National Natural Science Foundation of China

In recent decades, there has been a tendency toward increased summer floods in south China, increased drought in north China, and moderate cooling in China and India while most of the world has been warming. We used a global climate model to investigate possible aerosol contributions to these trends. We found precipitation and temperature changes in the model that were comparable to those observed if the aerosols included a large proportion of absorbing black carbon ("soot"), similar to observed amounts. Absorbing aerosols heat the air, alter regional atmospheric stability and vertical motions, and affect the large-scale circulation and hydrologic cycle with significant regional climate effects.

Abstract To bridge the gaps between traditional mesoscale modelling and microscale modelling, the National Center for Atmospheric Research, in collaboration with other agencies and research groups, has developed an integrated urban modelling system coupled to the weather research and forecasting (WRF) model as a community tool to address urban environmental issues. The core of this WRF/urban modelling system consists of the following: (1) three methods with different degrees of freedom to parameterize urban surface processes, ranging from a simple bulk parameterization to a sophisticated multi‐layer urban canopy model with an indoor–outdoor exchange sub‐model that directly interacts with the atmospheric boundary layer, (2) coupling to fine‐scale computational fluid dynamic Reynolds‐averaged Navier–Stokes and Large‐Eddy simulation models for transport and dispersion (T&D) applications, (3) procedures to incorporate high‐resolution urban land use, building morphology, and anthropogenic heating data using the National Urban Database and Access Portal Tool (NUDAPT), and (4) an urbanized high‐resolution land data assimilation system. This paper provides an overview of this modelling system; addresses the daunting challenges of initializing the coupled WRF/urban model and of specifying the potentially vast number of parameters required to execute the WRF/urban model; explores the model sensitivity to these urban parameters; and evaluates the ability of WRF/urban to capture urban heat islands, complex boundary‐layer structures aloft, and urban plume T&D for several major metropolitan regions. Recent applications of this modelling system illustrate its promising utility, as a regional climate‐modelling tool, to investigate impacts of future urbanization on regional meteorological conditions and on air quality under future climate change scenarios. Copyright © 2010 Royal Meteorological Society

ABSTRACT We study the impact of directors with foreign experience on firm performance in emerging markets. Using a unique data set from China, we exploit the introduction of policies to attract talented emigrants and increase the supply of individuals with foreign experience in different provinces at different times. We document that performance increases after firms hire directors with foreign experience and identify the channels through which the emigration of talent may lead to a brain gain. Our findings provide evidence on how directors transmit knowledge about management practices and corporate governance to firms in emerging markets.

Understanding how gut flora influences gut-brain communications has been the subject of significant research over the past decade. The broadening of the term "microbiota-gut-brain axis" from "gut-brain axis" underscores a bidirectional communication system between the gut and the brain. The microbiota-gut-brain axis involves metabolic, endocrine, neural, and immune pathways which are crucial for the maintenance of brain homeostasis. Alterations in the composition of gut microbiota are associated with multiple neuropsychiatric disorders. Although a causal relationship between gut dysbiosis and neural dysfunction remains elusive, emerging evidence indicates that gut dysbiosis may promote amyloid-beta aggregation, neuroinflammation, oxidative stress, and insulin resistance in the pathogenesis of Alzheimer's disease (AD). Illustration of the mechanisms underlying the regulation by gut microbiota may pave the way for developing novel therapeutic strategies for AD. In this narrative review, we provide an overview of gut microbiota and their dysregulation in the pathogenesis of AD. Novel insights into the modification of gut microbiota composition as a preventive or therapeutic approach for AD are highlighted.

