Damage to living trees contributes to almost half of the biomass losses in tropical forests

Accurate estimates of forest biomass stocks and fluxes are needed to quantify global carbon budgets and assess the response of forests to climate change. However, most forest inventories consider tree mortality as the only aboveground biomass (AGB) loss without accounting for losses via damage to living trees: branchfall, trunk breakage, and wood decay. Here, we use ~151,000 annual records of tree survival and structural completeness to compare AGB loss via damage to living trees to total AGB loss (mortality + damage) in seven tropical forests widely distributed across environmental conditions. We find that 42% (3.62 Mg ha⁻¹ year⁻¹; 95% confidence interval [CI] 2.36–5.25) of total AGB loss (8.72 Mg ha⁻¹ year⁻¹; CI 5.57–12.86) is due to damage to living trees. Total AGB loss was highly variable among forests, but these differences were mainly caused by site variability in damage-related AGB losses rather than by mortality-related AGB losses. We show that conventional forest inventories overestimate stand-level AGB stocks by 4% (1%–17% range across forests) because assume structurally complete trees, underestimate total AGB loss by 29% (6%–57% range across forests) due to overlooked damage-related AGB losses, and overestimate AGB loss via mortality by 22% (7%–80% range across forests) because of the assumption that trees are undamaged before dying. Our results indicate that forest carbon fluxes are higher than previously thought. Damage on living trees is an underappreciated component of the forest carbon cycle that is likely to become even more important as the frequency and severity of forest disturbances increase.     

Do the Levels of Maternal Plasma Trace Elements Affect Fetal Nuchal Translucency Thickness?

Objective

Fetal nuchal translucency (NT) thickness is an important marker for prenatal screening; however, studies focusing on the correlation between maternal trace element levels and NT thickness are limited. The aim of this study was to evaluate maternal trace element levels during the first trimester and to investigate the association between maternal trace element levels and fetal NT thickness.

Methods

In total, 113 samples were obtained from singleton pregnant women. Maternal plasma samples were collected in the first trimester of gestation. Plasma trace element levels were measured using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Nuchal translucency thickness was measured using ultrasonography at 10–14 weeks of gestation.

Results

We found that maternal plasma potassium (K) levels had a significant negative correlation with both NT (r = -0.230, p < 0.05) and NT Multiples of the Median (NT MoM) (r = -0.206, p < 0.05). After adjustment for potential confounders, log-transformed maternal plasma potassium levels in the first trimester were significantly associated with fetal NT (NT MoM: β = -0.68, p < 0.05; NT: β = -1.20, p < 0.01). Although not statistically significant, the As, Hg and Pb levels in maternal plasma were positively correlated with NT, and the Mg, Cu, Zn, Na and Ca levels were negatively correlated with NT.

Conclusion

Maternal plasma K levels during the first trimester appeared to be associated with NT thickness. The essential elements tended to decrease NT thickness, and non-essential elements tended to increase it.     

Discovery of protein phosphorylation motifs through exploratory data analysis

Background

The need for efficient algorithms to uncover biologically relevant phosphorylation motifs has become very important with rapid expansion of the proteomic sequence database along with a plethora of new information on phosphorylation sites. Here we present a novel unsupervised method, called Motif Finder (in short, F-Motif) for identification of phosphorylation motifs. F-Motif uses clustering of sequence information represented by numerical features that exploit the statistical information hidden in some foreground data. Furthermore, these identified motifs are then filtered to find “actual” motifs with statistically significant motif scores.

Results and Discussion

We have applied F-Motif to several new and existing data sets and compared its performance with two well known state-of-the-art methods. In almost all cases F-Motif could identify all statistically significant motifs extracted by the state-of-the-art methods. More importantly, in addition to this, F-Motif uncovers several novel motifs. We have demonstrated using clues from the literature that most of these new motifs discovered by F-Motif are indeed novel. We have also found some interesting phenomena. For example, for CK2 kinase, the conserved sites appear only on the right side of S. However, for CDK kinase, the adjacent site on the right of S is conserved with residue P. In addition, three different encoding methods, including a novel position contrast matrix (PCM) and the simplest binary coding, are used and the ability of F-motif to discover motifs remains quite robust with respect to encoding schemes.

Conclusions

An iterative algorithm proposed here uses exploratory data analysis to discover motifs from phosphorylated data. The effectiveness of F-Motif has been demonstrated using several real data sets as well as using a synthetic data set. The method is quite general in nature and can be used to find other types of motifs also. We have also provided a server for F-Motif at http://f-motif.classcloud.org/, http://bio.classcloud.org/f-motif/ or http://ymu.classcloud.org/f-motif/.     

Identification of single- and multiple-class specific signature genes from gene expression profiles by group marker index

Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing drug on other diseases as well as designing a single drug for multiple diseases.     

