional distress were stronger at higher levels of dysfunctional coping strategies (ps < 0.001). This study highlights the importance of paying attention to the adverse impacts of perceived stress and dysfunctional coping on long-term mental health outcomes among local government officials after disasters. [ABSTRACT FROM AUTHOR]

Background Encouraging antitumor activity of nab-paclitaxel plus S-1 (AS) has been shown in several small-scale studies. This study compared the efficacy and safety of AS versus standard-of-care nab-paclitaxel plus gemcitabine (AG) as a first-line treatment for advanced pancreatic cancer (PC). Methods In this multicenter, randomized, phase II trial, eligible patients with unresectable, locally advanced, or metastatic PC were recruited and randomly assigned (1:1) to receive AS (nab-paclitaxel 125 mg/m2 on days 1 and 8; S-1 twice daily on days 1 through 14) or AG (nab-paclitaxel 125 mg/m2 on days 1 and 8; gemcitabine 1000 mg/m2 on days 1 and 8) for 6 cycles. The primary endpoint was progression-free survival (PFS). Results Between July 16, 2019, and September 9, 2022, 62 patients (AS, n  = 32; AG, n  = 30) were treated and evaluated. With a median follow-up of 8.36 months at preplanned interim analysis (data cutoff, March 24, 2023), the median PFS (8.48 vs 4.47 months; hazard ratio [HR], 0.402; P  = .002) and overall survival (OS; 13.73 vs 9.59 months; HR, 0.226; P  < .001) in the AS group were significantly longer compared to the AG group. More patients had objective response in the AS group than AG group (37.50% vs 6.67%; P  = .005). The most common grade 3-4 adverse events were neutropenia and leucopenia in both groups, and gamma glutamyl transferase increase was observed only in the AG group. Conclusion The first-line AS regimen significantly extended both PFS and OS of Chinese patients with advanced PC when compared with the AG regimen, with a comparable safety profile. (ClinicalTrials.gov Identifier: NCT03636308). [ABSTRACT FROM AUTHOR]

Drought stress, as one of the most common environmental factors, seriously affects seed- ling establishment as well as plant growth and productivity. The growth of Toona ciliata is constrained by soil moisture deficit, and drought stress can reduce its productivity and limit its suitable growing environment. To explore the molecular mechanism of Toona ciliata responding to drought stress, leaves of two-year-old Toona ciliata seedlings were used as experimental materials for transcriptome sequencing and physiological index measurements. Under drought stress, the contents of Chl, MDA, POD, SP, SS, and RWC all change differently. We performed transcriptome sequencing, obtaining 4830 differential genes. The enrichment analysis indicates that the primary effects on the leaves of Toona ciliata under drought stress are related to photosynthesis and responses to plant hormone signal transduction. Transcription factor families associated with drought resistance include the NAC, WRKY, bZIP, bHLH, AP2-EREBP, C3H, GRAS, and FRAI transcription factor families. A weighted gene co-expression network analysis (WGCNA) analysis successfully identified 10 hub genes in response to drought stress in Toona ciliata leaves. Real-time quantitative PCR (RT-qPCR) validated the reliability of the transcriptomic data, and the analysis of its results showed a close correlation with the data obtained from RNA-seq. This study clarifies the transcriptional response of Toona ciliata to drought stress, contributing to the revelation of the molecular mechanisms of drought adaptation. [ABSTRACT FROM AUTHOR]

The discrete Hopfield neural network 3-satisfiability (DHNN-3SAT) model represents an innovative application of deep learning techniques to the Boolean SAT problem. Existing research indicated that the DHNN-3SAT model demonstrated significant advantages in handling 3SAT problem instances of varying scales and complexities. Compared to traditional heuristic algorithms, this model converged to local minima more rapidly and exhibited enhanced exploration capabilities within the global search space. However, the model faced several challenges and limitations. As constraints in SAT problems dynamically increased, decreased, or changed, and as problem scales expanded, the model's computational complexity and storage requirements may increase dramatically, leading to reduced performance in handling large-scale SAT problems. To address these challenges, this paper first introduced a method for designing network synaptic weights based on fundamental logical clauses. This method effectively utilized the synaptic weight information from the original SAT problem within the DHNN network, thereby significantly reducing redundant computations. Concrete examples illustrated the design process of network synaptic weights when constraints were added, removed, or updated, offering new approaches for managing the evolving constraints in SAT problems. Subsequently, the paper presented a DHNN-3SAT model optimized by genetic algorithms combined with K-modes clustering. This model employed genetic algorithm-optimized K-modes clustering to effectively cluster the initial space, significantly reducing the search space. This approach minimized the likelihood of redundant searches and reduced the risk of getting trapped in local minima, thus improving search efficiency. Experimental tests on benchmark datasets showed that the proposed model outperformed traditional DHNN-3SAT models, DHNN-3SAT models combined with genetic algorithms, and DHNN-3SAT models combined with imperialist competitive algorithms across four evaluation metrics. This study not only broadened the application of DHNN in solving 3SAT problems but also provided valuable insights and guidance for future research. [ABSTRACT FROM AUTHOR]

