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ObjectiveThis study aimed to determine the sensitivity and specificity of metagenomic next−generation sequencing (mNGS) for detecting pathogens in spinal infections and to identify the differences in the diagnostic performance between mNGS and targeted next−generation sequencing (tNGS).MethodsA total of 76 consecutive patients with suspected spinal infections who underwent mNGS, culture, and histopathological examinations were retrospectively studied. The final diagnosis of the patient was determined by combining the clinical treatment results, pathological examinations, imaging changes and laboratory indicators. The sensitivity and specificity of mNGS and culture were determined.ResultsThe difference between the two detection rates was statistically significant (p < 0.001), with mNGS exhibiting a significantly higher detection rate (77.6% versus 18.4%). The average diagnosis time of mNGS was significantly shorter than that of bacterial culture (p < 0.001, 1.65 versus 3.07 days). The sensitivity and accuracy of mNGS were significantly higher than that of the culture group (p < 0.001, 82.3% versus 17.5%; 75% versus 27.6%), whereas the specificity of mNGS (42.9%) was lower than that of the culture group (p > 0.05, 42.9% versus 76.9%). The sensitivity, specificity, accuracy, and positive predictive value (PPV) of pus were higher than those of tissue samples for mNGS, whereas for culture, the sensitivity, specificity, accuracy, and PPV of tissue samples were higher than those of pus. tNGS demonstrated higher sensitivity and accuracy in diagnosing tuberculosis (TB) than mNGS (80% versus 50%; 87.5% versus 68.8%).ConclusionmNGS for spinal infection demonstrated better diagnostic value in developing an antibiotic regimen earlier, and it is recommended to prioritize pus samples for testing through mNGS. Moreover, tNGS outperformed other methods for diagnosing spinal TB and identifying antibiotic-resistance genes in drug-resistant TB.

We aim to explore the association between caffeine and its metabolites and bone mineral density (BMD) in postmenopausal women. Data of 4286 postmenopausal women were extracted from the National Health and Nutrition Examination Survey (NHANES) database in 2009–14 in this cross-sectional study. Weighted linear regression and stepwise regression analyses were used to screen the covariates. Weighted univariate and multivariate linear regression analyses were used to explore the associations between caffeine and its metabolites and BMD. The evaluation index was estimated value (β) with 95 % confidence intervals (CIs). We also explored these relationships in age subgroups. The median BMD level among the eligible women was 0⋅7 gm/cm2. After adjusting for covariates including age, body mass index (BMI), fat intake, Calcium (Ca) supplements, diabetes mellitus (DM), angina pectoris, parental history of osteoporosis (OP), anti-osteoporosis therapy, poverty income ratio (PIR), vitamin D (VD) supplements, coronary heart disease (CHD), and previous fracture, we found that caffeine intake was not significantly related to the BMD reduction (β = 0, P = 0⋅135). However, caffeine metabolites, including MethyluricAcid3, MethyluricAcid7, MethyluricAcid37, Methylxanthine3, and Methylxanthine37, were negatively associated with the BMD (all P < 0⋅05). In addition, MethyluricAcid37 and Methylxanthine37 were negatively associated with BMD in females aged

