Efficacy of Random Forest Models in Predicting Multidrug-Resistant Gram-Negative Bacterial Nosocomial Infections Compared to Traditional Logistic Regression Models.

This study evaluates whether random forest (RF) models are as effective as traditional Logistic Regression (LR) models in predicting multidrug-resistant Gram-negative bacterial nosocomial infections. Data were collected from 541 patients with hospital-acquired Gram-negative bacterial infections at two tertiary-level hospitals in Urumqi, Xinjiang, China, from August 2022 to November 2023. Relevant literature informed the selection of significant predictors based on patients' pre-infection clinical information and medication history. The data were split into a training set of 379 cases and a validation set of 162 cases, adhering to a 7:3 ratio. Both RF and LR models were developed using the training set and subsequently evaluated on the validation set. The LR model achieved an accuracy of 84.57%, sensitivity of 82.89%, specificity of 80.10%, positive predictive value of 84%, negative predictive value of 85.06%, and a Yoden index of 0.69. In contrast, the RF model demonstrated superior performance with an accuracy of 89.51%, sensitivity of 90.79%, specificity of 88.37%, positive predictive value of 87.34%, negative predictive value of 91.57%, and a Yoden index of 0.79. Receiver operating characteristic curve analysis revealed an area under the curve of 0.91 for the LR model and 0.94 for the RF model. These findings indicate that the RF model surpasses the LR model in specificity, sensitivity, and accuracy in predicting hospital-acquired multidrug-resistant Gram-negative infections, showcasing its greater potential for clinical application.

MXene-Integrated Perylene Anode with Ultra-Stable and Fast Ammonium-Ion Storage for Aqueous Micro Batteries.

The aqueous micro batteries (AMBs) are expected to be one of the most promising micro energy storage devices for its safe operation and cost-effectiveness. However, the performance of the AMBs is not satisfactory, which is attributed to strong interaction between metal ions and the electrode materials. Here, the first AMBs are developed with NH4 + as charge carrier. More importantly, to solve the low conductivity and the dissolution during the NH4 + intercalation/extraction problem of perylene material represented by perylene-3,4,9,10-tetracarboxylic dianhydride (PTCDA), the Ti3 C2 Tx MXene with high conductivity and polar surface terminals is introduced as a conductive skeleton (PTCDA/Ti3 C2 Tx MXene). Benefitting from this, the PTCDA/Ti3 C2 Tx MXene electrodes exhibit ultra-high cycle life and rate capability (74.31% after 10 000 galvanostatic chargedischarge (GCD) cycles, and 91.67 mAh g-1 at 15.0 A g-1 , i.e., capacity retention of 45.2% for a 30-fold increase in current density). More significantly, the AMBs with NH4 + as charge carrier and PTCDA/Ti3 C2 Tx MXene anode provide excellent energy density and power density, cycle life, and flexibility. This work will provide strategy for the development of NH4 + storage materials and the design of AMBs.

Enhancing and Adapting in the Clinic: Source-Free Unsupervised Domain Adaptation for Medical Image Enhancement.

Medical imaging provides many valuable clues involving anatomical structure and pathological characteristics. However, image degradation is a common issue in clinical practice, which can adversely impact the observation and diagnosis by physicians and algorithms. Although extensive enhancement models have been developed, these models require a well pre-training before deployment, while failing to take advantage of the potential value of inference data after deployment. In this paper, we raise an algorithm for source-free unsupervised domain adaptive medical image enhancement (SAME), which adapts and optimizes enhancement models using test data in the inference phase. A structure-preserving enhancement network is first constructed to learn a robust source model from synthesized training data. Then a teacher-student model is initialized with the source model and conducts source-free unsupervised domain adaptation (SFUDA) by knowledge distillation with the test data. Additionally, a pseudo-label picker is developed to boost the knowledge distillation of enhancement tasks. Experiments were implemented on ten datasets from three medical image modalities to validate the advantage of the proposed algorithm, and setting analysis and ablation studies were also carried out to interpret the effectiveness of SAME. The remarkable enhancement performance and benefits for downstream tasks demonstrate the potential and generalizability of SAME. The code is available at https://github.com/liamheng/Annotation-free-Medical-Image-Enhancement.

Comparison of different internal fixation models in ankle arthrodesis using 3D finite-element analysis.