Abstract. Land-cover change has been identified as an important cause or driving force of global climate change and is a significant research topic. Over the past few decades, global land-cover mapping has progressed; however, long-time-series global land-cover-change monitoring data are still sparse, especially those at 30 m resolution. In this study, we describe GLC_FCS30D, a novel global 30 m land-cover dynamics monitoring dataset containing 35 land-cover subcategories and covering the period 1985–2022 in 26 time steps (maps were updated every 5 years before 2000 and annually after 2000). GLC_FCS30D has been developed using continuous change detection and all available Landsat imagery based on the Google Earth Engine platform. Specifically, we first take advantage of the continuous change-detection model and the full time series of Landsat observations to capture the time points of changed pixels and identify the temporally stable areas. Then, we apply a spatiotemporal refinement method to derive the globally distributed and high-confidence training samples from these temporally stable areas. Next, local adaptive classification models are used to update the land-cover information for the changed pixels, and a temporal-consistency optimization algorithm is adopted to improve their temporal stability and suppress some false changes. Further, the GLC_FCS30D product is validated using 84 526 globally distributed validation samples from 2020. It achieves an overall accuracy of 80.88 % (±0.27 %) for the basic classification system (10 major land-cover types) and 73.04 % (±0.30 %) for the LCCS (Land Cover Classification System) level-1 validation system (17 LCCS land-cover types). Meanwhile, two third-party time-series datasets used for validation from the United States and Europe Union are also collected for analyzing accuracy variations, and the results show that GLC_FCS30D offers significant stability in terms of variation across the accuracy time series and achieves mean accuracies of 79.50 % (±0.50 %) and 81.91 % (±0.09 %) over the two regions. Lastly, we draw conclusions about the global land-cover-change information from the GLC_FCS30D dataset; namely, that forest and cropland variations have dominated global land-cover change over past 37 years, the net loss of forests reached about 2.5 million km2, and the net gain in cropland area is approximately 1.3 million km2. Therefore, the novel dataset GLC_FCS30D is an accurate land-cover-dynamics time-series monitoring product that benefits from its diverse classification system, high spatial resolution, and long time span (1985–2022); thus, it will effectively support global climate change research and promote sustainable development analysis. The GLC_FCS30D dataset is available via https://doi.org/10.5281/zenodo.8239305 (Liu et al., 2023).

AIM: To investigate the relationship between the gut-liver axis and nonalcoholic fatty liver disease (NAFLD), we performed a meta-analysis to evaluate the effects of probiotic therapy in NAFLD. METHODS: We searched PubMed, Medline, Embase, Web of Science, the Cochrane Library and Chinese Biomedicine Database for all relevant randomized controlled trials on probiotics in patients with NAFLD/nonalcoholic steatohepatitis (NASH). A statistical analysis was performed using RevMan 5.0 software. RESULTS: Four randomized trials involving 134 NAFLD/NASH patients were included. The results showed that probiotic therapy significantly decreased alanine aminotransferase (ALT), aspartate transaminase (AST), total-cholesterol (T-chol), high density lipoprotein (HDL), tumor necrosis factor (TNF)-α and homeostasis model assessment of insulin resistance (HOMA-IR) [ALT: weighted mean difference (WMD) -23.71, 95%CI: -33.46--13.95, P < 0.00001; AST: WMD -19.77, 95%CI: -32.55--7.00, P = 0.002; T-chol: WMD -0.28, 95%CI: -0.55--0.01, P = 0.04; HDL: WMD -0.09, 95%CI: -0.16-0.01, P = 0.03; TNF-α: WMD -0.32, 95%CI: -0.48--0.17, P < 0.0001; HOMA-IR: WMD -0.46, 95%CI: -0.73--0.19, P = 0.0008]. However, the use of probiotics was not associated with changes in body mass index (BMI), glucose (GLU) and low density lipoprotein (LDL) (BMI: WMD 0.05, 95%CI: -0.18-0.29, P = 0.64; GLU: WMD 0.05, 95%CI: -0.25-0.35, P = 0.76; LDL: WMD -0.38, 95%CI: -0.78-0.02, P = 0.06). CONCLUSION: Probiotic therapies can reduce liver aminotransferases, total-cholesterol, TNF-α and improve insulin resistance in NAFLD patients. Modulation of the gut microbiota represents a new treatment for NAFLD.

