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Changes in aerosol loadings and properties are important for the understanding of atmospheric environment and climate change. This study investigates the characteristics and long-term trends of aerosols of different sizes and types in Asia from 2000 to 2020 by considering multi-source aerosol data and novel analysis methods and perspectives, all of which provides the groundwork for promoting the acquisition of new discoveries that are different from the past. The geometric mean aggregation method is applied, and serial autocorrelation is considered to avoid the overestimation of trend significance. Among regions in Asia, high values of aerosol optical depth (AOD) are mainly concentrated in East Asia (EA) and South Asia (SA) and are closely related to the population density. The AOD in EA showed the most significant negative trend, with a value of -5.28×10-4 per year, mainly owing to decreases in organic carbon (OC), black carbon (BC), and dust aerosols. It is also worth noting that this observed large-scale decrease in OC and BC is a unique and significant phenomenon to region of EA and mainly around China. In contrast, the aerosol concentrations in SA generally show a positive trend, with an increased value of AOD of 1.25 × 10−3 per year. This increase is mainly due to large emissions of fine-mode aerosols, such as OC and sulfate aerosol. Additionally, the high aerosol loading in northern SA has a lower AOD variability when compared with that of East China Plain, revealing a relatively more persistent air pollution situation. Over the whole region of Asia, the characteristics of the percentage changes in different types of AOD are seen in the increases in BC (6.23 %) and OC (17.09 %) AOD, together with a decrease in dust (−5.51 %), sulfate (−3.07 %), and sea salt (−9.80 %) AOD. Except for anthropogenic emissions, the large increase in the percentage of OC is also due to wildfires found in northern Asia in the summer, whereas the differently sized AOD only shows slight changes in Asia in that small-sized AOD decreases (−3.34 %) and that the total AOD did not show a significant change. This suggests that, from a trend perspective, decreases in aerosol in recent years have mostly been offsetting earlier increases in the anthropogenic emissions over Asia. To summarize, the above findings analyse the comprehensive characteristics of aerosol distributions and reappraise the long-term trends of different aerosol parameters, which will greatly enhance the understanding of regional and global aerosol environment and climatology, in addition to filling the gaps and breaking through the limitations of past knowledge.

Abstract Background Hydrological extremes such as floods generally have multidimensional attributes with complex dependence structures. This leads to the urgent demand of hydrological risk analysis within a multivariate context. In this study, the bivariate hydrologic risk framework is proposed based on the bivariate copula method. In the proposed risk analysis framework, bivariate flood frequency would be analyzed for different flood variable pairs (i.e., flood peak-volume, flood peak-duration, flood volume-duration), and the bivariate hydrologic risk is then derived based on the joint return period of a flood variable pair. The distribution of one flood variable conditional on another flood variable can also be obtained through the copula method. Results The proposed method is applied to the risk analysis for the Xiangxi River in the Three Gorges Reservoir area, China, based on 50 years streamflow measurements. The results indicate that the bivariate risk for flood peak flow-duration would keep constant for some time and then decrease as the increase of the flood duration. The bivariate risk for flood peak-volume holds a similar trend with the bivariate risk of flood peak-duration. The probability density functions (PDFs) of the flood volume and duration conditional on flood peak can also be generated through the best fitted copula function. Conclusion The results indicate that the distributions of flood volume would be highly influenced by the flood peak flows, in which the flood volume would be expected to increase as the increase of flood return period. Conversely, the distribution of the flood duration would not change significantly with the variation in the flood peak return period. The obtained conclusions from the bivariate hydrologic analysis can provide decision support for flood control and mitigation.