BACKGROUND: The purpose of this study is to use three-dimensional finite-element analysis to better understand the biomechanical features of various internal fixators for ankle arthrodesis. METHODS: We used finite-element analysis to compare four different types of internal fixations in ankle arthrodesis: Group A had three crossed screws (Ø6.5 mm); Group B had two crossed screws (Ø6.5 mm) and an anterior plate (Ø2.7 mm); Group C only had an anterior anatomical plate (Ø3.5 mm); Group D had one anterior anatomical plate (Ø3.5 mm) and one posterior-lateral screw (Ø6.5 mm). We adopted Ansys 21.0 software to analyze and compare the four types in terms of the displacement of the arthrodesis surface and the stress peak and stress distribution of these models under intorsion, extorsion, dorsiflexion torque, and neutral vertical load. RESULTS: ① Displacement of the arthrodesis surface: In Group A, the maximum displacement was larger than Group D under neutral vertical load and dorsiflexion torque but less than it under intorsion and extorsion torque. In Group B, the maximum displacement against dorsiflexion, neutral vertical load, intorsion, and extorsion was less than that in the other three fixation models. In Group C, the maximum displacement against the above four loading patterns were significantly higher than that in another three fixation models. ② Stress peak and stress distribution: based on the stress distribution of the four models, the peak von Mises stress was concentrated in the central sections of the compression screws, plate joints, and bending parts of the plates. CONCLUSIONS: The fixation model consisting of two crossed screws and an anterior outperformed the other three fixation models in terms of biomedical advantages; thus, this model can be deemed a safe and reliable internal fixation approach for ankle arthrodesis.

A generic fundus image enhancement network boosted by frequency self-supervised representation learning.

Fundus photography is prone to suffer from image quality degradation that impacts clinical examination performed by ophthalmologists or intelligent systems. Though enhancement algorithms have been developed to promote fundus observation on degraded images, high data demands and limited applicability hinder their clinical deployment. To circumvent this bottleneck, a generic fundus image enhancement network (GFE-Net) is developed in this study to robustly correct unknown fundus images without supervised or extra data. Levering image frequency information, self-supervised representation learning is conducted to learn robust structure-aware representations from degraded images. Then with a seamless architecture that couples representation learning and image enhancement, GFE-Net can accurately correct fundus images and meanwhile preserve retinal structures. Comprehensive experiments are implemented to demonstrate the effectiveness and advantages of GFE-Net. Compared with state-of-the-art algorithms, GFE-Net achieves superior performance in data dependency, enhancement performance, deployment efficiency, and scale generalizability. Follow-up fundus image analysis is also facilitated by GFE-Net, whose modules are respectively verified to be effective for image enhancement.

Suppressing ASPARTIC PROTEASE 1 prolongs photosynthesis and increases wheat grain weight.

The elongation of photosynthesis, or functional staygreen, represents a feasible strategy to propel metabolite flux towards cereal kernels. However, achieving this goal remains a challenge in food crops. Here we report the cloning of wheat CO2 assimilation and kernel enhanced 2 (cake2), the mechanism underlying the photosynthesis advantages and natural alleles amenable to breeding elite varieties. A premature stop mutation in the A-genome copy of the ASPARTIC PROTEASE 1 (APP-A1) gene increased the photosynthesis rate and yield. APP1 bound and degraded PsbO, the protective extrinsic member of photosystem II critical for increasing photosynthesis and yield. Furthermore, a natural polymorphism of the APP-A1 gene in common wheat reduced APP-A1's activity and promoted photosynthesis and grain size and weight. This work demonstrates that the modification of APP1 increases photosynthesis, grain size and yield potentials. The genetic resources could propel photosynthesis and high-yield potentials in elite varieties of tetraploid and hexaploid wheat.

Suppression of ZEAXANTHIN EPOXIDASE 1 restricts stripe rust growth in wheat.

