The purpose of this study was to analyse the clinical characteristics of patients with severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) PCR re-positivity after recovering from coronavirus disease 2019 (COVID-19). Patients (n = 1391) from Guangzhou, China, who had recovered from COVID-19 were recruited between 7 September 2021 and 11 March 2022. Data on epidemiology, symptoms, laboratory test results and treatment were analysed. In this study, 42.7% of recovered patients had re-positive result. Most re-positive patients were asymptomatic, did not have severe comorbidities, and were not contagious. The re-positivity rate was 39%, 46%, 11% and 25% in patients who had received inactivated, mRNA, adenovirus vector and recombinant subunit vaccines, respectively. Seven independent risk factors for testing re-positive were identified, and a predictive model was constructed using these variables. The predictors of re-positivity were COVID-19 vaccination status, previous SARs-CoV-12 infection prior to the most recent episode, renal function, SARS-CoV-2 IgG and IgM antibody levels and white blood cell count. The predictive model could benefit the control of the spread of COVID-19.

Microplastics (MPs) pollution has been a hot research topic in recent years. MPs are ubiquitous throughout the ecological environment and are eventually accumulated in organisms through inhalation or ingestion. However, given that MPs are inert pollutants, their effects on organisms are not clear. In previous study, we have investigated the effects of polyethylene terephthalate MPs on physiology of Drosophila. What is the effect of polypropylene microplastics (PP-MPs)? The results of our experiments show that being exposed to high concentration of PP-MPs have significant effect on Drosophila. PP-MPs exposure can significantly increase locomotor activity and shorten the time of group sleep in Drosophila. In the presence of high concentrations of PP-MPs, the triglyceride content was reduced in females and their ability of egg production was affected. However, there was no significant effect on the level of protein and carbohydrate, or on the food intake. Our experimental results can provide some preliminary data for assessing the potential hazard of PP-MPs to other organisms.

The random fiber laser (RFL) has been an excellent platform for exploring novel optical dynamics and developing new functional optoelectronic devices. However, it is challenging for RFLs to regulate their emission into regular narrow pulses due to their intrinsic randomness. Here, through engineering the laser configuration (cavity Q value, gain distribution and nonlinearity), we demonstrate that narrow (~2.5 ns) pulses with record peak power as high as 64.3  kW are achieved from a self-Q-switched random ytterbium fiber laser. Based on high intracavity intensity and efficient interplay of multiple nonlinear processes (stimulated Brillouin scattering, stimulated Raman scattering and four-wave mixing), an over-one-octave visible-near-infrared (NIR) Raman-frequency comb is generated from single-mode silica fibers for the first time. After spectrally filtering the Raman peaks, wavelength-tunable pulses with durations of several hundreds of picoseconds are obtained. Such a high-peak-power random Q-switched fiber laser and wide frequency comb in the visible-NIR region can find applications in diverse areas, such as spectroscopy, biomedical imaging and quantum information.

The dependence of fishbone cycle on energetic particle intensity has been investigated in EAST low-magnetic-shear plasmas. It is observed that the fishbone mode growth rate, saturation amplitude as well as fishbone cycle frequency clearly increase with increasing neutral beam injection (NBI) power. Moreover, enhanced electron density and temperature perturbations as well as energetic particle loss were observed with greater injected NBI power. Simulation results using M3D-K code show that as the NBI power increases, the resonant frequency and the energy of the resonant particles become higher, and the saturation amplitude of the mode also changes, due to the non-perturbative energetic particle contribution. The relationship between the calculated energetic particle pressure ratio and fishbone cycle frequency is obtained as ${f_{\textrm{FC}}} = 2.2{(1000{\beta _{\textrm{ep,calc}}} - 0.1)^{5.9 \pm 0.5}}$. Results consistent with the experimental observations have been achieved based on a predator–prey model.

This study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. Machine-learning methods (Random Forest and XGboost) were used to rank clinical features for mortality risk. Multivariate logistic regression models were applied to identify clinical features with statistical significance. The predictors of mortality were lactate dehydrogenase (LDH), C-reactive protein (CRP) and age based on 500 bootstrapped samples. A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission.

This paper presents a soft robot which can imitate the crawling locomotion of an earthworm. Locomotion of the robot can be achieved by expanding and contracting the body that is made of flexible material. A link of the earthworm-like robot is combined with three modules, and a multi-cavity earthworm-like soft robot is combined with multiple links. The multiple links of the earthworm-like soft robot are fabricated by silicone in the three-dimensional printed customized molds. Experiments on a single module, two-links, and three-links show that the soft robot can move and bend on condition of modules extension and contraction in a specified gait. The development of the earthworm-like soft robot shows a great prospect in many complicated environments such as pipeline detection.