To address the residual stress induced during the cure of fibre reinforced thermoset polymer composites, two different approaches were suggested for coupling a non-dominated sorting genetic algorithm (NSGA-II) with finite element (FE) simulations based on a viscoelastic constitutive law. These two approaches were proposed with consideration of different ways of integrating NSGA-II and the FE model. In Approach A, NSGA-II was performed based on results from a series of simulations under various combinations of cure variables. Alternatively, Approach B employed NSGA-II to iteratively update and optimise the cure profile for subsequent simulations. Results indicated that both approaches achieved simultaneous reductions in cure time and macroscale residual stress, with Approach B showing further improvements due to the direct coupling between the NSGA-II and simulations. Specifically, the maximum residual stress and cure time optimised by Approach A were reduced by 5%–9% and 22%–50% respectively, while those obtained by Approach B were reduced by 7%–10% and 32%–49% respectively, compared to those based on the manufacturer recommended cure profile. The evolution of stress in composites based on optimised cure profiles from these two approaches was also elucidated. Additionally, microscale modelling further revealed a 3%–5% reduction in the average residual stress within a representative volume element (RVE) model was also shown, depending upon the approach adopted. Ultimately, by combining a NSGA-II and FE simulations, the optimisation of cure time and residual stress at the macroscale and cure time together with a reduction of microscale stress could be realised. [ABSTRACT FROM AUTHOR]

Transforming carbon dioxide (CO2) into value-added chemicals is an innovative approach to tackling environmental issues and reducing greenhouse impacts. Owing to the excellent synergistic interaction between substrates and dopants, single-atom alloys (SAAs) exhibit promising catalytic performance in converting CO2 into valuable chemicals. In this theoretical investigation, we employed DFT to explore the catalytic potential of single-atom-doped Cu(111) surfaces in the conversion of CO2 to formic acid (HCOOH). Our investigation provides an in-depth analysis of the catalytic properties of Pd/Cu(111) and Pt/Cu(111) surfaces in relation to the Cu(111) surface. We have identified that *CO2 hydrogenation to *HCOO is the rate-determining step. This step exhibits greater kinetic feasibility on the Pd/Cu(111) surface than on the Pt/Cu(111) surface. The electronic state analysis reveals that the single atoms doped in the Cu(111) surface modify the local chemical environment at the nanoscale. The microkinetic simulation results indicate that Pd/Cu(111) achieves the highest turnover frequency (TOF), with a value of 3.78 × 10–5 s–1·site–1 at 500 K and 30 bar, identifying it as a promising catalyst for CO2 hydrogenation. Additionally, our findings suggest that adjusting temperatures at a constant pressure can lead to stabilization of the TOF values on Pd/Cu(111). This work contributes valuable insights for screening effective SAAs in CO2 hydrogenation to HCOOH, offering a promising direction for the development of catalyst design for HCOOH production. [ABSTRACT FROM AUTHOR]

To tackle the problem of difficult signal detection and carrier frequency synchronization faced by wireless communication among stations of the multistatic passive detection system in interference environments, an adaptive signal detection and carrier frequency offset (CFO) estimation method based on a virtual array is proposed in this paper. This is a data-aided method that utilizes a training sequence composed of three segments of sub-training sequences with different symbols. This method first uses spatial spectrum estimation to obtain the coarse frequency estimations of interference signals and CFO from virtual array signals constructed from the first two sub-training sequences. Then, beamforming is conducted accordingly on the virtual array signals constructed from the third sub-training sequence to suppress the in-band interferences and protrude the expected signal. Finally, improved performance of signal detection and CFO estimation is obtained with the beamformed signals. Simulation experiments show that a missed detection probability as low as 1 × 10−4, with a false detection probability of 1 × 10−3, can be obtained under a signal-to-interference ratio (SIR) of −10 dB and Eb/N0 of 1 dB. Moreover, the proposed method can also simultaneously achieve a CFO estimation error that is lower than 3%, with the condition of Eb/N0 being as low as −5 dB under different SIRs. Simulation results validate the proposed method and demonstrate the promising application prospects of the proposed method in networked passive detection scenarios. [ABSTRACT FROM AUTHOR]

