Soil N2O emission in Cinnamomum camphora plantations along an urbanization gradient altered by changes in litter input and microbial community composition.

Urbanization alters land use, increasing the rate of greenhouse gas (GHG) emissions and hence atmospheric compositions. Nitrous oxide (N2O) is a major GHG that contributes substantially to global warming. N2O emissions are sensitive to changes in substrate availabilities, such as litter and N input, as well as micro-environmental factors caused by land-use change upon urbanization. However, the potential impacts of changing litter and N on soil N2O emissions along urban-rural gradients is not well understood. Here, we conducted an in situ study over 19 months in Cinnamomum camphora plantations along an urban-rural gradient, to examine the effects of the urban-rural gradient, N and litter input on N2O emissions from C. camphora plantation soils and the underlying mechanisms via N, litter and microbial communities. The results showed that urban soil N2O emissions were 105% and 196% higher than those from suburban and rural soil, respectively, and co-occurred with a higher abundance of AOA, nirS and nirK genes. Litter removal increased cumulative N2O emissions by 59.7%, 50.9% and 43.3% from urban, suburban and rural soils, respectively. Compared with litter kept treatment, increases in AOA and nirK abundance were observed in urban soil, and higher rural nirS abundance occurred following litter removal. Additionally, the relatively higher soil temperature and available N content in the urban soil increased N2O emissions compared with the suburban and rural soil. Therefore, in addition to changes in microbial communities and abiotic environmental factors, litter kept in C. camphora plantations along an urban-rural gradient is also important in mitigating N2O emissions, providing a potential strategy for the mitigation of N2O emissions.

The Construction Model of the TCM Clinical Knowledge Coding Database Based on Knowledge Organization.

Based on the knowledge organization method, this paper explores the construction method of the traditional Chinese medicine (TCM) clinical knowledge coding model by taking TCM clinical electronic medical record data as the research object. Firstly, extracting technology is used to obtain the required data in the electronic medical record. Then, by constructing the clinical knowledge coding model, the tacit knowledge is made explicit, establishing the clinical knowledge base and exploring the connotation of TCM clinical knowledge. It provides necessary data resources for deepening the expression level of TCM clinical knowledge, constructing accurate TCM clinical diagnosis, intervention, and evaluation models, and promoting the inheritance, innovation, and development of TCM. In this paper, we extracted the data of 318 cases of distention and established the TCM clinical database from the basic information of patients, clinical diagnosis information, clinical diagnosis and treatment information, and clinical evaluation information. Based on the knowledge coding model and the connotation of knowledge attributes, the established TCM clinical knowledge base was to explore the law of TCM clinical precision diagnosis and treatment.

Random-Forest-Algorithm-Based Applications of the Basic Characteristics and Serum and Imaging Biomarkers to Diagnose Mild Cognitive Impairment.

BACKGROUND: Mild cognitive impairment (MCI) is considered the early stage of Alzheimer's Disease (AD). The purpose of our study was to analyze the basic characteristics and serum and imaging biomarkers for the diagnosis of MCI patients as a more objective and accurate approach. METHODS: The Montreal Cognitive Test was used to test 119 patients aged ≥65. Such serum biomarkers were detected as preprandial blood glucose, triglyceride, total cholesterol, Aß1-40, Aß1-42, and P-tau. All the subjects were scanned with 1.5T MRI (GE Healthcare, WI, USA) to obtain DWI, DTI, and ASL images. DTI was used to calculate the anisotropy fraction (FA), DWI was used to calculate the apparent diffusion coefficient (ADC), and ASL was used to calculate the cerebral blood flow (CBF). All the images were then registered to the SPACE of the Montreal Neurological Institute (MNI). In 116 brain regions, the medians of FA, ADC, and CBF were extracted by automatic anatomical labeling. The basic characteristics included gender, education level, and previous disease history of hypertension, diabetes, and coronary heart disease. The data were randomly divided into training sets and test ones. The recursive random forest algorithm was applied to the diagnosis of MCI patients, and the recursive feature elimination (RFE) method was used to screen the significant basic features and serum and imaging biomarkers. The overall accuracy, sensitivity, and specificity were calculated, respectively, and so were the ROC curve and the area under the curve (AUC) of the test set. RESULTS: When the variable of the MCI diagnostic model was an imaging biomarker, the training accuracy of the random forest was 100%, the correct rate of the test was 86.23%, the sensitivity was 78.26%, and the specificity was 100%. When combining the basic characteristics, the serum and imaging biomarkers as variables of the MCI diagnostic model, the training accuracy of the random forest was found to be 100%; the test accuracy was 97.23%, the sensitivity was 94.44%, and the specificity was 100%. RFE analysis showed that age, Aß1-40, and cerebellum_4_6 were the most important basic feature, serum biomarker, imaging biomarker, respectively. CONCLUSION: Imaging biomarkers can effectively diagnose MCI. The diagnostic capacity of the basic trait biomarkers or serum biomarkers for MCI is limited, but their combination with imaging biomarkers can improve the diagnostic capacity, as indicated by the sensitivity of 94.44% and the specificity of 100% in our model. As a machine learning method, a random forest can help diagnose MCI effectively while screening important influencing factors.