Land subsidence is a geological disaster. It will lead to the decline of land elevation, resulting in the potential safety hazards of urban facilities. Thus, the prediction of land subsidence displacement is significant. Among the existing prediction methods, the methods based on the time-series prediction model only analyze the settlement series data without considering the settlement mechanism, so they are easy to apply. However, they less consider the influence of other factors on land subsidence. Besides, they independently input displacement time-series data from different monitoring points without considering their relationship. To solve these problems, we take the monitoring point as the entity and take the DEM, soil type, building height, and land subsidence displacement sequence at the corresponding position of the monitoring point as the attributes to construct the knowledge graph. And then, we propose a framework Graph-TAP for modeling temporal attribute prediction of the knowledge graph’s entity. This framework learns the representation of events with the specific entity at first. Then it captures the temporal dependency between historical events using GRU. Finally, it predicts the entity’s displacement attribute. We randomly selected 61 subsidence monitoring points in Shenzhen, China. We used the land subsidence displacement InSAR time-series data (12-day time interval) and other attribute data from June 22, 2015, to April 5, 2016, for model training, validation, and testing. The experimental results show that our method is better than the time-series prediction based on the LSTM model and the DARNET (a knowledge graph temporal attribute prediction framework).

The vertical distribution of aerosol extinction coefficient (EC) measured by lidar systems has been used to retrieve the profile of particle matter with a diameter µm (PM2.5). However, the traditional linear model (LM) cannot consider the influence of multiple meteorological variables sufficiently and then induce the low inversion accuracy. Generally, the machine learning (ML) algorithms can input multiple features which may provide us with a new way to solve this constraint. In this study, the surface aerosol EC and meteorological data from January 2014 to December 2017 were used to explore the conversion of aerosol EC to PM2.5 concentrations. Four ML algorithms were used to train the PM2.5 prediction models: random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) and extreme gradient boosting decision tree (XGB). The mean absolute error (root mean square error) of LM, RF, KNN, SVM and XGB models were 11.66 (15.68), 5.35 (7.96), 7.95 (11.54), 6.96 (11.18) and 5.62 (8.27) µg/m3, respectively. This result shows that the RF model is the most suitable model for PM2.5 inversions from EC and meteorological data. Moreover, the sensitivity analysis of model input parameters was also conducted. All these results further indicated that it is necessary to consider the effect of meteorological variables when using EC to retrieve PM2.5 concentrations. Finally, the diurnal and seasonal variations of transport flux (TF) and PM2.5 profiles were analyzed based on the lidar data. The large PM2.5 concentration occurred at approximately 13:00–17:00 local time (LT) in 0.2–0.8 km. The diurnal variations of the TF show a clear conveyor belt at approximately 12:00–18:00 LT in 0.5–0.8 km. The results indicated that air pollutant transport over Wuhan mainly occurs at approximately 12:00–18:00 LT in 0.5–0.8 km. The TF near the ground usually has the highest value in winter (0.26 mg/m2 s), followed by the autumn and summer (0.2 and 0.19 mg/m2 s, respectively), and the lowest value in spring (0.14 mg/m2 s). These findings give us important information on the atmospheric profile and provide us sufficient confidence to apply lidar in the study of air quality monitoring.

Serious urban ozone (O3) pollution was observed during the campaign of 2016 G20 summit in Hangzhou, China, while other pollutants had been significantly reduced by the short-term emission control measures. To understand the underlying mechanism, the Weather Research Forecast with Chemistry (WRF-Chem) model is used to investigate the spatial and temporal O3 variations in Hangzhou from 24 August to 6 September 2016. The model is first successfully evaluated and validated for local and regional meteorological and chemical parameters by using the ground and upper-air level observed data. High ozone concentrations, temporally during most of the daytime emission control period and spatially from the surface to the top of the planetary boundary layer, are captured in Hangzhou and even the whole Yangtze River Delta region. Various atmospheric processes are further analyzed to determine the influential factors of local ozone formation through the integrated process rate method. Interesting horizontal and vertical advection circulations of O3 are observed during several short periods, and the effects of these processes are nearly canceled out. As a result, ozone pollution is mainly attributed to the local photochemical reactions that are not obviously influenced by the emission reduction measures. The ratio of reduction of Volatile Organic Compounds (VOCs) to that of NOx is a critical parameter that needs to be carefully considered for future alleviation of ozone formation. In addition, the vertical diffusion from the upper-air background O3 also plays an important role in shaping the surface ozone concentration. These results provide insight into urban O3 formation in Hangzhou and support the Model Intercomparison Study Asia Phase III (MICS-Asia Phase III).

