Developing fatigue-resistant ferroelectrics using interlayer sliding switching.

Ferroelectric materials have switchable electrical polarization that is appealing for high density non-volatile memories. However, inevitable fatigue hinders practical applications of these materials. Fatigue-free ferroelectric switching could dramatically improve the endurance of devices. We report a fatigue-free ferroelectric system based on the sliding ferroelectricity of bilayer 3R-MoS2. The memory performance of this ferroelectric device does not show the "wake-up effect" at low cycles or a substantial "fatigue effect" after 106 switching cycles under different pulse widths. The total stress time of device under an electric field is up to 105 s, which is long relative to other devices. Our theoretical calculation uncovers that the fatigue-free feature of sliding ferroelectricity is due to the immobile charge defects in sliding ferroelectricity.

Accurate Recognition of Vascular Lumen Region from 2D Ultrasound Cine Loops for Bubble Detection.

BACKGROUND: Accurate identification of vascular lumen region founded the base of bubble detection and bubble grading, which played a significant role in the detection of vascular gas emboli for the diagnosis of decompression sickness. OBJECTIVES: To assist in the detection of vascular bubbles, it is crucial to develop an automatic algorithm that could identify vascular lumen areas in ultrasound videos with the interference of bubble presence. METHODS: This article proposed an automated vascular lumen region recognition (VLRR) algorithm that could sketch the accurate boundary between vessel lumen and tissues from dynamic 2D ultrasound videos. It adopts 2D ultrasound videos of the lumen area as input and outputs the frames with circled vascular lumen boundary of the videos. Normalized cross-correlation method, distance transform technique, and region growing technique were adopted in this algorithm. Results A double-blind test was carried out to test the recognition accuracy of the algorithm on 180 samples in the images of 6 different grades of bubble videos, during which, intersection over union and pixel accuracy were adopted as evaluation metrics. The average IOU on the images of different bubble grades reached 0.76. The mean PA on 6 of the images of bubble grades reached 0.82. CONCLUSION: It is concluded that the proposed method could identify the vascular lumen with high accuracy, potentially applicable to assist clinicians in the measurement of the severity of vascular gas emboli in clinics.

Combining quantitative and qualitative analysis for scoring pleural line in lung ultrasound.

Objective.Accurate assessment of pleural line is crucial for the application of lung ultrasound (LUS) in monitoring lung diseases, thereby aim of this study is to develop a quantitative and qualitative analysis method for pleural line.Approach.The novel cascaded deep learning model based on convolution and multilayer perceptron was proposed to locate and segment the pleural line in LUS images, whose results were applied for quantitative analysis of textural and morphological features, respectively. By using gray-level co-occurrence matrix and self-designed statistical methods, eight textural and three morphological features were generated to characterize the pleural lines. Furthermore, the machine learning-based classifiers were employed to qualitatively evaluate the lesion degree of pleural line in LUS images.Main results.We prospectively evaluated 3770 LUS images acquired from 31 pneumonia patients. Experimental results demonstrated that the proposed pleural line extraction and evaluation methods all have good performance, with dice and accuracy of 0.87 and 94.47%, respectively, and the comparison with previous methods found statistical significance (P< 0.001 for all). Meanwhile, the generalization verification proved the feasibility of the proposed method in multiple data scenarios.Significance.The proposed method has great application potential for assessment of pleural line in LUS images and aiding lung disease diagnosis and treatment.

Deep learning based ultrasound analysis facilitates precise distinction between parotid pleomorphic adenoma and Warthin tumor.

