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BACKGROUND: Acute pancreatitis (AP) is a disease caused by abnormal activation of pancreatic enzymes and can lead to self-digestion of pancreatic tissues and dysfunction of other organs. Enteral nutrition plays a vital role in the treatment of AP because it can meet the nutritional needs of patients, promote the recovery of intestinal function, and maintain the barrier and immune functions of the intestine. However, the risk of aspiration during enteral nutrition is high; once aspiration occurs, it may cause serious complications, such as aspiration pneumonia, and suffocation, posing a threat to the patient's life. This study aims to establish and validate a prediction model for enteral nutrition aspiration during hospitalization in patients with AP. AIM: To establish and validate a predictive model for enteral nutrition aspiration during hospitalization in patients with AP. METHODS: A retrospective review was conducted on 200 patients with AP admitted to Chengdu Shangjin Nanfu Hospital, West China Hospital of Sichuan University from January 2020 to February 2024. Clinical data were collected from the electronic medical record system. Patients were randomly divided into a validation group (n = 40) and a modeling group (n = 160) in a 1:4 ratio, matched with 200 patients from the same time period. The modeling group was further categorized into an aspiration group (n = 25) and a non-aspiration group (n = 175) based on the occurrence of enteral nutrition aspiration during hospitalization. Univariate and multivariate logistic regression analyses were performed to identify factors influencing enteral nutrition aspiration in patients with AP during hospitalization. A prediction model for enteral nutrition aspiration during hospitalization was constructed, and calibration curves were used for validation. Receiver operating characteristic curve analysis was conducted to evaluate the predictive value of the model. RESULTS: There was no statistically significant difference in general data between the validation and modeling groups (P > 0.05). The comparison of age, gender, body mass index, smoking history, hypertension history, and diabetes history showed no statistically significant difference between the two groups (P > 0.05). However, patient position, consciousness status, nutritional risk, Acute Physiology and Chronic Health Evaluation (APACHE-II) score, and length of nasogastric tube placement showed statistically significant differences (P < 0.05) between the two groups. Multivariate logistic regression analysis showed that patient position, consciousness status, nutritional risk, APACHE-II score, and length of nasogastric tube placement were independent factors influencing enteral nutrition aspiration in patients with AP during hospitalization (P < 0.05). These factors were incorporated into the prediction model, which showed good consistency between the predicted and actual risks, as indicated by calibration curves with slopes close to 1 in the training and validation sets. Receiver operating characteristic analysis revealed an area under the curve (AUC) of 0.926 (95%CI: 0.8889-0.9675) in the training set. The optimal cutoff value is 0.73, with a sensitivity of 88.4 and specificity of 85.2. In the validation set, the AUC of the model for predicting enteral nutrition aspiration in patients with AP patients during hospitalization was 0.902, with a standard error of 0.040 (95%CI: 0.8284-0.9858), and the best cutoff value was 0.73, with a sensitivity of 91.9 and specificity of 81.8. CONCLUSION: A prediction model for enteral nutrition aspiration during hospitalization in patients with AP was established and demonstrated high predictive value. Further clinical application of the model is warranted.

