Identification of quantitative trait loci influencing early height growth in longleaf pine (Pinus palustris Mill)

The delay in early height growth (EHG) has been a limiting factor for artificial regeneration of longleaf pine (Pinus palustris Mill.). Simple Sequence Repeat (SSR) markers have been used to map the genome and quantitative trait loci controlling the EHG in a backcross family (longleaf pine x slash pine) x longleaf pine. A total of 228 locus specific SSR markers were screened against 6 longleaf pine recurrent parents and a sample of 7 longlef x slash pine hybrid parents. In total, 135 polymorphic markers were identified. Based on the genetic variance in EHG, available sample size, and the number of SSR marker polymorphisms, a half-sib family with a common paternal parent (Derr488) and 6 longleaf maternal parents were selected from 27 backcross families as the final mapping population. One hundred and twenty three (123) polymorphic markers showed polymorphisms across the half-sib family. An individual linkage map was built for each full-sib family first, and then the linkage maps from different full-sib families were integrated by common orthologous SSR markers with software JoinMap (ver3.0). There were 112 polymorphic markers mapped to the integrated map which contained 16 linkage groups. The observed map length was 1874.3 cM and covered 79.85% of genome. The estimated 95% confidence interval for genome length was 1781.3-2411.6 cM. Seventeen (17) QTLs were identified by single marker regression using 305 backcross progenies. For the interval mapping, the tallest and shortest 8 percent of seedlings were selected for QTL detection (phase I), and then random selections of 8 percent of the seedlings from the rest of the population and 25 seedlings from both tails of the within family distributions were used for unbiased QTL verification and mapping (phase II). Nine QTLs were detected and verified as associated with the 5 growth traits under P=0.05 chromosome-wide threshold. There was only weak evidence of QTL stability during the three years of growth under this study

Promoting hydrogen production and minimizing catalyst deactivation from the pyrolysis-catalytic steam reforming of biomass on nanosized NiZnAlOx catalysts

Hydrogen production from the thermochemical conversion of biomass was carried out with nano-sized NiZnAlOx catalysts using a two-stage fixed bed reactor system. The gases derived from the pyrolysis of wood sawdust in the first stage were catalytically steam reformed in the second stage. The NiZnAlOx catalysts were synthesized by a co-precipitation method with different Ni molar fractions (5, 10, 15, 25 and 35%) and a constant Zn:Al molar ratio of 1:4. The catalysts were characterized by a wide range of techniques, including N2 adsorption, SEM, XRD, TEM and temperature-programmed oxidation (TPO) and reduction (TPR). Fine metal particles of size around 10–11 nm were obtained and the catalysts had high stability characteristics, which improved the dispersion of active centers during the reaction and promoted the performance of the catalysts. The yield of gas was increased from 49.3 to 74.8 wt.%, and the volumetric concentration of hydrogen was increased from 34.7 to 48.1 vol.%, when the amount of Ni loading was increased from 5 to 35%. Meanwhile, the CH4 fraction decreased from 10.2 to 0.2 vol.% and the C2–C4 fraction was reduced from 2.4 vol.% to 0.0 vol.%. During the reaction, the crystal size of all catalysts was successfully maintained at around 10–11 nm with lowered catalyst coke formation, (particularly for the 35NiZn4Al catalyst where negligible coke was found) and additionally no obvious catalyst sintering was detected. The efficient production of hydrogen from the thermochemical conversion of renewable biomass indicates that it is a promising sustainable route to generate hydrogen from biomass using the NiZnAl metal oxide catalyst prepared in this work via a two-stage reaction system

Catalytic steam reforming of volatiles released via pyrolysis of wood sawdust for hydrogen-rich gas production on Fe–Zn/Al2O3 nanocatalysts

Thermo-chemical processing of biomass is a promising alternative to produce renewable hydrogen as a clean fuel or renewable syngas for a sustainable chemical industry. However, the fast deactivation of catalysts due to coke formation and sintering limits the application of catalytic thermo-chemical processing in the emerging bio-refining industry. In this research, Fe-Zn/Al2O3 nanocatalysts have been prepared for the production of hydrogen through pyrolysis catalytic reforming of wood sawdust. Through characterization, it was found that Fe and Zn were well distributed on the surface with a narrow particle size. During the reactions, the yield of hydrogen increased with the increase of Zn content, as Zn is an efficient metal promoter for enhancing the performance of the Fe active site in the reaction. The 20% Fe/Al2O3 catalyst with Zn/Al ratio of 1:1 showed the best performance in the process in relation to the hydrogen production and resistance to coke formation on the surface of the reacted catalyst. All the catalysts showed ultra-high stability during the process and nearly no sintering were observed on the used catalysts. Therefore, the nanocatalysts prepared from natural-abundant and low-cost metals in this work have promising catalytic properties (high metal dispersion and stability) to produce H2-rich syngas with optimal H2/CO ratio from the thermo-chemical process of biomass

