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The reaction characteristics and mechanism of coke with different coke reactivity indices (CRIs) in the high-temperature zone of a blast furnace should be fully understood to correctly evaluate the coke quality and optimize ironmaking. In this work, low-CRI coke (coke A) and high-CRI coke (coke B) were charged into a thermogravimetric analyzer to separately study their microstructural changes, gasification characteristics, and reaction mechanism under simulated cohesive zone conditions in a blast furnace. The results show that both coke A and coke B underwent pyrolysis, polycondensation, and graphitization during the heat treatment. The pyrolysis, polycondensation, gasification speed, and dissolution speed rates of coke B were higher than those of coke A. Direct and indirect reduction between sinter and coke occurred in the cohesive zone and had different stages. The consumption rate of coke B was faster than that of coke A during the coke-sinter reduction. The carbon molecules of coke A must absorb more energy to break away from the skeleton than those of coke B.

It has been shown that the same canonical cortical circuit model with mutual inhibition and a fatigue process can explain perceptual rivalry and other neurophysiological responses to a range of static stimuli. However, it has been proposed that this model cannot explain responses to dynamic inputs such as found in intermittent rivalry and rivalry memory, where maintenance of a percept when the stimulus is absent is required. This challenges the universality of the basic canonical cortical circuit. Here, we show that by including an overlooked realistic small nonspecific background neural activity, the same basic model can reproduce intermittent rivalry and rivalry memory without compromising static rivalry and other cortical phenomena. The background activity induces a mutual-inhibition mechanism for short-term memory, which is robust to noise and where fine-tuning of recurrent excitation or inclusion of sub-threshold currents or synaptic facilitation is unnecessary. We prove existence conditions for the mechanism and show that it can explain experimental results from the quartet apparent motion illusion, which is a prototypical intermittent rivalry stimulus.

ObjectiveSarcopenia and cognitive impairment often coexist in the elderly. In this study, we investigated the causal relationship between sarcopenia-related muscle characteristics and cognitive performance.MethodsWe used linkage disequilibrium score regression (LDSC) and Mendelian Randomization (MR) analyses to estimate genetic correlations and causal relationships between genetically predicted sarcopenia-related muscle traits and cognitive function, as well as cognitive function-based discovery samples and replicated samples. Estimated effect sizes were derived from a fixed-effects meta-analysis.ResultsOur univariate genome-wide association study (GWAS) meta-analysis indicated a causal relationship between appendicular lean mass (ALM) (β = 0.049; 95% confidence interval (CI): 0.032-0.066, P < 0.001) and walking pace (β = 0.349; 95% CI: 0.210-0.487, P < 0.001) with cognitive function, where a causal relationship existed between ALM in both male and female (βALM-Male(M) = 0.060; 95% CI: 0.031-0.089, PALM-M < 0.001; βALM-Female(F) = 0.045; 95% CI: 0.020-0.069, PALM-F < 0.001) with cognitive function. Low grip strength was not causally associated with cognitive function (β = -0.045; 95% CI: -0.092 - -0.002, P = 0.062). A reverse causality GWAS meta-analysis showed a causal relationship between cognitive function and ALM (β = 0.033; 95% CI: 0.018-0.048, P < 0.001) and walking pace (β = 0.039; 95% CI: 0.033-0.051, P < 0.001), where ALM in both male and female showed a causality (βALM-M = 0.041; 95% CI: 0.019-0.063, PALM-M < 0.001; βALM-F = 0.034; 95% CI: 0.010-0.058, PALM-F = 0.005). Cognitive function was not causally related to low grip strength (β = -0.024; 95% CI: -0.073-0.025, P = 0.344). Multivariable MR1 (MVMR1) analyses showed a significant causal relationship for ALM (β = 0.077; 95% CI: 0.044-0.109, P = 0.000) and walking pace (β = 0.579; 95% CI: 0.383-0.775, P = 0.000) and cognitive function. Multivariable MR2 (MVMR2) multivariate analysis showed that ALM causality remained (β = 0.069; 95% CI: 0.033-0.106, P = 0.000), and walking pace (β = 0.589; 95% CI: 0.372-0.806, P = 0.000).ConclusionsBidirectional two-sample MR demonstrated that sarcopenia-related muscle characteristics and cognitive performance were positive causal genetic risk factors for each other, while a multivariable MR study demonstrated that low ALM and a slow walking pace were causally involved in reduced cognitive performance. This study suggests a causal relationship between sarcopenia and cognitive impairment in older adults and provide new ideas for prevention and treatment.

Since SARS-CoV-2 emerged in late 2019, it spread from China to the rest of the world. An initial concern was the potential for vaccine- or antibody-dependent enhancement (ADE) of disease as had been reported with other coronaviruses. To evaluate this, we first developed a ferret model by exposing ferrets to SARS-CoV-2 by either mucosal inoculation (intranasal/oral/ocular) or inhalation using a small particle aerosol. Mucosal inoculation caused a mild fever and weight loss that resolved quickly; inoculation via either route resulted in virus shedding detected in the nares, throat, and rectum for 7-10 days post-infection. To evaluate the potential for ADE, we then inoculated groups of ferrets intravenously with 0.1, 0.5, or 1 mg/kg doses of a human polyclonal anti-SARS-CoV-2 IgG from hyper-immunized transchromosomic bovines (SAB-185). Twelve hours later, ferrets were challenged by mucosal inoculation with SARS-CoV-2. We found no significant differences in fever, weight loss, or viral shedding after infection between the three antibody groups or the controls. Signs of pathology in the lungs were noted in infected ferrets but no differences were found between control and antibody groups. The results of this study indicate that healthy, young adult ferrets of both sexes are a suitable model of mild COVID-19 and that low doses of specific IgG in SAB-185 are unlikely to enhance the disease caused by SARS-CoV-2.

The construction of an integrated national strategic system and capability is an essential goal of implementing the strategy of military-civilian integration in the contemporary era. And the collaborative innovation of military-civilian S&T is an inevitable choice to achieve this goal. Due to the dynamic, complex, and stochastic characteristics of military-civilian S&T collaborative innovation, the level of S&T innovation is highly volatile. This paper takes the internal and external stochastic disturbance factors of military-civilian S&T collaborative innovation as the perspective, studies the strategy selection problem of military-civilian S&T collaborative innovation under military domination, constructs a differential game model to explore the innovation strategies under the non-cooperative model without military subsidies, the non-cooperative model with military subsidies, and the collaborative model. Finally, we use numerical experiments to verify the validity of the conclusions. The study shows that: (1) Within a reasonable range of values of the benefit distribution coefficient, the system can achieve the Pareto optimum, and the collaborative model is conducive to improving the S&T innovation level and the optimum benefit level of the system. (2) Military subsidies can increase the benefits of the system and the parties involved to achieve Pareto improvement. (3) The level of S&T innovation under the collaborative model has dynamic evolutionary characteristics of maximum expectation and variance. As the intensity of disturbance increases, the stability of the system may be destroyed. Risk-averse civil enterprises prefer the cooperative mode, whereas risk-averse civil enterprises prefer the non-cooperative model.