Background: Pleomorphic adenoma (PA), often with the benign-like imaging appearances similar to Warthin tumor (WT), however, is a potentially malignant tumor with a high recurrence rate. It is worse that pathological fine-needle aspiration cytology (FNAC) is difficult to distinguish PA and WT for inexperienced pathologists. This study employed deep learning (DL) technology, which effectively utilized ultrasound images, to provide a reliable approach for discriminating PA from WT. Methods: 488 surgically confirmed patients, including 266 with PA and 222 with WT, were enrolled in this study. Two experienced ultrasound physicians independently evaluated all images to differentiate between PA and WT. The diagnostic performance of preoperative FNAC was also evaluated. During the DL study, all ultrasound images were randomly divided into training (70%), validation (20%), and test (10%) sets. Furthermore, ultrasound images that could not be diagnosed by FNAC were also randomly allocated to training (60%), validation (20%), and test (20%) sets. Five DL models were developed to classify ultrasound images as PA or WT. The robustness of these models was assessed using five-fold cross-validation. The Gradient-weighted Class Activation Mapping (Grad-CAM) technique was employed to visualize the region of interest in the DL models. Results: In Grad-CAM analysis, the DL models accurately identified the mass as the region of interest. The area under the receiver operating characteristic curve (AUROC) of the two ultrasound physicians were 0.351 and 0.598, and FNAC achieved an AUROC of only 0.721. Meanwhile, for DL models, the AUROC value for discriminating between PA and WT in the test set was from 0.828 to 0.908. ResNet50 demonstrated the optimal performance with an AUROC of 0.908, an accuracy of 0.833, a sensitivity of 0.736, and a specificity of 0.904. In the test set of cases that FNAC failed to provide a diagnosis, DenseNet121 demonstrated the optimal performance with an AUROC of 0.897, an accuracy of 0.806, a sensitivity of 0.789, and a specificity of 0.824. Conclusion: For the discrimination of PA and WT, DL models are superior to ultrasound and FNAC, thereby facilitating surgeons in making informed decisions regarding the most appropriate surgical approach.

OptiGait: Gait Monitoring Using An Ankle-Worn Stereo Camera System.

We developed an ankle-worn gait monitoring system for tracking gait parameters, including length, width, and height. The system utilizes ankle bracelets equipped with wide-angle infrared (IR) stereo cameras tasked with monitoring a marker on the opposing ankle. A computer vision algorithm we have also developed processes the imaged marker positions to estimate the length, width, and height of the person's gait. Through testing on multiple participants, the prototype of the proposed gait monitoring system exhibited notable performance, achieving an average accuracy of 96.52%, 94.46%, and 95.29% for gait length, width, and height measurements, respectively, despite distorted wide-angle images. The OptiGait system offers a cost-effective and user-friendly alternative compared to existing gait parameter sensing systems, delivering comparable accuracy in measuring gait length and width. Notably, the system demonstrates a novel capability in measuring gait height, a feature not previously reported in the literature.

Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning.

Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD diagnosis. We chose four standard views in pediatric cardiac ultrasound to identify atrial septal defects; the four standard views were as follows: subcostal sagittal view of the atrium septum (subSAS), apical four-chamber view (A4C), the low parasternal four-chamber view (LPS4C), and parasternal short-axis view of large artery (PSAX). We enlist data from 300 children patients as part of a double-blind experiment for five-fold cross-validation to verify the performance of our model. In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training). In our model, we present a block random selection, maximal agreement decision, and frame sampling strategy for training and testing respectively, resNet18 and r3D networks are used to extract the frame features and aggregate them to build a rich video-level representation. We validate our model using our private dataset by five cross-validation. For ASD detection, we achieve 89.33 ± 3.13 AUC, 84.95 ± 3.88 accuracy, 85.70 ± 4.91 sensitivity, 81.51 ± 8.15 specificity, and 81.99 ± 5.30 F1 score. The proposed model is a multiple instances learning-based deep learning model for video atrial septal defect detection which effectively improves ASD detection accuracy when compared to the performances of previous networks and clinical doctors.

Pharmacokinetics and tissue distribution study of two novel quinazoline PI3K/mTOR dual-inhibitors, potential anticancer agents.

S1 and S2, two structurally similar quinazoline derivatives, are novel anticancer drugs targeting the PI3K/AKT/mTOR signaling pathway channel. However, their pharmacokinetic and tissue distribution characteristics are unknown, which has hindered further development and in-depth studies. In this study, a simple, rapid and sensitive method using high performance liquid chromatography was established and validated to quantitatively study the pharmacokinetics and tissue distribution profiles of S1 and S2 in rats following intravenous injection. The results indicated that after intravenous injection, the elimination of S1 and S2 fit the two-compartment model and linear pharmacokinetics characteristics were observed. Furthermore, S1 and S2 were widely distributed and found in high concentrations in liver and kidney tissues and a small proportion of S1 and S2 could cross the blood-brain barrier and be distributed in the brain. The current findings will contribute to interpretation and understanding the relationship between dosage and pharmacodynamic effects of S1 and S2.