Magnetic skyrmions are thought to possess a tube-like structure in three dimensions, but this has not been directly observed in experiment. Here, Birch et al. report real-space imaging of skyrmion tubes in a lamella of FeGe.

Abstract In this study, an iterative factorial multimodel Bayesian copula (IFMBC) framework was developed for revealing uncertainties in risk inferences of compound extremes under the consideration of diverse model structures and parameter sets. Particularly, an iterative factorial analysis (IFA) method would be advanced in IFMBC to track the dominant contributors to the imprecise predictions of multi‐hazard risks. The developed IFMBC framework was applied for the risk assessment of compound floods at two estuarine systems (i.e., Washington and Philadelphia) in US. The results indicate that the most likely compound events, under predefined return periods, exhibit noticeable uncertainties. Those uncertainties also present multiple hotspots which may be attributed to different impacts from different factors. By applying the IFA method, the results suggest the copula structure would likely be ranked as one of the top 2 impact factors for predictions of failure probabilities (FPs) in the scenarios of AND, and Kendall, with its contributions higher than 30% for FP in Kendall (more than 40% at Washington) and more than 25% for FP in Kendall (larger than 40% at Philadelphia). In comparison, the copula structure may not pose a visible effect on the predictive uncertainty for FP in OR, with its contribution possibly less than 5% under long‐term service time periods. However, the marginal distributions would have higher effects on FP in OR than the effects on the other two FPs. Particularly, the marginal distribution for the extreme variable with high skewness and kurtosis values tends to be ranked as one of the most significant impact factors for FP in OR. Also, the overall impacts from parametric uncertainties in both marginal and dependence models cannot be neglected for the predictions of all three FPs with their contributions probably larger than 20% under a short service time period. Compared with the traditional multilevel factorial analysis, the IFA method can provide more reliable characterization for uncertainty contributors in multi‐hazard risk analyses, since the traditional method seems to significantly overestimate the contributions from parameter uncertainties.

Objective: To describe a population of dogs with acute spontaneous hemoperitoneum (ASH) that were treated with either surgical or medical management in the first 12 h after presentation, and to perform a preliminary investigation into whether there were differences in achieving cardiovascular stabilization or patterns of red blood cell (RBC) transfusion between patients treated with early (

Two tree peony species, Paeonia ostii and Paeonia rockii, from eight different provenances in China (four provenances for each species) were collected for their photosynthetic characteristics. Their diurnal variations of photosynthesis, the light- and CO2-response curves and chlorophyll content were determined. Among all eight provenances, Ziwuling P. rockii had a significantly higher average daily net photosynthetic rate, showing a superior photosynthetic capacity, followed by Fengxiang P. ostii and Xunyang P. ostii. In addition, Ziwuling P. rockii had the highest maximum net photosynthetic rates, thus, presented a strong photosynthetic potential. Furthermore, Xunyang P. ostii, Fengxiang P. ostii, and Heze P. ostii also exhibited a great photosynthetic capacity and light adaptability, which was consistent with their high chlorophyll a/b values. Taken together, Ziwuling P. rockii had the strongest photosynthetic capacity among all tested sources, followed by Fengxiang P. ostii, and Xunyang P. ostii, which are recommended to be used as seed sources for oil peony cultivation and cross-breeding.