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BACKGROUND: Study on influencing factors of gastric retention before endoscopic retrograde cholangiopancreatography (ERCP) background: With the wide application of ERCP, the risk of preoperative gastric retention affects the smooth progress of the operation. The study found that female, biliary and pancreatic malignant tumor, digestive tract obstruction and other factors are closely related to gastric retention, so the establishment of predictive model is very important to reduce the risk of operation. AIM: To analyze the factors influencing preoperative gastric retention in ERCP and establish a predictive model. METHODS: A retrospective analysis was conducted on 190 patients admitted to our hospital for ERCP preparation between January 2020 and February 2024. Patient baseline clinical data were collected using an electronic medical record system. Patients were randomly matched in a 1:4 ratio with data from 190 patients during the same period to establish a validation group (n = 38) and a modeling group (n = 152). Patients in the modeling group were divided into the gastric retention group (n = 52) and non-gastric retention group (n = 100) based on whether gastric retention occurred preoperatively. General data of patients in the validation group and modeling group were compared. Univariate and multivariate logistic regression analyses were performed to identify factors influencing preoperative gastric retention in ERCP patients. A predictive model for preoperative gastric retention in ERCP patients was constructed, and calibration curves were used for validation. The receiver operating characteristic (ROC) curve was analyzed to evaluate the predictive value of the model. RESULTS: We found no statistically significant difference in general data between the validation group and modeling group (P > 0.05). The comparison of age, body mass index, hypertension, and diabetes between the two groups showed no statistically significant difference (P > 0.05). However, we noted statistically significant differences in gender, primary disease, jaundice, opioid use, and gastrointestinal obstruction between the two groups (P < 0.05). Multivariate logistic regression analysis showed that gender, primary disease, jaundice, opioid use, and gastrointestinal obstruction were independent factors influencing preoperative gastric retention in ERCP patients (P < 0.05). The results of logistic regression analysis revealed that gender, primary disease, jaundice, opioid use, and gastrointestinal obstruction were included in the predictive model for preoperative gastric retention in ERCP patients. The calibration curves in the training set and validation set showed a slope close to 1, indicating good consistency between the predicted risk and actual risk. The ROC analysis results showed that the area under the curve (AUC) of the predictive model for preoperative gastric retention in ERCP patients in the training set was 0.901 with a standard error of 0.023 (95%CI: 0.8264-0.9567), and the optimal cutoff value was 0.71, with a sensitivity of 87.5 and specificity of 84.2. In the validation set, the AUC of the predictive model was 0.842 with a standard error of 0.013 (95%CI: 0.8061-0.9216), and the optimal cutoff value was 0.56, with a sensitivity of 56.2 and specificity of 100.0. CONCLUSION: Gender, primary disease, jaundice, opioid use, and gastrointestinal obstruction are factors influencing preoperative gastric retention in ERCP patients. A predictive model established based on these factors has high predictive value.

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BACKGROUND: Peripherally inserted central catheters (PICCs) are commonly used in hospitalized patients with liver cancer for the administration of chemotherapy, nutrition, and other medications. However, PICC-related thrombosis is a serious complication that can lead to morbidity and mortality in this patient population. Several risk factors have been identified for the development of PICC-related thrombosis, including cancer type, stage, comorbidities, and catheter characteristics. Understanding these risk factors and developing a predictive model can help healthcare providers identify high-risk patients and implement preventive measures to reduce the incidence of thrombosis. AIM: To analyze the influencing factors of PICC-related thrombosis in hospitalized patients with liver cancer, construct a predictive model, and validate it. METHODS: Clinical data of hospitalized patients with liver cancer admitted from January 2020 to December 2023 were collected. Thirty-five cases of PICC-related thrombosis in hospitalized patients with liver cancer were collected, and 220 patients who underwent PICC placement during the same period but did not develop PICC-related thrombosis were randomly selected as controls. A total of 255 samples were collected and used as the training set, and 77 cases were collected as the validation set in a 7:3 ratio. General patient information, case data, catheterization data, coagulation indicators, and Autar Thrombosis Risk Assessment Scale scores were analyzed. Univariate and multivariate unconditional logistic regression analyses were performed on relevant factors, and the value of combined indicators in predicting PICC-related thrombosis in hospitalized patients with liver cancer was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: Univariate analysis showed statistically significant differences (P < 0.05) in age, sex, Karnofsky performance status score (KPS), bedridden time, activities of daily living impairment, parenteral nutrition, catheter duration, distant metastasis, and bone marrow suppression between the thrombosis group and the non-thrombosis group. Other aspects had no statistically significant differences (P > 0.05). Multivariate regression analysis showed that age ≥ 60 years, KPS score ≤ 50 points, parenteral nutrition, stage III to IV, distant metastasis, bone marrow suppression, and activities of daily living impairment were independent risk factors for PICC-related thrombosis in hospitalized patients with liver cancer (P < 0.05). Catheter duration of 1-6 months and catheter duration > 6 months were protective factors for PICC-related thrombosis (P < 0.05). The predictive model for PICC-related thrombosis was obtained as follows: P predictive probability = [exp (Logit P)]/[1 + exp (Logit P)], where Logit P = age × 1.907 + KPS score × 2.045 + parenteral nutrition × 9.467 + catheter duration × 0.506 + tumor-node-metastasis (TNM) staging × 2.844 + distant metastasis × 2.065 + bone marrow suppression × 2.082 + activities of daily living impairment × 13.926. ROC curve analysis showed an area under the curve (AUC) of 0.827 (95%CI: 0.724-0.929, P < 0.001), with a corresponding optimal cut-off value of 0.612, sensitivity of 0.755, and specificity of 0.857. Calibration curve analysis showed good consistency between the predicted occurrence of PICC-related thrombosis and actual occurrence (P > 0.05). ROC analysis showed AUCs of 0.888 and 0.729 for the training and validation sets, respectively. CONCLUSION: Age, KPS score, parenteral nutrition, TNM staging, distant metastasis, bone marrow suppression, and activities of daily living impairment are independent risk factors for PICC-related thrombosis in hospitalized patients with liver cancer, while catheter duration is a protective factor for the disease. The predictive model has an AUC of 0.827, indicating high predictive accuracy and clinical value.