Detecting fa leptin receptor mutation in Zucker rats with tetra-primer amplification-refractory mutation system (ARMS)-PCR.

Due to the genetic mutation (fa) in the gene encoding for leptin receptor, homozygous Zucker rats (fa-/-) develop excessive adiposity and become an experimental animal model in obesity and metabolic-related diseases research. Based on tetra-primer amplification refractory mutation system-polymerase chain reaction (ARMS-PCR), we developed a method to quickly genotype Zucker rats with a mutated fa allele from their wildtype littermates. The three genotypes are clearly discriminated on 2.0% agarose gel. Our method can be used as a reliable tool to set up and maintain the breeding colony in animal facilities as well as assign animals to control and treatment groups based on their genotypes for animal studies.

Association of liver function with health-related physical fitness: a cross-sectional study.

BACKGROUND: In this study, by analyzing the correlation between various components of health-related physical fitness (HPF) and liver function indicators, the indicators of physical fitness that were highly correlated with liver function and could be monitored at home were screened to prevent more serious liver disease in the future, and to provide experimental basis for prescribing personalized exercise. METHODS: A total of 330 faculties (female = 198) of a university were recruited. The indicators of HPF and liver function were measured. Spearman correlation analysis, multivariate linear regression, and cross-lagged panel model was used to data statistics. RESULTS: In males, body fat (BF) was positively correlated with alanine aminotransferase (ALT); vital capacity and the vital capacity index were positively correlated with albumin; and vertical jump was positively correlated with globulin and negatively correlated with the albumin-globulin ratio (P < 0.05). However, there was no significant correlation among all indicators controlled confounding factors. In females, BF was negatively correlated with direct bilirubin; VO2max was positively correlated with indirect bilirubin; and vertical jump was positively correlated with the albumin-globulin ratio and significantly negatively correlated with globulin (P < 0.05). Controlled confounding factors, body fat percentage was positively correlated with globulin (ß = 0.174) and negatively correlated with direct bilirubin (ß = -0.431), and VO2max was positively correlated with indirect bilirubin (ß = 0.238, P < 0.05). Cross-lagged panel analysis showed that BF percentage can negatively predict direct bilirubin levels with great significance (ß = -0.055, P < 0.05). CONCLUSIONS: HPF may play a crucial role in liver function screening, particularly for female faculty members. For males, BF, vertical jump, vital capacity and vital capacity index could be associated with liver function but are susceptible to complex factors such as age, smoking, diabetes, and hypertension. In females, BF percentage is an important predictor of abnormal liver function in addition to VO2max and vertical jump, which are not affected by complex factors.

Metabolic engineering of Bacillus amyloliquefaciens for efficient production of α-glucosidase inhibitor1-deoxynojirimycin.

Owing to the feature of strong α-glucosidase inhibitory activity, 1-deoxynojirimycin (1-DNJ) has broad application prospects in areas of functional food, biomedicine, etc., and this research wants to construct an efficient strain for 1-DNJ production, basing on Bacillus amyloliquefaciens HZ-12. Firstly, using the temperature-sensitive shuttle plasmid T2 (2)-Ori, gene ptsG in phosphotransferase system (PTS) was weakened by homologous recombination, and non-PTS pathway was strengthened by deleting its repressor gene iolR, and 1-DNJ yield of resultant strain HZ-S2 was increased by 4.27-fold, reached 110.72 mg/L. Then, to increase precursor fructose-6-phosphate (F-6-P) supply, phosphofructokinase was weaken, fructose phosphatase GlpX and 6-phosphate glucose isomerase Pgi were strengthened by promoter replacement, moreover, regulator gene nanR was deleted, 1-DNJ yield was further increased to 267.37 mg/L by 2.41-fold. Subsequently, promoter of 1-DNJ synthetase cluster was optimized, as well as 5'-UTRs of downstream genes in synthetase cluster, and 1-DNJ produced by the final strain reached 478.62 mg/L. Last but not the least, 1-DNJ yield of 1632.50 mg/L was attained in 3 L fermenter, which was the highest yield of 1-DNJ reported to date. Taken together, our results demonstrated that metabolic engineering was an effective strategy for 1-DNJ synthesis, this research laid a foundation for industrialization of functional food and drugs based on 1-DNJ.

Artificial intelligence in breast imaging: potentials and challenges.

Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.

Effects of freeze-thaw cycling on the engineering properties of vegetation concrete.