Rural communities are resource-constrained and at higher risk of obesity and obesity-related conditions. Thus, studying self-assessed health status and underlying vulnerabilities is critical to provide insights to the program planners for effective and efficient planning of obesity prevention programs. This study aims to investigate the correlates of self-assessed health status and subsequently determine the obesity vulnerability level of residents in rural communities. Randomly sampled data were obtained from in-person community surveys in three rural Louisiana counties-East Carroll, Saint Helena, and Tensas-in June 2021. The association of social-demographic factors, grocery store choice, and exercise frequency with self-assessed health was investigated using the ordered logit model. An obesity vulnerability index was constructed using the weights obtained from the principal component analysis. The results show that gender, race, education, possession of children, exercise frequency, and grocery store choice significantly influence self-assessed health status. Around 20% of respondents fall into the most-vulnerable segment and 65% of respondents are vulnerable to obesity. The obesity vulnerability index ranged from -4.036 to 4.565, indicating a wide heterogeneity in the vulnerability level of rural residents. The findings show that the self-assessed health status of rural residents is not promising along with a high level of vulnerability to obesity. The findings from this study could serve as a reference in the policy discussion regarding an effective and efficient suite of interventions in rural communities to address obesity and promote well-being.

In order to improve the laws and regulations of the financial system, in the construction of laws and regulations, the traditional financial risk Early Warning (EW) model is optimized. The financial prevention and control measures with legal protection are implemented to warn the financial risks, which plays an important role in the construction of the rule of law in the Financial Market (FM) and the establishment of financial risk prevention and control laws and regulations. This paper combines the deep learning model and the Markov regime Switching Vector Auto Regression (MS-VAR) model and constructs a regional financial risk EW model from the following aspects: macroeconomic operation EW indicators, regional economic risk EW indicators, regional financial institution risk EW indicators. The model is empirically researched and analyzed. The results show that the fluctuation trend of the macroeconomic pressure index in the time series is relatively large, and the overall fluctuation of the regional economic pressure index is small, and fluctuates around 0 in most periods. After the financial crisis, local governments stepped up their supervision of non-performing corporate and household loans. From 2011 to 2018, the non-performing loan ratio began to decline, and the overall fluctuation of the regional financial comprehensive stress index was small, fluctuating around 0. Due to the lack of legal regulation, from the perspective of the regional economy, the risk level is more likely to change from low risk to moderate risk, while the risk status is less likely to change from high risk to moderate risk. From the perspective of regional financial institutions, the probabilities of maintaining low risk and moderate risk are 0.98 and 0.97, respectively, which is stronger than maintaining the stability of high risk. From the perspective of the state transition of the regional financial risk composite index, the probability of maintaining low risk and high risk is 0.97 and 0.93, which is higher than maintaining the stability of medium risk. The Deep Learning (DL) regional financial risk EW MS-VAR model has strong risk prediction ability. The model can better analyze the conversion probability of regional financial risk EW index and has better risk EW ability. This paper enhances the role of legal systems in financial risk prevention and control. The regional financial risk EW model incorporating financial legal indicators can better describe the regional financial risk level, and the EW results are basically consistent with the actual situation. In order to effectively prevent financial risks and ensure the safety of the financial system, it is recommended that the government improve local debt management, improve financial regulations and systems, and improve the legislative level of financial legal supervision.

ObjectiveWe aimed to use mathematical models of SARS-COV-2 to assess the potential efficacy of non-pharmaceutical interventions on transmission in the parcel delivery and logistics sector.MethodsWe devloped a network-based model of workplace contacts based on data and consultations from companies in the parcel delivery and logistics sectors. We used these in stochastic simulations of disease transmission to predict the probability of workplace outbreaks in this settings. Individuals in the model have different viral load trajectories based on SARS-CoV-2 in-host dynamics, which couple to their infectiousness and test positive probability over time, in order to determine the impact of testing and isolation measures.ResultsThe baseline model (without any interventions) showed different workplace infection rates for staff in different job roles. Based on our assumptions of contact patterns in the parcel delivery work setting we found that when a delivery driver was the index case, on average they infect only 0.14 other employees, while for warehouse and office workers this went up to 0.65 and 2.24 respectively. In the LIDD setting this was predicted to be 1.40, 0.98, and 1.34 respectively. Nonetheless, the vast majority of simulations resulted in 0 secondary cases among customers (even without contact-free delivery). Our results showed that a combination of social distancing, office staff working from home, and fixed driver pairings (all interventions carried out by the companies we consulted) reduce the risk of workplace outbreaks by 3-4 times.ConclusionThis work suggests that, without interventions, significant transmission could have occured in these workplaces, but that these posed minimal risk to customers. We found that identifying and isolating regular close-contacts of infectious individuals (i.e. house-share, carpools, or delivery pairs) is an efficient measure for stopping workplace outbreaks. Regular testing can make these isolation measures even more effective but also increases the number of staff isolating at one time. It is therefore more efficient to use these isolation measures in addition to social distancing and contact reduction interventions, rather than instead of, as these reduce both transmission and the number of people needing to isolate at one time.

The screening performance of urine flow cytometry parameters (e.g., white blood cell and bacteria) for urinary tract infection (UTI) has been widely recognized. The majority of previous studies, however, investigated the screening performance of Sysmex UF-1000i urine flow cytometer. This study aimed to investigate the screening performance of Sysmex UF-5000 analyzer, a third-generation urinary flow cytometer, for UTI and its novel parameter named Gram flag for discriminating gram-positive and negative pathogens. Urine specimens sent to the clinical microbiology laboratory of our hospital for bacterial culture between September 13, 2021, and November 15, 2021, were prospectively and consecutively collected. The Sysmex UF-5000 analyzer was used to determine urine white blood cell (WBC) and bacteria simultaneously. A chemical strip was used to assess urine nitrate. UTI was defined as positive urine bacterial culture > 104 CFU /ml. The receiver operating characteristics (ROC) curve, nomogram, decision tree, and decision curve were used to determine the screening performance of urine WBC, nitrate, and bacterial. A total of 246 UTIs and 425 non-UTIs were enrolled. The areas under the ROC curve (AUCs) for WBC and bacterial were 0.74 and 0.86, respectively. The decision curve showed that urine bacteria had a higher benefit than WBC. The nomogram indicated that urine bacterial had the largest effect on the probability of UTI. The sensitivity and specificity of the decision tree were 0.69 and 0.95, respectively. The flag of Gram-negative had a positive predictive value (PPV) of 0.93 in patients with urine bacteria > 1367 /μl. Therefore, we conclude that urine bacteria determined by the Sysmex UF-5000 had higher screening performance and greater benefit than WBC. The decision tree can be used to improve the screening performance of routine urinary parameters. The flag of Gram-negative is a reliable indicator to confirm gram-negative bacteria infection in UTI patients.

Cryptococcus gattii (C. gattii) is a fungal pathogen that once caused an outbreak of cryptococcosis on Vancouver Island, and had spread worldwide, while few data were available in China. In this study, seven clinical isolates of C. gattii VGII were collected from 19 hospitals, Multi-locus Sequence Typing (MLST) analysis and whole-genome sequencing (WGS) was performed, combined with published data for phylogenetic analysis. In addition, in vitro antifungal susceptibility testing, phenotypic analysis, and in vivo virulence studies were performed, subsequently, histopathological analysis of lung tissue was performed. C.gattii VGII infected patients were mainly immunocompetent male, and most of them had symptoms of central nervous system (CNS) involvement. MLST results showed that isolates from China exhibited high genetic diversity, and sequence type (ST) 7 was the major ST among the isolates. Some clinical isolates showed a close phylogenetic relationship with strains from Australia and South America. All clinical isolates did not show resistance to antifungal drugs. In addition, there was no correlation between virulence factors (temperature, melanin production, and capsule size) and virulence while in vivo experiments showed significant differences in virulence among strains. Lung fungal burden and damage to lung tissue correlated with virulence, and degree of damage to lung tissue in mice may highlight differences in virulence. Our work seeks to provide useful data for molecular epidemiology, antifungal susceptibility, and virulence differences of C. gattii VGII in China.