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Antibacterial materials have a wide range of potential applications in bio-antimicrobial, environmental antimicrobial, and food antimicrobial fields due to their intrinsic antimicrobial properties, which can circumvent the development of drug resistance in bacteria. Understanding the intricate mechanisms and intrinsic nature of diverse antibacterial materials is significant for the formulation of guidelines for the design of materials with rapid and efficacious antimicrobial action and a high degree of biomedical material safety. Herein, this review highlights the recent advances in investigating antimicrobial mechanisms of different antibacterial materials with a particular focus on tailored computer simulations and theoretical analysis. From the view of structure and function, we summarize the characteristics and mechanisms of different antibacterial materials, introduce the latest advances of new antibacterial materials, and discuss the design concept and development direction of new materials. In addition, we underscore the significance of employing simulation and theoretical methodologies to elucidate the intrinsic antimicrobial mechanisms, which is crucial for a comprehensive comprehension of the control strategies, safer biomedical applications, and the management of health and environmental concerns associated with antibacterial materials. This review could potentially stimulate further endeavors in fundamental research and facilitate the extensive utilization of computational and theoretical approaches in the design of novel functional nanomaterials.

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In recent years, there has been considerable push toward the biomedical applications with active particles, which have great potential to revolutionize disease diagnostics and therapy. The direct penetration of active particles through the cell membrane leads to more efficient intracellular delivery than previously considered endocytosis processes but may cause membrane disruption. Understanding fundamental behaviors of cell membranes in response to such extreme impacts by active particles is crucial to develop active particle-based biomedical technologies and manage health and safety issues in this emerging field. Unfortunately, the physical principles underlying the nonequilibrium behaviors from endocytosis to direct penetration remain elusive, and experiments are challenging. Here, we present a computed dynamic phase diagram for transmembrane transport of active particles and identify four characteristic dynamic phases in endocytosis and direct penetration according to the particle activity and membrane tension. The boundaries dividing these phases are analytically obtained with theoretical models, elucidating the nonequilibrium physics and criteria for the transition between different phases. Furthermore, we numerically and experimentally show three distinct dynamic regimes related to the interplay between necking and wrapping during the endocytosis process of active particles, which strikingly contrast the regimes for passive particles. Overall, these findings could be useful for sharpening the understanding of basic principles underlying biological issues related to the safe and efficient biomedical applications of such emerging matters.

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Antibiotics and triazole fungicides coexist in varying concentrations in natural aquatic environments, resulting in complex mixtures. These mixtures can potentially affect aquatic ecosystems. Accurately distinguishing synergistic and antagonistic mixtures and predicting mixture toxicity are crucial for effective mixture risk assessment. We tested the toxicities of 75 binary mixtures of antibiotics and fungicides against Auxenochlorella pyrenoidosa. Both regression and classification models for these mixtures were developed using machine learning models: random forest (RF), k-nearest neighbors (KNN), and kernel k-nearest neighbors (KKNN). The KKNN model emerged as the best regression model with high values of determination coefficient (R2 = 0.977), explained variance in prediction leave-one-out (Q2LOO = 0.894), and explained variance in external prediction (Q2F1 = 0.929, Q2F2 = 0.929, and Q2F3 = 0.923). The RF model, the leading classifier, exhibited high accuracy (accuracy = 1 for the training set and 0.905 for the test set) in distinguishing the synergistic and antagonistic mixtures. These results provide crucial value for the risk assessment of mixtures.