Abstract Background Candida africana is distributed worldwide and colonized in human genitalia and cause mainly vulvovaginal candidiasis (VVC). We report the multilocus sequence typing (MLST) analysis of C. africana from VVC. Methods MLST analysis of 43 strains of C. africana, which were isolated from vaginal specimens of patients with VVC, was performed. The enzymatic activity of phospholipase, esterase and haemolysis enzyme production was evaluated.The level of virulent genes and resistant genes mRNA expression was determined by using real-time PCR. Antifungal susceptibilities of the isolates were assayed by using the broth microdilution method. The statistical of the results was determined by the T test and Pearson chi-squared test. Results The MLST analysis revealed a substantial degree of genetic homogeneity. The DST782 and DST182 were the main MLST genotypes in C. africana. All the patients were symptomatic and with a high mycological cure rate when treated with commonly used antifungal agents.There were statistically significant differences in biofilm formation and phospholipase activity between C. africana and C.albicans. The level of virulent genes and resistant genes mRNA expression was higher in fluconazole-resistant strains. All C. africana isolates were susceptible to fluconazole, itraconazole, voriconazole, caspofungin, and micafungin. These isolates also exhibited low MICs to amphotericin B, flucytosine, and posaconazole. Conclusions Candida africana appear to be with a low level of sequence variation in MLST loci. Candida africana, a lower virulence candida, is susceptible to commonly used antifungal agents. This paper was presented at the conference of 8th Trend in Medical Mycology (6–9 October 2017, Belgrade, Serbia) and was published on conference abstract.

Abstract Background This retrospective study was undertaken to determine if the plasma circulating tumor DNA (ctDNA) level and tumor biological features in patients with advanced solid tumors affected the detection of genomic alterations (GAs) by a plasma ctDNA assay. Method Cell-free DNA (cfDNA) extracted from frozen plasma (N = 35) or fresh whole blood (N = 90) samples were subjected to a 62-gene hybrid capture-based next-generation sequencing assay FoundationACT. Concordance was analyzed for 51 matched FoundationACT and FoundationOne (tissue) cases. The maximum somatic allele frequency (MSAF) was used to estimate the amount of tumor fraction of cfDNA in each sample. The detection of GAs was correlated with the amount of cfDNA, MSAF, total tumor anatomic burden (dimensional sum), and total tumor metabolic burden (SUVmax sum) of the largest ten tumor lesions on PET/CT scans. Results FoundationACT detected GAs in 69 of 81 (85%) cases with MSAF > 0. Forty-two of 51 (82%) cases had ≥ 1 concordance GAs matched with FoundationOne, and 22 (52%) matched to the National Comprehensive Cancer Network (NCCN)-recommended molecular targets. FoundationACT also detected 8 unique molecular targets, which changed the therapy in 7 (88%) patients who did not have tumor rebiopsy or sufficient tumor DNA for genomic profiling assay. In all samples (N = 81), GAs were detected in plasma cfDNA from cancer patients with high MSAF quantity (P = 0.0006) or high tumor metabolic burden (P = 0.0006) regardless of cfDNA quantity (P = 0.2362). Conclusion This study supports the utility of using plasma-based genomic assays in cancer patients with high plasma MSAF level or high tumor metabolic burden.

Climate change is resulting in more frequent rainstorms and more rain-induced debris flows in mountainous areas. The prediction of likely hazard zones is important for debris flow risk assessment and management. Existing numerical methods for debris flow analysis often require the input of hydrographs at prescribed initiation locations, ignoring the initiation process and leading to large uncertainties in debris flow initiation locations, times, and volumes when applied to regional debris flow analysis. The evolution of the flowing mixture in time and space is also barely addressed. This paper presents a new integrated numerical model, EDDA 2.0, to simulate the whole process of debris flow initiation, motion, entrainment, deposition, and property changes. Two physical initiation mechanisms are modelled: transformation from slope failures and surface erosion. Three numerical tests and field application to a catastrophic debris flow event are conducted to verify the model components and evaluate the model performance. The results indicate that the integrated model is capable of simulating the initiation and subsequent flowing process of rain-induced debris flows, as well as the physical evolution of the flowing mixture. The integrated model provides a powerful tool for analysing multi-hazard processes, hazard interactions, and regional debris flow risk assessment in the future.

