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Abstract Ectomycorrhizal fungi (EMF) play pivotal roles in determining temperate forest ecosystem processes. We tracked root EMF community succession across saplings, juveniles, and adults of three temperate broadleaf trees (Acer mono, Betula platyphylla, and Quercus mongolica) in Northeast China. Adult stages showed higher alpha diversity but lower community dissimilarity compared to earlier stages. In particular, the EMF alpha diversity of Quercus mongolica marginally increased along with host developmental stages and ranked as sapling 99%). LEfSe was employed to identify stage- and/or host-specific EMF indicators. This study highlighted the assembly of EMF community during the development of broadleaf trees in temperate forests, thereby advancing understanding of the succession and coevolution of symbiotic relationships.

Antibiotics are effective in treating bacterial infections, but they also disrupt the gut microbiota, thereby causing side effects such as diarrhea, flatulence and indigestion. Probiotics and fecal microbiota transplantation (FMT) have attracted widespread attention due to their ability to restore the balance of the gut microbiota and alleviate various conditions. In this study, the mechanism of action of FMT and the probiotic Lacticaseibacillus paracasei HNU502 in alleviating the side effects of cefotaxime, a common antibiotic, using combination of metagenomics, pathological analysis and gas chromatography. The findings suggested that FMT alleviated antibiotic side effects by restoring the disturbed gut microbiota, while HNU502 worked by reestablishing the balance of the gut microbiota. Both FMT and probiotic supplementation improved gut microbiota composition, but to varying degrees, prevented goblet cell loss, and increased the contents of mucin-2 (MUC-2) and zonula occludens-1 (ZO-1), thus aiding gut mucosal recovery. Moreover, the two approaches ameliorated the metabolic disorders of short-chain fatty acids, inhibited the secretion of pro-inflammatory cytokines such as interleukin (IL)-1β, IL-6 and tumor necrosis factor (TNF)-α as well as blood urea nitrogen (BUN), and effectively reduced virulence factor enrichment. This study contributes to a better understanding of the link between human health and gut microbiota, and provides new therapeutic options for mitigating antibiotic side effects, which is of great significance for improving patients’ health status and quality of life.

This paper proposes a novel fast attitude fault-tolerant control (FTC) scheme for over-actuated hypersonic flight vehicles experiencing actuator failures, such as stuck faults and loss of effectiveness faults. The FTC scheme comprises two main components: a systematic algorithm in the control layer and a control allocation method in the reconfiguration layer, working together to achieve fault tolerance. Drawing inspiration from model predictive static programming, a step-by-step algorithm is developed to quickly resolve control torque under fault conditions, ensuring prescribed tracking performance. The vehicle system's coupling and uncertain disturbances are treated as total disturbance and estimated in real-time using an extended state observer. In the reconfiguration layer, a pseudo-inverse-based control allocation method is utilized to distribute the desired control torque across redundant rudder actuators. The proposed FTC scheme is applied to a hypersonic flight vehicle system subject to stuck faults, loss of effectiveness faults, modeling errors, and external disturbances. Simulation results are presented to demonstrate the superiority and effectiveness of the proposed FTC scheme. Owing to its fast computational speed, the scheme holds potential for online implementation, enabling rapid reconstruction of aerodynamic rudder actuators during failures.

Pilots face complex working environments during flight missions, which can easily lead to excessive workload and affect flight safety. Physiological signals are commonly used to evaluate a pilot’s workload because they are objective and can directly reflect physiological mental states. However, existing methods have shortcomings in temporal modeling, making it challenging to fully capture the dynamic characteristics of physiological signals over time. Moreover, fusing features of data from different modalities is also difficult.To address these problems, we proposed a temporal relation modeling and multimodal adversarial alignment network (TRM-MAAN) for pilot workload evaluation. Specifically, a Transformer-based temporal relationship modeling module was used to learn complex temporal relationships for better feature extraction. In addition, an adversarial alignment-based multi-modal fusion module was applied to capture and integrate multi-modal information, reducing distribution shifts between different modalities. The performance of the proposed TRM-MAAN method was evaluated via experiments of classifying three workload states using electroencephalogram (EEG) and electromyography (EMG) recordings of eight healthy pilots.Experimental results showed that the classification accuracy and F1 score of the proposed method were significantly better than the baseline model across different subjects, with an average recognition accuracy of $91.90~\pm ~1.72\%$ and an F1 score of $91.86~\pm ~1.75\%$ .This work provides essential technical support for improving the accuracy and robustness of pilot workload evaluation and introduces a promising way for enhancing flight safety, offering broad application prospects. Clinical and Translational Impact Statement: The proposed scheme provides a promising solution for workload evaluation based on electrophysiological signals, with potential applications in aiding the clinical monitoring of fatigue, mental status, cognitive psychology, and other disorders.