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The design and development of new large-capacity and selective materials for extracting rare precious metals via electronic waste is practically essential. In this paper, a new efficient UiO-66-NCS has been obtained as a consequence of the modification of the classical Zr-MOF (UiO-66-NH2), and its ability to recover gold has been investigated. These batch results adequately illustrated that UiO-66-NCS exhibited good adsorption capacity (675.5 mg g-1) and exceptional selectivity. In addition, UiO-66-NCS achieved faster adsorption equilibrium times of about 120 min. Adsorption kinetics and isotherms demonstrated that the pseudo-second-order adsorption scheme and a Langmuir-type procedure were shown by the adsorption of Au(III) on UiO-66-NCS. Characterized by pH effect experiments, TEM, XRD, and XPS, the adsorption of UiO-66-NCS with Au(III) relies on coordination, which further results in reduction, and the generated Au(0) is uniformly dispersed in the MOF. The adsorbent has considerable advantages for cyclic regeneration. Finally, DFT fitting results showed that the adsorption binding energy of UiO-66-NCS with [AuCl4]- was -8.63 kcal mol-1 for the adsorption process. UiO-66-NCS is likely to be an ideal substance for gold recovery.

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Hard X-rays are needed for non-destructive nano-imaging of solid matter. Synchrotron radiation facilities (SRF) provide the highest-quality images with single-digit nm resolution using advanced techniques such as X-ray ptychography. However, the resolution or field of view is ultimately constrained by the available coherent flux. To address this, the beam's incoherent fraction can be exploited using multiple parallel beams in an X-ray multibeam ptychography (MBP) approach. This expands the domain of X-ray ptychography to larger samples or more rapid measurements. Both qualities favor the study of complex composite or functional samples, such as catalysts, energy materials, or electronic devices. The challenge of performing ptychography at high energy and with many parallel beams must be overcome to extract the full advantages for extended samples while minimizing beam attenuation. Here, that challenge is overcome by creating a lens array using cutting-edge laser printing technology and applying it to perform scanning with MBP with up to 12 beams and at photon energies of 13 and 20 keV. This exceeds the measurement limits of conventional hard X-ray ptychography without compromising image quality for various samples: Siemens star test pattern, Ni/Al2O3 catalyst, microchip, and gold nano-crystal clusters.

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Capillary electrophoresis with capacitively coupled contactless conductivity detection (CE-C4D) has proven to be an efficient technique for the separation and detection of charged inorganic, organic, and biochemical analytes. It offers several advantages, including cost-effectiveness, nanoliter injection volume, short analysis time, good separation efficiency, suitability for miniaturization, and portability. However, the routine determination of common inorganic cations (NH4+, K+, Na+, Ca2+, Mg2+, and Li+) and inorganic anions (F-, Cl-, Br-, NO2-, NO3-, PO43-, and SO42-) in water quality monitoring typically exhibits limits of detection of about 0.3-1 µM without preconcentration. This sensitivity often proves insufficient for the applications of CE-C4D in trace analysis situations. Here, we explore methods to push the detection limits of CE-C4D through a comprehensive consideration of signal and noise sources. In particular, we (i) studied the model of C4D and its guiding roles in C4D and CE-C4D, (ii) optimized the bandwidth and noise performance of the current-to-voltage (I-V) converter, and (iii) reduced the noise level due to the strong background signal of the background electrolyte by adaptive differential detection. We characterized the system with Li+; the 3-fold signal-to-noise (S/N) detection limit for Li+ was determined at 20 nM, with a linear range spanning from 60 nM to 1.6 mM. Moreover, the optimized CE-C4D method was applied to the analysis of common mixed inorganic cations (K+, Na+, Ca2+, Mg2+, and Li+), anions (F-, Cl-, Br-, NO2-, NO3-, PO43-, and SO42-), toxic halides (BrO3-) and heavy metal ions (Pb2+, Cd2+, Cr3+, Co2+, Ni2+, Zn2+, and Cu2+) at trace concentrations of 200 nM. All electropherograms showed good S/N ratios, thus proving its applicability and accuracy. Our results have shown that the developed CE-C4D method is feasible for trace ion analysis in water quality control.