It is now more than 3 years since the ratification of the 2030 Agenda for Sustainable Development framework [1] but, according to the latest progress report, unless the rate of progress increases, it is challenging that all of the Sustainable Development Goals (SDGs) will be achieved by 2030 [2]. With only 11 years remaining until the 2030 deadline, it has become imperative to develop a plan of action to enable the full agenda to be realized. Specifically, there is an urgent need for a holistic approach to clarify the interrelationships between the 17 SDGs, while also taking into account their complexity and their sometimes mutually reinforcing or conflicting nature. Trying to achieve these goals separately in succession is nonsensical, but pursuing them simultaneously is impractical [3]. The fundamental logic underlying these complex relationships between the goals must be systematically expressed; only then will we be able to fully achieve them. Many studies have been conducted on the interlinkages among these 17 ‘holistic’ and ‘indivisible’ goals, and they have taken a range of different approaches. These include applying a nexus approach to the various goals [4], investigating the degree of interactions among the different goals [5], employing network analysis [6] and even developing a sustainable-development model [7]. However, these studies have only analysed the synergies and trade-offs among the various goals, and they have usually championed the goals believed to be the most important while often ignoring the importance of others. This situation has made it impossible for policymakers to recommend ways in which to holistically achieve the full set of SDGs. Here, we regard sustainable development as a product of society that is produced through the cooperation of society as a whole to achieve a balance between human development and environmental protection. Given that a society typically acts to maximize benefits while minimizing production inputs through the continuous accumulation of experiences, including both technological innovation and institutional change, we divided the 17 SDGs into three categories: essential needs, expected objectives and governance (Fig.1). We then used this novel ‘matrix’ approach to analyse the complex framework of interactions among the various SDGs, with the overall objective of promoting a coherent policy. SDG categories: essential needs, governance and objectives. Sustainable development can be seen as a social product. Through appropriate governance, providing maximum outputs with minimal inputs can be realized. For this process to work, minimizing essential needs to improve resource-use efficiency will depend heavily on natural science and technological innovation. Realizing maximum expected goals to better equitably effect the distribution of goods and services will depend more heavily on social science or ethical constraints. The first category—essential needs—represents the basic guarantee of human survival as part of the intrinsic right to realize sustainable development. This category includes food (SDG 2), water (SDG 6) and energy (SDG 7) resources, all of which require ecosystem services provided by land (SDG 15) and the oceans (SDG 14). To achieve sustainable development, these resources must be able to sustain human survival for a long period of time. This requires minimizing their use by reducing resource waste and improving resource-use efficiency—two fundamental requirements for sustainable development in the Anthropocene to both satisfy the needs of the present society and safeguard Earth's life-support system [8]. Scientists and engineers are required to devise innovative solutions to ensure the provision of these essential needs at their lowest optimal rates of consumption and to alleviate the current global-resource crisis by replacing consumption with recycling. Traditional methods of analysis suggest that there is a trade-off between food and energy, especially with regard to water-resource competition [3]. For example, in water-limited regions, industries that require the use of massive amounts of energy not only require water to cool machinery, but also put groundwater and other water resources at risk of contamination, ultimately impacting local food production. Innovation in production technology is the most effective measure by which to overcome such a trade-off between these food and water targets [3]. The development and implementation of these SDGs will rely heavily on scientific contributions at national, regional and global scales; there is also the aspect of the equitable distribution of these essential needs to consider. Such a distribution requires engineering-based improvements in transportation, but also increased assistance from developed to developing countries and regions. Meeting essential needs guarantees human survival. However, only by meeting the second category of goals, namely expected objectives, can we hope to live prosperous and happy lives. These objectives include living poverty-free (SDG 1) while guaranteeing health (SDG 3), education (SDG 4), gender equality (SDG 5), economic and labour rights (SDG 8) and social equality (SDG 10) and developing a more egalitarian and inclusive society (SDG 16). Unlike the goals in the essential-needs category, which will generally be achieved through an improvement in resource-use efficiency, the key to achieving goals in the expected-objectives category is to reform the manner in which goods and services are distributed. Achieving these goals will rely more on institutional changes, which will necessitate inputs from both the social science and ethics domains [9]. For example, China's 9-year compulsory education system is crucial in achieving SDG 4 (quality education), but further institutional change is required to make higher education more affordable. Similarly, all of the goals in this category must be addressed at the institutional level, which will require great effort from researchers in fields such as sociology. Diverse geographical, political and economic environments and their relative contexts will present various challenges that restrict the distribution of benefits derived from the SDGs. Therefore, it is necessary to understand the needs and concerns of different groups and individuals, especially in areas subject to ethical constraints. Social disciplines, such as psychology and economics, will undoubtedly play a decisive role in policymaking. The third category is governance, which encompasses