Abstract Background Benign and malignant breast tumors differ in their microvasculature morphology and distribution. Histologic biomarkers of malignant breast tumors are also correlated with the microvasculature. There is a lack of imaging technology for evaluating the microvasculature. Ultrasound localization microscopy (ULM) can provide detailed microvascular architecture at super-resolution. The objective of this trial is to explore the role of ULM in distinguishing benign from malignant breast tumors and to explore the correlations between ULM qualitative and quantitative parameters and histologic biomarkers in malignant breast tumors. Methods/design This prospective and multicenter study will include 83 patients with breast tumors that will undergo ULM. 55 patients will be assigned to the malignant group, and 28 patients will be assigned to the benign group. The primary outcome is the differences in the qualitative parameters (microvasculature morphology, distribution, and flow direction) between benign and malignant breast tumors on ULM. Secondary outcomes include (1) differences in the quantitative parameters (microvasculature density, tortuosity, diameter, and flow velocity) between benign and malignant breast tumors based on ULM; (2) diagnostic performance of the qualitative parameters in distinguishing benign and malignant breast tumors; (3) diagnostic performance of the quantitative parameters in distinguishing benign and malignant breast tumors; (4) relationships between the qualitative parameters and histologic biomarkers in malignant breast tumors; (5) relationships between the quantitative parameters and histologic biomarkers in malignant breast tumors; and (6) the evaluation of inter-reader and intra-reader reproducibility. Discussion Detecting vascularity in breast tumors is of great significance to differentiate benign from malignant tumors and to predict histologic biomarkers. These histologic biomarkers, such as ER, PR, HER2 and Ki67, are closely related to prognosis evaluation. This trial will provide maximum information about the microvasculature of breast tumors and thereby will help with the formulation of subsequent differential diagnosis and the prediction of histologic biomarkers. Trial registration number/date Chinese Clinical Trial Registry ChiCTR2100048361/6th/July/2021. This study is a part of that clinical trial.

IntroductionBetula tianschanica Rupr. is distributed in regions such as China, Kyrgyzstan, and Tajikistan. Owing to the impacts of climate change, it is increasingly threatened by habitat fragmentation, resulting in a precipitous decline in its population. Currently listed as endangered on the Red List of Trees of Central Asia, this species is predominantly found in the Tianshan Mountains. Examining the influence of climate change on the geographical distribution pattern of Betula tianschanica is crucial for the management and conservation of its wild resources.MethodsThis study employed two models, maximum entropy (MaxEnt) and random forest (RF), combined with 116 distribution points of Betula tianschanica and 27 environmental factor variables, to investigate the environmental determinants of the distribution of Betula tianschanica and project its potential geographical distribution areas.ResultsThe MaxEnt model and the RF model determined the primary environmental factors influencing the potential distribution of Betula tianschanica. The MaxEnt model showed that the percentage of gravel volume in the lower soil layer and elevation are the most significant, while the RF model considered elevation and precipitation of the wettest quarter to be the most crucial. Both models unanimously asserted that elevation is the pivotal environmental element affecting the distribution of Betula tianschanica.The mean area under the curve (AUC) scores for the MaxEnt model and RF were 0.970 and 0.873, respectively, revealing that the MaxEnt model outperformed the RF model in predictive accuracy. Consequently, the present study employed the estimated geographical area for Betula tianschanica modeled by the MaxEnt model as a reference. Following the MaxEnt model’s projected outcomes, Betula tianschanica is mainly located in territories such as the Tianshan Mountains, Ili River Basin, Lake Issyk-Kul, Turpan Basin, Irtysh River, Ulungur River, Bogda Mountains, Kazakh Hills, Lake Balkhash, Amu River, and the middle reaches of the Syr River.Within the MaxEnt model, the total suitable habitat area exhibits growth across all scenarios, with the exception of a decline observed during the 2041–2060 period under the SSP2-4.5 scenario. Remarkably, under the SSP58.5 scenario for the same timeframe, this area expands significantly by 42.7%. In contrast, the RF model demonstrated relatively minor fluctuations in the total suitable habitat area, with the highest recorded increase being 12.81%. This paper recommends establishing protected areas in the Tianshan Mountains, conducting long-term monitoring of its population dynamics, and enhancing international cooperation. In response to future climate change, climate refuges should be established and adaptive management implemented to ensure the survival and reproduction of Betula tianschanica.