Effects of thin Covers on the Release of Coal Gangue Contaminants

AbstractThe effects of the different ecological covers on the release of coal gangue contaminants were evaluated by the batch pot of experiments. The tests were carried for 12 weeks on the coal gangue by different approaches, which were the coverings with 1-2cm artificial matrix that contained acid buffer and plant ameliorant(Tr1:thin matrix cover), 1-2cm slurry of artificial matrix (Tr2: thin coating),and control groups(CK),and planted Lolium perenne, Chenopodium ambrosioides L, and sporopollen of Funaria hygrometrica Hedw on the surface layer, respectively. During the pot experiments, the leachates were collected and analyzed for pH, electrical conductivity (EC), and concentration of Fe, Mn, Cu, Zn, SO42-, F- .The results showed that the coal gangue was uninterruptedly oxidized to form acidic when it was exposed to open air, and 3 or 5 weeks later, dissolution of Fe, Mn, Cu, Zn and SO42-, F− in the coal gangue started and increased significantly, and this is a typical acid mine drainage (AMD) formation process. Compared to the CK, thin matrix cover could retard the allotted time of the production of acidity and release of contaminants, but was easily invalid to long-term. The pH of thin coating was at a high value with time, and the concentration of Fe, Mn, Cu, Zn, SO42− and F- reduced significantly. The data indicated that the thin coating could effectively stop or retard the production of acidity and the release of contaminants generated by coal gangue. It suggests that thin coating covers on the coal gangue could be a suitable method for pollution abatement and controlling on-site

A Comprehensive Molecular Interaction Map for Rheumatoid Arthritis

Computational biology contributes to a variety of areas related to life sciences and, due to the growing impact of translational medicine - the scientific approach to medicine in tight relation with basic science -, it is becoming an important player in clinical-related areas. In this study, we use computation methods in order to improve our understanding of the complex interactions that occur between molecules related to Rheumatoid Arthritis (RA).Due to the complexity of the disease and the numerous molecular players involved, we devised a method to construct a systemic network of interactions of the processes ongoing in patients affected by RA. The network is based on high-throughput data, refined semi-automatically with carefully curated literature-based information. This global network has then been topologically analysed, as a whole and tissue-specifically, in order to translate the experimental molecular connections into topological motifs meaningful in the identification of tissue-specific markers and targets in the diagnosis, and possibly in the therapy, of RA.’

Expression of Peroxiredoxin 1 and 4 Promotes Human Lung Cancer Malignancy

Members of the Peroxiredoxin (Prx) family are major cellular antioxidants that scavenge hydrogen peroxide and play essential roles in oxidative stress and cell signaling. 2-Cys Prxs, including Prx1, 2, 3 and 4, have been indicated in multiple oncogenic signaling pathways and thus may contribute to various processes of cancer development. The significance of 2-Cys Prxs in lung cancer development and their biological function in signal transduction have not been fully investigated. In this study we analyzed the expression of 2-Cys Prxs in lung cancer, and examined their levels of expression in a variety of cell lines established from human lung normal or cancer tissues. We found that 2-Cys Prxs, in particular, Prx1 and Prx4, were preferentially expressed in cell lines derived from human lung cancer. Through isoform specific knockdown of individual Prx, we demonstrated that Prx1 and Prx4 (but not Prx3) were required for human lung cancer A549 cells to form soft agar colony and to invade through matrigel in culture. Knockdown of Prx1 or Prx4 significantly reduced the activation of c-Jun and repressed the AP-1 mediated promoter activity. In mouse xenograft models, knockdown of Prx4 in A549 cells reduced subcutaneous tumor growth and blocked metastasis formation initiated through tail vein injection. Moreover, overexpression of Prx1 or Prx4 further enhanced the malignancy of A549 cells both in culture and in mouse xenografts in vivo. These data provide an in-depth understanding of the contribution of Prx1 and Prx4 to lung cancer development and are of importance for future development of therapeutic methods that targeting 2-Cys Prxs

Current Status and Outlook of Research on Emergency Drills for Public Health Emergencies