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Atomic clocks with higher frequency stability and accuracy than traditional space-borne atomic clocks are the cornerstone of long-term autonomous operation of space-time-frequency systems. We proposed a space cold atoms clock based on an intracavity cooling scheme, which captures cold atoms at the center of a microwave cavity and then executes in situ interactions between the cold atoms and microwaves. As a result of the microgravity environment in space, the cold atoms can interact with the microwaves for a longer time, which aids in realizing a high-precision atomic clock in space. This paper presents the overall design, operational characteristics, and reliability test results of the space atomic clock based on the intracavity cooling scheme designed for the operation onboard the China space station. In addition, the engineering prototype performance of the space cold atoms microwave clock is also presented. The ground test results for the clock show a fractional frequency stability of 1.1 × 10-12 τ-1/2 reaching 2.5 × 10-15 at 200,000 s, providing solid technical and data support for its future operation in orbit.

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BACKGROUND: Multiples of resting metabolic rate (RMR) are often used to classify physical activity intensity, a concept known as the metabolic equivalent of task (MET). However, the METs metrics may misclassify physical activity intensity in older adults because of age-related changes in RMR and maximal aerobic capacity (V˙O2max). This study aimed to (i) compare classifications of activity intensity by estimated (METsestimated) and measured (METsmeasured) METs and (ii) compare physical activity classified by absolute (METsmeasured) versus relative intensity (%V˙O2Reserve) in older adults. METHODS: Ninety-eight adults aged 75-90 years participated in the study. RMR and V˙O2 during sitting, standing, daily activities, and 6-minute walking test were measured. V˙O2Reserve was defined as the difference between V˙O2max and RMR. Moderate and vigorous intensity was classified as 3 and 6 METs and 40% and 60% of V˙O2Reserve, respectively. Paired t tests and a confusion matrix were used to investigate aims 1 and 2, respectively. RESULTS: METsmeasured was 24% lower than the standard 1 MET of 3.5 mL O2·min-1·kg-1. METsestimated underestimated the intensity during daily and walking activities when compared to METsmeasured. Nevertheless, when comparing METsmeasured to percentages of V˙O2Reserve, a mismatch was shown for moderate intensity in 47%-67% of the participants during daily activities and 21% of the participants during self-selected gait speed. CONCLUSIONS: Applying METsestimated for older adults leads to potential underestimation of physical activity intensity, suggesting that current classification metrics should be revised for older adults. V˙O2Reserve is a candidate metric for establishing precise physical activity intensity cut points for older adults. Clinical Trials Registration Number: NCT04821713.

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BACKGROUND Micro-needle knife (MNK) therapy releases the superficial fascia to alleviate pain and improve joint function in patients with acute ankle sprains (AAS). We aimed to evaluate the efficacy and safety of MNK therapy vs that of acupuncture. MATERIAL AND METHODS This blinded assessor, randomized controlled trial allocated 80 patients with AAS to 2 parallel groups in a 1: 1 ratio. The experimental group received MNK therapy; the control group underwent conventional acupuncture treatment at specified acupoints. Clinical efficacy differences between the 2 groups before (time-point 1 [TP1]) and after treatment (TP2) were evaluated using the visual analogue scale (VAS) and Kofoed ankle score. Safety records and evaluations of adverse events were documented. One-month follow-up after treatment (TP3) was conducted to assess the intervention scheme's reliability. RESULTS VAS and Kofoed ankle scores significantly improved in both groups. No patients dropped due to adverse events. At TP1, there were no significant differences between the 2 groups in terms of VAS and Kofoed scores (P>0.05). However, at TP2, efficacy of MNK therapy in releasing the superficial fascia was significantly superior to that of acupuncture treatment (P<0.001). At TP3, no significant differences in scores existed between the groups (P>0.05). CONCLUSIONS This study demonstrates that 6 sessions of MNK therapy to release the superficial fascia safely and effectively alleviated pain and enhanced ankle joint function in patients with AAS, surpassing the efficacy of conventional acupuncture treatment. Future studies should increase the sample size and introduce additional control groups to further validate the superior clinical efficacy of this intervention.

