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Due to the unique structures and properties of graphene and graphene-analogous two-dimensional (2D) nanosheets, numerous methods were developed to prepare these 2D nanosheets; however, it is still changeable to fabricate high-quality 2D nanosheets cost-effectively in large scale. Electrochemical exfoliation has emerged as an efficient and mild technology for the preparation of 2D nanosheets, and this paper reviewed the recent development of this strategy. Electrochemical exfoliation was divided into two main categories, anodic and cathodic exfoliation. The merits of each category were summarized and analyzed as well as drawbacks in detail. It provides an avenue to design and fabricate different kinds of high-quality 2D nanosheets cost-effectively in large scale for energy storage and conversion, catalysis, sensors, electronics and so on.

Background Heterosis is a phenomenon in which hybrids exhibit superior performance relative to parental phenotypes. In addition to the heterosis of above-ground agronomic traits on which most existing studies have focused, root heterosis is also an indispensable component of heterosis in the entire plant and of major importance to plant breeding. Consequently, systematic investigations of root heterosis, particularly in reproductive-stage rice, are needed. The recent advent of RNA sequencing technology (RNA-Seq) provides an opportunity to conduct in-depth transcript profiling for heterosis studies. Results Using the Illumina HiSeq 2000 platform, the root transcriptomes of the super-hybrid rice variety Xieyou 9308 and its parents were analyzed at tillering and heading stages. Approximately 391 million high-quality paired-end reads (100-bp in size) were generated and aligned against the Nipponbare reference genome. We found that 38,872 of 42,081 (92.4%) annotated transcripts were represented by at least one sequence read. A total of 829 and 4186 transcripts that were differentially expressed between the hybrid and its parents (DG HP ) were identified at tillering and heading stages, respectively. Out of the DG HP , 66.59% were down-regulated at the tillering stage and 64.41% were up-regulated at the heading stage. At the heading stage, the DG HP were significantly enriched in pathways related to processes such as carbohydrate metabolism and plant hormone signal transduction, with most of the key genes that are involved in the two pathways being up-regulated in the hybrid. Several significant DG HP that could be mapped to quantitative trait loci (QTLs) for yield and root traits are also involved in carbohydrate metabolism and plant hormone signal transduction pathways. Conclusions An extensive transcriptome dataset was obtained by RNA-Seq, giving a comprehensive overview of the root transcriptomes at tillering and heading stages in a heterotic rice cross and providing a useful resource for the rice research community. Using comparative transcriptome analysis, we detected DG HP and identified a group of potential candidate transcripts. The changes in the expression of the candidate transcripts may lay a foundation for future studies on molecular mechanisms underlying root heterosis.

Background Paravertebral block (PVB) is commonly used for analgesia postoperatively while rarely as anesthesia during surgical stabilization for rib fractures. This study aimed to explore the feasibility and safety of PVB analgesia alone during surgical stabilization for patients with multiple rib fractures (MRF) under conscious state. Methods This prospective single-arm pilot study was conducted in patients with MRF who schedule for surgical stabilization using PVB analgesia in Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine between September 2019 and September 2020. The outcomes were the vital signs, postoperative pain and nausea and vomiting (PONV). Those who underwent general anesthesia (GA) during the same period were included for post hoc analysis. Results Eighteen patients (aged 62 ± 10.64 years; 8 males) were enrolled. The vital signs, including SpO 2 , systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, of the patients at baseline, perioperative, intraoperative, and postoperative day 1 were kept normal. The postoperative numerical rating scale (NRS) pain scores at 6, 12, and 24 h were 2.67 ± 1.36, 2.44 ± 0.80, and 2.33 ± 0.86, respectively, which were improved compared with baseline (5.78 ± 1.00). No PONV, postoperative morbidity, pulmonary infections, or incision infections were observed. Additionally, post-hoc analysis for the comparison of patients who underwent GA with PVB (in the pilot study) showed a similar number of rib fracture fixation ( P  = 0.06) and analgesic effect ( P  = 0.06) after operation, while a significantly shorter total length of hospital stay ( P  < 0.01), postoperative hospital stay ( P  < 0.01), lower dose of sufentanil citrate use ( P  < 0.01),and total costs( P  < 0.03)in patients who underwent PVB. Conclusions PVB analgesia during surgical stabilization for MRF under a conscious state might be feasible and safe. Compared with GA, PVB analgesia might reduce the dose of narcotics, shorten the length of hospital stay, and reduce the cost of hospitalization. Clinical registration : www.clinicaltrials.gov (#NCT04536311).