the effective regulation of competitive relationships and the establishment of equitable rules and systems that will guarantee meeting at least a minimum number of essential needs while maximizing the expected objectives. In this category, building disaster-resistant infrastructure (SDG 9) and sustainable cities and communities (SDG 11) can effectively guarantee the provision of essential needs while maintaining stable economic growth, which is the foundation of social stability. At the same time, establishing responsible production and consumption models (SDG 12) and climate-change control standards (SDG 13) is essential to control emissions from agricultural production and energy consumption. Finally, strengthening global partnerships (SDG 17) can help complement the mutual advantages gained by both developed and developing countries, which will greatly contribute to the implementation of all 17 SDGs. Given that many environmental and social problems are typically derived from the failure of economic models or management systems [10], appropriate measures are necessary to provide for essential needs and to achieve the desired goals that contribute to human well-being. However, effective governance relies heavily on interdisciplinary guidance, which is to say that inputs from the natural sciences and social sciences are equally important in this regard. For example, in the case of SDG 12 (responsible consumption and production), technological advances could boost the production of clean-energy (green) vehicles. However, the promotion and application of such vehicles will require support from increased public education and preferential policies. Hence, effective governance measures necessitate interdisciplinary collaboration between the various stakeholders, such as scientists, policymakers and entrepreneurs [11]. The relationships between the 17 SDGs are inherently complex. Quantitative-analysis methods, such as the study of correlations and different scenarios, are not always reliable due to the lack of monitoring data for some of the sustainable-development indicators [12]. Addi-tionally, theoretical analyses of the relationships between the various SDGs through the construction of a nexus framework have also been criticized for being subjective, which limits the application of research results [13]. However, viewing the relationships between the SDGs from different perspectives is important in promoting theoretical innovation and adopting SDGs in national policies requires objective evidence [14]. The fundamental difference between our approach and those used in other studies is that we take a holistic view of the relationships among the 17 SDGs, rather than simply analysing their synergies and trade-offs. This approach ensures the comprehensive implementation of the goals and is in contrast to the approach of using a few important indicators, which may not account for the integral application of sustainable development. The purpose of dividing the 17 SDGs into three distinct categories is to simplify the inherently complex relationships between them. This division will also help to improve the management of the various goals, while also promoting interdependent cooperation among different managing bodies and avoiding the narrow perspectives that researchers have used in the past. Sustainable development is the most important objective of modern times and can only be achieved through a holistic societal approach. Such a broad-based approach is also an effective means to accelerate its realization through a reasonable division of labour and cooperation among managing bodies. Scientific and technological progress can contribute to improving production efficiency to better meet essential needs, providing a greater number of ecosystem services without exceeding the carrying capacity of our planet [15]. Improvements in education, health and social equality (i.e. expected objectives), however, can only be achieved through institutional change, which is strongly influenced by the social sciences. Finally, all of the goals in the governance category are multi-faceted and include meeting the needs of local enterprises and community residents. Moreover, the realization of these goals will help support achieving the objectives in the other two categories. An in-depth discussion of the synergies and trade-offs among the different SDGs is beyond the scope of this paper, and these issues have been comprehensively discussed by others. Although a change in each goal or indicator will have an impact on other goals or indicators, clustering different goals into groups may facilitate the interactions of various managing bodies and improve the operability of goal management. In addition, this approach could also transform the way we manage SDG relationships from among the 17 individual SDGs to among the three categories, thereby reducing analytical difficulties through simplification. However, in future studies, it is inevitable that the relationships among the 17 goals will have to be analysed separately, which will be a key process in further implementing the SDGs. By grouping the 17 SDGs of the 2030 Agenda for Sustainable Development framework into three categories, we showed that the implementation of the goals within a complex global system is an optimization process governed by a compromise between meeting essential needs and maximizing the realization of expected objectives. Although there are overlaps, trade-offs and synergies among the 17 SDGs, if they are considered holistically, negative externalities among each goal can effectively be excluded. Controlling the consumption of essential needs to their lowest optimal rate is the basic foundation of realizing sustainable development, whereas maximizing expected objectives will satisfy peoples’ material and psychological needs and promote environmental protection. Effective measures of governance are the key to successfully maintaining a balance between meeting essential needs and expected objectives, and each country must find a path of development that suits its own national conditions. Finally, policy coherence, whether horizontal (the interactions between different objectives) or vertical (the interactions between different policy levels), can be ensured by applying this analytical framework to both developed and developing countries. This work was supported by the National Key Research and Development Program of China (2017YFA0604701) and Chinese Academy of Sciences (QYZDY-SSW-DQC025).