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The unclear pathogenesis of chronic itch originating from several systemic disorders poses challenges to clinical intervention. Recent studies recapitulate the spinal neurocircuits associated with neuroinflammation and synaptic plasticity responsible for pruriceptive sensations. The resolution of nociception and inflammation by Annexin 1 (ANXA1) has been identified. Given that pain and itch share many neural mechanisms, we employed two mice models of chronic itch to study the underlying targets and therapeutic potential of ANXA1, comprising allergic contact dermatitis-induced itch and cholestatic itch. Herein, we report that spinal expression of ANXA1 is down-regulated in mice with dermatitis-induced itch and cholestatic itch. Repetitive injections of ANXA1-derived peptide Ac2-26 (intrathecal, 10 µg) reduce itch-like scratching behaviors following dermatitis and cholestasis. Single exposure to Ac2-26 (intrathecal, 10 µg) alleviates the established itch phenotypes. Moreover, systemic delivery of Ac2-26 (intravenous, 100 µg) is effective against chronic dermatitis-induced itch and cholestatic itch. Strikingly, Ac2-26 therapy inhibits transferrin receptor 1 over-expression, iron accumulation, cytokine IL-17 release and the production of its receptor IL-17R, as well as astrocyte activation in the dorsal horn of spinal cord in mouse with dermatitis and cholestasis. Pharmacological intervention with iron chelator deferoxamine impairs chronic itch behaviors and spinal iron accumulation after dermatitis and cholestasis. Also, spinal IL-17/IL-17R neutralization attenuates chronic itch. Taken together, this current research indicates that ANXA1 protects against the beginning and maintenance of long-term dermatitis-induced itch and cholestatic itch, which may occur via the spinal suppression of IL-17-mediated neuroinflammation, astrocyte activation and iron overload.

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Correction for 'A compact and high-performance setup of capillary electrophoresis with capacitively coupled contactless conductivity detection (CE-C4D)' by Lin Li et al., Analyst, 2024, https://doi.org/10.1039/d4an00354c.

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Zygnematophyceae are the algal sisters of land plants. Here we sequenced four genomes of filamentous Zygnematophyceae, including chromosome-scale assemblies for three strains of Zygnema circumcarinatum. We inferred traits in the ancestor of Zygnematophyceae and land plants that might have ushered in the conquest of land by plants: expanded genes for signaling cascades, environmental response, and multicellular growth. Zygnematophyceae and land plants share all the major enzymes for cell wall synthesis and remodifications, and gene gains shaped this toolkit. Co-expression network analyses uncover gene cohorts that unite environmental signaling with multicellular developmental programs. Our data shed light on a molecular chassis that balances environmental response and growth modulation across more than 600 million years of streptophyte evolution.

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Objectives: This study aimed to explore and visualize the relationships among multiple symptoms in patients with Meniere's disease (MD) and aid clinical nurses in the design of accurate, individualized interventions. Methods: This study included 790 patients with MD at the Eye and ENT Hospital of Fudan University from October 2014 to December 2021. A self-designed symptom checklist was used to assess 15 MD-related symptoms and construct contemporaneous networks with all 15 symptoms in R software. Qgraph package and Fruchterman-Reingold layout were used for network visualization. Bootstrapping methods were performed to assess network accuracy and stability, and three centrality indices were adopted to describe relationships among symptoms. Results: Symptom networks showed good accuracy and stability. "Anxiety and nervousness"(98.2%), "aural fullness"(84.4%) and "tinnitus"(82.7%) were the common symptom in MD patients, while "tinnitus", "aural fullness" and "decline in word recognition", were more serious. MD patients with longer disease duration had higher prevalence and severity for all symptoms (P < 0.05). Symptom networks showed good accuracy and stability. "Decline in word recognition," "fatigue," and "anxiety and nervousness" were at the center of the symptom networks, which had the largest strength values and closeness. "Decline in word recognition," "headache," and "spatial discrimination and poor orientation" were the symptoms with the highest betweenness with the strongest bridging effect. The ≥1-year disease group exhibited higher centralities for "drop attack" and "anxiety and nervousness," and a lower centrality for "headache" compared with the <1-year disease group. Conclusions: The symptom networks of MD patients with varying disease durations were revealed. Clinicians and nurses must provide precision interventions tailored to modifying symptom severity and centrality. Nursing interventions should focus on word recognition issues and associated discomfort in MD patients with multiple symptoms.

