Objective A diagnostic model was established to discriminate infectious diseases from non-infectious diseases.
Methods The clinical data of patients with fever of unknown origin (FUO) hospitalized in Xiangya Hospital Central South University, from January, 2006 to April, 2011 were retrospectively analyzed. Patients enrolled were divided into two groups. The first group was used to develop a diagnostic model: independent variables were recorded and considered in a logistic regression analysis to identify infectious and non-infectious diseases (αin = 0.05, αout = 0.10). The second group was used to evaluate the diagnostic model and make ROC analysis.
Results The diagnostic rate of 143 patients in the first group was 87.4%, the diagnosis included infectious disease (52.4%), connective tissue diseases (16.8%), neoplastic disease (16.1%) and miscellaneous (2.1%). The diagnostic rate of 168 patients in the second group was 88.4%, and the diagnosis was similar to the first group. Logistic regression analysis showed that decreased white blood cell count (WBC < 4.0×109/L), higher lactate dehydrogenase level (LDH > 320 U/L) and lymphadenectasis were independent risk factors associated with non-infectious diseases. The odds ratios were 14.74, 5.84 and 5.11 (P ≤ 0.01) , respectively. In ROC analysis, the sensitivity and specificity of the positive predictive values was 62.1% and 89.1%, respectively, while that of negative predicting values were 75% and 81.7%, respectively (AUC = 0.76, P = 0.00).
Conclusions The combination of WBC < 4.0×109/L, LDH > 320 U/L and lymphadenectasis may be useful in discriminating infectious diseases from non-infectious diseases in patients hospitalized as FUO.
A facility of BaPS (Barometric Process Separation) and indoor incubation experiments were used to determine the effect of soil salinity on soil respiration and nitrogen transformation. The rates of soil respiration, gross nitrification, denitrification, ammonium and nitrate nitrogen concentrations and relevant soil parameters were measured. Results showed that soil respiration and nitrification and denitrification rates were all affected by soil salinity. Furthermore, the effect of soil salinity level on nitrification and denitrification rates had a threshold value (EC1:5 = 1.13 dS/m). When soil salinity level was smaller to this threshold value, the rates of nitrification and denitrification increased with soil salinity while they were reduced when soil salinity level was larger than the threshold value. Moreover, the changing law of soil respiration rate with soil salinity was similar with the nitrification and denitrification rates while the variation tendency was opposite. In addition, the transformation form urea to ammonium and nitrate nitrogen was also reduced with the increase of soil salinity and the reduced effect could be expressed by exponential functions.
Intermittent irrigation has attracted much attention as a water-saving technology in arid and semi-arid regions. For understanding the effect of intermittent irrigation on water and solute storage varied from irrigation amount per time (IRA), irrigation application frequency (IRAF), irrigation intervals (IRI) and even soil texture (ST), intermittent irrigation experiment was carried out in 33 micro-plots in Inner Mongolia, China. The experiment results were used for the calibration and validation of HYDRUS-1D software. Then 3 ST (silty clay loam, silty loam, and silty clay), 5 IRA (2, 4, 6, 8, and 10 cm), 4 IRAF (2, 3, 4, and 5 times) and 4 IRI (1, 2, 3, and 4 days) were combined and total 240 scenarios were simulated by HYDRUS-1D. Analysis of variance (ANVOA) of simulated results indicated that ST, IRA, and IRAF had significant effect on salt and nitrate nitrogen (NO3−-N) storage of 0-40 cm depth soil in intermittent irrigation while only ST affected soil water storage obviously. Furthermore, salt leaching percentage (SLP) and water use efficiency (WUE) of 0-40 cm depth were calculated and statistical prediction models for SLP were established based on the ANOVA using multiple regression analysis in each soil texture. Then constraint conditions of soil water storage (around field capacity), salt storage (smaller than 168 mg·cm−2), WUE (as large as possible) in 0-40 cm depth and total irrigation water amount (less than 25 cm) were proposed to find out the optimal intermittent irrigation strategies. Before sowing, the optimal irrigation strategy for silty clay loam soil was 6 cm IRA, 3 times IRAF, and 2 days IRI respectively. For silty loam and silty clay soils, IRA, IRAF, and IRI were 8 cm, 3 times, and 2 days respectively.
Objective: The objective of this work is to search for a novel method to explore the disrupted pathways associated with periodontitis (PD) based on the network level.