Purpose: Timely and accurate monitoring of soil salinity content (SSC) is essential for precise irrigation management of large-scale farmland. Uncrewed aerial vehicle (UAV) low-altitude remote sensing with high spatial and temporal resolution provides a scientific and effective technical means for SSC monitoring. Many existing soil salinity inversion models have only been tested by a single variable selection method or machine learning algorithm, and the influence of variable selection method combined with machine learning algorithm on the accuracy of soil salinity inversion remain further studied. Methods: Firstly, based on UAV multispectral remote sensing data, by extracting the spectral reflectance of each sampling point to construct 30 spectral indexes, and using the pearson correlation coefficient (PCC), gray relational analysis (GRA), variable projection importance (VIP), and support vector machine-recursive feature elimination (SVM-RFE) to screen spectral index and realize the selection of sensitive variables. Subsequently, screened and unscreened variables as model input independent variables, constructed 20 soil salinity inversion models based on the support vector machine regression (SVM), back propagation neural network (BPNN), extreme learning machine (ELM), and random forest (RF) machine learning algorithms, the aim is to explore the feasibility of different variable selection methods combined with machine learning algorithms in SSC inversion of crop-covered farmland. To evaluate the performance of the soil salinity inversion model, the determination coefficient (R2), root mean square error (RMSE) and performance deviation ratio (RPD) were used to evaluate the model performance, and determined the best variable selection method and soil salinity inversion model by taking alfalfa covered farmland in arid oasis irrigation areas of China as the research object. Results: The variable selection combined with machine learning algorithm can significantly improve the accuracy of remote sensing inversion of soil salinity. The performance of the models has been improved markedly using the four variable selection methods, and the applicability varied among the four methods, the GRA variable selection method is suitable for SVM, BPNN, and ELM modeling, while the PCC method is suitable for RF modeling. The GRA-SVM is the best soil salinity inversion model in alfalfa cover farmland, with Rv2 of 0.8888, RMSEv of 0.1780, and RPD of 1.8115 based on the model verification dataset, and the spatial distribution map of soil salinity can truly reflect the degree of soil salinization in the study area. Conclusion: Based on our findings, the variable selection combined with machine learning algorithm is an effective method to improve the accuracy of soil salinity remote sensing inversion, which provides a new approach for timely and accurate acquisition of crops covered farmland soil salinity information. [ABSTRACT FROM AUTHOR]

A 64-year-old man presented with diffuse patchy alopecia for one month. He had a 17-year history of moderate to severe plaque psoriasis. Significant resolution of psoriasis lesions occurred during preventive anti-tuberculosis therapy using isoniazid and rifampicin. However, diffuse patchy alopecia areata developed. After withdrawal of anti-tuberculosis drugs and administration of glucocorticoids, alopecia areata was in remission gradually. However, psoriatic lesions returned. Anti-tuberculosis drugs may exert anti-inflammatory and immunomodulatory effects through multiple key factors. [ABSTRACT FROM AUTHOR]

As a newly developing method, tool path generation based on vector fields often requires pre-partitioning of the surface, leading to non-smooth paths at the boundaries between surface segments. To tackle this challenge, we propose a novel global tool path planning method based on vector fields. Our approach eliminates the need for pre-partitioning of the surface and incorporates considerations to achieve uniform variance of the scallop height while ensuring path smoothness. As a result, our method enables the generation of smooth paths globally while simultaneously achieving length optimality. Experimental results demonstrate the effectiveness of our proposed method, as it achieves both smoothness and length optimality compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The reliability issues in silicon carbide (SiC)-based devices with gate dielectric formed through heat oxidation are significant factors limiting their application in power devices. Aluminum oxide (Al2O3) was chosen as a high-k material to form the gate oxide layer on top of a SiC substrate. Atomic layer deposition (ALD) was used to fabricate an Al2O3/4H-SiC heterostructure, and the quality of the ALD Al2O3 layer was examined by XPS and electrical experiments. The XPS analysis suggests that the created heterojunction is a type-I heterojunction with ΔEC = 1.89 eV and ΔEV = 1.83 eV. Metal-insulated semiconductor structures with upper and lower Al electrodes were prepared for investigating leakage current and breakdown voltage characteristics. The leakage current density and breakdown electric field of an MOS capacitor structure with an Al2O3/4H-SiC heterojunction are ∼10−10 A/cm2 and 9.3 MV/cm, respectively. The interfacial defect density (Dit) near the conduction band of the MOS capacitive structure with the SiC substrate is 1.44 × 1012 eV−1 cm−2, while the interface charge (Neff) of the oxide layer of this sample can also be controlled at a level of 1011 cm−2. The Al2O3/SiC structure fabricated in this work exhibits superior electrical performance compared to the heterostructure based on the Si substrate and other relevant heterostructures documented in previous studies. [ABSTRACT FROM AUTHOR]