Chlorophyll-a (Chl-a) plays a vital role in assessing environmental health and understanding the response of marine ecosystems to physical factors and climate change. In situ sampling, remote sensing, and moored buoys or floats are commonly employed methods for obtaining Chl-a in marine science research. Although in situ sampling, buoys, and floats could provide accurate data, they are limited by the spatial and temporal resolution. Remote sensing offers continuous and broad spatial coverage, while it is often hindered by cloud cover in the South China Sea (SCS). This study discussed the feasibility of a predictive model by linking the physical factors [e.g., wind field, surface currents, sea surface height (SSH), and sea surface temperature (SST)] with surface Chl-a in the SCS based on the ResUnet. The ResUnet architecture performs well in capturing non-linear relationships between variables, with the model achieving a prediction accuracy exceeding 90%. The results indicate that (1) the combination of oceanic dynamical and meteorological data could effectively estimate the Chl-a based on deep learning methods; (2) the combination of meteorological and SST effectively reproduces Chl-a in the northern SCS, while adding surface currents and SSH improves model performance in the southern SCS; (3) With the addition of surface currents and SSH, the model effectively captures the high Chl-a patches induced by eddies. This research presents a viable method for estimating surface Chl-a concentrations in regions where they are highly correlated with dynamic factors, using deep learning and comprehensive oceanic and atmospheric data.

Atrial fibrillation (AF) is the most common tachyarrhythmia and seriously affects human health. Key targets of AF bioinformatics analysis can help to better understand the pathogenesis of AF and develop therapeutic targets. The left atrial appendage tissue of 20 patients with AF and 10 patients with sinus rhythm were collected for sequencing, and the expression data of the atrial tissue were obtained. Based on this, 2578 differentially expressed genes were obtained through differential analysis. Different express genes (DEGs) were functionally enriched on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), mainly focusing on neuroactive ligand-receptor interactions, neuronal cell body pathways, regulation of neurogenesis, and neuronal death, regulation of neuronal death, etc. Secondly, 14 significant module genes were obtained by analyzing the weighted gene co-expression network of DEGs. Next, LASSO and SVM analyzes were performed on the differential genes, and the results were in good agreement with the calibration curve of the nomogram model for predicting AF constructed by the weighted gene co-expression network key genes. The significant module genes obtained by the area under the ROC curve (AUC) analysis were analyzed. Through crossover, two key disease characteristic genes related to AF, HOXA2 and RND2, were screened out. RND2 was selected for further research, and qPCR verified the expression of RND2 in sinus rhythm patients and AF patients. Patients with sinus rhythm were significantly higher than those in AF patients. Our research indicates that RND2 is significantly associated with the onset of AF and can serve as a potential target for studying its pathogenesis.

Currently, ethanol detection in alcoholic beverages typically relies on complicated and costly approaches, including gas chromatography, liquid chromatography, and mass spectrometry, etc. Herein, we developed a non-toxicity, practical, low-cost and portable visual indicator for ethanol detection based on OCDs/I2O4 nanopowders. By color change, the OCDs/I2O4 can visually indicate ethanol concentration ranging from 14 % vol to 56 % vol and the detection limit can be further extended to 2 % vol with a UV-visible spectrum. The OCDs/I2O4 indicator shows good stability and long-term endurance for at least 18 months, and it can be used under different temperatures (at least from 3 °C to 40 °C) and humidity (at least from 10 % to 90 %). A possible mechanism for the color change has been proposed based on the covalent-link hydrogen bonding change between OCDs and I2O4. The proposed OCDs/I2O4 composite offers a convenient and practical solution for ethanol monitoring in various alcoholic beverages.