Biochar derived from spent mushroom substrate reduced N2O emissions with lower water content but increased CH4 emissions under flooded condition from fertilized soils in Camellia oleifera plantations.

Agricultural soils are major sources of greenhouse gases (GHGs) that related with intensive fertilizer input. Biochar is widely used to mitigate GHGs, which may interact with soil water content impacting GHG emissions. Camellia oleifera fruit shell (FS) and spent mushroom substrate (MS) are ideal biochar feedstocks. However, the impact of water content and biochar on soil GHG emissions has not been thoroughly understood. Here, we examined CH4 and N2O emissions from C. oleifera plantation soils as affected by biochar (derived from MS or FS, 1 g 25 g-1 soil), water content (60%, 120%, 240% or 360% water holding capacity, WHC), and fertilization (control or chicken manure, CM 2.5 g 25 g-1 soil). We determined the abundance of related microbial functional genes to obtain the underlining mechanisms. The results showed that higher N2O emissions occurred in soils with 120%WHC, due to increased abundance of AOA, AOB and nirS. MS or FS biochar differed in their effects on soil GHG emissions with different WHC. MS biochar was higher in pH, C/N and specific surface area, and mitigated more N2O emissions from soils with CM and 120%WHC relative to FS biochar (by 92.9% and 34.6%, respectively). MS biochar significantly decreased abundance of nitrification related functional genes (AOA, AOB) in soils with 120%WHC and CM, which explained the decrease in N2O emissions. However, MS biochar increased cumulative CH4 emissions from flooded soils via increase in mcrA abundance. Thereby, biochar feedstocks should be considered in CH4 and N2O mitigations from soils with different water contents.

[Long-term water use characteristics and patterns of typical tree species in seasonal drought regions]. / å­£èŠ‚æ€§å¹²æ—±åœ°åŒºå ¸åž‹æ ‘ç§é•¿æœŸæ°´åˆ†åˆ©ç”¨ç‰¹å¾ä¸Žæ¨¡å¼.

In the areas with seasonal drought, water is the key factor affecting plant growth and development. Based on long-term continuous observation data, it is of great significance to explore plant water use patterns for vegetation construction in areas with seasonal drought. Taking Platycladus orientalis in Beijing mountainous area as the research object, stable hydrogen and oxygen isotope technique was applied to measure the isotopic composition of water from the soil, plant branches, and precipitation from 2012 to 2017. The relative contribution of soil water from different soil layers to P. orientalis was quantified by the MixSIAR model. The results showed that soil water in the deep layer (40-100 cm) was more stable than that in the shallow layer (0-40 cm). The variation of soil water content and water isotope values in the shallow layer were more obvious due to the effects of evaporation and precipitation. P. orientalis mainly absorbed stable deep soil water, with a relative contribution rate of 55.7%. In the dry season, with the decreases of soil water content, the absorption depth of plants to soil water gradually shifted to the shallow layer. Under conditions of moist, natural condition, mild drought and moderate drought, the relative contribution rates of deep soil water were 59.8%, 57.9%, 54.6%, 52.7%, respectively. To maintain higher transpiration in the wet season, P. orientalis relied more on deep soil water under mild and moderate drought conditions than in the dry season. Under the conditions of moist, natural condition, mild drought and moderate drought, the relative contribution rates of deep soil water were 58.9%, 57.6%, 56.4%, and 57.1%, respectively. The adaptive characteristic of P. orientalis, which adjusts the depth of root water absorption according to soil moisture condition, is of great significance for tree species selection in ecological afforestation and long-term management planning in areas with seasonal drought.