Methods: Firstly, the differential expression genes (DEGs) between PD patients and cognitively normal subjects were inferred based on LIMMA package. Then, the protein-protein interactions (PPI) in each pathway were explored by Empirical Bayesian (EB) co-expression program. Specifically, we determined the 100th weight value as the threshold value of the disrupted pathways of PPI by constructing the randomly model and confirmed the weight value of each pathway. Meanwhile, we dissected the disrupted pathways under the weight value > the threshold value. Pathways enrichment analyses of DEGs were carried out based on Expression Analysis Systematic Explored (EASE) test. Finally, the better method was selected based on the more rich and significant obtained pathways by comparing the two methods.
Results: After the calculation of LIMMA package, we estimated 524 DEGs in all. Then we determined 0.115222 as the threshold value of the disrupted pathways of PPI. When the weight value>0.115222, there were 258 disrupted pathways of PPI enriched in. Additionally, we observed those 524 DEGs that were enriched in 4 pathways under EASE=0.1.
Conclusion: We proposed a novel network method inferring the disrupted pathway for PD. The disrupted pathways might be underlying biomarkers for treatment associated with PD.
For improving the understanding of interactions between hyperspectral reflectance and soil salinity, in situ hyperspectral inversion of soil salt content at a depth of 0-10 cm was conducted in Hetao Irrigation District, Inner Mongolia, China. Six filtering methods were used to preprocess soil reflectance data, and waveband selection combined by VIP (variable importance in projection) and b-coefficients (regression coefficients of model) was also applied to simplify model. Then statistical methods of partial least square regression (PLS) and orthogonal projection to latent structures (OPLS) were processed to establish the inversion models. Our findings indicate that the selected sensitive wavebands for the 6 filtering methods are different, among which the multiplicative signal correction (MSC) and standard normal variate methods (SNV) have some similar sensitive wavebands with unfiltered data. Derivatives (DF1 and DF2) could characterize sensitive wavebands along the scale of VNIR (350-1100 nm), especially the second derivative (DF2). The sensitive wavebands for continuum-removed reflectance method (CR) have protruded many narrow absorption features. For orthogonal signal correction method (OSC), the selected wavebands are centralized in the range of 565-1013 nm. The calibration and evaluation processes have demonstrated the second order derivate filtering method (DF2) combined with waveband selection is superior to other processes, for it has high R2 (larger than 0.7) both in PLS and OPLS models for calibration and evaluation, by choosing only 156 wavebands from the whole 700 wavebands. Meanwhile, OPLS method was considered to be more suitable for the analyzing than PLS in most of our situations.
Being an economical and endangered species, microsatellite markers of Taxus chinensis var. mairei were very limited. We have developed a set of microsatellite markers, which was benefit for future genetic analysis of this rare species. Polymorphic loci were developed from congeneric species by cross-species amplification methods, and new primers were redesigned to test for potential null alleles. 15 loci showed polymorphism. The number of alleles per locus varied from 2 to 23 tested in 48 individuals. The observed heterozygosity (Ho) and expected heterozygosity (He) values ranged form 0.000 to 0.854 and 0.082 to 0.827, respectively. Newly redesigned primer confirmed that no null allele existed in most suspected loci. These microsatellite markers will be useful for future genetic analysis and conservation of this endangered species.
For estimation of root-zone moisture content from EO-1/Hyperion imagery, surface soil moisture was first predicted by hyperspectral reflectance data using partial least square regression (PLSR) analysis. The textures of more than 300 soil samples extracted from a 900 m × 900 m field site located within the Hetao Irrigation District in China were used to parameterize the HYDRUS-1D numerical model. The study area was spatially discretized into 18,000 compartments (30 m × 30 m × 0.02 m), and Monte Carlo simulations were applied to generate 2000 different soil-particle size distributions for each compartment. Soil hydraulic properties for each realization were determined by application of artificial neural network analysis and used to parameterize HYDRUS-1D to simulate averaged soil-moisture contents within the root zone (0-40 cm) and surface (approximately 0-4 cm). Then the link between surface moisture and root zone was established by use of linear regression analysis, resulting in R and RMSE of 0.38 and 0.03, respectively. Kriging and co-kriging with observed surface moisture, and co-kriging with surface moisture obtained from Hyperion imagery were also used to estimate root-zone moisture. Results indicated that PLSR is a powerful tool for soil moisture estimation from hyperspectral data. Furthermore, co-kriging with observed surface moisture had the highest R (0.41) and linear regression model, and HYDRUS Monte Carlo simulations had a lowest RMSE (0.03) among the four methods. In regions that have similar climatic and soil conditions to our study area, a linear regression model with HYDRUS Monte Carlo simulations is a practical method for root-zone moisture estimation before sowing and it can be easily coupled with remote sensing technology.