Vegetation concrete has been widely applied for the ecological restoration of bare steep slopes in short-term frozen and non-frozen soil regions in China. However, field experiments conducted in seasonally frozen soil regions have revealed decreases in the bulk density, nutrient content and vegetation coverage. This study aimed to clarify the evolution process and mechanism of the engineering properties of vegetation concrete under atmospheric freeze-thaw (F-T) test conditions. The physical, mechanical, and nutrient properties of vegetation concrete were investigated using six F-T cycles (0, 1, 2, 5, 10 and 20) and two initial soil water contents (18 and 22%). The results revealed decreases in the acoustic wave velocity and cohesive forces and an increase in the permeability coefficient of the vegetation concrete owing to F-T action. X-ray diffraction tests indicated that the decreased cohesive force was closely related to the overall decrease in the content of gelling hydration products in the vegetation concrete. Additionally, the contents of NH4+-N, PO43-P and K+ in the vegetation concrete increased, whereas that of NO3--N decreased. The loss rates of these soluble nutrients increased, indicating that the nutrient retention capacity of the vegetation concrete had decreased. Specifically, the decreased nutrient retention capacity was mainly related to the disintegration and fragmentation of larger aggregates due to F-T action. This study provides theoretical support for future research on improving the anti-freezing capability of ecological slope protection substrates in seasonally frozen soil regions.

Evidence for a causal effect of major depressive disorder, anxiety on prostatitis risk: A univariate and multivariate Mendelian randomization study.

BACKGROUND: Observational studies have shown an association between major depressive disorder (MDD), anxiety, and prostatitis. However, the causal relationship between MDD, anxiety, and prostatitis remains controversial. Therefore, we aimed to use two-sample Mendelian randomization (MR) to assess the causal effects of MDD and anxiety on prostatitis. METHODS: We conducted univariable and multivariable MR analyses using summary statistics from publicly available genome-wide association studies to estimate the causal relationships between MDD, anxiety, and prostatitis risk. In the main MR analysis, the inverse-variance weighted (IVW) method was used, while secondary methods included the weighted median, weighted mode, MR-Egger regression, and MR pleiotropy residual and outlier (MR-PRESSO) tests to detect and correct for the presence of pleiotropy. RESULTS: MDD had 97 independent instrumental variables (IVs) and anxiety had 15 IVs. Univariable MR analysis showed that genetically determined MDD had a detrimental causal effect on prostatitis (IVW: odds ratio [OR] = 1.47, 95% confidence interval [CI] = 1.12-1.92, p = 0.005), while no causal relationship was found between anxiety and prostatitis (IVW: OR = 0.25, 95% CI = 0.02-2.82, p = 0.26). More convincingly, after adjusting for confounding factors such as body mass index, alcohol consumption, and smoking, the genetic liability for MDD remained associated with prostatitis risk, with no strong evidence of anxiety affecting prostatitis incidence. CONCLUSION: This study supports the notion that MDD has a detrimental effect on prostatitis risk, and strategies focused on addressing MDD may be one of the cornerstones for treating prostatitis. The potential preventive value of treating MDD for prostatitis should be further investigated in future research.

Reciprocal Effect of Environmental Stimuli to Regulate the Adipogenesis and Osteogenesis Fate Decision in Bone Marrow-Derived Mesenchymal Stem Cells (BM-MSCs).

Mesenchymal stem cells derived from bone marrow (BM-MSCs) can differentiate into adipocytes and osteoblasts. Various external stimuli, including environmental contaminants, heavy metals, dietary, and physical factors, are shown to influence the fate decision of BM-MSCs toward adipogenesis or osteogenesis. The balance of osteogenesis and adipogenesis is critical for the maintenance of bone homeostasis, and the interruption of BM-MSCs lineage commitment is associated with human health issues, such as fracture, osteoporosis, osteopenia, and osteonecrosis. This review focuses on how external stimuli shift the fate of BM-MSCs towards adipogenesis or osteogenesis. Future studies are needed to understand the impact of these external stimuli on bone health and elucidate the underlying mechanisms of BM-MSCs differentiation. This knowledge will inform efforts to prevent bone-related diseases and develop therapeutic approaches to treat bone disorders associated with various pathological conditions.

Differentiation between Phyllodes Tumors and Fibroadenomas through Breast Ultrasound: Deep-Learning Model Outperforms Ultrasound Physicians.