Soil C-N-P pools and stoichiometry as affected by intensive management of camellia oleifera plantations.

Intensive management of C. oleifera has produced many pure C. oleifera plantations. The transmission of C. oleifera plantation will potentially affect soil C, N, and P pools as well as their stoichiometric characteristics both in top soil layer and vertical soil profile due to the intensive management. To understand changes in vertical pools and stoichiometric characteristics of soil C, N, and P as affected by intensive management of C. oleifera plantations, both mixed and pure C. oleifera plantations were studied. We conducted studies in five locations in Jiangxi, China with both pure and mixed C. oleifera plantations, to compare changes in vertical pools and stoichiometry of C, N, and P. Both C and N pools were significantly different between mixed and pure plantation types of C. oleifera. However, the ratio of C:N, C:P, and N:P was consistently higher in mixed plantations with C:P and N:P altered but C:N ratio did not change with soil depth. The intensive management significantly impact both C and N pools and the stoichiometry of C, N, and P. Intensive management of C. oleifera plantations decreased both C and N pools, especially at the depth of 30-50 cm soil layer. C. oleifera plantation alteration from mixed to pure should be considered in future forest management practice considering the substantial effects on soil element cycling and distribution along vertical soil profile.

JRmGRN: joint reconstruction of multiple gene regulatory networks with common hub genes using data from multiple tissues or conditions.

Motivation: Joint reconstruction of multiple gene regulatory networks (GRNs) using gene expression data from multiple tissues/conditions is very important for understanding common and tissue/condition-specific regulation. However, there are currently no computational models and methods available for directly constructing such multiple GRNs that not only share some common hub genes but also possess tissue/condition-specific regulatory edges. Results: In this paper, we proposed a new graphic Gaussian model for joint reconstruction of multiple gene regulatory networks (JRmGRN), which highlighted hub genes, using gene expression data from several tissues/conditions. Under the framework of Gaussian graphical model, JRmGRN method constructs the GRNs through maximizing a penalized log likelihood function. We formulated it as a convex optimization problem, and then solved it with an alternating direction method of multipliers (ADMM) algorithm. The performance of JRmGRN was first evaluated with synthetic data and the results showed that JRmGRN outperformed several other methods for reconstruction of GRNs. We also applied our method to real Arabidopsis thaliana RNA-seq data from two light regime conditions in comparison with other methods, and both common hub genes and some conditions-specific hub genes were identified with higher accuracy and precision. Availability and implementation: JRmGRN is available as a R program from: https://github.com/wenpingd. Supplementary information: Supplementary data are available at Bioinformatics online.

TGMI: an efficient algorithm for identifying pathway regulators through evaluation of triple-gene mutual interaction.

Despite their important roles, the regulators for most metabolic pathways and biological processes remain elusive. Presently, the methods for identifying metabolic pathway and biological process regulators are intensively sought after. We developed a novel algorithm called triple-gene mutual interaction (TGMI) for identifying these regulators using high-throughput gene expression data. It first calculated the regulatory interactions among triple gene blocks (two pathway genes and one transcription factor (TF)), using conditional mutual information, and then identifies significantly interacted triple genes using a newly identified novel mutual interaction measure (MIM), which was substantiated to reflect strengths of regulatory interactions within each triple gene block. The TGMI calculated the MIM for each triple gene block and then examined its statistical significance using bootstrap. Finally, the frequencies of all TFs present in all significantly interacted triple gene blocks were calculated and ranked. We showed that the TFs with higher frequencies were usually genuine pathway regulators upon evaluating multiple pathways in plants, animals and yeast. Comparison of TGMI with several other algorithms demonstrated its higher accuracy. Therefore, TGMI will be a valuable tool that can help biologists to identify regulators of metabolic pathways and biological processes from the exploded high-throughput gene expression data in public repositories.

