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Background Although observational studies have established some socioeconomic traits to be independent risk factors for pelvic organ prolapse (POP), they can not infer causality since they are easily biased by confounding factors and reverse causality. Moreover, it remains ambiguous which one or several of socioeconomic traits play predominant roles in the associations with POP risk. Mendelian randomization (MR) overcomes these biases and can even determine one or several socioeconomic traits predominantly accounting for the associations. Objective We conducted a multivariable Mendelian randomization (MVMR) analysis to disentangle whether one or more of five categories of socioeconomic traits, “age at which full-time education completed (abbreviated as “EA”)”, “job involving heavy manual or physical work (“heavy work”)”, “average total household income before tax (income)”, “Townsend deprivation index at recruitment (TDI)”, and “leisure/social activities” exerted independent and predominant effects on POP risk. Methods We first screened single-nucleotide polymorphisms (SNPs) as proxies for five individual socioeconomic traits and female genital prolapse (FGP, approximate surrogate for POP due to no GWASs for POP) to conduct Univariable Mendelian randomization (UVMR) analyses to estimate causal associations of five socioeconomic traits with FGP risk using IVW method as major analysis. Additionally, we conducted heterogeneity, pleiotropy, and sensitivity analysis to assess the robustness of our results. Then, we harvested a combination of SNPs as an integrated proxy for the five socioeconomic traits to perform a MVMR analysis based on IVW MVMR model. Results UVMR analyses based on IVW method identified causal effect of EA (OR 0.759, 95%CI 0.629–0.916, p  = 0.004), but denied that of the other five traits on FGP risk (all p  > 0.05). Heterogeneity analyses, pleiotropy analyses, “leave-one-out” sensitivity analyses and MR-PRESSO adjustments did not detect heterogeneity, pleiotropic effects, or result fluctuation by outlying SNPs in the effect estimates of six socioeconomic traits on FGP risk (all p  > 0.05). Further, MVMR analyses determined a predominant role of EA playing in the associations of socioeconomic traits with FGP risk based on both MVMR Model 1 (OR 0.842, 95%CI 0.744–0.953, p  = 0.006) and Model 2 (OR 0.857, 95%CI 0.759–0.967, p  = 0.012). Conclusion Our UVMR and MVMR analyses provided genetic evidence that one socioeconomic trait, lower educational attainment, is associated with risk of female genital prolapse, and even independently and predominantly accounts for the associations of socioeconomic traits with risk of female genital prolapse.

Effective teaching behavior in physical education has an important impact on the quality of classroom teaching. To overcome the shortcomings in the existing university teaching quality assessment procedure, this paper designs an evaluation model for physical education departments based on long short-term memory networks (LSTM) with the improved particle swarm optimization (PSO). The model is constructed by analyzing the connotation and index system of teacher education teaching quality and constructing a fan-leaf structure model of teacher education teaching quality. Then, an improved PSO-LSTM model is proposed to train the teaching samples. The evaluation model applies the improved LSTM model and optimizes the network structure by dynamically adjusting the learning rate. The model is then optimized for the number of neurons and iterations of the network using the improved PSO. The results of the experiment indicate that the proposed model is effective in evaluating the quality of physical education. Moreover, the model analysis’ accuracy has greatly improved. This helps teachers have a comprehensive understanding of classroom dynamics and improve their professional competence and classroom teaching quality.

Investigators in China report the results of an open-label, randomized clinical trial of lopinavir–ritonavir for the treatment of Covid-19 in 199 infected adult patients. The primary end point was the time to clinical improvement.

Effects of nitrogen (N) and phosphorus (P) from different sources and at different concentrations on the growth of Levanderina fissa (= Gyrodinium instriatum) were studied in laboratory conditions. The findings might explain the recurrent blooms of this species in Pearl River Estuary, China. Results showed that nutrient limitation significantly inhibited the growth of L. fissa. The values of specific growth rate (μmax) and half-saturation nutrient concentration (KS) were 0.37 divisions/d and 8.49 μmol L?1 for N, and 0.39 divisions/d and 1.99 μmol L?1 for P, respectively. Based on KS values, dissolved inorganic N level in PRE was sufficient to support the high proliferation of L. fissa, while dissolved inorganic P concentration was far lower than the minimum requirement for its effective growth. L. fissa was not able to utilize dissolved organic N (DON) compounds such as urea, amino acids, and uric acid. However, it grew well by using a wide variety of dissolved organic P (DOP) sources like nucleotides, glycerophosphate, and 4-nitrophenylphosphate. The results from this study suggested that the ability in DOP utilization of L. fissa offers this species a competitive advantage in phytoplankton communities. The high level and continuous supply of DIN, enrichment of DOP, together with warm climate and low salinity in the Pearl River Estuary provided a suitable nutrient niche for the growth of L. fissa, and resulted in the recurrent blooms in the estuary.