The preoperative differentiation of breast phyllodes tumors (PTs) from fibroadenomas (FAs) plays a critical role in identifying an appropriate surgical treatment. Although several imaging modalities are available, reliable differentiation between PT and FA remains a great challenge for radiologists in clinical work. Artificial intelligence (AI)-assisted diagnosis has shown promise in distinguishing PT from FA. However, a very small sample size was adopted in previous studies. In this work, we retrospectively enrolled 656 breast tumors (372 FAs and 284 PTs) with 1945 ultrasound images in total. Two experienced ultrasound physicians independently evaluated the ultrasound images. Meanwhile, three deep-learning models (i.e., ResNet, VGG, and GoogLeNet) were applied to classify FAs and PTs. The robustness of the models was evaluated by fivefold cross validation. The performance of each model was assessed by using the receiver operating characteristic (ROC) curve. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated. Among the three models, the ResNet model yielded the highest AUC value, of 0.91, with an accuracy value of 95.3%, a sensitivity value of 96.2%, and a specificity value of 94.7% in the testing data set. In contrast, the two physicians yielded an average AUC value of 0.69, an accuracy value of 70.7%, a sensitivity value of 54.4%, and a specificity value of 53.2%. Our findings indicate that the diagnostic performance of deep learning is better than that of physicians in the distinction of PTs from FAs. This further suggests that AI is a valuable tool for aiding clinical diagnosis, thereby advancing precision therapy.

Exercise against nonalcoholic fatty liver disease: Possible role and mechanism of lipophagy.

Nonalcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease worldwide. NAFLD is prevalent in about 30% of people worldwide. The lack of physical activity is considered as one of the risks for NAFLD, and approximately one-third of NAFLD patients hardly engage in physical activity. It is acknowledged that exercise is one of the optimal non-pharmacological methods for preventing and treating NAFLD. Different forms of exercise such as aerobic exercise, resistance exercise and even simply physical activity in a higher level can be beneficial in reducing liver lipid accumulation and disease progression for NAFLD patients. In NAFLD patients, exercise is helpful in lowering steatosis and enhancing liver function. The mechanisms underlying the prevention and treatment of NAFLD by exercise are various and complex. Current studies on the mechanisms have focused on the pro-lipolytic, anti-inflammatory, and antioxidant and lipophagy. Promotion of lipophagy is regarded as an important mechanism for prevention and improvement of NAFLD by exercise. Recent studies have investigated the above mechanism, yet the potential mechanism has not been completely elucidated. Thus, in this review, we cover the recent advances of exercise-promoted lipophagy in NAFLD treatment and prevention. Furthermore, given the fact that exercise activates SIRT1, we discuss the possible regulatory mechanisms of lipophagy by SIRT1 during exercise. These mechanisms need to be verified by further experimental studies.

A combined radiomic model distinguishing GISTs from leiomyomas and schwannomas in the stomach based on endoscopic ultrasonography images.

BACKGROUND: Endoscopic ultrasonography (EUS) is recommended as the best tool for evaluating gastric subepithelial lesions (SELs); nonetheless, it has difficulty distinguishing gastrointestinal stromal tumors (GISTs) from leiomyomas and schwannomas. GISTs have malignant potential, whereas leiomyomas and schwannomas are considered benign. PURPOSE: This study aimed to establish a combined radiomic model based on EUS images for distinguishing GISTs from leiomyomas and schwannomas in the stomach. METHODS: EUS images of pathologically confirmed GISTs, leiomyomas, and schwannomas were collected from five centers. Gastric SELs were divided into training and testing datasets based on random split-sample method (7:3). Radiomic features were extracted from the tumor and muscularis propria regions. Principal component analysis, least absolute shrinkage, and selection operator were used for feature selection. Support vector machine was used to construct radiomic models. Two radiomic models were built: the conventional radiomic model included tumor features alone, whereas the combined radiomic model incorporated features from the tumor and muscularis propria regions. RESULTS: A total of 3933 EUS images from 485 cases were included. For the differential diagnosis of GISTs from leiomyomas and schwannomas, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve were 74.5%, 72.2%, 78.7%, and 0.754, respectively, for the EUS experts; 76.8%, 74.4%, 81.0%, and 0.830, respectively, for the conventional radiomic model; and 90.9%, 91.0%, 90.6%, and 0.953, respectively, for the combined radiomic model. For gastric SELs <20 mm, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the combined radiomic model were 91.4%, 91.6%, 91.1%, and 0.960, respectively. CONCLUSIONS: We developed and validated a combined radiomic model to distinguish gastric GISTs from leiomyomas and schwannomas. The combined radiomic model showed better diagnostic performance than the conventional radiomic model and could assist EUS experts in non-invasively diagnosing gastric SELs, particularly gastric SELs <20 mm.