SSGA and MSGA: two seed-growing algorithms for constructing collaborative subnetworks.

The establishment of a collaborative network of transcription factors (TFs) followed by decomposition and then construction of subnetworks is an effective way to obtain sets of collaborative TFs; each set controls a biological process or a complex trait. We previously developed eight gene association methods for genome-wide coexpression analysis between each TF and all other genomic genes and then constructing collaborative networks of TFs but only one algorithm, called Triple-Link Algorithm, for building collaborative subnetworks. In this study, we developed two more algorithms, Single Seed-Growing Algorithm (SSGA) and Multi-Seed Growing Algorithm (MSGA), for building collaborative subnetworks of TFs by identifying the fully-linked triple-node seeds from a decomposed collaborative network and then growing them into subnetworks with two different strategies. The subnetworks built from the three algorithms described above were comparatively appraised in terms of both functional cohesion and intra-subnetwork association strengths versus inter-subnetwork association strengths. We concluded that SSGA and MSGA, which performed more systemic comparisons and analyses of edge weights and network connectivity during subnetwork construction processes, yielded more functional and cohesive subnetworks than Triple-Link Algorithm. Together, these three algorithms provide alternate approaches for acquiring subnetworks of collaborative TFs. We also presented a framework to outline how to use these three algorithms to obtain collaborative TF sets governing biological processes or complex traits.

Recursive random forest algorithm for constructing multilayered hierarchical gene regulatory networks that govern biological pathways.

BACKGROUND: Present knowledge indicates a multilayered hierarchical gene regulatory network (ML-hGRN) often operates above a biological pathway. Although the ML-hGRN is very important for understanding how a pathway is regulated, there is almost no computational algorithm for directly constructing ML-hGRNs. RESULTS: A backward elimination random forest (BWERF) algorithm was developed for constructing the ML-hGRN operating above a biological pathway. For each pathway gene, the BWERF used a random forest model to calculate the importance values of all transcription factors (TFs) to this pathway gene recursively with a portion (e.g. 1/10) of least important TFs being excluded in each round of modeling, during which, the importance values of all TFs to the pathway gene were updated and ranked until only one TF was remained in the list. The above procedure, termed BWERF. After that, the importance values of a TF to all pathway genes were aggregated and fitted to a Gaussian mixture model to determine the TF retention for the regulatory layer immediately above the pathway layer. The acquired TFs at the secondary layer were then set to be the new bottom layer to infer the next upper layer, and this process was repeated until a ML-hGRN with the expected layers was obtained. CONCLUSIONS: BWERF improved the accuracy for constructing ML-hGRNs because it used backward elimination to exclude the noise genes, and aggregated the individual importance values for determining the TFs retention. We validated the BWERF by using it for constructing ML-hGRNs operating above mouse pluripotency maintenance pathway and Arabidopsis lignocellulosic pathway. Compared to GENIE3, BWERF showed an improvement in recognizing authentic TFs regulating a pathway. Compared to the bottom-up Gaussian graphical model algorithm we developed for constructing ML-hGRNs, the BWERF can construct ML-hGRNs with significantly reduced edges that enable biologists to choose the implicit edges for experimental validation.

Using carbon nanotubes-gold nanocomposites to quench energy from pinnate titanium dioxide nanorods array for signal-on photoelectrochemical aptasensing.