The diagnosis of neurological diseases is one of the biggest challenges in modern medicine, which is a major issue at the moment. Electroencephalography (EEG) recordings is usually used to identify various neurological diseases. EEG produces a large volume of multi-channel time-series data that neurologists visually analyze to identify and understand abnormalities within the brain and how they propagate. This is a time-consuming, error-prone, subjective, and exhausting process. Moreover, recent advances in EEG classification have mostly focused on classifying patients of a specific disease from healthy subjects using EEG data, which is not cost effective as it requires multiple systems for checking a subject's EEG data for different neurological disorders. This forces researchers to advance their work and create a single, unified classification framework for identifying various neurological diseases from EEG signal data. Hence, this study aims to meet this requirement by developing a machine learning (ML) based data mining technique for categorizing multiple abnormalities from EEG data. Textural feature extractors and ML-based classifiers are used on time-frequency spectrogram images to develop the classification system. Initially, noises and artifacts are removed from the signal using filtering techniques and then normalized to reduce computational complexity. Afterwards, normalized signals are segmented into small time segments and spectrogram images are generated from those segments using short-time Fourier transform. Then two histogram based textural feature extractors are used to calculate features separately and principal component analysis is used to select significant features from the extracted features. Finally, four different ML based classifiers are used to categorize those selected features into different disease classes. The developed method is tested on four real-time EEG datasets. The obtained result has shown potential in classifying various abnormality types, indicating that it can be utilized to identify various neurological abnormalities from brain signal data.

BackgroundThe prevailing consensus from large epidemiological studies is that breastfeeding is associated with improved IQ and cognitive functioning in later childhood and adolescence. Current research is exploring the association between breastfeeding and early brain development in preterm infants.ObjectiveTo explore the differences in brain gray matter between breastmilk-fed and formula-fed preterm infants using structural and functional magnetic resonance imaging.MethodsA convenience sample of breastmilk-fed preterm infants(n = 34) and formula-fed infants (n = 22) aged approximately 32 weeks. At near term-equivalent age, MR scanning was performed. Gray matter structural and functional differences between the two groups were assessed using MATLAB software for voxel-based morphometry (VBM) and amplitude of low-frequency fluctuation (ALFF) analysis.ResultsMaternal and neonatal demographic characteristics showed no significant difference between the two groups. Breastmilk-fed infants had greater regional gray matter volume on MRI than formula-fed infants in multiple brain regions, including the bilateral frontal lobe (BA11, BA46), right temporal lobe (BA37), and left caudate nucleus, at a statistical threshold of p40 voxels. Compared with formula-fed infants, breastmilk-fed infants showed increased brain activation on fMRI in the right superior temporal gyrus (BA41).ConclusionBreastmilk-fed infants had greater regional gray matter development and increased regional gray matter function compared with formula-fed infants at near term-equivalent age, suggesting breastmilk feeding in the early period after birth may have some degree of influence on early brain development in preterm infants.

The COVID-19 epidemic has a catastrophic impact on global well-being and public health. More than 27 million confirmed cases have been reported worldwide until now. Due to the growing number of confirmed cases, and challenges to the variations of the COVID-19, timely and accurate classification of healthy and infected patients is essential to control and treat COVID-19. We aim to develop a deep learning-based system for the persuasive classification and reliable detection of COVID-19 using chest radiography. Firstly, we evaluate the performance of various state-of-the-art convolutional neural networks (CNNs) proposed over recent years for medical image classification. Secondly, we develop and train CNN from scratch. In both cases, we use a public X-Ray dataset for training and validation purposes. For transfer learning, we obtain 100% accuracy for binary classification (i.e., Normal/COVID-19) and 87.50% accuracy for tertiary classification (Normal/COVID-19/Pneumonia). With the CNN trained from scratch, we achieve 93.75% accuracy for tertiary classification. In the case of transfer learning, the classification accuracy drops with the increased number of classes. The results are demonstrated by comprehensive receiver operating characteristics (ROC) and confusion metric analysis with 10-fold cross-validation.

Nonalcoholic fatty liver disease (NAFLD) affects approximately a quarter of the population worldwide, and persistent overnutrition is one of the major causes. However, the underlying molecular basis has not been fully elucidated, and no specific drug has been approved for this disease. Here, we identify a regulatory mechanism that reveals a novel function of Rab2A in the progression of NAFLD based on energy status and PPARγ. The mechanistic analysis shows that nutrition repletion suppresses the phosphorylation of AMPK-TBC1D1 signaling, augments the level of GTP-bound Rab2A, and then increases the protein stability of PPARγ, which ultimately promotes the hepatic accumulation of lipids in vitro and in vivo. Furthermore, we found that blocking the AMPK-TBC1D1 pathway in TBC1D1S231A-knock-in (KI) mice led to a markedly increased GTP-bound Rab2A and subsequent fatty liver in aged mice. Our studies also showed that inhibition of Rab2A expression alleviated hepatic lipid deposition in western diet-induced obesity (DIO) mice by reducing the protein level of PPARγ and the expression of PPARγ target genes. Our findings not only reveal a new molecular mechanism regulating the progression of NAFLD during persistent overnutrition but also have potential implications for drug discovery to combat this disease.

The quality of cultivated land determines the production capacity of cultivated land and the level of regional development, and also directly affects the food security and ecological safety of the country. This paper starts from the perspective of spatial pattern of cultivated land quality and uses spatial autocorrelation analysis to study the spatial aggregation characteristics and differences of cultivated land quality in Henan Province at the county level scale, and also uses bivariate spatial autocorrelation to analyze the influence of neighboring influences on the quality of cultivated land in the target area. The spatial autoregressive model was used to further analyze the driving factors affecting the quality of cultivated land, and the influence of cultivated land area index was coupled in the process of rating analysis, which was finally used as a basis to propose more precise measures for the protection of cultivated land zoning. The results show that: (1) The quality of cultivated land in Henan Province has a strong spatial correlation (global Moran's I≈0.710) and shows an obvious aggregation pattern in spatial distribution; positive correlation types (high-high and low-low) are concentrated in north-central and western mountainous areas of Henan Province, respectively; negative correlation types are discrete. The negative correlation types are distributed in a discrete manner. (2) The bivariate spatial autocorrelation results show that Slope (Moran's I≈-0.505), Irrigation guarantee rate (IGR, 0.354), Urbanization rate (-0.255), Total agricultural machinery power (TAMP, 0.331) and Pesticide use (0.214) are the main influencing factors. (3) According to the absolute values of the regression coefficients, it can be seen that the magnitude of the influence of different factors on the quality of cultivated land is: Slope (0.089) >IGR (0.025) > Urbanization rate (0.002) > TAMP (0.001) > Pesticide use (1.96e-006). (4) Based on the spatial pattern presented by the spatial autocorrelation results, we proposed corresponding protection zoning measures to provide more scientific reference decisions and technical support for the implementation of refined cultivated land management in Henan Province.

Commerce has had positive impacts on the whole agri-food value chain at different stages, it was developed rapidly in rural China in the past few years. E-commerce participation can promote the use intensity of organic fertilizers (OF) and this could achieve many benefits for different stakeholders including ensuring food safety, positive environmental impacts and promoting the adoption of green production technologies. Therefore, this study has used primary data collected from 733 fruit farmers in rural China to explore the impact of e-commerce participation on fruit farmers' use intensity of (OF). Unlike previous studies investigating the dichotomous decision of (OF) adoption, this study captures the use intensity of (OF) from both input quantity and cost aspects. We employed an endogenous switching regression (ESR) model to address selectivity bias caused by observed and unobserved factors. The results show that e-commerce participation significantly increases the use intensity of (OF) in input quantity and cost by 19.48% and 29.50%, respectively. Heterogeneous analysis further reveals that compared to fruit farmers with a low e-commerce participation level, fruit farmers with a high e-commerce participation level have higher (OF) use intensity. The findings also show that risk preference, human capital, cultivated area, cooperative membership and government restraint mechanisms positively and significantly affect the probability of fruit farmers' participation in e-commerce and fruit farmers' use intensity of (OF). The results emphasize that e-commerce promotion is an efficient way to encourage farmers to adopt (OF), which help improve product quality and promote sustainable agricultural development.