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Weed is a major biological factor causing declines in crop yield. However, widespread herbicide application and indiscriminate weeding with soil disturbance are of great concern because of their environmental impacts. Site-specific weed management (SSWM) refers to a weed management strategy for digital agriculture that results in low energy loss. Deep learning is crucial for developing SSWM, as it distinguishes crops from weeds and identifies weed species. However, this technique requires substantial annotated data, which necessitates expertise in weed science and agronomy. In this study, we present a channel attention mechanism-driven generative adversarial network (CA-GAN) that can generate realistic synthetic weed data. The performance of the model was evaluated using two datasets: the public segmented Plant Seedling Dataset (sPSD), featuring nine common broadleaf weeds from arable land, and the Institute for Sustainable Agro-ecosystem Services (ISAS) dataset, which includes five common summer weeds in Japan. Consequently, the synthetic dataset generated by the proposed CA-GAN obtained an 82.63% recognition accuracy on the sPSD and 93.46% on the ISAS dataset. The Fréchet inception distance (FID) score test measures the similarity between a synthetic and real dataset, and it has been shown to correlate well with human judgments of the quality of synthetic samples. The synthetic dataset achieved a low FID score (20.95 on the sPSD and 24.31 on the ISAS dataset). Overall, the experimental results demonstrated that the proposed method outperformed previous state-of-the-art GAN models in terms of image quality, diversity, and discriminability, making it a promising approach for synthetic agricultural data generation.

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Active matter systems, which convert internal chemical energy or energy from the environment into directed motion, are ubiquitous in nature and exhibit a range of emerging non-equilibrium behaviors. However, most of the current works on active matter have been devoted to particles, and the study of active polymers has only recently come into the spotlight due to their prevalence within living organisms. The intricate interplay between activity and conformational degrees of freedom gives rise to novel structural and dynamical behaviors of active polymers. Research in active polymers remarkably broadens diverse concepts of polymer physics, such as molecular architecture, dynamics, scaling and so on, which is of significant importance for the development of new polymer materials with unique performance. Furthermore, active polymers are often found in strongly interacting and crowded systems and in complex environments, so that the understanding of this behavior is essential for future developments of novel polymer-based biomaterials. This review thereby focuses on the study of active polymers in complex and crowded environments, and aims to provide insights into the fundamental physics underlying the adaptive and collective behaviors far from equilibrium, as well as the open challenges that the field is currently facing.

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Theobroma cacao, the chocolate tree, is indigenous to the Amazon basin, the greatest biodiversity hotspot on earth. Recent advancement in plant genomics highlights the importance of de novo sequencing of multiple reference genomes to capture the genome diversity present in different cacao populations. In this study, three high-quality chromosome-level genomes of wild cacao were constructed, de novo assembled with HiFi long reads sequencing, and scaffolded using a reference-free strategy. These genomes represent the three most important genetic clusters of cacao trees from the Upper Amazon region. The three wild cacao genomes were compared with two reference genomes of domesticated cacao. The five cacao genetic clusters were inferred to have diverged in the early and middle Pleistocene period, approximately 1.83-0.69 million years ago. The results shown here serve as an example of understanding how the Amazonian biodiversity was developed. The three wild cacao genomes provide valuable resources for studying genetic diversity and advancing genetic improvement of this species.

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