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We report in this work a practical design of pentamode acoustic cloak with microstructure. The proposed cloak is assembled by pentamode lattice made of a single-phase solid material. The function of rerouting acoustic wave round an obstacle has been demonstrated numerically. It is also revealed that shear related resonance due to weak shear resistance in practical pentamode lattices punctures broadband feature predicted based on ideal pentamode cloak. As a consequence, the latticed pentamode cloak can only conceal the obstacle in segmented frequency ranges. We have also shown that the shear resonance can be largely reduced by introducing material damping and an improved broadband performance can be achieved. These works pave the way for experimental demonstration of pentamode acoustic cloak.

Cognitive decline is one of the complications of type 2 diabetes (T2D). Intermittent fasting (IF) is a promising dietary intervention for alleviating T2D symptoms, but its protective effect on diabetes-driven cognitive dysfunction remains elusive. Here, we find that a 28-day IF regimen for diabetic mice improves behavioral impairment via a microbiota-metabolites-brain axis: IF enhances mitochondrial biogenesis and energy metabolism gene expression in hippocampus, re-structures the gut microbiota, and improves microbial metabolites that are related to cognitive function. Moreover, strong connections are observed between IF affected genes, microbiota and metabolites, as assessed by integrative modelling. Removing gut microbiota with antibiotics partly abolishes the neuroprotective effects of IF. Administration of 3-indolepropionic acid, serotonin, short chain fatty acids or tauroursodeoxycholic acid shows a similar effect to IF in terms of improving cognitive function. Together, our study purports the microbiota-metabolites-brain axis as a mechanism that can enable therapeutic strategies against metabolism-implicated cognitive pathophysiologies. Intermittent fasting (IF) has been shown beneficial in reducing metabolic diseases. Here, using a multi-omics approach in a T2D mouse model, the authors report that IF alters the composition of the gut microbiota and improves metabolic phenotypes that correlate with cognitive behavior.

The intra-articular corticosteroid injection (IACI) and arthroscopic partial meniscectomy (APM) are crucial treatment options for meniscus tears and are widely used in clinical practice. To determine whether there are differences in the progression of osteoarthritis (OA) after APM and IACI treatments for degenerative meniscal tears, and to identify the influencing factors. We finally collected the minimum joint space width (JSW), WOMAC score, and KOOS score of 189 patients after 4 years of follow-up. The mixed effects model and general estimating equation were used to analyze the differences in the progression of knee osteoarthritis and the factors affecting the progression of knee osteoarthritis in patients with degenerative meniscus tears who received different treatments. Over a 48-month follow-up period, all three groups showed a decreasing trend in knee JSW, with the IACI group having the fastest JSW decline rate at -0.020 mm/month (95% CI: -0.027 to -0.013, p  < 0.01). There was no statistically significant difference in the JSW decline rate among the three groups. The WOMAC total scores for both the IACI and APM groups showed an improving trend, at -0.123/month (95% CI: -0.211 to -0.036, p  < 0.01) and − 0.115/month (95% CI: -0.201 to -0.028, p  < 0.01) respectively, with no statistical difference between the two groups. BMI was also a significant factor affecting postoperative JSW (regression coefficients: -0.012, 95% CI: -0.022 to 0.001, p  = 0.03) and WOMAC total scores (regression coefficients: 0.189, 95% CI: 0.008 to 0.370, p  = 0.04). Compared to single IACI, multiple IACI treatments resulted in faster JSW decline (B: 0.430, 95% CI: 1.012 to 2.336, p  = 0.04). Patients with degenerative meniscal tears who undergo either IACI or APM treatment exhibit more pronounced progression of knee osteoarthritis compared to those in the non-treatment group. This form of deterioration is mainly driven by BMI.

Microplastics (MPs) have been considered as a global environmental problem threatening the ecological security. However, studies on MPs’ pollution in freshwaters and the associated risk assessment remain limited in the literature. In this study, the concentrations, distributions, and the potential ecological risks of MPs were analyzed in Yongjiang River, which is an important drinking water source flowing through Nanning City, the mega city of China. The MPs’ abundances in surface waters and sediments ranged from 500 to 7700 n/m 3 and from 90 to 550 n/kg, respectively. Spatial distribution highlighted the significant impact of anthropogenic activity on the MPs’ accumulation. Polyethylene and polypropylene were the most common polymer compositions investigated. Shape, size, and color were examined to analyze the characteristics of MPs in the river. To assess the ecological risk of MPs, the predicted no-effect concentration (PNEC) values were derived from a species’ sensitivity distribution model based on the toxicity data of MPs for freshwater species available in the literature. The PNEC for MPs in surface water was derived to be 4920 n/m 3 . Risk assessment results through risk quotient (RQ) method suggest that most of the monitored sites in Yongjiang River posed negligible risks to freshwater biota, except the two sites with high risk in the urban center. The results provided a basis for ecological risk assessment of MPs in freshwaters.