Lingonberry (Vaccinium vitis-idaea L.) extract contains various active ingredients with strong inhibitory effects on cancer cell growth. HepG2 cells were treated with various concentrations of lingonberry extract, cell inhibition rate was measured by CCK-8 assay, and apoptosis rate by annexin-propidium iodide double-staining assay. The cell cycle was analyzed by flow cytometry, and cell migration and invasion by transwell assay. Real-time reverse transcription-PCR and western blotting were employed to analyze the expression of C-X-C motif chemokine ligand 3 (CXCL3). Ki-67, TUNEL, and transwell assays were used to verify the relationship between CXCL3 expression and cell proliferation, apoptosis, migration, and invasion. The composition of lingonberry extract was: 37.58% cyanidin-3-O-glucoside, 10.96% kaempferol 3-O-arabinoside, 4.52% epicatechin, 4.35% chlorogenic acid, 3.83% catechinic acid, 1.54% isoquercitrin, 1.05% 4-hydroxycinnamon acid, 1.03% cyanidin chloride, 0.85% 2,3-dihydroxybenzoic acid, 0.55% quercetin, 0.36% D-(-)-quininic acid, 0.96% caffeic acid, 0.16% ferulic acid, 0.12% oleanolic acid, and 0.03% ursolic acid. Lingonberry extract inhibited the proliferation of HepG2 cells in a dose-dependent manner. After 48 h exposure to 100 μg/mL extract the inhibition rate and IC50 were 80.89±6.05% and 22.62 μg/mL, respectively. Lingonberry extract promoted late apoptosis in HepG2 cells and arrested the cell cycle at G2/M and S phases. Lingonberry extract also promoted the apoptosis of HepG2 cancer cells, inhibiting their proliferation, migration, and invasion by regulating the expression of CXCL3. This study offers new insight into the antihepatoma activity of lingonberry extract and provides a basis for the development of pilot antitumor drugs.

The interferon-regulated antiviral responses are essential for the induction of both innate and adaptive immunity in mammals. Production of virus-derived small-interfering RNAs (vsiRNAs) to restrict virus infection by RNA interference (RNAi) is a recently identified mammalian immune response to several RNA viruses, which cause important human diseases such as influenza and Zika virus. However, little is known about Dicer processing of viral double-stranded RNA replicative intermediates (dsRNA-vRIs) in mammalian somatic cells. Here we show that infected somatic cells produced more influenza vsiRNAs than cellular microRNAs when both were produced by human Dicer expressed de novo, indicating that dsRNA-vRIs are not poor Dicer substrates as previously proposed according to in vitro Dicer processing of synthetic long dsRNA. We report the first evidence both for canonical vsiRNA production during wild-type Nodamura virus infection and direct vsiRNA sequestration by its RNAi suppressor protein B2 in two strains of suckling mice. Moreover, Sindbis virus (SINV) accumulation in vivo was decreased by prior production of SINV-targeting vsiRNAs triggered by infection and increased by heterologous expression of B2 in cis from SINV genome, indicating an antiviral function for the induced RNAi response. These findings reveal that unlike artificial long dsRNA, dsRNA-vRIs made during authentic infection of mature somatic cells are efficiently processed by Dicer into vsiRNAs to direct antiviral RNAi. Interestingly, Dicer processing of dsRNA-vRIs into vsiRNAs was inhibited by LGP2 (laboratory of genetics and physiology 2), which was encoded by an interferon-stimulated gene (ISG) shown recently to inhibit Dicer processing of artificial long dsRNA in cell culture. Our work thus further suggests negative modulation of antiviral RNAi by a known ISG from the interferon response.

13 Reasons Why is a Netflix original series adapted from Jay Asher’s 2007 young adult novel with the same title. Season 1 premiered on March 31, 2017 and featured the sensitive issue of teen suicide along with bullying, substance use, depression, and sexual assault. Unlike the typical teen dramas on popular streaming platforms, this show was created not only for entertainment, but also to stimulate conversations about taboo topics that people often shy away from. However, it also caused significant controversy, especially criticism around the main character Hannah’s suicide scene. More than three years into the initial controversy and at least two dozen scholarly publications later, this study is the first to examine the entertainment narrative content of 13 Reasons Why Season 1 to better understand how these health and social issues were portrayed in the show, what specific examples we could identify as potential behavioral modeling, and to what degree it complied with the 2017 WHO guidelines for media professionals. We used the framing theory and social cognitive theory in communication research and media studies as our guiding conceptual frameworks and a narrative analysis approach to investigate a total of 660 cut scenes in all 13 episodes. Our findings provided empirical evidence, along with contextual information and detailed examples, to demonstrate that a popular entertainment program like the Netflix series 13 Reasons Why serves as a double-edged sword. The production team’s good will and due diligence are commendable. Yet, additional steps can be taken in the future to effectively promote professional resources and reduce viewers’ risks, especially the most vulnerable groups.

[This corrects the article DOI: 10.1371/journal.pone.0118261.].

Semantic segmentation of medical images provides an important cornerstone for subsequent tasks of image analysis and understanding. With rapid advancements in deep learning methods, conventional U-Net segmentation networks have been applied in many fields. Based on exploratory experiments, features at multiple scales have been found to be of great importance for the segmentation of medical images. In this paper, we propose a scale-attention deep learning network (SA-Net), which extracts features of different scales in a residual module and uses an attention module to enforce the scale-attention capability. SA-Net can better learn the multi-scale features and achieve more accurate segmentation for different medical image. In addition, this work validates the proposed method across multiple datasets. The experiment results show SA-Net achieves excellent performances in the applications of vessel detection in retinal images, lung segmentation, artery/vein(A/V) classification in retinal images and blastocyst segmentation. To facilitate SA-Net utilization by the scientific community, the code implementation will be made publicly available.

Contact tracing has historically been used to retard the spread of infectious diseases, but if it is exercised by hand in large-scale, it is known to be a resource-intensive and quite deficient process. Nowadays, digital contact tracing has promptly emerged as an indispensable asset in the global fight against the coronavirus pandemic. The work at hand offers a meticulous study of all the official Android contact tracing apps deployed hitherto by European countries. Each app is closely scrutinized both statically and dynamically by means of dynamic instrumentation. Depending on the level of examination, static analysis results are grouped in two axes. The first encompasses permissions, API calls, and possible connections to external URLs, while the second concentrates on potential security weaknesses and vulnerabilities, including the use of trackers, in-depth manifest analysis, shared software analysis, and taint analysis. Dynamic analysis on the other hand collects data pertaining to Java classes and network traffic. The results demonstrate that while overall these apps are well-engineered, they are not free of weaknesses, vulnerabilities, and misconfigurations that may ultimately put the user security and privacy at risk.

[This corrects the article DOI: 10.1371/journal.pone.0251867.].