On the basis of the absorption and emission spectra overlap, an enhanced resonance energy transfer caused by excition-plasmon resonance between carbon nanotubes-gold nanoparticles (CNTs-Au) and pinnate titanium dioxide nanorods array (P-TiO2 NA) was obtained. Three-dimensional single crystalline P-TiO2 were prepared successfully on fluorine-doped tin oxide conducting glass (FTO glass), and its optical absorption properties and photoelectrochemical (PEC) properties were investigated. With the synergy of CNTs-Au as energy acceptor, it resulted in the enhancement of energy transfer between excited P-TiO2 NA and CNTs-Au. Upon the novel sandwichlike structure formed via DNA hybridization, the exciton produced in P-TiO2 NA was annihilated and a damped photocurrent was obtained. With the use of carcinoembryonic antigen (CEA) as a model which bonded to its specific aptamer and destroyed the sandwichlike structure, the energy transfer efficiency was lowered, leading to PEC response augment. Thus a signal-on PEC aptasensor was constructed. Under the optimal conditions, the PEC aptasensor for CEA determination exhibited a linear range from 0.001 to 2.5ngmL(-1) with a detection limit of 0.39pgmL(-1) and was satisfactory for clinical sample detection. Furthermore, the proposed aptasensor shows satisfying performance, such as easy preparation, rapid detection and so on. Moreover, since different aptamer can specifically bind to different target molecules, the designed strategy has an expansive application for the construction of versatile PEC platforms.

Bottom-up GGM algorithm for constructing multilayered hierarchical gene regulatory networks that govern biological pathways or processes.

BACKGROUND: Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways. RESULTS: A bottom-up graphic Gaussian model (GGM) algorithm was developed for constructing ML-hGRN operating above a biological pathway using small- to medium-sized microarray or RNA-seq data sets. The algorithm first placed genes of a pathway at the bottom layer and began to construct a ML-hGRN by evaluating all combined triple genes: two pathway genes and one regulatory gene. The algorithm retained all triple genes where a regulatory gene significantly interfered two paired pathway genes. The regulatory genes with highest interference frequency were kept as the second layer and the number kept is based on an optimization function. Thereafter, the algorithm was used recursively to build a ML-hGRN in layer-by-layer fashion until the defined number of layers was obtained or terminated automatically. CONCLUSIONS: We validated the algorithm and demonstrated its high efficiency in constructing ML-hGRNs governing biological pathways. The algorithm is instrumental for biologists to learn the hierarchical regulators associated with a given biological pathway from even small-sized microarray or RNA-seq data sets.

An aldehyde group-based P-acid probe for selective fluorescence turn-on sensing of cysteine and homocysteine.

A highly sensitive and selective turn on fluorescent probe P-acid-aldehyde (P-CHO) is developed for the determination of cysteine (Cys) and homocysteine (Hcy). The probe is designed and synthesized by incorporating the specific functional group aldehyde group for thiols into a stable π-conjugated material 4,4'-(2,5-dimethoxy-1,4-phenylene) bis(ethyne-2,1-diyl) dibenzoic acid (P-acid). The probe fluorescence is quenched through donor photoinduced electron transfer (d-PET) between the fluorophore (P-acid) and the recognition group (aldehyde group). In the presence of thiols, Cys and Hcy can selectively react with aldehyde group of the probe because the inhibition of d-PET between fluorophore and recognition group. Therefore, a turn-on fluorescent sensor was established for the fluorescence recovery. Under the optimized conditions, the fluorescence response of probe is directly proportional to the concentration of Cys in the range of 4-95 NM L(-1), with a detection limit 3.0 nM. In addition, the sensing system exhibits good selectively toward Cys and Hcy in the presence of other amino acids. It has been successfully applied for bioimaging of Cys and Hcy in living cells with low cell toxicity.

Application of ZnO quantum dots dotted carbon nanotube for sensitive electrochemiluminescence immunoassay based on simply electrochemical reduced Pt/Au alloy and a disposable device.