Although animals such as spiders, fish, and birds have very different anatomies, the basic mechanisms that govern their perception, decision-making, learning, reproduction, and death have striking similarities. These mechanisms have apparently allowed the development of general intelligence in nature. This led us to the idea of approaching artificial general intelligence (AGI) by constructing a generic artificial animal (animat) with a configurable body and fixed mechanisms of perception, decision-making, learning, reproduction, and death. One instance of this generic animat could be an artificial spider, another an artificial fish, and a third an artificial bird. The goal of all decision-making in this model is to maintain homeostasis. Thus actions are selected that might promote survival and reproduction to varying degrees. All decision-making is based on knowledge that is stored in network structures. Each animat has two such network structures: a genotype and a phenotype. The genotype models the initial nervous system that is encoded in the genome (“the brain at birth”), while the phenotype represents the nervous system in its present form (“the brain at present”). Initially the phenotype and the genotype coincide, but then the phenotype keeps developing as a result of learning, while the genotype essentially remains unchanged. The model is extended to ecosystems populated by animats that develop continuously according to fixed mechanisms for sexual or asexual reproduction, and death. Several examples of simple ecosystems are given. We show that our generic animat model possesses general intelligence in a primitive form. In fact, it can learn simple forms of locomotion, navigation, foraging, language, and arithmetic.
Objective Fusion protein is a subunit of the human respiratory syncytial virus (HRSV) and a potential vaccine candidate. Thus, a study on the genetic characteristics of F protein was considered important for further investigations in this field. The aim of this study was to determine the prevalence and genetic diversity of the F gene of HRSV infections in hospitalized pediatric patients in Beijing with acute lower respiratory tract infections and to compare the circulating genotypes that are currently found worldwide.
Methods HRSV particles were amplified by RT-PCR and the PCR products were purified for sequencing. Further analysis was carried out by Bioedit and MEGA 3.0 biological software programs.
Results Seventy-six samples (23.1%) were positive for HRSV. The percentage of cases in patients younger than 1 year was 84.21%. Among the six Beijing isolates, four belonged to subgroup A, whose respective F genes shared 97.0%-97.4% nucleotide sequence identity and 92.1%-93.0% amino acid sequence identity. The other two isolates belonged to subgroup B. Here, 97.3% and 98.2% sequence identity were found at nucleotide and amino acid levels, respectively.
Conclusions Phylogenetic analysis of nucleotide sequences revealed that those four isolates within subgroup A were monophyletic and closely related to each other, but those two within subgroup B distributed in two distinct clusters. Subgroup A and B strains co-circulated, indicating that two different transmission chains occurred in Beijing from 2003-2004.
The chemokine C-C motif ligand 11, also known as eotaxin-1, has been identified as a novel mediator of inflammatory bone resorption. However, little is known regarding a potential role for CCL11/Eotaxin-1 in postmenopausal osteoporosis.
The scope of this study was to explore the relationship between serum CCL11/Eotaxin-1 concentrations and disease progression of postmenopausal females with osteoporosis.
A total of 83 postmenopausal women diagnosed with osteoporosis were enrolled. Meanwhile, 82 postmenopausal women with normal bone mineral density (BMD) and 85 healthy controls inner child-bearing age were enrolled as control. The Dual-energy X-ray absorptiometry was used to examine the BMDs at the femoral neck, lumbar spine 1-4 and total hip of all participants. Serum CCL11/Eotaxin-1 levels were examined by enzyme-linked immunosorbent assay. We also included inflammation marker interleukin-6 (IL-6) as well as a serum marker of bone resorption C-telopeptide cross-linked collagen type 1 (CTX-1). The Visual Analogue Scale (VAS) and Oswestry Disability Index (ODI) were recorded to evaluate the clinical severity in POMP females.
Serum CCL11/Eotaxin-1 levels were significantly elevated in postmenopausal osteoporotic patients PMOP patients compared with PMNOP and healthy controls. We observed a significant negative correlation of serum CCL11/Eotaxin-1 levels with lumbar spine, femoral neck and total hip BMD. Furthermore, serum CCL11/ Eotaxin-1 concentrations were also positively related to the VAS and ODI scores. Last, serum CCL11/ Eotaxin-1 concentrations were positively associated with IL-6 and CTX-1 levels. These correlations remain significant after adjusting for age and BMI. Multivariate linear regression analysis demonstrated that CCL11/Eotaxin-1 could serve as an independent marker.
Serum CCL 11/Eotaxin-1 may serve as a candidate biomarker for postmenopausal osteoporosis. Therapeutics targeting CCL11/Eotaxin-1 and its related signalling way to prevent and slow progression of PMOP deserve further study.