Skid-steered technology has been widely applied to wheeled vehicles due to its advanced pivot steering capabilities and adaptable power transmission systems. To better understand the steering characteristics of skid-steered wheeled vehicles and derive general conclusions, a torque differential-based (TD-based) two-degree-of-freedom (2-DOF) dynamic model was developed. This model is grounded in the vehicle’s steering mechanism and the single-wheel dynamics model, and a steering radius model based on TD input was established using the concept of the instantaneous center of rotation (ICR). Additionally, an expression for the stability factor was derived, and both the steady-state and transient steering characteristics were analyzed. Finally, the real vehicle tests demonstrated that the TD-based dynamic model responds quickly, maintains high precision, and remains stable across different steering input frequencies. Compared with the SD-based dynamic model, the TD-based dynamic model has a 2.553% higher calculation accuracy for the steering radius, a 6.251% higher comprehensive response accuracy for the steering input, and a response speed advantage of 1.035%.

A chromosome segment substituted line (CSSL) represents an ideal resource for studying quantitative traits like thermotolerance. To develop wheat inter-varietal CSSLs with E6015-3S (a heat-sensitive genotype) being the recipient parent, genome-wide unique DNA markers are urgently needed for marker-assisted selection. In this study, 11,016 primer pairs targeting 5036 indel sites were successfully designed for E6015-3S, with an average density of 0.36 indels per Mbp. These primer pairs are believed to be unique and polymorphic in the wheat genome; as gathered from the evidence, (i) 76.18 to 99.34% of the 11,016 primer pairs yielded a single hit during sequence alignment with 18 sequenced genomes, (ii) 83.59 to 90.98% of 1042 synthesized primer pairs amplified a single band in 16 wheat accessions, and (iii) 59.69 to 99.81% of the tested 1042 primer pairs were polymorphic between E6015-3S and 15 individual wheat accessions. These primer pairs are also anticipated with excellent resolvability on agarose or polyacrylamide gels, since most of them have indel sizes from 15 to 46 bp, amplicon sizes from 141 to 250 bp, and polymorphism ratios from 6.0 to 25.0%. Collectively, these primer pairs are ideal DNA markers for inter-varietal CSSL development and more broad applications, like germplasm classification, seed purity testing, genetic linkage mapping, and marker-assisted breeding in wheat, owing to their uniqueness, polymorphism, and easy-to-use characteristics.

Background: An association between testicular cancer and Down syndrome has been reported by several studies. Down syndrome with cryptorchidism and retroperitoneal mixed germ cell tumours is rare, and yolk sac tumours are often considered secondary components of mixed germ cell tumours. Herein, we present a rare case of retroperitoneal mixed germ cell tumour with cryptorchidism accompanied by yolk sac tumour and seminoma in a patient with Down syndrome, along with its imaging features. Case presentation: A 42-year-old man was admitted to the hospital for 6 months due to a worsening abdominal pain that was followed by syncope for 8 h. There was a significant increase in AFP and β-HCG levels. An enhanced computed tomography (CT) scan of the entire abdomen showed a mixed cystic solid mass in the retroperitoneal space. Fluorine-2-fluoro-2-deoxy-d-glucose (18F-FDG) positron emission tomography/CT examination showed an abnormal increase in the FDG uptake in the parenchymal part of the mass, with a maximum standardised uptake value of approximately 10.5. The pathological diagnosis was retroperitoneal mixed germ cell tumour (yolk sac tumour + seminoma). One and a half months postoperatively, the tumour recurred. Consequently, the patient underwent chemotherapy, and after one course of treatment, the patient developed bone marrow suppression. Finally, he died due to complications. Conclusions: Yolk sac tumours, the main components of mixed germ cell tumours, are rare in adults and exhibit rapid growth, heightened malignancy, and poor prognoses. CT features play a crucial role in diagnosis. Down syndrome is a high-risk factor for malignant testicular germ cell tumours. Therefore, comprehensive examinations for gonadal and germ cell tumours in patients with Down syndrome are imperative and should be prioritised by clinicians. [ABSTRACT FROM AUTHOR]