We report on a disposable microdevice suitable for sandwich-type electrochemiluminescence (ECL) detection of prostate specific antigen (PSA). The method is making use of ZnO quantum dots dotted carbon nanotube (ZnO@CNT) and simply electrochemical reduced Pt/Au alloy. The latter was selected as immunosensing probe to modify screen-printed carbon electrode, due to its excellent electrical property. For further ultrasensitive, low-potential and stable ECL detection, ZnO@CNT composite was first synthesized using a facile solvothermal method, and employed as signal amplification label. In this work, two working electrodes in one device were used for one determination to obtain more exact results based on screen-print technique. Taking advantage of dual-amplification effects of the Pt/Au and ZnO@CNT, this immunosensor could detect the PSA quantitatively, in the range of 0.001-500 ng mL(-1), with a low detection limit of 0.61 pg mL(-1). The resulting versatile immunosensor possesses high sensitivity, satisfactory reproducibility and regeneration. This simple and specific strategy has vast potential to be used in other biological assays.

A dual amplification strategy for ultrasensitive electrochemiluminescence immunoassay based on a Pt nanoparticles dotted graphene-carbon nanotubes composite and carbon dots functionalized mesoporous Pt/Fe.

A facile and sensitive electrochemiluminescence (ECL) immunosensor for the detection of human carcinoembryonic antigen (CEA) was designed. The immunosensor used Pt nanoparticles dotted graphene-carbon nanotubes composites (Pt/Gr-CNTs) as a platform and carbon dots functionalized Pt/Fe nanoparticles (Pt/Fe@CDs) as bionanolabels. The Pt/Gr-CNTs was first synthesized using a facile ultrasonic method to modify the working electrode, which increases the surface area to capture a large amount of primary anti-CEA antibodies as well as improving the electronic transmission rate. The bionanolabels Pt/Fe@CDs prepared through ethanediamine linking, showed good ECL signal amplification performance. The reason is that the Pt/Fe@CDs nanocomposites as signal tags can increase CDs loading per immunoreaction in comparison with single CDs. The approach provided a good linear response range from 0.003 to 600 ng mL(-1) with a low detection limit of 0.8 pg mL(-1). The immunosensor showed good specificity, acceptable stability and reproducibility. Satisfactory results were obtained in the determination of CEA in human serum albumin samples. Hence, the proposed ECL immunosensor could become a promising method for tumor marker detection.

A highly sensitive electrically driven electrochemiluminescent assay for quantification of bile acids in human serum.

A capillary electrically driven assay with electrochemiluminescent (ECL) detection for total bile acids in human serum was developed and fully validated. Quantification was performed by multiple reactions. First, the bile acids react with nicotinamide adenine dinucleotide (NAD(+)) under catalysis of 3α-hydroxysteroid dehydrogenase (3α-HSD), which is converted to 3-ketosteroid and concomitantly NAD(+) turns into reduced nicotinamide adenine dinucleotide (NADH). And then Ru(bpy)3(2+) is oxidized to be Ru(bpy)3(3+), which serves as an electron mediator, and reacts immediately with NADH coexisting in a carrier solution and is converted to Ru(bpy)3(2+*). NADH transfers an electron to form NAD(+) and the unstable excited-state species, Ru(bpy)3(2+*), which emits photons and gives out light when it decays to the ground state, Ru(bpy)3(2+). Consequently, the concentration of total bile acids could be determined by the electrochemiluminescent intensity. The assay was linear from 0.1 fmol L(-1) to 1000 fmol L(-1), with a detection limit of 0.02 fmol L(-1). The intra-day and inter-day precision had a coefficient of variation of less than 5.0%. The developed ECL assay had an acceptable correlation with an enzymatic cycling method commonly adopted in clinics for the determination of total bile acids (r = 0.7216). Based on the above-mentioned principle, we established a simple, accurate and highly sensitive approach for the determination of total bile acids. Furthermore, this assay has been applied successfully to the detection of total bile acids in human serum, indicating its practicality for bioanalysis.

Primary bile acids as potential biomarkers for the clinical grading of intrahepatic cholestasis of pregnancy.