Deep learning has shown great promise in hydrological modeling, especially when large sample data sets are used to capture generalizable patterns across basins. However, challenges remain in addressing data scarcity and ensuring model reliability, particularly when key hydrological observations are modeled as individual tasks. In this study, we shift from traditional single‐task learning (STL) to multi‐task learning (MTL) to leverage the interconnections among hydrological variables and potentially improve modeling outcomes in data‐limited settings. Using a Long Short‐Term Memory (LSTM) neural network with the Catchment Attributes and Meteorology for Large‐Sample Studies data set, we developed an MTL model to predict streamflow and evapotranspiration across 591 basins. The MTL model exhibited comparable predictions for streamflow and evapotranspiration to STL models, with similar spatiotemporal generalization across varying data sizes. MTL's strength appeared when using LSTM cell state probes to predict the non‐target variable, surface soil moisture (SSM), showing slightly higher correlation coefficients. This highlights MTL's ability to capture intrinsic hydrological rules, enhancing model reliability. Leveraging this ability, we further explored MTL's advantages under two data‐limited scenarios: one with less‐observed SSM data and another with no available streamflow data. In both cases, MTL, supported by another well‐observed variable, outperformed STL models by a notable difference. These findings highlight MTL's potential to address the challenges of hydrological modeling in data‐limited basins. As Earth observation data continues to grow, MTL could become a valuable approach for building more reliable and generalizable hydrological models.

Light quality strongly impacts the growth and flower quality of ornamental plants. The optimum light quality for the growth and flowering of Hippeastrum remains to be validated. In the present study, we investigated the effect of the red/blue light ratio of LEDs on the growth and flowering quality of H. hybrid ‘Red Lion’. Two LEDs with red/blue light ratio of 1:9 (R 10 B 90 ) and 9:1 (R 90 B 10 ) were designed. LEDs of white light were the control. In the earlier vegetative and reproductive growth phase, R 90 B 10 increased the biomass of the bulbs, leaves, and flowers. Compared with the control and R 10 B 90 group, R 90 B 10 LEDs delayed flowering by 2.30 d and 3.26 d, respectively. Based on chlorophyll contents, photosynthetic capacity, chlorophyll fluorescence parameters, and carbohydrate contents, the photosynthesis rate was higher in the R 10 B 90 group. Optimal red and blue light intensity promoted the accumulation of carbohydrates and early flowering and prolonged the flowering period of H. hybrid . Microscopic analysis showed that stomatal density was high, and the number of chloroplasts was large in the R 10 B 90 treatment group, which enhanced photosynthesis. Particularly, R 10 B 90 promoted the expression of seven key genes related to chlorophyll synthesis. R 10 B 90 also promoted early overexpression of the HpCOL gene that promotes early flowering. Thus, higher blue light and 10% red light intensities promote early and extended flowering, while higher red light and 10% blue light promote vegetative plant growth but delay flowering.

Background Ancestry estimation of skulls is under a wide range of applications in forensic science, anthropology, and facial reconstruction. This study aims to avoid defects in traditional skull ancestry estimation methods, such as time-consuming and labor-intensive manual calibration of feature points, and subjective results. Results This paper uses the skull depth image as input, based on AlexNet, introduces the Wide module and SE-block to improve the network, designs and proposes ANINet, and realizes the ancestry classification. Such a unified model architecture of ANINet overcomes the subjectivity of manually calibrating feature points, of which the accuracy and efficiency are improved. We use depth projection to obtain the local depth image and the global depth image of the skull, take the skull depth image as the object, use global, local, and local + global methods respectively to experiment on the 95 cases of Han skull and 110 cases of Uyghur skull data sets, and perform cross-validation. The experimental results show that the accuracies of the three methods for skull ancestry estimation reached 98.21%, 98.04% and 99.03%, respectively. Compared with the classic networks AlexNet, Vgg-16, GoogLenet, ResNet-50, DenseNet-121, and SqueezeNet, the network proposed in this paper has the advantages of high accuracy and small parameters; compared with state-of-the-art methods, the method in this paper has a higher learning rate and better ability to estimate. Conclusions In summary, skull depth images have an excellent performance in estimation, and ANINet is an effective approach for skull ancestry estimation.