In this work, the spatial extent of new particle formation (NPF) events and the relative probability of observing particles originating from different spatial origins around three rural sites in eastern China were investigated using the NanoMap method, using particle number size distribution (PNSD) data and air mass back trajectories. The length of the datasets used were 7, 1.5, and 3 years at rural sites Shangdianzi (SDZ) in the North China Plain (NCP), Mt. Tai (TS) in central eastern China, and Lin'an (LAN) in the Yangtze River Delta region in eastern China, respectively. Regional NPF events were observed to occur with the horizontal extent larger than 500 km at SDZ and TS, favoured by the fast transport of northwesterly air masses. At LAN, however, the spatial footprint of NPF events was mostly observed around the site within 100–200 km. Difference in the horizontal spatial distribution of new particle source areas at different sites was connected to typical meteorological conditions at the sites. Consecutive large-scale regional NPF events were observed at SDZ and TS simultaneously and were associated with a high surface pressure system dominating over this area. Simultaneous NPF events at SDZ and LAN were seldom observed. At SDZ the polluted air masses arriving over the NCP were associated with higher particle growth rate (GR) and new particle formation rate (J) than air masses from Inner Mongolia (IM). At TS the same phenomenon was observed for J, but GR was somewhat lower in air masses arriving over the NCP compared to those arriving from IM. The capability of NanoMap to capture the NPF occurrence probability depends on the length of the dataset of PNSD measurement but also on topography around the measurement site and typical air mass advection speed during NPF events. Thus the long-term measurements of PNSD in the planetary boundary layer are necessary in the further study of spatial extent and the probability of NPF events. The spatial extent, relative probability of occurrence, and typical evolution of PNSD during NPF events presented in this study provide valuable information to further understand the climate and air quality effects of new particle formation.

It has been demonstrated that microRNAs (miRNAs) play important roles in the control of melanogenesis and hair color in mammals. By comparing miRNA expression profiles between brown and white alpaca skin, we previously identified miR508-3p as a differentially expressed miRNA suggesting its potential role in melanogenesis and hair color formation. The present study was conducted to determine the role of miR508-3p in melanogenesis in alpaca melanocytes. In situ hybridization showed that miR508-3p is abundantly present in the cytoplasma of alpaca melanocytes. miR508-3p was predicted to target the gene encoding microphthalmia transcription factor (MITF) and a luciferase reporter assay indicated that miR508-3p regulates MITF expression by directly targeting its 3′UTR. Overexpression of miR508-3p in alpaca melanocytes down-regulated MITF expression both at the messenger RNA and protein level and resulted in decreased expression of key melanogenic genes including tyrosinase and tyrosinase-related protein 2. Overexpression of miR508-3p in melanocytes also resulted in decreased melanin production including total alkali-soluble melanogenesis, eumelanogenesis and pheomelanogenesis. Results support a functional role of miR508-3p in regulating melanogenesis in alpaca melanocytes by directly targeting MITF.

Inhibiting high-voltage-activated calcium channels (HVACCs; CaV1/CaV2) is therapeutic for myriad cardiovascular and neurological diseases. For particular applications, genetically-encoded HVACC blockers may enable channel inhibition with greater tissue-specificity and versatility than is achievable with small molecules. Here, we engineered a genetically-encoded HVACC inhibitor by first isolating an immunized llama nanobody (nb.F3) that binds auxiliary HVACC CaVβ subunits. Nb.F3 by itself is functionally inert, providing a convenient vehicle to target active moieties to CaVβ-associated channels. Nb.F3 fused to the catalytic HECT domain of Nedd4L (CaV-aβlator), an E3 ubiquitin ligase, ablated currents from diverse HVACCs reconstituted in HEK293 cells, and from endogenous CaV1/CaV2 channels in mammalian cardiomyocytes, dorsal root ganglion neurons, and pancreatic β cells. In cardiomyocytes, CaV-aβlator redistributed CaV1.2 channels from dyads to Rab-7-positive late endosomes. This work introduces CaV-aβlator as a potent genetically-encoded HVACC inhibitor, and describes a general approach that can be broadly adapted to generate versatile modulators for macro-molecular membrane protein complexes.