Seefeldt`s classic motor development pyramid model recognizes the significance of fundamental movement skills (FMS) in physical activities and proposes a "proficiency barrier" between FMS and higher-level specific sports skills during middle childhood. However, the relationship between the layers of the conceptual model has not been empirically tested. This study investigated motor fitness (MF), FMS, and quality of movement patterns (QMP) in 7-10 years old children and evaluated the relationships among them. A total of 117 children were randomly selected to take tests of MF, the Test of Gross Motor Development-2 (TGMD-2), and the Functional Movement Screen (FMS™). MF and FMS levels were classified according to percentile ranges. Two multiple (R×C) Chi-Square tests were applied to analyze the relationships between MF, FMS, and QMP. Post-hoc testing estimated the possibility of FMS and QMP to predict MF. The results showed that boys scored significantly higher on the object-control subtest and on the TGMD-2 compared to girls (p

Activities attested since at least 2.6 Myr, such as stone knapping, marrow extraction, and woodworking may have allowed early hominins to recognize the technological potential of discarded skeletal remains and equipped them with a transferable skillset fit for the marginal modification and utilization of bone flakes. Identifying precisely when and where expedient bone tools were used in prehistory nonetheless remains a challenging task owing to the multiple natural and anthropogenic processes that can mimic deliberately knapped bones. Here, we compare a large sample of the faunal remains from Lingjing, a 115 ka-old site from China which has yielded important hominin remains and rich faunal and lithic assemblages, with bone fragments produced by experimentally fracturing Equus caballus long bones. Our results provide a set of qualitative and quantitative criteria that can help zooarchaeologists and bone technologists distinguish faunal remains with intentional flake removal scars from those resulting from carcass processing activities. Experimental data shows marrow extraction seldom generates diaphyseal fragments bearing more than six flake scars arranged contiguously or in interspersed series. Long bone fragments presenting such characteristics can, therefore, be interpreted as being purposefully knapped to be used as expediency tools. The identification, based on the above experimental criteria, of 56 bone tools in the Lingjing faunal assemblage is consistent with the smaller size of the lithics found in the same layer. The continuity gradient observed in the size of lithics and knapped bones suggests the latter were used for tasks in which the former were less or not effective.

BackgroundThis study aimed to assess the comparability among assays using freshly frozen human sera and external quality assessment (EQA) data in China.MethodsTwenty-nine serum samples and two commercial EQA materials, obtained from the National Center for Clinical Laboratories (NCCL), were analyzed in triplicate using eight routine TSH assays. The commutability of commercial EQA materials (NCCL materials) was evaluated in accordance with the CLSI EP30-A and IFCC bias analysis. Median values obtained for the NCCL EQA materials were used to determine the systematic and commutability-related biases among immunoassays through back-calculation. The comparability of TSH measurements from a panel of clinical samples and NCCL EQA data was determined on the basis of Passing-Bablok regression. Furthermore, human serum pools were used to perform commutable EQA.ResultsNCCL EQA materials displayed commutability among three or five of seven assay combinations according CLSI or IFCC approach, respectively. The mean of systematic bias ranged from -13.78% to 9.85% for the eight routine TSH assays. After correcting for systematic bias, averaged commutability-related biases ranged between -42.26% and 12.19%. After correction for systematic and commutability -related biases, the slopes indicating interassay relatedness ranged from 0.801 to 1.299 using individual human sera, from 0.735 to 1.254 using NCCL EQA data, and from 0.729 to 1.115 using pooled human serum EQA(the commutable EQA).ConclusionsThe harmonization of TSH measurement is challenging; hence, systematic and commutability-related biases should be determined and corrected for accurate comparisons among assays when using human individual serum and the commercial EQA materials.

ObjectiveIrritable bowel syndrome (IBS) affects children's quality of life and learning. The purpose of this research was to systematically evaluate the efficacy of probiotic adjuvant therapy for IBS in children.MethodsThe Web of Science, PubMed, Cochrane Library, EMBASE and Clinical Trials databases were electronically searched for randomized controlled trials (RCTs) published prior to January 2021 exploring the use of probiotic adjuvant therapy for IBS in children. Strict screening and quality evaluations of the eligible articles were performed independently by 2 researchers. Outcome indexes were extracted, and a meta-analysis of the data was performed using RevMan 5.4.1 and STATA 16 software. Finally, the risk of bias in the included studies was assessed with the RCT bias risk assessment tool recommended in the Cochrane Handbook for Systematic Reviews of Interventions (5.1.0).ResultsA total of nine RCTs were included. In children, probiotics significantly reduced the abdominal pain score (I2 = 95%, SMD = -1.15, 95% (-2.05, -0.24), P = 0.01) and Subject's Global Assessment of Relief (SGARC) score (I2 = 95%, MD = -3.84, 95% (-6.49, -1.20), P = 0.004), increased the rate of abdominal pain treatment success (I2 = 0%, RR = 3.44, 95% (1.73, 6.87), P = 0.0005) and abdominal pain relief (I2 = 40%, RR = 1.48, 95% (0.96, 2.28), P = 0.08), and reduced the frequency of abdominal pain (I2 = 2%, MD = -0.82, 95% (-1.57, -0.07), P = 0.03). However, we found that it might not be possible to relieve abdominal pain by increasing the daily intake of probiotics.ConclusionsProbiotics are effective at treating abdominal pain caused by IBS in children, however, there was no significant correlation between abdominal pain and the amount of probiotics ingested. More attention should be given to IBS in children, and a standardized evaluation should be adopted.

Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal communication, and restricted or repetitive behaviors. Currently Electroencephalography (EEG) is the most popular tool to inspect the existence of neurological disorders like autism biomarkers due to its low setup cost, high temporal resolution and wide availability. Generally, EEG recordings produce vast amount of data with dynamic behavior, which are visually analyzed by professional clinician to detect autism. It is laborious, expensive, subjective, error prone and has reliability issue. Therefor this study intends to develop an efficient diagnostic framework based on time-frequency spectrogram images of EEG signals to automatically identify ASD. In the proposed system, primarily, the raw EEG signals are pre-processed using re-referencing, filtering and normalization. Then, Short-Time Fourier Transform is used to transform the pre-processed signals into two-dimensional spectrogram images. Afterward those images are evaluated by machine learning (ML) and deep learning (DL) models, separately. In the ML process, textural features are extracted, and significant features are selected using principal component analysis, and feed them to six different ML classifiers for classification. In the DL process, three different convolutional neural network models are tested. The proposed DL based model achieves higher accuracy (99.15%) compared to the ML based model (95.25%) on an ASD EEG dataset and also outperforms existing methods. The findings of this study suggest that the DL based structure could discover important biomarkers for efficient and automatic diagnosis of ASD from EEG and may assist to develop computer-aided diagnosis system.

Mesosulfuron-methyl is always applied by foliar spraying in combination with the safener mefenpyr-diethyl to avoid phytotoxicity on wheat (Triticum aestivum L.) cultivars. However, it was observed that the tolerance of Tausch's goatgrass (Aegilops tauschii Coss.) to mesosulfuron-methyl significantly increased in the presence of mefenpyr-diethyl by performing bioassay. This confirmed phenomenon may lead to overuse of mesosulfuron-methyl and weed resistance evolution in field conditions. Therefore, we tested the effect of wheat seed dressing with mefenpyr-diethyl as a possible alternative and disclosed the underlying mechanisms by herbicide dissipation study, enzymatic analysis and transcriptome profiling. The results suggest that increase of ALS activity, enhancement of metabolic processes, and other stress responses are crucial for the regulation of herbicide detoxification induced by mefenpyr-diethyl. Additionally, transcription factors such as AP2/ERF-ERF, bHLH, NAC, and MYB, and protein kinase such as RLK-Pelle_DLSV might play vital regulatory roles. The current study has important implications for mesosulfuron-methyl application in wheat field to control Tausch's goatgrass and provides a comprehensive understanding of the protective effect of mefenpyr-diethyl.