Mechanical force is the basis of cardiovascular development, homeostasis, and diseases. The perception and response of mechanical force by the cardiovascular system are crucial. However, the molecular mechanisms mediating mechanotransduction in the cardiovascular system are not yet understood. PIEZO1, a novel transmembrane mechanosensitive cation channel known for its regulation of touch sensation, has been found to be widely expressed in the mammalian cardiovascular system. In this review, we elucidate the role and mechanism of PIEZO1 as a mechanical sensor in cardiovascular development, homeostasis, and disease processes, including embryo survival, angiogenesis, cardiac development repair, vascular inflammation, lymphangiogenesis, blood pressure regulation, cardiac hypertrophy, cardiac fibrosis, ventricular remodeling, and heart failure. We further summarize chemical molecules targeting PIEZO1 for potential translational applications. Finally, we address the controversies surrounding emergent concepts and challenges in future applications. [ABSTRACT FROM AUTHOR]

This study utilized a theory proposed by Mohan, Slater, Luo, and Jaipal (2002) regarding the Developmental Path of Cause to investigate AWE score use in classroom contexts. This "path" has the potential to support validity arguments because it suggests how causal linguistic features can be organized in hierarchical order. Utilization of this path enabled this study to investigate AWE scores by comparing them to ratings based on teachers' intuitions as well as to scores based on assignment rubrics. Qualitative focus group data suggested that the path can help teachers articulate their intuitions. Quantitative results showed that the grades provided by raters trained to use the path tended to support AWE scores from Criterion, a web-based AWE system, more strongly than did rubric-generated grades. The findings from this study suggest that Criterion scores not only closely correlated with teachers' intuitions and with raters trained to use the path, but that the use of the path for teaching may support the implementation of AWE systems in classroom contexts, and would help students focus on the core of a cause-effect essay: appropriateness and sophistication of causal language.

Lentiviral vector mobilization following HIV-1 infection of vector-transduced cells poses biosafety risks to vector-treated patients and their communities. The self-inactivating (SIN) vector design has reduced, however, not abolished mobilization of integrated vector genomes. Furthermore, an earlier study demonstrated the ability of the major product of reverse transcription, a circular SIN HIV-1 vector comprising a single- long terminal repeat (LTR) to support production of high vector titers. Here, we demonstrate that configuring the internal vector expression cassette in opposite orientation to the LTRs abolishes mobilization of SIN vectors. This additional SIN mechanism is in part premised on induction of host PKR response to double-stranded RNAs comprised of mRNAs transcribed from cryptic transcription initiation sites around 3'SIN-LTR's and the vector internal promoter. As anticipated, PKR response following transfection of opposite orientation vectors, negatively affects their titers. Importantly, shRNA-mediated knockdown of PKR rendered titers of SIN HIV-1 vectors comprising opposite orientation expression cassettes comparable to titers of conventional SIN vectors. High-titer vectors carrying an expression cassette in opposite orientation to the LTRs efficiently delivered and maintained high levels of transgene expression in mouse livers. This study establishes opposite orientation expression cassettes as an additional PKR-dependent SIN mechanism that abolishes vector mobilization from integrated and episomal SIN lentiviral vectors.

BackgroundAs key regulators of gene expression in eukaryotes, small RNAs have been characterized in many seed plants, and pathways for their biogenesis, degradation, and action have been defined in model angiosperms. However, both small RNAs themselves and small RNA pathways are not well characterized in other land plants such as lycophytes and ferns, preventing a comprehensive evolutionary perspective on small RNAs in land plants.ResultsUsing 25 representatives from major lineages of lycophytes and ferns, most of which lack sequenced genomes, we characterized small RNAs and small RNA pathways in these plants. We identified homologs of DICER-LIKE (DCL), ARGONAUTE (AGO), and other genes involved in small RNA pathways, predicted over 2600 conserved microRNA (miRNA) candidates, and performed phylogenetic analyses on small RNA pathways as well as miRNAs. Pathways underlying miRNA biogenesis, degradation, and activity were established in the common ancestor of land plants, but the 24-nucleotide siRNA pathway that guides DNA methylation is incomplete in sister species of seed plants, especially lycophytes. We show that the functional diversification of key gene families such as DCL and AGO as observed in angiosperms occurred early in land plants followed by parallel expansion of the AGO family in ferns and angiosperms. We uncovered a conserved AGO subfamily absent in angiosperms.ConclusionsOur phylogenetic analyses of miRNAs in bryophytes, lycophytes, ferns, and angiosperms refine the time-of-origin for conserved miRNA families as well as small RNA machinery in land plants.