Public health emergencies are major infectious disease outbreaks, mass unexplained diseases, major food and occupational poisoning and other events that seriously affect public health that occur suddenly and cause serious damage to the public health of society. In the absence of certain preplanned preparations and experience in emergency response, the outbreak of public health emergencies always catches us off guard and poses a huge challenge and burden to public health and social safety. At present, due to the late start of the emergency management of public health emergencies, the lack of a perfect theoretical system of emergency management, the weak public awareness of emergencies, the lack of scientific emergency measures, the lack of a perfect and flexible public health emergency management system, and the lack of an advanced emergency management level of society as a whole, all of these factors have led to inefficiency, communication and a lack of experience in dealing with public health emergencies such as the new coronary pneumonia outbreak in the early stage. In this context, it is important to take a deeper look at the problems of inefficiency and poor communication in handling public health emergencies. In this context, it is important to examine the current research status of emergency drills for public health emergencies in China, investigate the typical problems, and propose a series of innovative practical strategies for emergency drills for public health emergencies, such as exploring the closed-loop management mode of emergency drills for public health emergencies and giving full play to the role of information technology as a driver of innovation, according to the current development level of China as a whole

Sno/scaRNAbase: a curated database for small nucleolar RNAs and cajal body-specific RNAs

Small nucleolar RNAs (snoRNAs) and Cajal body-specific RNAs (scaRNAs) are named for their subcellular localization within nucleoli and Cajal bodies (conserved subnuclear organelles present in the nucleoplasm), respectively. They have been found to play important roles in rRNA, tRNA, snRNAs, and even mRNA modification and processing. All snoRNAs fall in two categories, box C/D snoRNAs and box H/ACA snoRNAs, according to their distinct sequence and secondary structure features. Box C/D snoRNAs and box H/ACA snoRNAs mainly function in guiding 2′-O-ribose methylation and pseudouridilation, respectively. ScaRNAs possess both box C/D snoRNA and box H/ACA snoRNA sequence motif features, but guide snRNA modifications that are transcribed by RNA polymerase II. Here we present a Web-based sno/scaRNA database, called sno/scaRNAbase, to facilitate the sno/scaRNA research in terms of providing a more comprehensive knowledge base. Covering 1979 records derived from 85 organisms for the first time, sno/scaRNAbase is not only dedicated to filling gaps between existing organism-specific sno/scaRNA databases that are focused on different sno/scaRNA aspects, but also provides sno/scaRNA scientists with an opportunity to adopt a unified nomenclature for sno/scaRNAs. Derived from a systematic literature curation and annotation effort, the sno/scaRNAbase provides an easy-to-use gateway to important sno/scaRNA features such as sequence motifs, possible functions, homologues, secondary structures, genomics organization, sno/scaRNA gene's chromosome location, and more. Approximate searches, in addition to accurate and straightforward searches, make the database search more flexible. A BLAST search engine is implemented to enable blast of query sequences against all sno/scaRNAbase sequences. Thus our sno/scaRNAbase serves as a more uniform and friendly platform for sno/scaRNA research. The database is free available at

Epileptic prediction using spatiotemporal information combined with optimal features strategy on EEG

ObjectiveEpilepsy is the second most common brain neurological disease after stroke, which has the characteristics of sudden and recurrence. Seizure prediction is seriously important for improving the quality of patients’ lives.MethodsFrom the perspective of multiple dimensions including time-frequency, entropy and brain network, this paper proposed a novel approach by constructing the optimal spatiotemporal feature set to predict seizures. Based on strong independence and large information capabilities, the two-dimensional feature screening algorithm is performed to eliminate unnecessary redundant features. In order to verify the effectiveness of the optimal feature set, support vector machine (SVM) was used to classify the preictal and interictal states on both the Kaggle intracranial EEG and CHB-MIT scalp EEG dataset.ResultsThis model achieved an average accuracy of 98.01%, AUC of 0.96, F-Score of 98.3% and FPR of 0.0383/h on the Kaggle dataset; On the CHB-MIT dataset, the average accuracy, AUC, F-score and FPR were 95.93%, 0.92, 94.97% and 0.0473/h, respectively. Further ablation experiments have confirmed that the temporal and spatial features fusion has better performance than the individual temporal or spatial features.ConclusionCompared to the state-of-the-art methods, our approach outperforms most of these existing techniques. The results show that our approach can effectively extract the spatiotemporal information of epileptic EEG signals to predict epileptic seizures with high performance