Reducing losses caused by pathogens is an effective strategy for stabilizing crop yields. Daunting challenges remain in cloning and characterizing genes that inhibit stripe rust, a devastating disease of wheat (Triticum aestivum) caused by Puccinia striiformis f. sp. tritici (Pst). We found that suppression of wheat zeaxanthin epoxidase 1 (ZEP1) increased wheat defense against Pst. We isolated the yellow rust slower 1 (yrs1) mutant of tetraploid wheat in which a premature stop mutation in ZEP1-B underpins the phenotype. Genetic analyses revealed increased H2O2 accumulation in zep1 mutants and demonstrated a correlation between ZEP1 dysfunction and slower Pst growth in wheat. Moreover, wheat kinase START 1.1 (WKS1.1, Yr36) bound, phosphorylated, and suppressed the biochemical activity of ZEP1. A rare natural allele in the hexaploid wheat ZEP1-B promoter reduced its transcription and Pst growth. Our study thus identified a novel suppressor of Pst, characterized its mechanism of action, and revealed beneficial variants for wheat disease control. This work opens the door to stacking wheat ZEP1 variants with other known Pst resistance genes in future breeding programs to enhance wheat tolerance to pathogens.

B7-H3 but not PD-L1 is involved in the antitumor effects of Dihydroartemisinin in non-small cell lung cancer.

Dihydroartemisinin (DHA), an active antimalaria metabolite derived from artemisinin, has received increasing attention for its anticancer activities. However, little is known about the anticancer mechanisms of DHA, although the existing data define its antimalaria effects by producing excessive reactive oxygen species (ROS). In this study, we showed that DHA effectively suppresses in vitro and in vivo tumor growth of non-small cell lung cancer (NSCLC) without perceptible toxicity on heart, liver, spleen, lung, and kidney tissues. Of note, DHA inhibited the expression of B7-H3 rather than PD-L1, whereas overexpression of B7-H3 completely rescued DHA's inhibition on cell proliferation and migration of NSCLC A549 and HCC827 cells. B7-H3 overexpression also largely inhibited DHA's induction on the apoptosis of the two cell lines. Furthermore, DHA treatment led to increased infiltration of CD8+ T Lymphocytes in the xenografts as compared with that of negative controls. Taken together, our results suggest that B7-H3 but not PD-L1 is involved in the antitumor effects of DHA in NSCLC, which may be indicative of an effective B7-H3 blockade and further combination with anti-PD-L1/PD-1 immunotherapy.

Deep multi-modal intermediate fusion of clinical record and time series data in mortality prediction.

In intensive care units (ICUs), mortality prediction is performed by combining information from these two sources of ICU patients by monitoring patient health. Respectively, time series data generated from each patient admission to the ICU and clinical records consisting of physician diagnostic summaries. However, existing mortality prediction studies mainly cascade the multimodal features of time series data and clinical records for prediction, ignoring thecross-modal correlation between the underlying features in different modal data. To address theseissues, we propose a multimodal fusion model for mortality prediction that jointly models patients' time-series data as well as clinical records. We apply a fine-tuned Bert model (Bio-Bert) to the patient's clinical record to generate a holistic embedding of the text part, which is then combined with the output of an LSTM model encoding the patient's time-series data to extract valid features. The global contextual information of each modal data is extracted using the improved fusion module to capture the correlation between different modal data. Furthermore, the improved fusion module can be easily added to the fusion features of any unimodal network and utilize existing pre-trained unimodal model weights. We use a real dataset containing 18904 ICU patients to train and evaluate our model, and the research results show that the representations obtained by themodel can achieve better prediction accuracy compared to the baseline.

HSP90.2 promotes CO2 assimilation rate, grain weight and yield in wheat.

Wheat fixes CO2 by photosynthesis into kernels to nourish humankind. Improving the photosynthesis rate is a major driving force in assimilating atmospheric CO2 and guaranteeing food supply for human beings. Strategies for achieving the above goal need to be improved. Here, we report the cloning and mechanism of CO2 ASSIMILATION RATE AND KERNEL-ENHANCED 1 (CAKE1) from durum wheat (Triticum turgidum L. var. durum). The cake1 mutant displayed a lower photosynthesis rate with smaller grains. Genetic studies identified CAKE1 as HSP90.2-B, encoding cytosolic molecular chaperone folding nascent preproteins. The disturbance of HSP90.2 decreased leaf photosynthesis rate, kernel weight (KW) and yield. Nevertheless, HSP90.2 over-expression increased KW. HSP90.2 recruited and was essential for the chloroplast localization of nuclear-encoded photosynthesis units, for example PsbO. Actin microfilaments docked on the chloroplast surface interacted with HSP90.2 as a subcellular track towards chloroplasts. A natural variation in the hexaploid wheat HSP90.2-B promoter increased its transcription activity, enhanced photosynthesis rate and improved KW and yield. Our study illustrated an HSP90.2-Actin complex sorting client preproteins towards chloroplasts to promote CO2 assimilation and crop production. The beneficial haplotype of Hsp90.2 is rare in modern varieties and could be an excellent molecular switch promoting photosynthesis rate to increase yield in future elite wheat varieties.