BackgroundThe reinfection rate of SARS-CoV-2 Omicron variant is high; thus, exploring the risk factors for reinfection is important for the effective control of the epidemic. This study aimed to explore the effects of psychological and sleep factors on re-positivity with Omicron.MethodsThrough a prospective cohort study, 933 adult patients diagnosed with Omicron BA.2.2 infection and testing negative after treatment were included for screening and follow-up. We collected data on patients' demographic characteristics, SARS-CoV-2 Omicron vaccination status, anxiety, depression, and sleep status. Patients underwent nucleic acid testing for SARS-CoV-2 Omicron for 30 days. Regression and Kaplan-Meier analyses were used to determine the risk factors for re-positivity of Omicron.ResultsUltimately, 683 patients were included in the analysis. Logistic regression analysis showed that older age (P = 0.006) and depressive status (P = 0.006) were two independent risk factors for Omicron re-positivity. The odds ratios of re-positivity in patients aged ≥60 years and with a Patient Health Questionnaire-9 (PHQ-9) score ≥5 was 1.82 (95% confidence interval:1.18–2.78) and 2.22 (1.27–3.85), respectively. In addition, the time from infection to recovery was significantly longer in patients aged ≥60 years (17.2 ± 4.5 vs. 16.0 ± 4.4, P = 0.003) and in patients with PHQ-9≥5 (17.5 ± 4.2vs. 16.2 ± 4.5, P = 0.026). Kaplan–Meier analysis showed that there was a significantly higher primary re-positivity rate in patients aged ≥60 years (P = 0.004) and PHQ-9 ≥ 5 (P = 0.007).ConclusionThis study demonstrated that age of ≥60 years and depressive status were two independent risk factors for re-positivity with Omicron and that these factors could prolong the time from infection to recovery. Thus, it is necessary to pay particular attention to older adults and patients in a depressive state.

The biggest challenge of texture filtering is to smooth the strong gradient textures while maintaining the weak structures, which is difficult to achieve with current methods. Based on this, we propose a scale-adaptive texture filtering algorithm in this paper. First, the four-directional detection with gradient information is proposed for structure measurement. Second, the spatial kernel scale for each pixel is obtained based on the structure information; the larger spatial kernel is for pixels in textural regions to enhance the smoothness, while the smaller spatial kernel is for pixels on structures to maintain the edges. Finally, we adopt the Fourier approximation of range kernel, which reduces computational complexity without compromising the filtering visual quality. By subjective and objective analysis, our method outperforms the previous methods in eliminating the textures while preserving main structures and also has advantages in structure similarity and visual perception quality.

In this study, a deep learning model is proposed to predict groundwater levels. The model is able to accurately complete the prediction task even when the data utilized are insufficient. The hybrid model that we have developed, CNN-LSTM-ML, uses a combined network structure of convolutional neural networks (CNN) and long short-term memory (LSTM) network to extract the time dependence of groundwater level on meteorological factors, and uses a meta-learning algorithm framework to ensure the network’s performance under sample conditions. The study predicts groundwater levels from 66 observation wells in the middle and lower reaches of the Heihe River in arid regions and compares them with other data-driven models. Experiments show that the CNN-LSTM-ML model outperforms other models in terms of prediction accuracy in both the short term (1 month) and long term (12 months). Under the condition that the training data are reduced by 50%, the MAE of the proposed model is 33.6% lower than that of LSTM. The results of ablation experiments show that CNN-LSTM-ML is 26.5% better than the RMSE of the original CNN-LSTM structure. The model provides an effective method for groundwater level prediction and contributes to the sustainable management of water resources in arid regions.

HD-ZIP is a unique type of transcription factor in plants, which are closely linked to the regulation of plant growth and development, the response to abiotic stress, and disease resistance. However, there is little known about the HD-ZIP gene family of pepper. In this study, 40 HD-ZIP family members were analyzed in the pepper genome. The analysis indicated that the introns number of Ca-HD-ZIP varied from 1 to 17; the number of amino acids was between 119 and 841; the theoretical isoelectric point was between 4.54 and 9.85; the molecular weight was between 14.04 and 92.56; most of them were unstable proteins. The phylogenetic tree divided CaHD-ZIP into 4 subfamilies; 40 CaHD-ZIP genes were located on different chromosomes, and all of them contained the motif 1; two pairs of CaHD-ZIP parallel genes of six paralogism genes were fragment duplications which occurred in 58.28~88.24 million years ago. There were multiple pressure-related action elements upstream of the start codon of the HD-Z-IP family. Protein interaction network proved to be coexpression phenomenon between ATML1 (CaH-DZ22, CaHDZ32) and At4g048909 (CaHDZ12, CaHDZ31), and three regions of them were highly homology. The expression level of CaHD-ZIP gene was different with tissues and developmental stages, which suggested that CaHD-ZIP may be involved in biological functions during pepper progress. In addition, Pepper HD-ZIP I and II genes played a major role in salt stress. CaHDZ03, CaHDZ 10, CaHDZ17, CaHDZ25, CaHDZ34, and CaHDZ35 were significantly induced in response to salt stress. Notably, the expression of CaHDZ07, CaHDZ17, CaHDZ26, and CaHDZ30, homologs of Arabidopsis AtHB12 and AtHB7 genes, was significantly upregulated by salt stresses. CaHDZ03 possesses two closely linked ABA action elements, and its expression level increased significantly at 4 h under salt stress. qRT-P-CR and transcription analysis showed that the expression of CaHDZ03 and CaHDZ10 was upregulated under short-term salt stress, but CaHDZ10 was downregulated with long-term salt stress, which provided a theoretical basis for research the function of Ca-HDZIP in response to abiotic stress.