Effects of nitrogen（N） and phosphorus（P） from different sources and at different concentrations on the growth of Levanderina fissa（= Gyrodinium instriatum） were studied in laboratory conditions. The findings might explain the recurrent blooms of this species in Pearl River Estuary, China. Results showed that nutrient limitation significantly inhibited the growth of L. fissa. The values of specific growth rate（μmax） and half-saturation nutrient concentration（KS） were 0.37 divisions/d and 8.49 μmol L-1 for N, and 0.39 divisions/d and 1.99 μmol L-1 for P, respectively. Based on KS values, dissolved inorganic N level in PRE was sufficient to support the high proliferation of L. fissa, while dissolved inorganic P concentration was far lower than the minimum requirement for its effective growth. L. fissa was not able to utilize dissolved organic N（DON） compounds such as urea, amino acids, and uric acid. However, it grew well by using a wide variety of dissolved organic P（DOP） sources like nucleotides, glycerophosphate, and 4-nitrophenylphosphate. The results from this study suggested that the ability in DOP utilization of L. fissa offers this species a competitive advantage in phytoplankton communities. The high level and continuous supply of DIN, enrichment of DOP, together with warm climate and low salinity in the Pearl River Estuary provided a suitable nutrient niche for the growth of L. fissa, and resulted in the recurrent blooms in the estuary.

Carbon dots (CDs) have a wide range of applications in chemical, physical and biomedical research fields. We are particularly interested in the use of CDs as fluorescence nanomaterials for targeted tumor cell imaging. One of the important aspects of success is to enhance the fluorescence quantum yields (QY) of CDs as well as increase their targetability to tumor cells. However, most of the reported CDs are limited by relative low QY. In the current study, for the first time, one-step synthesis of highly luminescent CDs by using folic acid (FA) as single precursor was obtained in natural water through hydrothermal method. The as-prepared CDs exhibited QY as high as 94.5% in water, which is even higher than most of organic fluorescent dyes. The obtained CDs showed excellent photoluminescent activity, high photostability and favorable biocompatibility. The FA residuals in CDs led to extraordinary targetability to cancer cells and promoted folate receptor-mediated cellular uptake successfully, which holds a great potential in biological and bioimaging studies.

Spatial analysis of microbiomes at single cell resolution with high multiplexity and accuracy has remained challenging. Here we present spatial profiling of a microbiome using sequential error-robust fluorescence in situ hybridization (SEER-FISH), a highly multiplexed and accurate imaging method that allows mapping of microbial communities at micron-scale. We show that multiplexity of RNA profiling in microbiomes can be increased significantly by sequential rounds of probe hybridization and dissociation. Combined with error-correction strategies, we demonstrate that SEER-FISH enables accurate taxonomic identification in complex microbial communities. Using microbial communities composed of diverse bacterial taxa isolated from plant rhizospheres, we apply SEER-FISH to quantify the abundance of each taxon and map microbial biogeography on roots. At micron-scale, we identify clustering of microbial cells from multiple species on the rhizoplane. Under treatment of plant metabolites, we find spatial re-organization of microbial colonization along the root and alterations in spatial association among microbial taxa. Taken together, SEER-FISH provides a useful method for profiling the spatial ecology of complex microbial communities in situ. Spatial analysis of microbiomes at single cell resolution is challenging. Here the authors report a highly multiplexed method for spatial profiling, sequential error-robust fluorescence in situ hybridisation (SEER-FISH), and show that this allows mapping of microbial communities at micron-scale.

Polarity is an integral microenvironment parameter in biological systems closely associated with a multitude of cellular processes. Abnormal polarity variations accompany the initiation and development of pathophysiological processes. Thus, monitoring the abnormal polarity is of scientific and practical importance. Current state-of-the-art monitoring techniques are primarily based on fluorescence imaging which relies on a single emission intensity and may cause inaccurate detection due to heterogeneous accumulation of the probes. Herein, we report carbon dots (CDs) with ultra-sensitive responses to polarity. The CDs exhibit two linear relationships: one between fluorescence intensity and polarity and the other between polarity and the maximum emission wavelength. The emission spectrum is an intrinsic property of the probes, independent of the excitation intensity or probe concentration. These features enable two-color imaging/quantitation of polarity changes in lipid droplets (LDs) and in the cytoplasm via in situ emission spectroscopy. The probes reveal the polarity heterogeneity in LDs which can be applied to make a distinction between cancer and normal cells, and reveal the polarity homogeneity in cytoplasm.

With the increasing demand for location-based services, Wi-Fi-based indoor positioning technology has attracted much attention in recent years because of its ubiquitous deployment and low cost. Considering that Wi-Fi signals fluctuate greatly with time, extracting robust features of Wi-Fi signals is the key point to maintaining good positioning accuracy. To handle the dynamic fluctuation with time and sparsity of Wi-Fi signals, we propose an SDAE (Stacked Denoising Autoencoder)-based feature extraction method, which can obtain a robust and time-independent Wi-Fi fingerprint by learning the reconstruction distribution from a raw Wi-Fi signal and an artificial-noise-added Wi-Fi signal. We also leverage the strong representation ability of MLP (Multi-Layer Perceptron) to build a regression model, which maps the extracted features to the corresponding location. To fully evaluate the performance of our proposed algorithm, three datasets are applied, which represent three different scenarios, namely, spacious area with time interval, no time interval, and complex area with large time interval. The experimental results confirm the validity of our proposed SDAE-based feature extraction method, which can accurately reflect Wi-Fi signals in corresponding locations. Compared with other regression models, our proposed regression model can better map the extracted features to the target position. The average positioning error of our proposed algorithm is 4.24 m when there is a 52-day interval between training dataset and testing dataset. That confirms that the proposed algorithm outperforms other state-of-the-art positioning algorithms when there is a large time interval between training dataset and testing dataset.