A measurement of the cosmic ray positron fraction e+/(e++e−) in the energy range of 1–30 GeV is presented. The measurement is based on data taken by the AMS-01 experiment during its 10 day Space Shuttle flight in June 1998. A proton background suppression on the order of 106 is reached by identifying converted bremsstrahlung photons emitted from positrons.

STUDY DESIGN: A systematic review and meta-analysis. OBJECTIVE: The objective of this study was to investigate the incidence of surgical site infection (SSI) in patients following spine surgery and the rate of microorganisms in these cases. SUMMARY OF BACKGROUND DATA: Many studies have investigated the incidence and risk factors of SSI following spinal surgery, whereas no meta-analysis studies have been conducted regarding the comprehensive epidemiological incidence of SSI after spine surgery. METHODS: We searched the PubMed, Embase, and Cochrane Library databases for relevant studies that reported the incidence of SSI after spine surgery, and manually screened reference lists for additional studies. Relevant incidence estimates were calculated. Subgroup analysis, sensitivity analysis, and publication bias assessment were also performed. RESULTS: Our meta-analysis included 27 studies, with 603 SSI cases in 22,475 patients. The pooled SSI incidence was 3.1%. Subgroup analysis revealed that the incidence of superficial SSI was 1.4% and the incidence of deep SSI was 1.7%. Highest incidence (13.0%) was found in patients with neuromuscular scoliosis among the different indications. The incidences of SSI in cervical, thoracic, and lumbar spine were 3.4%, 3.7%, and 2.7%, respectively. Compared with posterior approach surgery (5.0%), anterior approach showed a lower incidence (2.3%) of SSI. Instrumented surgery had a higher incidence of SSI than noninstrumented surgery (4.4% vs. 1.4%). Patients with minimally invasive surgery (1.5%) had a lower SSI incidence than open surgery (3.8%). Lower incidence of SSI was found when vancomycin powder was applied locally during the surgery (1.9%) compared with those not used (4.8%). In addition, the rates of Staphylococcus aureus, Staphylococcus epidermidis, and methicillin-resistant Staphylococci in microbiological culture results were 37.9%, 22.7%, and 23.1%, respectively. CONCLUSION: The pooled incidence of SSI following spine surgery was 3.1%. These figures may be useful in the estimation of the probability of SSI following spine surgery. LEVEL OF EVIDENCE: 3.

BACKGROUND: Fibrosis is the common end stage of most liver diseases, for which, unfortunately, there is no effective treatment available currently. It has been shown that mesenchymal stem cells (MSCs) from bone marrow (BM) could engraft in the lung after bleomycin exposure and ameliorate its fibrotic effects. This study was designed to evaluate the effect of Flk1 MSCs from murine BM (termed here Flk1 mMSCs) on fibrosis formation induced by carbon tetrachloride (CCl4). METHODS: A CCl(4)-induced hepatic fibrosis model was used. Flk1 mMSCs were systemically infused immediately or 1 week after mice were challenged with CCl(4). Control mice received only saline infusion. Fibrosis index and donor-cell engraftment were assessed 2 or 5 weeks after CCl(4) challenge. RESULTS: We found that Flk1 mMSCs transplantation immediately, but not 1 week after exposure to CCl(4), significantly reduced CCl(4)-induced liver damage and collagen deposition. In addition, levels of hepatic hydroxyproline and serum fibrosis markers in mice receiving immediate Flk1 mMSCs transplantation after CCl(4) challenge were significantly lower compared with those of control mice. More importantly, histologic examination suggested that hepatic damage recovery was much better in these immediately Flk1 mMSCs-treated mice. Immunofluorescence, polymerase chain reaction, and fluorescence in situ hybridization analysis revealed that donor cells engrafted into host liver, had epithelium-like morphology, and expressed albumin, although at low frequency. CONCLUSION: These results suggest that Flk1 mMSCs might initiate endogenous hepatic tissue regeneration, engraft into host liver in response to CCl(4) injury, and ameliorate its fibrogenic effects.