A Task Complexity Analysis Method to Study the Emergency Situation under Automated Metro System.

System upgrades and team members interactions lead to changes in task structure. Therefore, in order to handle emergencies efficiently and safely, a comprehensive method of the traffic dispatching team task complexity (TDTTC) is proposed based on team cognitive work analysis (Team-CWA) and network feature analysis. The method comes from the perspective of the socio-technical system. Two stages were included in this method. In the first stage, four phases of Team-CWA, i.e., team work domain analysis, team control task analysis, team strategies analysis, and team worker competencies analysis, were applied in the qualitative analysis of TDTTC. Then in the second stage, a mapping process was established based on events and information cues. After the team task network was established, the characteristic indexes of node degree/average degree, average shortest path length, agglomeration coefficient, and overall network performance for TDTTC were extracted to analyze TDTTC quantitatively. The cases of tasks for screen door fault under grade of automation GOA1-GOA4 were compared. The results revealed that the more nodes and communication between nodes, the larger the network scale was, which would lead to the TDTTC being more complicated no matter what level of automation system it was under. This method is not only the exploration of cognitive engineering theory in the field of task complexity, but also the innovation of team task complexity in the development of automatic metro operation.

P-ResUnet: Segmentation of brain tissue with Purified Residual Unet.

Brain tissue of Magnetic Resonance Imaging is precisely segmented and quantified, which aids in the diagnosis of neurological diseases such as epilepsy, Alzheimer's, and multiple sclerosis. Recently, UNet-like architectures are widely used for medical image segmentation, which achieved promising performance by using the skip connection to fuse the low-level and high-level information. However, In the process of integrating the low-level and high-level information, the non-object information (noise) will be added, which reduces the accuracy of medical image segmentation. Likewise, the same problem also exists in the residual unit. Since the output and input of the residual unit are fused, the non-object information (noise) of the input of the residual unit will be in the integration. To address this challenging problem, in this paper we propose a Purified Residual U-net for the segmentation of brain tissue. This model encodes the image to obtain deep semantic information and purifies the information of low-level features and the residual unit from the image, and acquires the result through a decoder at last. We use the Dilated Pyramid Separate Block (DPSB) as the first block to purify the features for each layer in the encoder without the first layer, which expands the receptive field of the convolution kernel with only a few parameters added. In the first layer, we have explored the best performance achieved with DPB. We find the most non-object information (noise) in the initial image, so it is good for the accuracy to exchange the information to the max degree. We have conducted experiments with the widely used IBSR-18 dataset composed of T-1 weighted MRI volumes from 18 subjects. The results show that compared with some of the cutting-edge methods, our method enhances segmentation performance with the mean dice score reaching 91.093% and the mean Hausdorff distance decreasing to 3.2606.

A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy.

Magnetoencephalography is a noninvasive neuromagnetic technology to record epileptic activities for the pre-operative localization of epileptogenic zones, which has received increasing attention in the diagnosis and surgery of epilepsy. As reported by recent studies, pathological high frequency oscillations (HFOs), when utilized as a biomarker to localize the epileptogenic zones, result in a significant reduction in seizure frequency, even seizure elimination in around 80% of cases. Thus, objective, rapid, and automatic detection and recommendation of HFOs are highly desirable for clinicians to alleviate the burden of reviewing a large amount of MEG data from a given patient. Despite the advantage, the performance of existing HFOs rarely satisfies the clinical requirement. Consequently, no HFOs have been successfully applied to real clinical applications so far. In this work, we propose a multi-head self-attention-based detector for recommendation, termed MSADR, to detect and recommend HFO signals. Taking advantage of the state-of-the-art multi-head self-attention mechanism in deep learning, the proposed MSADR achieves a more superior accuracy of 88.6% than peer machine learning models in both detection and recommendation tasks. In addition, the robustness of MSADR is also extensively assessed with various ablation tests, results of which further demonstrate the effectiveness and generalizability of the proposed approach.

Hormone-response transcriptional landscapes of wheat seedlings and the development of a JA fluorescent reporter.