Background Osteoporosis is a common metabolic skeletal disease and usually lacks obvious symptoms. Many individuals are not diagnosed until osteoporotic fractures occur. Bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis detection. However, only a limited percentage of people with osteoporosis risks undergo the DXA test. As a result, it is vital to develop methods to identify individuals at-risk based on methods other than DXA. Results We proposed a hierarchical model with three layers to detect osteoporosis using clinical data (including demographic characteristics and routine laboratory tests data) and CT images covering lumbar vertebral bodies rather than DXA data via machine learning. 2210 individuals over age 40 were collected retrospectively, among which 246 individuals’ clinical data and CT images are both available. Irrelevant and redundant features were removed via statistical analysis. Consequently, 28 features, including 16 clinical data and 12 texture features demonstrated statistically significant differences ( p  < 0.05) between osteoporosis and normal groups. Six machine learning algorithms including logistic regression (LR), support vector machine with radial-basis function kernel, artificial neural network, random forests, eXtreme Gradient Boosting and Stacking that combined the above five classifiers were employed as classifiers to assess the performances of the model. Furthermore, to diminish the influence of data partitioning, the dataset was randomly split into training and test set with stratified sampling repeated five times. The results demonstrated that the hierarchical model based on LR showed better performances with an area under the receiver operating characteristic curve of 0.818, 0.838, and 0.962 for three layers, respectively in distinguishing individuals with osteoporosis and normal BMD. Conclusions The proposed model showed great potential in opportunistic screening for osteoporosis without additional expense. It is hoped that this model could serve to detect osteoporosis as early as possible and thereby prevent serious complications of osteoporosis, such as osteoporosis fractures.

Obesity is a public health crisis, presenting a huge burden on health care and the economic system in both developed and developing countries. According to the WHO’s latest report on obesity, 39% of adults of age 18 and above are obese, with an increase of 18% compared to the last few decades. Metabolic energy imbalance due to contemporary lifestyle, changes in gut microbiota, hormonal imbalance, inherent genetics, and epigenetics is a major contributory factor to this crisis. Multiple studies have shown that probiotics and their metabolites (postbiotics) supplementation have an effect on obesity-related effects in vitro, in vivo, and in human clinical investigations. Postbiotics such as the SCFAs suppress obesity by regulating metabolic hormones such as GLP-1, and PPY thus reducing feed intake and suppressing appetite. Furthermore, muramyl di-peptides, bacteriocins, and LPS have been tested against obesity and yielded promising results in both human and mice studies. These insights provide an overview of targetable pharmacological sites and explore new opportunities for the safer use of postbiotics against obesity in the future.

In this paper, a learning and recognition approach is proposed for univariate time series composed of output measurements of general nonlinear dynamical systems. Firstly, a class of dynamical systems in the canonical form is derived to describe the univariate time series by introducing coordinate transformation. An observer-based deterministic learning technique is then adopted to achieve dynamical modeling of the associated transformed systems of the training univariate time series, and the modeling results in the form of radial basis function network (RBFN) models are stored in a pattern library. Subsequently, multiple observer-based dynamical estimators containing the RBFN models in the pattern library are constructed for a test univariate time series, and a recognition decision scheme is proposed by the derived recognition indicator. On this basis, more concise recognition conditions are provided, which is beneficial for verifying the recognition results. Finally, simulation studies on the Rossler system and aero-engine stall warning verify the effectiveness of the proposed approach.