Taiga–tundra boundary ecosystems are affected by climate change. Methane (CH4) emissions in taiga–tundra boundary ecosystems have sparsely been evaluated from local to regional scales. We linked in situ CH4 fluxes (2009–2016) with vegetation cover, and scaled these findings to estimate CH4 emissions at a local scale (10 × 10 km) using high-resolution satellite images in an ecosystem on permafrost (Indigirka lowland, north-eastern Siberia). We defined nine vegetation classes, containing 71 species, of which 16 were dominant. Distribution patterns were affected by microtopographic height, thaw depth and soil moisture. The Indigirka lowland was covered by willow-dominated dense shrubland and cotton-sedge-dominated wetlands with sparse larch forests. In situ CH4 emissions were high in wetlands. Lakes and rivers were CH4 sources, while forest floors were mostly neutral in terms of CH4 emission. Estimated local CH4 emissions (37 mg m−2 d−1) were higher than those reported in similar studies. Our results indicate that: (i) sedge and emergent wetland ecosystems act as hot spots for CH4 emissions, and (ii) sparse tree coverage does not regulate local CH4 emissions and balance. Thus, larch growth and distribution, which are expected to change with climate, do not contribute to decreasing local CH4 emissions.

We examined nasopharyngeal pneumococcal colonization density patterns surrounding acute respiratory illnesses (ARI) in young children in Peru. Pneumococcal densities were dynamic, gradually increasing leading up to an ARI, peaking during the ARI, and decreasing after the ARI. Rhinovirus co-infection was associated with higher pneumococcal densities.

The validation and analysis of X-ray crystallographic data is essential for reproducibility and the development of crystallographic methods. Here, the authors describe a repository for crystallographic datasets and demonstrate some of the ways it could serve the crystallographic community.

We report small-angle x-ray scattering measurements of the skyrmion lattice in two 200-nm-thick Cu_{2}OSeO_{3} lamellae aligned with the applied magnetic field parallel to the out of plane [110] or [100] crystallographic directions. Our measurements show that the equilibrium skyrmion phase in both samples is expanded significantly compared to bulk crystals, existing between approximately 30 and 50 K over a wide region of magnetic field. This skyrmion state is elliptically distorted at low fields for the [110] sample, and symmetric for the [100] sample, possibly due to crystalline anisotropy becoming more important at this sample thickness than it is in bulk samples. Furthermore, we find that a metastable skyrmion state can be observed at low temperature by field cooling through the equilibrium skyrmion pocket in both samples. In contrast to the behavior in bulk samples, the volume fraction of metastable skyrmions does not significantly depend on cooling rate. We show that a possible explanation for this is the change in the lowest temperature of the skyrmion state in this lamellae compared to bulk, without requiring different energetics of the skyrmion state.

Traditional direction-of-arrival (DOA) estimation techniques perform Nyquist-rate sampling of the received signals and as a result they require high storage. To reduce sampling ratio, we introduce level-crossing (LC) sampling which captures samples whenever the signal crosses predetermined reference levels, and the LC-based analog-to-digital converter (LC ADC) has been shown to efficiently sample certain classes of signals. In this paper, we focus on the DOA estimation problem by using second-order statistics based on the LC samplings recording on one sensor, along with the synchronous samplings of the another sensors, a sparse angle space scenario can be found by solving an $ell_1$ minimization problem, giving the number of sources and their DOA's. The experimental results show that our proposed method, when compared with some existing norm-based constrained optimization compressive sensing (CS) algorithms, as well as subspace method, improves the DOA estimation performance, while using less samples when compared with Nyquist-rate sampling and reducing sensor activity especially for long time silence signal.

The pattern synthesis of sparse antenna arrays has many practical applications in situations where the weights, size, and cost of antennas are limited. In this work the antenna array synthesis problem, with minimum number of elements, is studied from the new perspective of sparseness constrained optimization. The number of antenna elements in the array can be efficiently reduced by casting the pattern synthesis problem into the compressive sensing (CS) framework of sparseness constrained optimization and solving with the reweighted l1-norm minimization algorithm. Besides, the proposed method allows exploitation of the array orientation diversity in the CS framework to address left-right radiation pattern ambiguity problem. Numerical examples are presented to show the high efficiency of achieving the desired radiation pattern with the minimum number of antenna elements.