Wheat is an essential energy and protein source for humans. Climate change brings daunting challenges to wheat yield through environmental stresses, in which phytohormones play critical roles. Nevertheless, the comprehensive understanding of wheat phytohormone responses remains elusive. Here, we investigated the transcriptome response of wheat seedlings to five phytohormones, cytokinin (6-BA), abscisic acid (ABA), gibberellic acid (GA), jasmonate (JA) and salicylic acid (SA). We further selected two JA marker genes and cloned their promoters to drive the expression of 3XEGFP (tandem trimeric enhanced green fluorescent protein) in transgenic lines. The JA fluorescent reporter displayed a fast and stable response to JA treatment as an ideal tool to follow JA dynamics during fungal and cold stresses at a cellular resolution. Overall, this study provided a transcriptional landscape and facilitated generating fluorescent reporters to monitor the dynamics of phytohormones in food crops.

Mid-term efficacy of surgical treatments for post-hysterectomy vaginal vault prolapse: a retrospective study.

Background: The surgical treatment of post-hysterectomy vaginal vault prolapse (PHVP) has been reported in several clinical studies, but mostly are short-term studies. This study aims to explore the mid-term efficacy of surgical treatments for PHVP. Methods: A total of 138 PHVP patients underwent surgery from January 2005 to January 2020 at the Fourth Medical Center of PLA General Hospital, Beijing. The clinical data of 119 patients who completed follow-up were retrospectively analyzed. Both groups of patients are diagnosed Pelvic Organ Prolapse Quantification system (POP-Q) III-IV stage of prolapse, with obvious prolapse-related symptoms and requiring surgical treatment. Among them, pelvic floor reconstruction surgery (RPS) was performed in patients who wanted to retain vaginal function and colpocleisis were used for frail patients who cannot tolerate RPS. We used the POP-Q scores for the objective efficacy evaluation, and use the Pelvic Floor Distress Inventory-Short Form 20 (PFDI-20) and Pelvic Floor Impact Questionnaire-7 (PFIQ-7) to evaluate the subjective symptom during follow up. Among them, 61 patients underwent pelvic floor RPS (Group R), and 58 received colpocleisis (Group C). Results: The surgeries in both groups were successfully completed, the median follow-up time after surgery was 4.3 years (0.25-13 years) and 5.3 years (0.33-15 years), respectively; the overall surgical success rate was 86.9% (53/61) and 100% (58/58), respectively; the subjective satisfaction rate was 90.2% (55/61) and 91.4% (53/58), respectively; and the PFDI-20 and PFIQ-7 scores in both groups were significantly improved compared with the preoperative levels (P<0.05). In Group R, 6 cases (9.8%, 6/61) were dissatisfied after surgery; in Group C, 5 cases (8.6%, 5/58) were dissatisfied after surgery. Conclusions: Reconstructive surgery and colpocleisis have a good mid-term effect on PHVP, with good outcome and few complications. The surgeon is expected to ascertain an appropriate surgical procedure based on the characteristics of the patient, the degree and the location of prolapse, in order to achieve the best surgical efficacy and minimize the damage.

Fusion of sequential visits and medical ontology for mortality prediction.

The goal of mortality prediction task is to predict the future death risk of patients according to their previous Electronic Healthcare Records (EHR). The main challenge of mortality prediction is how to design an accurate and robust predictive model with sequential, multivariate, sparse and irregular EHR data. In addition, the performance of model may be affected by lack of sufficient information of some patients with rare diseases in EHRs. To address these challenges, we propose a model to fuse Sequential visits and Medical Ontology to predict patients' death risk. SeMO not only learns reasonable embeddings for medical concepts from sequential and irregular visits, but also exploits medical ontology to improve the prediction performance. With integration of multivariate features, SeMO learns robust representations of medical codes, mitigating data insufficiency and insightful sequential dependencies among patient's visits. Experimental results on real world datasets prove that the proposed SeMO improves the prediction performance compared with the baseline approaches. Our model achieves an precision of up to 0.975. Compared with RNN, the precision has been improved up to 2.204%.

Method for counting labeled neurons in mouse brain regions based on image representation and registration.