Background Accurately diagnosing supraspinatus tears based on magnetic resonance imaging (MRI) is challenging and time-combusting due to the experience level variability of the musculoskeletal radiologists and orthopedic surgeons. We developed a deep learning-based model for automatically diagnosing supraspinatus tears (STs) using shoulder MRI and validated its feasibility in clinical practice. Materials and methods A total of 701 shoulder MRI data (2804 images) were retrospectively collected for model training and internal test. An additional 69 shoulder MRIs (276 images) were collected from patients who underwent shoulder arthroplasty and constituted the surgery test set for clinical validation. Two advanced convolutional neural networks (CNN) based on Xception were trained and optimized to detect STs. The diagnostic performance of the CNN was evaluated according to its sensitivity, specificity, precision, accuracy, and F1 score. Subgroup analyses were performed to verify its robustness, and we also compared the CNN’s performance with that of 4 radiologists and 4 orthopedic surgeons on the surgery and internal test sets. Results Optimal diagnostic performance was achieved on the 2D model, from which F1-scores of 0.824 and 0.75, and areas under the ROC curves of 0.921 (95% confidence interval, 0.841–1.000) and 0.882 (0.817–0.947) were observed on the surgery and internal test sets. For the subgroup analysis, the 2D CNN model demonstrated a sensitivity of 0.33–1.000 and 0.625–1.000 for different degrees of tears on the surgery and internal test sets, and there was no significant performance difference between 1.5 and 3.0 T data. Compared with eight clinicians, the 2D CNN model exhibited better diagnostic performance than the junior clinicians and was equivalent to senior clinicians. Conclusions The proposed 2D CNN model realized the adequate and efficient automatic diagnoses of STs, which achieved a comparable performance of junior musculoskeletal radiologists and orthopedic surgeons. It might be conducive to assisting poor-experienced radiologists, especially in community scenarios lacking consulting experts.

Extensive industrial activities and anthropogenic agricultural practices have led to substantial ammonia release to the environment. Although croplands can act as ammonia sinks, reduced crop production under high concentrations of ammonium has been documented. Alpha-ketoglutarate (AKG) is a critical carbon source, displaying pleiotropic physiological functions. The objective of the present study is to disclose the potential of AKG to enhance ammonium assimilation in poplars. It showed that AKG application substantially boosted the height, biomass, and photosynthesis activity of poplars exposed to excessive ammonium. AKG also enhanced the activities of key enzymes involved in nitrogen assimilation: glutamine synthetase (GS) and glutamate synthase (GOGAT), elevating the content of amino acids, sucrose, and the tricarboxylic acid cycle (TCA) metabolites. Furthermore, AKG positively modulated key genes tied to glucose metabolism and ATP synthesis, while suppressing ATP-depleting genes. Correspondingly, both H+-ATPase activity and ATP content increased. These findings demonstrate that exogenously applying AKG improves poplar growth under a high level of ammonium treatment. AKG might function through sufficient carbon investment, which enhances the carbon–nitrogen balance and energy stability in poplars, promoting ammonium assimilation at high doses of ammonium. Our study provides novel insight into AKG’s role in improving poplar growth in response to excess ammonia exposure.

Objectives Recently, NRG-GY018 clinical trial demonstrated that adding pembrolizumab to chemotherapy led to significantly longer progression-free survival than chemotherapy alone in the first-line treatment of advanced or recurrent endometrial cancer (a/rEC). This analysis aimed to estimate the cost-effectiveness of pembrolizumab plus paclitaxel plus carboplatin chemotherapy (PC) as the first-line treatment for a/rEC in the US and China. Methods A Markov model based on the clinical data from NRG-GY018 trial was established to estimate the cost and efficacy of PC and paclitaxel plus carboplatin groups for a/rEC in mismatch repair-proficient (pMMR) and mismatch repair–deficient (dMMR) populations. Direct medical costs and utility values were collected from the government databases, local databases, and published literatures. The main outcomes were incremental cost-effectiveness ratios (ICERs), incremental monetary benefit (INMB), and incremental net-health benefit (INHB). The robustness of the model was assessed using one-way and probabilistic sensitivity analyses. Results With the 5-year time horizon, treatment with PC gained an additional 0.87 QALYs (1.34 LYs) in pMMR and 4.17 QALYs (5.14 LYs) in the dMMR population. In the US, the ICERs of PC compared to chemotherapy were 404,575 $/QALY in pMMR and 124,406 $/QALY in dMMR patients, respectively. In China, the ICERs of PC compared to chemotherapy were 220,259 $/QALY and 70,207 $/QALY in pMMR and dMMR populations, respectively. The results of sensitivity analyses supported the robustness of our models. Conclusions For patients with a/rEC, PC was cost-effective compared with chemotherapy in the first-line treatment for dMMR populations in the US. However, the combination of pembrolizumab with chemotherapy was not a cost-effective strategy for pMMR a/rEC in the US and a/rEC in China regardless of the MMR status, a price reduction process is required to reach the traditional cost-effectiveness threshold.