Human calcium-sensing receptor (CaSR) is a G-protein-coupled receptor (GPCR) that maintains extracellular Ca2+ homeostasis through the regulation of parathyroid hormone secretion. It functions as a disulfide-tethered homodimer composed of three main domains, the Venus Flytrap module, cysteine-rich domain, and seven-helix transmembrane region. Here, we present the crystal structures of the entire extracellular domain of CaSR in the resting and active conformations. We provide direct evidence that L-amino acids are agonists of the receptor. In the active structure, L-Trp occupies the orthosteric agonist-binding site at the interdomain cleft and is primarily responsible for inducing extracellular domain closure to initiate receptor activation. Our structures reveal multiple binding sites for Ca2+ and PO43- ions. Both ions are crucial for structural integrity of the receptor. While Ca2+ ions stabilize the active state, PO43- ions reinforce the inactive conformation. The activation mechanism of CaSR involves the formation of a novel dimer interface between subunits.

Six genes, which encode enzymes involved in ascorbic acid (AsA) biosynthesis, including guanosine diphosphate (GDP)-mannose pyrophosphorylase (GMP), GDP-mannose-3',5'-epimerase (GME), GDP-galactose guanylyltransferase (GGT), L-galactose-1-phosphate phosphatase (GPP), L-galactose dehydrogenase (GDH) and L-galactono-1,4-lactone dehydrogenase (GLDH) were transformed into Arabidopsis thaliana, to evaluate the contribution of each gene to AsA accumulation. Additionally, two combinations, GGT-GPP and GGT-GLDH, were co-transformed into Arabidopsis with a reliable double-gene transformation system. AsA content of GGT transgenic lines was 2.9-fold higher as compared to the control, and co-transformation led up to 4.1-fold AsA enhancement. These results provided further evidence that GGT is the key enzyme in plant AsA biosynthesis.

A cDNA library from white alpaca (Vicugna pacos) skin was constructed using SMART technology to investigate the global gene expression profile in alpaca skin and identify genes associated with physiology of alpaca skin and pigmentation. A total of 5359 high-quality EST (expressed sequence tag) sequences were generated by sequencing random cDNA clones from the library. Clustering analysis of sequences revealed a total of 3504 unique sequences including 739 contigs (assembled from 2594 ESTs) and 2765 singletons. BLAST analysis against GenBank nr database resulted in 1287 significant hits (E-value < 10−10), of which 863 were annotated through gene ontology analysis. Transcripts for genes related to fleece quality, growth and coat color (e.g. collagen types I and III, troponin C2 and secreted protein acidic and rich in cysteine) were abundantly present in the library. Other genes, such as keratin family genes known to be involved in melanosome protein production, were also identified in the library. Members (KRT10, 14 and 15) of this gene family are evolutionarily conserved as revealed by a cross-species comparative analysis. This collection of ESTs provides a valuable resource for future research to understand the network of gene expression linked to physiology of alpaca skin and development of pigmentation.

To determine the effect of light stress under fragmental habitat on the physiology, this paper investigated the physiological responses of Monimopetalum chinense with different light intensities in the Xianyu Mountains (Anhui, China). The study showed that both weak and intense light brought about by habitat fragmentation could improve antioxidant enzymes activities, and promote electrical conductivity and malondialdehyde content of M. chinense leaves. However, too strong light could inhibit photosynthesis rates, superoxide dismutase, catalase, and ascorbate peroxidase activities. In addition, the characteristics of leaves were affected by light intensity at the fragmental habitat. Specifically, intense light was disadvantageous to photosynthesis and antioxidant enzymes of the species. Our results suggest that the biodiversity conservation of M. chinense is necessary, and that light intensity should be considered carefully when implementing conservation efforts.