Given a geographic query that is composed of query keywords and a location, a geographic search engine retrieves documents that are the most textually and spatially relevant to the query keywords and the location, respectively, and ranks the retrieved documents according to their joint textual and spatial relevances to the query. The lack of an efficient index that can simultaneously handle both the textual and spatial aspects of the documents makes existing geographic search engines inefficient in answering geographic queries. In this paper, we propose an efficient index, called IR-tree, that together with a top-k document search algorithm facilitates four major tasks in document searches, namely, 1) spatial filtering, 2) textual filtering, 3) relevance computation, and 4) document ranking in a fully integrated manner. In addition, IR-tree allows searches to adopt different weights on textual and spatial relevance of documents at the runtime and thus caters for a wide variety of applications. A set of comprehensive experiments over a wide range of scenarios has been conducted and the experiment results demonstrate that IR-tree outperforms the state-of-the-art approaches for geographic document searches.

Internet of Medical Things (IoMT) can connect many medical imaging equipment to the medical information network to facilitate the process of diagnosing and treating doctors. As medical image contains sensitive information, it is of importance yet very challenging to safeguard the privacy or security of the patient. In this work, a deep-learning-based image encryption and decryption network (DeepEDN) is proposed to fulfill the process of encrypting and decrypting the medical image. Specifically, in DeepEDN, the cycle-generative adversarial network (Cycle-GAN) is employed as the main learning network to transfer the medical image from its original domain into the target domain. The target domain is regarded as “hidden factors” to guide the learning model for realizing the encryption. The encrypted image is restored to the original (plaintext) image through a reconstruction network to achieve image decryption. In order to facilitate the data mining directly from the privacy-protected environment, a region of interest (ROI)-mining network is proposed to extract the interesting object from the encrypted image. The proposed DeepEDN is evaluated on the chest X-ray data set. Extensive experimental results and security analysis show that the proposed method can achieve a high level of security with a good performance in efficiency.

AIM: To investigate the photocatalytic killing effect of photoexcited TiO(2) nanoparticles on human colon carcinoma cell line (Ls-174-t) and to study the mechanism underlying the action of photoexcited TiO(2) nanoparticles on malignant cells. METHODS: Ls-174-t human colon carcinoma cells were cultured in RPMI 1640 medium supplemented with 199 mL/L calf serum in a humidified incubator with an atmosphere of 50 mL/L CO(2) at 37 degrees C. Viable cells in the samples were measured by using the MTT method. A GGZ-300 W high pressure Hg lamp with a maximum ultraviolet-A (UVA, 320-400 nm) irradiation peak at 365 nm was used as light source in the photocatalytic killing test. RESULTS: The photocatalytic killing of Ls-174-t cells was carried out in vitro with TiO(2) nanoparticles. The killing effect was weak by using UVA irradiation without TiO(2) nanoparticles. In our studies, the photocatalytic killing effect was correlated with the concentration of TiO(2) and illumination time. Once TiO(2) was added, Ls-174-t cells were killed at a much higher rate. In the presence of 1 000 microg/mL TiO(2), 44% of cells were killed after 10 min of UVA irradiation, and 88% of cells were killed after 30 min of UVA irradiation. CONCLUSION: When the concentration of TiO(2) is below 200 microg/mL, the photocatalytic killing effect on human colon carcinoma cells is almost the same as that of UVA irradiation alone. When the concentration of TiO(2) is above 200 microg/mL, the remarkable killing effect of photoexcited TiO(2) nanoparticles can be found.