OBJECTIVE: To identify possible biomarkers for the clinical grading of intrahepatic cholestasis of pregnancy (ICP) through serum bile acid (SBA) profiling in women with ICP. METHODS: Serum samples were collected in the last trimester of pregnancy from 33 women with severe ICP, 28 women with mild ICP, and 35 women with a normal pregnancy. The SBA levels were determined by high-performance liquid chromatography-tandem mass spectrometry. RESULTS: Patients with severe ICP had significantly higher serum levels of taurochenodeoxycholic acid, tauroursodeoxycholic acid, glycocholic acid (GCA), taurocholic acid (TCA), and glycochenodeoxycholic acid than women with mild ICP or a normal pregnancy. Primary bile acid species, in particular TCA and GCA, were the main bile acids detected and their levels were significantly higher in the severe ICP group than in the other 2 groups. CONCLUSION: There is an obvious difference in the SBA profiles of women with severe ICP and those with mild ICP, indicating that primary bile acids may be useful biomarkers for the clinical grading of ICP. Implementation of primary bile acid testing in clinical practice may help clinicians to determine the appropriate management strategy for patients with ICP.

CO2 in solid para-hydrogen: spectral splitting and the CO2···(o-H2)n clusters.

Complicated high-resolution spectral structures are often observed for molecules doped in solid molecular hydrogen. The structures can result from miscellaneous effects and are often interpreted differently in references. The spectrum of the ν(3) band of CO(2) in solid para-H(2) presents a model system which exhibits rich spectral structures. With the help of the potential energy simulation of the CO(2) molecule doped in para-hydrogen matrix, and extensive experiments with different CO(2) isotopologues and different ortho-hydrogen concentrations in the matrix, the spectral features observed in p-H(2) matrix are assigned to the CO(2)···(o-H(2))(n) clusters and also to energy level splitting that is due to different alignments of the doped CO(2) molecules in the matrix. The assignments are further supported by the dynamics analysis and also by the spectrum recorded with sample codoped with O(2) which serves as catalyst transferring o-H(2) to p-H(2) in the matrix at 4 K temperature. The observed spectral features of CO(2)/pH(2) can potentially be used as an alternative readout of the temperature and orthohydrogen concentration in the solid para-hydrogen.

Analysis of tryptophan catabolism in HBV patients by HPLC with programmed wavelength ultraviolet detection.

BACKGROUND: In order to understand the tryptophan catabolism in acute and chronic hepatitis B patients, we quantitatively analyzed plasma kynurenine and tryptophan simultaneously by high performance liquid chromatography with programmed wavelength ultraviolet detection. METHODS: A new and specific high performance liquid chromatography method to simultaneously measure plasma kynurenine and tryptophan with programmed wavelength ultraviolet detection using 3-nitro-tyrosine as internal standard was elaborated. Thirty patients were recruited (10 patients with acute HBV, 10 with chronic HBV and 10 healthy subjects). RESULTS: The retention times of kynurenine and tryptophan were 2.9 min and 4.4 min, respectively. For kynurenine, the assay was linear from 0.442 micromol/l to 18.3 micromol/l. For tryptophan, the linearity was from 3.67 to 470 micromol/l. The detection limits were 0.014 micromol/l for Kyn and 0.122 micromol/l for Trp, respectively. Its precision and recovery are satisfactory. In this study we found that the kynurenine per tryptophan ratio of acute group is higher than control group and chronic group. CONCLUSIONS: The method is simple, fast, accurate, and suitable for applicability to clinical measurement.

A frequency-stabilized difference frequency generation laser spectrometer for precise line profile studies in the midinfrared.

A midinfrared laser spectrometer is built up based on the difference frequency generation (DFG) of a Nd:YAG (yttrium aluminum garnet) laser and a tunable Ti:sapphire (Ti:Sa) laser. Tuning the Ti:Sa laser and operating properly with the periodically poled lithium niobate crystal, the DFG emission is tunable in the spectral range of 2.3-5.0 microm. The 1064 nm Nd:YAG laser frequency is stabilized to the 10(-6) cm(-1) level on a Doppler-broadened I(2) absorption line at 532 nm. As a result, the DFG emission frequency is stabilized within 1x10(-4) cm(-1). The measurement of an absorption line of CH(4) near 3 mum demonstrates that the DFG spectrometer is very suitable for the molecular absorption line profile studies in the midinfrared region.