An important step in brain image analysis is to divide specific brain regions by matching brain slices to standard brain reference atlases, and perform statistical analysis on the labeled neurons in each brain region. Taking mouse fluorescently labeled brain slices as an example, due to the noise and distortion introduced during the preparation of brain slices, and the modal differences with standard brain atlas, the brain slices cannot directly establish an accurate one-to-one correspondence with the brain atlas, which in turn affects the accuracy of the number of labeled neurons in each brain region. This paper introduces the idea of image representation, uses neural networks to realize the registration of different modal mouse brain slices and brain atlas, completes the regional localization of the brain slices, and uses threshold segmentation to detect and count the labeled neurons in each brain region. The method proposed in this paper can effectively solve the problem of large deviation of neurons count caused by the inaccurate division of brain regions in large deformed brain slices, and can automatically realize accurate count of labeled neurons in each brain region of brain slices. The whole framework of method for counting labeled neurons in mouse brain regions based on image representation and registration.

Robotic-Assisted Laparoscopic Sacrocolpopexy for Pelvic Organ Prolapse: A Single Center Experience in China.

OBJECTIVE: The aim is to investigate the efficiency and outcome of robotic-assisted sacrocolpopexy (RASC) in a cohort of patients with pelvic organ prolapse (POP) in our Gynecology Department. METHODS: We performed a retrospective study of female patients who underwent RASC in Chinese PLA General Hospital from January 2013 to December 2020. Their clinical features included age, degree of prolapse, menopause time, body mass index, pregnancy, delivery, operation time, and bleeding volume. All patients were followed up for more than 6 months. POP-Q was recorded to evaluate the position of prolapsed organs. PFDI-20, PFIQ-7, and PGI-I were used to evaluate the life quality after surgery. RESULTS: Twenty-four patients with POP received RASC in our center. The intraoperative bleeding was 86.9 ± 98.3 ml (20-300 ml). The operation time was 143.5 ± 47.3 min (60-240 minutes). The hospitalization time was 10.4 ± 2.1 days (8-16 days). And the follow-up time was 40.8 ± 22.0 months (6-72 months). In the POP-Q follow-up, postoperative Aa, Ba, Ap, Bp, and C were significantly improved than those before surgery (P < 0.05). The objective and subjective cure rate was 100%. PGI-I score was very good in 9 (9/24), very good in 10 (10/24), and good in 3 (3/24). Postoperative PFDI-20 and PFIQ-7 were 2.78 ± 3.82 and 1.57 ± 3.86, which decreased dramatically after surgery (P < 0.05). Mesh exposure occurred in 4 cases (16.7%) at 2-12 months. The exposed diameters were less than 1 cm in 3 cases (2 A/T3/S1) and 1-2 cm in 1 case (3 B/T3/S1). These mesh exposures healed after conservative observation or mesh excision. CONCLUSION: RASC for POP has the advantage of less bleeding and hospitalization time. It is a minimally invasive option for pelvic organ prolapse.

OsRAM2 Function in Lipid Biosynthesis Is Required for Arbuscular Mycorrhizal Symbiosis in Rice.

Arbuscular mycorrhiza (AM) is a mutualistic symbiosis formed between most land plants and Glomeromycotina fungi. During symbiosis, plants provide organic carbon to fungi in exchange for mineral nutrients. Previous legume studies showed that the required for arbuscular mycorrhization2 (RAM2) gene is necessary for transferring lipids from plants to AM fungi (AMF) and is also likely to play a "signaling" role at the root surface. To further explore RAM2 functions in other plant lineages, in this study, two rice (Oryza sativa) genes, OsRAM2 and OsRAM2L, were identified as orthologs of legume RAM2. Examining their expression patterns during symbiosis revealed that only OsRAM2 was strongly upregulated upon AMF inoculation. CRISPR/Cas9 mutagenesis was then performed to obtain three Osram2 mutant lines (-1, -2, and -3). After inoculation by AMF Rhizophagus irregularis or Funneliformis mosseae, all of the mutant lines showed extremely low colonization rates and the rarely observed arbuscules were all defective, thus supporting a conserved "nutritional" role of RAM2 between monocot and dicot lineages. As for the signaling role, although the hyphopodia numbers formed by both AMF on Osram2 mutants were indeed reduced, their morphology showed no abnormality, with fungal hyphae invading roots successfully. Promoter activities further indicated that OsRAM2 was not expressed in epidermal cells below hyphopodia or outer cortical cells enclosing fungal hyphae but instead expressed exclusively in cortical cells containing arbuscules. Therefore, this suggested an indirect role of RAM2 rather than a direct involvement in determining the symbiosis signals at the root surface.[Formula: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 "No Rights Reserved" license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2022.