Research and design of an intelligent fish tank system.

In order to improve the hardware configuration and interaction mode of the fish tank system and realize the diversification of client functions, the purpose of real-time remote monitoring and management is achieved. A set of IoT intelligent fish tank system composed of sensor unit, signal processing unit and wireless transmission unit was designed. The system improves the algorithm of the data collected by the sensor, and proposes an improved first-order lag average filtering algorithm. The system uses composite collection information, intelligent processing, chart data analysis and other methods to transmit the processed data to the cloud server through the WIFI communication module. An APP is designed on the remote monitoring and control end, and a visual data interface of the smart fish tank is made, and the user can modify the environmental parameters conducive to the biological survival inside the fish tank through the APP, it brings great convenience to the family fish tank, and the test shows that the system network is stable and fast in response, and the overall purpose of the intelligent fish tank system is achieved.

MOF-Derived Ru1Zr1/Co Dual-Atomic-Site Catalyst with Promoted Performance for Fischer-Tropsch Synthesis.

Cobalt-based catalysts have been widely used for Fischer-Tropsch synthesis (FTS) in industry; however, achieving rational catalyst design at the atomic level and thereby a higher activity and more long-chain-hydrocarbon products simultaneously remain an attractive and difficult challenge. The dual-atomic-site catalysts with unique electronic and geometric interface interactions offer a great opportunity for exploiting advanced FTS catalysts with improved performance. Herein, we designed a Ru1Zr1/Co catalyst with Ru and Zr dual atomic sites on the Co nanoparticle (NP) surface through a metal-organic-framework-mediated synthesis strategy which presents greatly enhanced FTS activity (high turnover frequency of 3.8 × 10-2 s-1 at 200 °C) and C5+ selectivity (80.7%). Control experiments presented a synergic effect between Ru and Zr single-atom site on Co NPs. Further density functional theory calculations of the chain growth process from C1 to C5 revealed that the designed Ru/Zr dual sites remarkably lower the rate-limiting barriers due to the significantly weakened C-O bond and promote the chain growth processes, resulting in the greatly boosted FTS performance. Therefore, our work demonstrates the effectiveness of dual-atomic-site design in promoting the FTS performance and provides new opportunities for developing efficient industrial catalysts.

Association between Different Types of Exercise and Intake of Nutrients including Carbohydrate, Fat, Protein, and B Vitamins in Young Adults.

PURPOSE: This cross-sectional study aimed to investigate the association between different types of exercise and nutrient intake to provide a basis for promoting the overall health of young adults. METHODS: A total of 427 young adults (217 women) aged 21 to 35 were recruited. Participants self-reported time spent (min/week) in endurance exercise, resistance exercise, sports, walking, and other structured physical activity (PA). Nutrient intake was determined via telephone-administered 24 h recalls. RESULTS: Resistance exercise was positively associated with intake of protein, vitamins B2, B3, B5, B6, and B12 and the percentage of total calories from protein (PCT-PRO), and negatively associated with the percentage of total calories from carbohydrate (PCT-CHO) (p < 0.05). Time spent in aerobic exercise was positively associated with fiber, pectin, and vitamin B6 intake, and negatively associated with PCT-PRO (p < 0.05). Time spent exercising was negatively associated with fiber and pectin intake (p < 0.05). Time spent performing other structured PA was positively associated with pectin intake (p < 0.05). Participants' total exercise time was positively associated with intake of vitamins B2, B5, B12, and PCT-Fat, PCT-PRO, and negatively associated with PCT-CHO (p < 0.05). CONCLUSION: The results showed an association between various exercise types and specific nutrients. It may be worthwhile to point out the negative association of exercise with CHO intake, which may need to be examined more closely in active young adults. In addition, the supplementation of B vitamins and pectin may be beneficial for their exercise performance and post-exercise recovery.