It has been previously demonstrated that genistein exhibits anticancer activity against breast cancer. However, the precise mechanisms underlying the anticancer effect of genistein, in particular the epigenetic basis, remain unclear. In this study, we investigated whether genistein could modulate the DNA methylation status and expression of cancer-related genes in breast cancer cells. We treated MCF-7 and MDA-MB-231 human breast cancer cells with genistein in vitro. We found that genistein decreased the levels of global DNA methylation, DNA methyltransferase (DNMT) activity and expression of DNMT1. Yet, the expression of DNMT3A and DNMT3B showed no significant change. Using molecular modeling, we observed that genistein might directly interact with the catalytic domain of DNMT1, thus competitively inhibiting the binding of hemimethylated DNA to the catalytic domain of DNMT1. Furthermore, genistein decreased DNA methylation in the promoter region of multiple tumor suppressor genes (TSGs) such as ataxia telangiectasia mutated (ATM), adenomatous polyposis coli (APC), phosphatase and tensin homolog (PTEN), mammary serpin peptidase inhibitor (SERPINB5), and increased the mRNA expression of these genes. However, we detected no significant changes in the DNA methylation status or mRNA expression of stratifin (SFN). These results suggest that the anticancer effect of genistein on breast cancer may be partly due to its ability to demethylate and reactivate methylation-silenced TSGs through direct interaction with the DNMT1 catalytic domain and inhibition of DNMT1 expression.

A self-driven closed-loop parallel testing system implements more challenging tests to accelerate evaluation and development of autonomous vehicles.

In this article local empirical likelihood-based inference for a varying coefficient model with longitudinal data is investigated. First, we show that the naive empirical likelihood ratio is asymptotically standard chi-squared when undersmoothing is employed. The ratio is self-scale invariant and the plug-in estimate of the limiting variance is not needed. Second, to enhance the performance of the ratio, mean-corrected and residual-adjusted empirical likelihood ratios are recommended. The merit of these two bias corrections is that without undersmoothing, both also have standard chi-squared limits. Third, a maximum empirical likelihood estimator (MELE) of the time-varying coefficient is defined, the asymptotic equivalence to the weighted least-squares estimator (WLSE) is provided, and the asymptotic normality is shown. By the empirical likelihood ratios and the normal approximation of the MELE/WLSE, the confidence regions of the time-varying coefficients are constructed. Fourth, when some components are of particular interest, we suggest using mean-corrected and residual-adjusted partial empirical likelihood ratios to construct the confidence regions/intervals. In addition, we also consider the construction of the simultaneous and bootstrap confidence bands. A simulation study is undertaken to compare the empirical likelihood, the normal approximation, and the bootstrap methods in terms of coverage accuracies and average areas/widths of confidence regions/bands. An example in epidemiology is used for illustration.

Covalently bonded ceramics exhibit preeminent properties—including hardness, strength, chemical inertness, and resistance against heat and corrosion—yet their wider application is challenging because of their room-temperature brittleness. In contrast to the atoms in metals that can slide along slip planes to accommodate strains, the atoms in covalently bonded ceramics require bond breaking because of the strong and directional characteristics of covalent bonds. This eventually leads to catastrophic failure on loading. We present an approach for designing deformable covalently bonded silicon nitride (Si 3 N 4 ) ceramics that feature a dual-phase structure with coherent interfaces. Successive bond switching is realized at the coherent interfaces, which facilitates a stress-induced phase transformation and, eventually, generates plastic deformability.

OBJECTIVE: To examine potential biomarkers in Lewis negative patients with pancreatic cancer. BACKGROUND: Carbohydrate antigen 19-9 (CA19-9) is currently the most important and widely used biomarker in pancreatic cancer. However, approximately 5 to 10% of the population are Lewis negative individuals, and they are documented to have scarce or no CA19-9 secretion. Therefore, it is necessary to explore potential biomarkers to compensate for this drawback. METHODS: Lewis genotypes were determined in a large cohort of patients with pancreatic cancer (682 cases) and controls (525 cases) by sequencing the Fucosyltransferase 3 (FUT3) gene from genomic DNA. Potential biomarkers were examined in patients with Lewis negative genotypes and normal subjects. The impact of potential biomarkers on tumor burden and survival was analyzed. RESULTS: Forty-seven (6.9%) patients with pancreatic cancer had Lewis negative genotypes. Carcinoembryonic antigen (CEA) and CA125 had greater sensitivity than other biomarkers in Lewis negative patients with pancreatic cancer [CEA, 63.8%; CA125, 51.1%; CA72-4, 25.5%; CA15-3, 21.3%; CA19-9, 19.1%; CA50, 12.8%; CA242, 10.6%; and alpha-fetoprotein (AFP), 0.0%]. In addition, both CEA (98.0%) and CA125 (93.8%) showed a high specificity. Compared with other biomarkers, CEA (60.9%) was sensitive for stage I, II diseases and CA125 (75.0%) was sensitive for stage III, IV diseases. CEA and CA125 were associated with tumor metastasis and therapeutic response. CONCLUSIONS: CEA and CA125 have the potential to be applied as biomarkers in Lewis negative patients with pancreatic cancer. CEA and CA125 should be routinely measured for all patients with pancreatic cancer.

