δ13C values of soil organic carbon and their responses to C3 and C4 plants shift in Mengzi karst graben basin, SW China

Understanding the controlling factors of soil organic carbon isotope (δ13CSOC) change and the vegetation succession process is crucial to guide ecological restoration and agricultural cultivation in karst rocky desertification region. However, the information about the combination of C3 and C4 plant distribution and rocky desertification remains unknown. Soils from different landforms, including basin, slope, and plateau, were sampled to investigate the spatial variance of the δ13CSOC distribution characteristics. The contribution of C3 and C4 plant species for δ13CSOC under the different rocky desertification grades (LRD: light rocky desertification; MRD: moderate rocky desertification; and SRD: severe rocky desertification) in Mengzi karst graben basin of Southwest (SW) China was also discussed. The δ13CSOC  value decreased with the increase of altitude from basin, slope to plateau. At the same landform, different rocky desertification grades had no significant effect on the δ13CSOC in slope and plateau. Nevertheless, there were significant differences of δ13CSOC C between LRD and SRD in the basin. The C4 plants account for more than 70% in the basin and slope, while C3 plants account for more than 70% in the plateau. This may be due to the long-term cultivation of corn in the historical period in the basin and slope. However, the plateau area is not suitable for the growth of C4 plants such as corn due to the cold climate. In addition, in the same landform, with the aggravation of rocky desertification, the proportion of C4 plants for δ13CSOC increased with the proportion of C3 plants decreased. With the aggravation of rocky desertification, the composition of vegetation species changed from arbour (C3 plants) to small shrubs and herbs (C4 plants).Razumevanje povezave med spremembami izotopa v organskem ogljiku v tleh (δ13CSOC) in procesi ekološke sukcesije je pomembno pri restavraciji in kultivaciji kraških degradiranih območij. O povezavi med porazdelitvijo sestoja rastlin C3 in C4 ter skalno dezertifikacijo na kraških območjih vemo le malo. Prostorsko spremenljivost δ13CSOC smo določali na različnih površinskih oblikah; v kotlini, na pobočjih in na kraški planoti na območju Mengzi v provinci Junan na jugovzhodu Kitajske. V vseh reliefnih oblikah smo obravnavali prispevek rastlinskih vrst C3 in C4 k vrednosti δ13CSOC . Posebno pozornost smo posvetili tudi različnim stopnjam dezertifikacije (LRD: majhna; MRD: zmerna; in SRD: visoka). V splošnem vrednost δ13CSOC pada z naraščanjem nadmorske višine. Na planoti in pobočjih stopnja dezertifikacije ne vpliva pomembno na δ13CSOC, v kotlini pa je značilna razlika med območji z nizko stopnjo dezertifikacije in območji z visoko stopnjo dezertifikacije. Sestoji rastlin C4 tvorijo več kot 70 % rastlinja v kotlini in na pobočjih, sestoji rastlin C3 pa 70 % rastlinja na planoti. Razlika je verjetno posledica pridelave koruze v kotlini in na pobočjih, saj planota zaradi hladne klime ni primerna za sajenje rastlin C4, kot je koruza. Sočasno z napredovanjem dezertifikacije upada delež rastlin C4 (npr. dreves) in narašča delež rastlin C3 (grmičevje in zeli)

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

BACKGROUND: The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. RESULTS: We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. CONCLUSION: As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential

miRecords: an integrated resource for microRNA–target interactions

MicroRNAs (miRNAs) are an important class of small noncoding RNAs capable of regulating other genes’ expression. Much progress has been made in computational target prediction of miRNAs in recent years. More than 10 miRNA target prediction programs have been established, yet, the prediction of animal miRNA targets remains a challenging task. We have developed miRecords, an integrated resource for animal miRNA–target interactions. The Validated Targets component of this resource hosts a large, high-quality manually curated database of experimentally validated miRNA–target interactions with systematic documentation of experimental support for each interaction. The current release of this database includes 1135 records of validated miRNA–target interactions between 301 miRNAs and 902 target genes in seven animal species. The Predicted Targets component of miRecords stores predicted miRNA targets produced by 11 established miRNA target prediction programs. miRecords is expected to serve as a useful resource not only for experimental miRNA researchers, but also for informatics scientists developing the next-generation miRNA target prediction programs. The miRecords is available at http://miRecords.umn.edu/miRecords

Loss of FHL1 induces an age-dependent skeletal muscle myopathy associated with myofibrillar and intermyofibrillar disorganization in mice