Linear aggregation is present in some animals, such as the coordinated movement of ants and the migration of caterpillars and spinylobsters, but none has been reported on rotifers. The rotifers were collected and clone cultured in the laboratory at 25 ± 1°C, under natural light (light intensity ~130 lx, L:D = 14:10). The culture medium(pH = 7.3) was formulated as described by Suga et al., and rotifers were fed on the micro algae Scenedesmus obliquus grown in HB-4 medium to the exponential growth stage. When density was high (150 individuals ml-1), the behavior of rotifers was observed using a stereo microscope (Motic ES-18TZLED). In this paper, linear aggregation in Brachionus calyciflorus was found for the first time, and experiments were carried out to verify the correlation between linear aggregation and culture density of B. calyciflorus. With the increase of density, the number of aggregations increase, the number of individuals in the aggregation increased, and the maintenance time of the aggregation was also increased. Therefore, we speculate that the formation of aggregates is related to density and may be a behavioral signal of density increase, which may transmit information between density increase and formation of dormant eggs.

Tilapia is one of the most important economic and fastest-growing species in aquaculture worldwide. In 2015, an epidemic associated with severe mortality occurred in adult tilapia in Hubei, China. The causative pathogen was identified as Tilapia parvovirus (TiPV) by virus isolation, electron microscopy, experimental challenge, In situ hybridization (ISH), indirect immunofluorescence (IFA), and viral gene sequencing. Electron microscopy revealed large numbers of parvovirus particles in the organs of diseased fish, including kidney, spleen, liver, heart, brain, gill, intestine, etc. The virions were spherical in shape, non-enveloped and approximately 30nm in diameter. The TiPV was isolated and propagated in tilapia brain cells (TiB) and induced a typical cytopathic effect (CPE) after 3 days post-infection (dpi). This virus was used to experimentally infect adult tilapia and clinical disease symptoms similar to those observed naturally were replicated. Additionally, the results of ISH and IFA showed positive signals in kidney and spleen tissues from TiPV-infected fish. To identify TiPV-specific sequences, the near complete genome of TiPV was obtained and determined to be 4269 bp in size. Phylogenetic analysis of the NS1 sequence revealed that TiPV is a novel parvovirus, forms a separate branch in proposed genus Chapparvovirus of Parvoviridae. Results presented here confirm that TiPV is a novel parvovirus pathogen that can cause massive mortality in adult tilapia. This provides a basis for the further studies to define the epidemiology, pathology, diagnosis, prevention and treatment of this emerging viral disease.

Burkholderia pseudomallei is the causative agent of melioidosis, endemic in Southeast Asia and Northern Australia, and increasingly recognized in southern China, especially in Hainan Province. Mycotic aneurysm caused by B. pseudomallei is a rare but potentially severe illness with a high mortality rate. The clinical features of the mycotic aneurysm secondary to melioidosis have not been illustrated in China. Over a seven-year period (2013 to 2019), 159 patients with bacteremic melioidosis were retrospectively analyzed in Hainan province, China, of whom eight patients were confirmed to have mycotic aneurysm through the combination of imaging examination, pathologic examination and aneurysm tissue culture. We summarized these eight patients' clinical characteristics, demographical features, treatments and outcomes. The susceptibilities to five commonly-used antibiotics for these eight B. pseudomallei isolates were also determined by E-test strips. Furthermore, the mycotic aneurysm cases secondary to melioidosis retrieved from the literature were also reviewed. Of the eight cases, six had abdominal mycotic aneurysms, one had a left iliac aneurysm, and the other one had an infectious mesenteric aneurysm. They were aged from 48 to 69 years old, and had the underlying risk factors of diabetes mellitus (2 patients), long-term smoking (4 patients), hypertension (6 patients), and soil and water contact history (6 patients), respectively. The positive arterial aneurysm imaging was observed in all patients via computed tomography (CT) or angiography. Eight B. pseudomallei isolates collected from both blood and mycotic aneurysm tissues remained 100% susceptible to imipenem and ceftazidime. After surgery combined with antibiotic administration, six patients survived, with a mortality rate of 25%. In melioidosis endemic areas, the mycotic aneurysm secondary to melioidosis might be underdiagnosed, and increased awareness of predisposing risk factors and clinical features of the mycotic aneurysm is required. Following a positive B. pseudomallei blood culture, the diagnosis of mycotic aneurysm should be under consideration in those with abdominal pain and/or hypertension. Imaging by CT or angiography is indispensable for its timely diagnosis and management.

Due to the popularity of Web-based applications, various developers have provided an abundance of Web services with similar functionality. Such similarity makes it challenging for users to discover, select, and recommend appropriate Web services for the service-oriented systems. Quality of Service (QoS) has become a vital criterion for service discovery, selection, and recommendation. Unfortunately, service registries cannot ensure the validity of the available quality values of the Web services provided online. Consequently, predicting the Web services' QoS values has become a vital way to find the most appropriate services. In this paper, we propose a novel methodology for predicting Web service QoS using source code metrics. The core component is aggregating software metrics using inequality distribution from micro level of individual class to the macro level of the entire Web service. We used correlation between QoS and software metrics to train the learning machine. We validate and evaluate our approach using three sets of software quality metrics. Our results show that the proposed methodology can help improve the efficiency for the prediction of QoS properties using its source code metrics.

BACKGROUND:The insect predator Coccinella septempunctata can effectively control many types of pests, such as aphids, whiteflies, and small lepidopteran larvae. We previously found that C. septempunctata fed an artificial diet showed diminished biological properties(e.g. fecundity, egg hatching rate, survival rate, etc.) compared with those fed natural prey (Aphis craccivora), likely due to different nutritional characteristics of the diet. In this study, we used transcriptome sequencing analysis to identify nutrition- and metabolism-related genes of C. septempunctata that were differentially expressed depending on diet. METHODOLOGY/PRINCIPAL FINDINGS:The Illumina HiSeq2000 was used to sequence 691,942,058 total clean reads from artificial diet-fed and A. craccivora-fed C. septempunctata libraries, and the clean reads were assembled using Trinity de novo software (Tabel 2). Comparison of transcriptome sequences revealed that expression of 38,315 genes was affected by the artificial diet, and 1,182 of these genes showed a significant change in expression levels (FDR ≤ 0.05,|log2FC|≥1, "FC" stands for "fold change"). These differentially expressed genes (DEGs) were likely associated with the decreased egg laying capacity, hatching rate, longevity, and increased sex ratio (♀:♂) of adult C. septempunctata observed in the group fed the artificial diet. Furthermore, in the most DEGs metabolic pathways for C. septempunctata feeding on the artificial diet accumulated amino acid metabolic pathways, lipid metabolic pathways, and starch and glucose metabolism were down-regulated. CONCLUSIONS/SIGNIFICANCE:We found some differentially expressed genes and metabolic pathways are related to nutrition, from which a more informative feedback for diet formulation was obtained and the artificial diet could be more efficiently optimized.

To provide the general information on corneal transplantation (CT) in China, China Cornea Society designed a questionnaire on CT from 2014 to 2018 and entrusted it to 31 committee members for implementation of the survey nationwide. This article presents the results of the survey and compares the indicators used in the survey and those in the annual statistical report released by the Eye Bank Association of America (EBAA). The number of corneal transplantations completed by the 64 hospitals from 2014 to 2018 was respectively 5377, 6394, 7595, 8270 and 8980, totally 36,616 (22,959 male and 13,657 female). The five largest hospitals by the number of corneal transplantations completed 15,994 surgeries in total, accounting for 43.68% of all the surgeries performed in the 64 hospitals. The most common indication for corneal transplantations was corneal leukoma (7683, 20.98%), followed by bacterial keratitis (4209, 11.49%), corneal dystrophies (4189, 11.44%), keratoconus (3578, 9.77%) and corneal perforation (2839, 7.75%). The main surgical techniques were penetrating keratoplasty (PK) (19,896, 54.34%), anterior lamellar keratoplasty (ALK) (13,869, 37.88%). The proportion of PK decreased from 57.97% in 2014 to 52.88% in 2018 while the proportion of ALK increased from 36.04% in 2014 to 37.92% in 2018. The geographical distribution of keratoplasties performed in China is unbalanced. PK and ALK were the main techniques of CT and corneal leukoma, bacterial keratitis and corneal dystrophies were the main indications for CT in China.