STUDY DESIGN: A meta-analysis. OBJECTIVE: To investigate whether robot-assisted techniques are superior to conventional techniques in terms of the accuracy of pedicle screw placement and clinical indexes. SUMMARY OF BACKGROUND DATA: Robot-assisted techniques are increasingly applied to spine surgery to reduce the rate of screw misplacement. However, controversy about the superiority of robot-assisted techniques over conventional freehand techniques remains. METHODS: We conducted a comprehensive search of PubMed, EMBASE, and Cochrane Library for potentially eligible articles. The outcomes were evaluated in terms of risk ratio (RR) or standardized mean difference and the associated 95% confidence intervals (CIs). Meta-analysis was performed using the RevMan 5.3 software and subgroup analyses were performed based on the robot type for the accuracy of pedicle screw placement. RESULTS: Nine randomized controlled trials with 696 patients were included in this meta-analysis. The results demonstrated that the robot-assisted technique was more accurate in pedicle screw placement than the freehand technique. Subgroup analyses showed that the TINAVI robot-assisted technique was more accurate in screw positions Grade A (RR, 1.10; 95% CI, 1.06-1.14), Grade B (RR, 0.46; 95% CI, 0.28-0.75), and Grades C + D + E (RR, 0.21; 95% CI, 0.09-0.45) than the freehand technique, whereas the Renaissance robot-assisted technique showed the same accuracy as the freehand technique in screw positions Grade A, Grade B, and Grades C + D + E. Furthermore, the robot-assisted techniques showed equivalent postoperative stay, visual analogue scale scores, and Oswestry disability index scores to those of the freehand technique and shorter intraoperative radiation exposure time, fewer radiation dose and proximal facet violations but longer surgical time than the freehand technique. CONCLUSION: The robot-assisted technique is more accurate in pedicle screw placement than the freehand technique. And TINAVI robot-assisted pedicle screw placement is a more accurate alternative to conventional techniques and the Renaissance robot-assisted procedure. LEVEL OF EVIDENCE: 1.

Abstract Aims It has been assumed that montane species will undergo upslope shifts in response to climate warming and their range sizes are therefore predicted to decrease. However, this view has been challenged because a recent study (Elsen &amp; Tingley, ) indicated that land surface area increases with increasing altitude in some mountains. To test this prediction, we used one of the world's biodiversity hotspots as a study system to examine overall patterns of plant distribution shift in response to climate warming. Location The Hengduan Mountains and adjacent regions. Methods Based on distribution data for 151 species at a resolution of 2.5 arc minutes, we employed ecological niche modelling to model their distributions under the climatic conditions of the Last Glacial Maximum, Current (2017), and 2050 separately. We examined the distributional shifts of these species, especially with respect to altitude and range size, in response to two periods of stepwise climate warming. Results All the montane plants sampled shifted upward during the two warming stages, but not only northward, some shifted westward or in other directions. In contrast with the expected consistent loss of range when shifting upward, 63.6% of the plants expanded their range size continuously since the LGM . Only 11.9% of the plants contracted their range size continuously from the LGM to 2050. Estimates of species richness in the regions studied changed greatly, but in an unbalanced manner, from the LGM to the Current and from the Current to 2050. Main conclusions Numerous montane plants in the Hengduan Mountains are predicted to expand their range sizes as they shift upslope in response to climate warming. Our results highlight the possibility that more available land surface area due to the heterogeneous topography along altitudinal gradients and the adjacent large Qinghai‐Tibet Plateau sensu stricto can mediate the range loss of the montane plants under climate warming. These findings are crucial for estimating the future range sizes of plants and planning biodiversity protection for mountain ecosystems under the anticipated warming of the world's climate.