Recent human genetic studies have provided evidences that sporadic or inherited missense mutations in four-and-a-half LIM domain protein 1 (FHL1), resulting in alterations in FHL1 protein expression, are associated with rare congenital myopathies, including reducing body myopathy and Emery–Dreifuss muscular dystrophy. However, it remains to be clarified whether mutations in FHL1 cause skeletal muscle remodeling owing to gain- or loss of FHL1 function. In this study, we used FHL1-null mice lacking global FHL1 expression to evaluate loss-of-function effects on skeletal muscle homeostasis. Histological and functional analyses of soleus, tibialis anterior and sternohyoideus muscles demonstrated that FHL1-null mice develop an age-dependent myopathy associated with myofibrillar and intermyofibrillar (mitochondrial and sarcoplasmic reticulum) disorganization, impaired muscle oxidative capacity and increased autophagic activity. A longitudinal study established decreased survival rates in FHL1-null mice, associated with age-dependent impairment of muscle contractile function and a significantly lower exercise capacity. Analysis of primary myoblasts isolated from FHL1-null muscles demonstrated early muscle fiber differentiation and maturation defects, which could be rescued by re-expression of the FHL1A isoform, highlighting that FHL1A is necessary for proper muscle fiber differentiation and maturation in vitro. Overall, our data show that loss of FHL1 function leads to myopathy in vivo and suggest that loss of function of FHL1 may be one of the mechanisms underlying muscle dystrophy in patients with FHL1 mutations

SVRMHC prediction server for MHC-binding peptides

BACKGROUND: The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. RESULTS: Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods. CONCLUSION: SVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers

Metal-organic framework derived hierarchical porous TiO2 nanopills as a super stable anode for Na-ion batteries

Hierarchical porous TiO2 nanopills were synthesized using a titanium metal-organic framework MIL-125(Ti) as precursor. The as-synthesized TiO2 nanopills owned a large specific surface area of 102 m2/g and unique porous structure. Furthermore, the obtained TiO2 nanopills were applied as anode materials for Na-ion batteries for the first time. The as-synthesized TiO2 nanopills achieved a high discharge capacity of 196.4 mAh/g at a current density of 0.1 A/g. A discharge capacity of 115.9 mAh/g was obtained at a high current density of 0.5 A/g and the capacity retention was remained as high as 90% even after 3000 cycles. The excellent electrochemical performance can be attributed to its unique hierarchical porous feature

Integrated siRNA design based on surveying of features associated with high RNAi effectiveness

BACKGROUND: Short interfering RNAs have allowed the development of clean and easily regulated methods for disruption of gene expression. However, while these methods continue to grow in popularity, designing effective siRNA experiments can be challenging. The various existing siRNA design guidelines suffer from two problems: they differ considerably from each other, and they produce high levels of false-positive predictions when tested on data of independent origins. RESULTS: Using a distinctly large set of siRNA efficacy data assembled from a vast diversity of origins (the siRecords data, containing records of 3,277 siRNA experiments targeting 1,518 genes, derived from 1,417 independent studies), we conducted extensive analyses of all known features that have been implicated in increasing RNAi effectiveness. A number of features having positive impacts on siRNA efficacy were identified. By performing quantitative analyses on cooperative effects among these features, then applying a disjunctive rule merging (DRM) algorithm, we developed a bundle of siRNA design rule sets with the false positive problem well curbed. A comparison with 15 online siRNA design tools indicated that some of the rule sets we developed surpassed all of these design tools commonly used in siRNA design practice in positive predictive values (PPVs). CONCLUSION: The availability of the large and diverse siRNA dataset from siRecords and the approach we describe in this report have allowed the development of highly effective and generally applicable siRNA design rule sets. Together with ever improving RNAi lab techniques, these design rule sets are expected to make siRNAs a more useful tool for molecular genetics, functional genomics, and drug discovery studies

Are survey stock price forecasts anchored by fundamental forecasts? A long-run perspective

This paper firstly shows that a wide range of asset pricing models, including full information and Bayesian rational expectations models, typically imply that agents use the long-run cointegration relationship between stock prices and fundamentals to forecast future stock prices. However, using several widely used survey forecast datasets, we provide robust new evidence that survey forecasts of aggregate stock price indices are not cointegrated with forecasts of fundamentals (aggregate consumption, dividend, and output), both at the consensus and individual level. We argue that it is crucial to relax investors’ common knowledge of the equilibrium pricing function to reconcile this finding