Tailings dams are constructed as storage dams for ore waste, serving as industrial waste piles and for drainage. The dam is negatively affected by rainfall, infiltration lines and its own gravity, which can cause its instability to gradually increase, leading to dam deformation. To predict the irregular changes of tailings dam deformation, empirical mode decomposition (EMD) is applied to the deformation data to obtain the trend and periodic components. The attention mechanism is used to assign different weights to the input variables to overcome the limitation that the long short-term memory (LSTM) model can only generate fixed-length vectors. The lagged autocorrelation coefficient is applied to each decomposed subregion to solve the lagging effect of external factors on dam deformation. Finally, the model is used to predict deformation in multiple directions to test the generalization ability. The proposed method can effectively mitigate the problems of gradient disappearance and gradient explosion. The applied results show that, compared with the control model EMD-LSTM, the evaluation indexes RMSE and MAE improve 23.66% and 27.90%, respectively. The method also has a high prediction accuracy in the remaining directions of the tailings dam, which has a wide practical application effect and provides a new idea for tailings dam deformation mechanism research.

Circular RNA (circRNA) is a group of endogenous noncoding RNA characterized by a covalently closed cyclic structure lacking poly-adenylated tails. Recent studies have suggested that circRNAs play a crucial role in regulating gene expression by acting as a microRNA sponge, RNA binding protein sponge and translational regulator. CircRNAs have become a research hotspot because of their close association with the development of diseases. Some circRNAs are reportedly expressed in a tissue- and development stage-specific manner. Furthermore, due to other features of circRNAs including stability, conservation and high abundance in body fluids, circRNAs are believed to be potential biomarkers for various diseases. In the present review, we provide the current understanding of biogenesis and gene regulatory mechanisms of circRNAs, summarize the recent studies on circRNAs as potential diagnostic and prognostic biomarkers, and highlight the major advantages and limitations of circRNAs as novel biomarkers based on existing knowledge. Keywords: Circular RNA, Biomarker, Diseases, Liquid biopsy

Wind energy has increasingly played a vital role in mitigating conventional resource shortages. Nevertheless, the stochastic nature of wind poses a great challenge when attempting to find an accurate forecasting model for wind power. Therefore, precise wind power forecasts are of primary importance to solve operational, planning and economic problems in the growing wind power scenario. Previous research has focused efforts on the deterministic forecast of wind power values, but less attention has been paid to providing information about wind energy. Based on an optimal Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Singular Spectrum Analysis (SSA), this paper develops a hybrid uncertainty forecasting model, IFASF (Interval Forecast-ANFIS-SSA-Firefly Alogorithm), to obtain the upper and lower bounds of daily average wind power, which is beneficial for the practical operation of both the grid company and independent power producers. To strengthen the practical ability of this developed model, this paper presents a comparison between IFASF and other benchmarks, which provides a general reference for this aspect for statistical or artificially intelligent interval forecast methods. The comparison results show that the developed model outperforms eight benchmarks and has a satisfactory forecasting effectiveness in three different wind farms with two time horizons. [ABSTRACT FROM AUTHOR]