Background China has 129 dialects with Mandarin as the standard and Chaoshan as the major dialect of the Chaoshan region in Guangdong. This study aimed to describe the dialect competence and usage, communication difficulty, impact of linguistic barriers, and subjective experience in healthcare. Methods Healthcare providers ( n  = 234) and healthcare consumers ( n  = 483) at two tertiary teaching hospitals in Shantou, Chaoshan region participated in an anonymous survey. Results Chaoshan and Mandarin were spoken respectively by ca. 80% and 6.1% of the participants. Monolinguals accounted for 28.5%, including 16.8% of Chaoshan-speaking healthcare providers and 18% of Mandarin-speaking healthcare consumers. The monolinguals preferentially used their competent dialect ( Ps  < 0.001) and had significant communication difficulties ( Ps  < 0.0001), with the mean (SD) score of 3.06 (0.96) out of 4 with Mandarin for healthcare providers and 2.18 (1.78) and 1.64 (1.40) with Mandarin and Chaoshan, respectively, for healthcare consumers. The monolingual healthcare providers perceived significant negative impacts of linguistic barriers on the entire healthcare delivery process ( Ps  < 0.0001). Regression analyses showed the length of stay in the Chaoshan region as a protective factor of linguistic barrier with a limited protective effect. Conclusions This is the first report of significant linguistic barriers in healthcare imposed by Mandarin and Chaoshan dialects in Chaoshan, China. With perceived adverse impacts on the entire healthcare delivery and risks to the healthcare quality and burden, interventions such as professional interpreter service, service-learning interpreter program, or mobile interpreting apps that are medically accurate and culturally sensitive are suggested for dialectally diverse China.

A three-dimensional (3D) fully-coupled fluid-structure model has been developed in this study to calculate the impact force of tsunamis on a flexible structure considering fluid-structure interactions. The propagation of a tsunami is simulated by solving the 3D Navier–Stokes equations using a finite volume method with the volume-of-fluid technique. The structure motion under the tsunami impact force is simulated by solving the motion equation using the generalized alpha method. The structure motion is fed back into the fluid solver via a technique that combines a sharp-interface immersed boundary method with the cut-cell method. The flow model predicts accurate impact forces of dam-break flows on rigid blocks in three experimental cases. The fully coupled 3D flow-structure model is tested with experiments on a large-scale (1:5) model bridge under nonbreaking and breaking solitary waves. The simulated wave propagation and structure restoring forces generally agree well with the measured data. Then, the fully-coupled fluid-structure model is compared with an uncoupled model and applied to assess the effect of flexibility on structure responses to tsunami loading, showing that the restoring force highly depends on the dynamic characteristics of the structure and the feedback coupling between fluid and structure. The maximum hydrodynamic and restoring forces decrease with increasing structure flexibility.

Transforming growth factor beta-inducing factor 1 (TGIF1) was reported to be dysregulated in several types of cancer. However, its expression pattern and functions in nonsmall cell lung cancer (NSCLC) remained unknown. In the present study, the expression of TGIF1 was found to be elevated in the clinical NSCLC tissues. TGIF1 promoted the growth and migration of NSCLC cells, while knocking down the expression of TGIF1 inhibited the growth and migration of NSCLC cells. Moreover, downregulation of TGIF1 impaired the metastasis of NSCLC cells. In the study for the molecular mechanisms, it was found that TGIF1 positively regulated beta-catenin/TCF signaling. In summary, our study demonstrated the oncogenic role of TGIF1 in NSCLC, and TGIF1 might be a therapeutic target for NSCLC.

Objective Osseous metastasis (OM) is the second most common site of thyroid cancer distant metastasis and presents a poor prognosis. Accurate prognostic estimation for OM has clinical significance. Ascertain the risk factors for survival and develop an effective model to predict the 3-year, 5-year overall survival (OS) and cancer-specific survival (CSS) for thyroid cancer patients with OM. Methods We retrieved the information of patients with OMs between 2010 and 2016 from the Surveillance, Epidemiology, and End Result Program. The Chi-square test, and univariate and multivariate Cox regression analyses were performed. Four machine learning (ML) algorithms, which were most commonly used in this field, were applied. Result A total of 579 patients having OMs were eligible. Advanced age, tumor size ≥ 40 mm, combined with other distant metastasis were associated with worse OS in DTC OMs patients. Radioactive iodine (RAI) significantly improved CSS in both males and females. Among four ML models [logistic regression, support vector machines, extreme gradient boosting, and random forest (RF)], RF had the best performance [area under the receiver-operating characteristic curve: 0.9378 for 3-year CSS, 0.9105 for 5-year CSS, 0.8787 for 3-year OS, 0.8909 for 5-year OS]. The accuracy and specificity of RF were also the best. Conclusions RF model shall be used to establish an accurate prognostic model for thyroid cancer patients with OM, not only from the SEER cohort but also intended for all thyroid cancer patients in the general population, which may be applicable in clinical practice in the future.