The privacy of Electronic Health Records (EHRs) is facing a major hurdle with outsourcing private health data in the cloud as there exists danger of leaking health information to unauthorized parties. In fact, EHRs are stored on centralized databases that increases the security risk footprint and requires trust in a single authority which cannot effectively protect data from internal attacks. This research focuses on ensuring the patient privacy and data security while sharing the sensitive data across same or different organisations as well as healthcare providers in a distributed environment. This research develops a privacy-preserving framework viz Healthchain based on Blockchain technology that maintains security, privacy, scalability and integrity of the e-health data. The Blockchain is built on Hyperledger fabric, a permissioned distributed ledger solutions by using Hyperledger composer and stores EHRs by utilizing InterPlanetary File System (IPFS) to build this healthchain framework. Moreover, the data stored in the IPFS is encrypted by using a unique cryptographic public key encryption algorithm to create a robust blockchain solution for electronic health data. The objective of the research is to provide a foundation for developing security solutions against cyber-attacks by exploiting the inherent features of the blockchain, and thus contribute to the robustness of healthcare information sharing environments. Through the results, the proposed model shows that the healthcare records are not traceable to unauthorized access as the model stores only the encrypted hash of the records that proves effectiveness in terms of data security, enhanced data privacy, improved data scalability, interoperability and data integrity while sharing and accessing medical records among stakeholders across the healthchain network.

As a novel concept of responding to disease epidemics, Fangcang shelter hospitals were deployed to expand the health system's capacity and provide medical services for non-severe COVID-19 patients during the outbreak in Wuhan. To give insights on patient management within Fangcang hospitals, we conducted a retrospective analysis to: 1) describe the characteristics of the patients admitted to Fangcang hospitals and 2) explore risk factors for longer length of stay (LOS). We enrolled 136 confirmed COVID-19 patients, including asymptomatic patients and those with mild symptoms, who were hospitalized in the Wuti Fangcang Hospital. 58 patients completed the treatment and discharged before 1 March 2020. After describing patients' demographic and clinical characteristics, exposure history, treatment received and time course of the disease, we conducted linear regression analysis to identify factors influencing LOS. We found that patients having fever before admission were hospitalized 3.5 days (95%CI 1.39 to 5.63, p = 0.002) longer than those without fever and that patients having bilateral pneumonia were hospitalized 3.4 days (95%CI 0.49 to 6.25, p = 0.023) longer than those with normal CT scan results. We also found weak evidence suggesting that patients with diabetes were hospitalized 3.2 days longer than those without diabetes (95%CI -0.2 to 6.56, p = 0.065). However, we observed no significant differences in LOS between symptomatic and asymptomatic patients and between patients who received treatment and those without treatment. Longer duration of hospitalization among non-severe COVID-19 patients is associated with having fever, bilateral pneumonia on CT scan and diabetes. However, being asymptomatic and using supportive medications at the early stage of infection do not have significant influences on LOS. Our study is a single-centered study with relatively small sample size. The findings provide evidence for predicting hospital bed demand in a novel response scenario and may help decision-makers in preparing for ramping up the health system capacity.

Telomere shortening is associated with aging and age-associated diseases. Additionally, telomere dysfunction resulting from telomerase gene mutation can lead to premature aging, such as apparent skin atrophy and hair loss. However, the molecular signaling linking telomere dysfunction to skin atrophy remains elusive. Here we show that dysfunctional telomere disrupts BMP/pSmad/P63 signaling, impairing epidermal stem cell specification and differentiation of skin and hair follicles. We find that telomere shortening mediated by Terc loss up-regulates Follistatin (Fst), inhibiting pSmad signaling and down-regulating P63 and epidermal keratins in an ESC differentiation model as well as in adult development of telomere-shortened mice. Mechanistically, short telomeres disrupt PRC2/H3K27me3-mediated repression of Fst. Our findings reveal that skin atrophy due to telomere dysfunction is caused by a previously unappreciated link with Fst and BMP signaling that could be explored in the development of therapies.

Recently identified broadly neutralizing antibodies (bnAbs) show great potential for clinical interventions against HIV-1 infection. However, resistant strains may impose substantial challenges. Here, we report on the identification and characterization of a panel of HIV-1 strains with broad and potent resistance against a large number of bnAbs, particularly those targeting the CD4-binding site (CD4bs). Site-directed mutagenesis revealed that several key epitope mutations facilitate resistance and are located in the inner domain, loop D, and β23/loop V5/β24 of HIV-1 gp120. The resistance is largely correlated with binding affinity of antibodies to the envelope trimers expressed on the cell surface. Our results therefore demonstrate the existence of broadly resistant HIV-1 strains against CD4bs neutralizing antibodies. Treatment strategies based on the CD4bs bnAbs must overcome such resistance to achieve optimal clinical outcomes.

Modified nucleosides in tRNAs are critical for protein translation. N1-methylguanosine-37 and N1-methylinosine-37 in tRNAs, both located at the 3'-adjacent to the anticodon, are formed by Trm5. Here we describe Arabidopsis thaliana AtTRM5 (At3g56120) as a Trm5 ortholog. Attrm5 mutant plants have overall slower growth as observed by slower leaf initiation rate, delayed flowering and reduced primary root length. In Attrm5 mutants, mRNAs of flowering time genes are less abundant and correlated with delayed flowering. We show that AtTRM5 complements the yeast trm5 mutant, and in vitro methylates tRNA guanosine-37 to produce N1-methylguanosine (m1G). We also show in vitro that AtTRM5 methylates tRNA inosine-37 to produce N1-methylinosine (m1I) and in Attrm5 mutant plants, we show a reduction of both N1-methylguanosine and N1-methylinosine. We also show that AtTRM5 is localized to the nucleus in plant cells. Proteomics data showed that photosynthetic protein abundance is affected in Attrm5 mutant plants. Finally, we show tRNA-Ala aminoacylation is not affected in Attrm5 mutants. However the abundance of tRNA-Ala and tRNA-Asp 5' half cleavage products are deduced. Our findings highlight the bifunctionality of AtTRM5 and the importance of the post-transcriptional tRNA modifications m1G and m1I at tRNA position 37 in general plant growth and development.

As antibiotic contamination increases in wastewater and aqueous environments, the reduction of antibiotics has become a pertinent topic of research regarding water treatment. Clay minerals, such as smectite or kaolinite, are important adsorbents used in water treatment, and sufficient removal of antibiotics by clay minerals is expected. In this study, the adsorption of oxytetracycline (OTC) on kaolinite was investigated. The experimental data of OTC adsorption on kaolinite fit the pseudo-second-order kinetics model well (R2>0.98). After 24 h, adsorption equilibrium of OTC on kaolinite was reached. The Langmuir model was better fitting with the adsorption isotherms generated from experimental data and OTC adsorption occurred on the external surface of kaolinite. The analysis of several thermodynamic parameters indicated that the adsorption of OTC on kaolinite was spontaneous and thermodynamically favorable. With the increase of the pH of a solution, the adsorption capacity increased and then decreased. The adsorption coefficient (Kd) of 102-103 were obtained for adsorption process of OTC on kaolinite.