Infant colic is a condition of unknown cause which can result in carer distress and attachment difficulties. Recent studies have implicated the gut microbiota in infant colic, and certain probiotics have demonstrated possible efficacy. We aim to investigate whether the intestinal microbiota composition in infants with colic is associated with cry/fuss time at baseline, persistence of cry/fuss at 4-week follow-up, or child behavior at 2 years of age. Fecal samples from infants with colic (n = 118, 53% male) were analyzed using 16S rRNA sequencing. After examining the alpha and beta diversity of the clinical samples, we performed a differential abundance analysis of the 16S data to look for taxa that associate with baseline and future behavior, while adjusting for potential confounding variables. In addition, we used random forest classifiers to evaluate how well baseline gut microbiota can predict future crying time. Alpha diversity of the fecal microbiota was strongly influenced by birth mode, feed type, and child gender, but did not significantly associate with crying or behavioral outcomes. Several taxa within the microbiota (including Bifidobacterium, Clostridium, Lactobacillus, and Klebsiella) associate with colic severity, and the baseline microbiota composition can predict further crying at 4 weeks with up to 65% accuracy. The combination of machine learning findings with associative relationships demonstrates the potential prognostic utility of the infant fecal microbiota in predicting subsequent infant crying problems.

The polymerization of 3,4-ethylenedioxythiophene (EDOT) onto nanosilica (SiO2) was synthesized in this study by using supercritical carbon dioxide (SCCO2). With the addition of dopants of p-toluenesulfonic acid (p-TSA) or decylbenzene sulfonic acid (DBSA), the PEDOT/SiO2 composite became conductive. The product was characterized by FTIR spectroscopy and the core-shell structure was confirmed through the TEM images. The electrical properties were analyzed by UV-vis absorbance and four-point probe measurement. DBSA is shown as the better dopants with the molar ratio (DBSA/EDOT) of 0.2 at the reaction time of 48 hours. The maximum coating percentage is 63 wt% under the optimal operation conditions at 40oC and 280 bar. The conductivity is tuned up to 6.6×10-2 S/cm after the coating process.

We aimed to investigate the association between plasma retinol and incident cancer among Chinese hypertensive adults. We conducted a nested case–control study, including 231 patients with incident cancer and 231 matched controls during a median 4·5-year follow-up of the China Stroke Primary Prevention Trial. There was a significant, inverse association between retinol levels and digestive system cancer (per 10 μg/dl increases: OR 0·79; 95 % CI 0·69, 0·91). When compared with participants in the first quartile of retinol (< 52·3 μg/dl), a significantly lower cancer risk was found in participants in quartile 2–4 ( ≥ 52·3 μg/dl: OR 0·31; 95 % CI 0·13, 0·71). However, there was a U-shaped association between retinol levels and non-digestive system cancers where the risk of cancers decreased (although not significantly) with each increment of plasma retinol (per 10 μg/dl increases: OR 0·89; 95 % CI 0·60, 1·31) in participants with retinol < 68·2 μg/dl, and then increased significantly with retinol (per 10 μg/dl increase: OR 1·65; 95 % CI 1·12, 2·44) in participants with retinol ≥ 68·2 μg/dl. In conclusion, there was a significant inverse dose–response association between plasma retinol and the risk of digestive system cancers. However, a U-shaped association was observed between plasma retinol and the risk of non-digestive cancers (with a turning point approximately 68·2 μg/dl).

Given the global water challenges, solar-driven steam generation has become a renewed topic recently as an energy-efficient way for clean water production. Here, a hybrid plasmonic structure consisting of a top layer of TiN nanoparticles (NPs) and a bottom layer of mesoporous anodized alumina membrane (AAM) was rationally designed and fabricated. The top TiN NPs with broadband light absorption acted as a plasmonic heating layer, which converted the absorbed light to heat efficiently for interfacial water heating. The AAM acted as the mechanical support layer, guaranteeing the heat isolation and continuous water replenishment. With optimized thickness of the TiN top layer, a solar steam generation efficiency of 87.7% was achieved in this study. This efficiency is comparable or even higher than prior studies. The current work proves the capability of the TiN NPs as an alternative photothermal material.

Employing self-determination theory, in this study, we explore how workplace ostracism (being ignored and excluded by others) affects newcomer’s voice behavior. Through an empirical study with 353 matched supervisor–subordinate pairs from a large high-technology company, we find that workplace ostracism has negative influence on newcomer’s both promotive and prohibitive voice through the mediating effect of psychological needs satisfaction. In addition, narcissism moderates the effects of ostracism on psychological needs satisfaction. Theoretical and practical implications of these findings are discussed.

Orthogonal frequency division multiplex (OFDM) system is a special cognitive radio system that is widely used in military and civilian applications. As a crucial aspect of spectrum monitoring and electronic countermeasures reconnaissance, it is important to identify the OFDM signal. An identification method based on fractal box dimension and pseudo-inverse spectrum (PIS) has been proposed in this paper for the recognition problem of OFDM signal under multipath channel. Firstly, by theoretically analyzing the fractal box dimension of OFDM signal and single carrier (SC) signal, it can be concluded that the fractal box dimension of OFDM signal and SC signal has obvious differences. Thus, the fractal box dimension of the two types of signal is used to discriminate OFDM signal and SC signal. Then, the PIS of an OFDM signal is constructed according to the characteristics of the OFDM signal. Through theoretical analysis and the experimental simulation, it illustrates that the classification feature could be extracted by detecting the periodical peak of the PIS of OFDM signal and used for identifying OFDM signal in the Gaussian noise. Simulation results demonstrate that the proposed algorithm has better performance than the conventional algorithm based on autocorrelation.