Online product reviews underpin nearly all e-shopping activities. The high volume of data, as well as various online review quality, puts growing pressure on automated approaches for informative content prioritization. Despite a substantial body of literature on review helpfulness prediction, the rationale behind specific feature selection is largely under-studied. Also, the current works tend to concentrate on domain- and/or platform-dependent feature curation, lacking wider generalization. Moreover, the issue of result comparability and reproducibility occurs due to frequent data and source code unavailability. This study addresses the gaps through the most comprehensive feature identification, evaluation, and selection. To this end, the 30 most frequently used content-based features are first identified from 149 relevant research papers and grouped into five coherent categories. The features are then selected to perform helpfulness prediction on six domains of the largest publicly available Amazon 5-core dataset. Three scenarios for feature selection are considered: (i) individual features, (ii) features within each category, and (iii) all features. Empirical results demonstrate that semantics plays a dominant role in predicting informative reviews, followed by sentiment, and other features. Finally, feature combination patterns and selection guidelines across domains are summarized to enhance customer experience in today's prevalent e-commerce environment. The computational framework for helpfulness prediction used in the study have been released to facilitate result comparability and reproducibility.

ObjectiveTo compare the efficacy of tenofovir and entecavir in nucleos(t)ide analogue-naive chronic hepatitis B.MethodsThe Web of Science, PubMed, Cochrane Library, EMBASE, Clinical Trials and China National Knowledge Infrastructure(CNKI) databases were electronically searched to collect randomized controlled trials (RCTs) regarding the comparison between tenofovir and entecavir in nucleos(t)ide analogue-naive chronic hepatitis B (CHB) since the date of database inception to July 2019. Two researchers independently screened and evaluated the obtained studies and extracted the outcome indexes. RevMan 5.3 software was used for the meta-analysis.ResultsEarly on, tenofovir had a greater ability to inhibit the hepatitis B virus, I2 = 0% [RR = 1.08, 95% CI (1.03, 1.13), PConclusionsTenofovir and entecavir were equally effective in the treatment of patients with nucleos(t)ide analogue-naive chronic hepatitis B. In addition, TDF has an advantage in the incidence of hepatocellular carcinoma. Additional RCTs and a large-sample prospective cohort study should be performed.

BackgroundTobacco smoking remains a public health problem among adolescents in the United States. While Web-based interventions for smoking prevention have been successful at the individual level, there is still an urgent need to understand their engagement capabilities and their effects at the social level. In the current study, we aimed to (1) learn about adolescents' subjective experience with a Web-based program called a smoking prevention interactive experience (ASPIRE), (2) obtain suggestions for improvement in ASPIRE content, (3) identify psychological outcomes of ASPIRE, and (4) explore outcomes of social interaction.Materials and methodsAfter a randomized controlled trial with 110 adolescents, 20 adolescent users of ASPIRE, aged 11-18, were randomly selected to participate in one-on-one interviews at four after-school programs in Houston, Texas. Interviews involved questions concerning adolescents' experience with the intervention. Qualitative data were coded and analyzed using a constant comparison approach for the generation of themes.ResultsDescribing their experience with ASPIRE, participants expressed comfort in material that is tailored to their demographic and preferred interactive activities over entertaining videos. Presenting suggestions for improvement, participants mainly reported the need to include gaming features into ASPIRE. Presenting psychological outcomes, they expressed emotional engagement in the program, shifts in attitudes and beliefs, and unwillingness to smoke. Finally, as outcomes of social interaction, participants reported engagement with others in discussions about tobacco and their need to hold smokers accountable for their actions.ConclusionsAdolescents' reports moved from their individual experience with ASPIRE to their active interactions with family members and friends and their attempt to persuade others to quit smoking. Future Web-based programs for adolescents may be designed with tailoring and game play in mind, in order to provide mobilization skills and foster social interactions against smoking.

A cluster of eleven patients, including eight family members and three healthcare workers with fever and thrombocytopenia occurred in Yixing County, Jiangsu Province, China, from October to November 1996. However, the initial investigation failed to identify its etiology. Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by SFTS bunyavirus (SFTSV), which was first discovered in 2009. The discovery of novel SFTSV resulted in our consideration to test SFTSV on the remaining samples of this cluster in September 2010.We retrospectively analyzed the epidemiological and clinical data of this cluster. The first case, one 55-year-old man with fulminant hemorrhagic diseases, died on October 14, 1996. His younger brother (the second case) developed similar hemorrhagic diseases after nursing him and then died on November 3. From November 4 to November 15, nine other patients, including six family members and three medical staffs, developed fever and thrombocytopenia after exposure to the second case. The sera of six patients were collected on November 24, 1996. IgM antibodies against SFTSV were detected in all of the six patients' sera using enzyme-linked immunosorbent assay (ELISA), while IgG antibodies were detected in one patient's serum using an indirect immunofluorescence assay (IFA). We also found that IgG antibodies against SFTSV were still detected in four surviving patients' sera 14 years after illness onset.The mysterious pathogen of the cluster in 1996 was proved to be SFTSV on the basis of its epidemiological data, clinical data and serological results. It suggests that SFTSV has been circulating in China for more than 10 years before being identified in 2009, and SFTSV IgG antibodies can persist for up to 14 years.

Heartbeat classification is an important step in the early-stage detection of cardiac arrhythmia, which has been identified as a type of cardiovascular diseases (CVDs) affecting millions of people around the world. The current progress on heartbeat classification from ECG recordings is facing a challenge to achieve high classification sensitivity on disease heartbeats with a satisfied overall accuracy. Most of the work take individual heartbeats as independent data samples in processing. Furthermore, the use of a static feature set for classification of all types of heartbeats often causes distractions when identifying supraventricular (S) ectopic beats. In this work, a pyramid-like model is proposed to improve the performance of heartbeat classification. The model distinguishes the classification of normal and S beats and takes advantage of the neighbor-related information to assist identification of S bests. The proposed model was evaluated on the benchmark MIT-BIH-AR database and the St. Petersburg Institute of Cardiological Technics(INCART) database for generalization performance measurement. The results reported prove that the proposed pyramid-like model exhibits higher performance than the state-of-the-art rivals in the identification of disease heartbeats as well as maintains a reasonable overall classification accuracy.

To investigate the molecular mechanisms of sub-acutely aging and demonstrate the effect of electroacupuncture (EA) at the Guanyuan (CV 4), Zusanli (ST 36) and Baihui (DU 20) acupoint on the sub-acutely aging brain, cDNA microarrays and bioinformatics analyses were carried out. Thirty Sprague-Dawley (SD) male rats were selected and randomly divided into three groups: the control group (C), the sub-acutely aging model group (M) and the electroacupuncture group (M+EA). Sub-acutely aging model rats were obtained by D-galactose s.c. injection continuously for 40 days. Total RNA was extracted from the hippocampus area of brains in three groups for cDNA microarrays. The data of different groups were compared and analyzed by differential expression analysis, Gene ontology (GO) term enrichment, Kyoto Encyclopedia of Genes Genomes (KEGG) pathway enrichment and quantitative real-time PCR. According to the results, 4052 DE genes were identified in our study. Among them, there were 3079 differentially expressed (DE) genes between group M and group C, and these genes are associated with the aging of rats. Moreover, 983 genes were expressed differently in group M+EA compared with group M, revealing that points stimuli could regulate gene expression in brain with aging. Gene ontology (GO) term enrichment and KEGG enrichment were performed to further classify the differential expression genes. Important GO terms and KEGG pathways connected with sub-acutely aging EA effects were identified. At last, 3 significant differentially expressed genes were selected for real-time quantitative PCR to clarify the cDNA microarray results. In conclusion, the cDNA microarray data first compared and analyzed the differences of gene expression profile in the hippocampus of rats in different groups, which contribute to our knowledge on the molecular mechanisms of EA towards sub-acutely aging.