NKT cells are required for complete Freund's adjuvant-mediated protection from autoimmune diabetes

Autoimmune diabetes in NOD mice can be prevented by application of Ags derived from Mycobacterium tuberculosis in the form of bacillus Calmette-Guérin or CFA. Disease protection by CFA is associated with a reduction in the numbers of pathogenic β-cell specific, self-reactive CTLs, a phenomenon dependent on the presence and function of NK cells. However, the mechanisms by which NK cells are activated and recruited by heat-killed M. tuberculosis within CFA are unclear. In this study, we report that CFA-mediated NK cell activation and mobilization is dependent on CD1d expression. The administration of M. tuberculosis from CFA results in rapid NKT cell activation and IFN-γ secretion both in vitro and in vivo. CFA-induced NKT cell activation is intact in MyD88(-/-) mice suggesting that the mechanism is independent of TLR signaling. Furthermore, CD1d expression was found to be essential for both M. tuberculosis-triggered NKT cell activation and CFA-mediated protection of NOD mice from diabetes. Collectively, these findings reveal hitherto previously unidentified roles for NKT cells in the adjuvant-promoting effects of CFA on innate and adaptive immunity.     

Mapping the essential structures of human ribosomal protein L7 for nuclear entry, ribosome assembly and function

Human large subunit protein L7 carries multiple nuclear localization signals (NLS) in its structure: there are three monobasic partite NLSs at the NH2-region of the first 54 amino acid residues and a bipartite in the middle section at position of 156-167. The C-region of the last 50 amino acid residues displays membrane binding nature, and might involve in forming a nuclear microbody for pre-nucleolar ribosome assembly. The middle section covers 144 amino acid residues which are essential for the structure and function of ribosome. This is evident from findings that truncated L7 without the NH2-region or the C-region, or missing both regions, is capable of reaching nucleolus and incorporating in ribosome, however, only ribosomes bearing truncated L7 without the NH2-region is capable of engaging in polysome formation. Combining with the phylogenic findings from homologous sequence alignment, the NH2-region of L7, besides being as a eukaryotic expansion segment, can be excluded from building a functional eukaryotic ribosome.     

Testing metabolic ecology theory for allometric scaling of tree size, growth and mortality in tropical forests

The theory of metabolic ecology predicts specific relationships among tree stem diameter, biomass, height, growth and mortality. As demographic rates are important to estimates of carbon fluxes in forests, this theory might offer important insights into the global carbon budget, and deserves careful assessment. We assembled data from 10 old-growth tropical forests encompassing censuses of 367 ha and > 1.7 million trees to test the theory's predictions. We also developed a set of alternative predictions that retained some assumptions of metabolic ecology while also considering how availability of a key limiting resource, light, changes with tree size. Our results show that there are no universal scaling relationships of growth or mortality with size among trees in tropical forests. Observed patterns were consistent with our alternative model in the one site where we had the data necessary to evaluate it, and were inconsistent with the predictions of metabolic ecology in all forests.     

Uric acid and urea in human sweat

The present study investigated whether thermal sweating may relieve elevated concentrations of serum uric acid or urea. Concentrations of uric acid and urea were measured in the sweat of sixteen male volunteers, who were treated with external heat after one hour of intense physical exercise. The same analytes were also measured in their urine and serum samples. Furthermore, creatinine and some electrolytes were determined in these specimens. The results show that the concentration of uric acid in the sweat is 24.5 micromol/L, which is only 6.3% of that in serum. The concentration of urea in the sweat is 22.2 mmol/L, which is 3.6 times that in serum. The results indicate that sweat uric acid concentration is quite minimal, and the estimated total uric acid excretion per day in normal physiological range is insignificant. However, the level of sweat urea was found at a much higher concentration than the serum level. No correlation could be established between the level of uric acid in sweat and in serum. There was also no correlation between the level of urea in sweat and that in serum. These results suggest it would not be effective to relieve the elevated serum uric acid concentration by thermal sweating when the renal excretion of uric acid is partly compromised. Nevertheless, the potential of urea excretion via profuse sweating is apparent particularly when the kidneys are damaged or their function is impaired. These findings also suggest that persons who take vigorous exercise or are exposed to hot environments should be well advised to drink adequate fluids since heavy sweating excretes only minimal uric acid, accompanied by significant diminution of urinary output and diminished urinary excretions of uric acid, which may induce elevated levels of serum uric acid.     

Optimization of formation and regeneration of protoplasts from biocontrol agents ofTrichoderma species

The optimal conditions necessary for a large yield and a high frequency of regeneration of protoplasts isolated from the biocontrol agentsTrichoderma koningii andT. harzianum were investigated. Protoplast yields were 1.2×10⁸/ml fromT. koningii and 6×10⁷/ml fromT. harzianum when 20-h mycelial culture was treated with a lytic enzyme solution containing Novozym 234 (15 mg/ml), sucrose (0.6 M) and citrate phosphate buffer (0.02 M), pH 5.6 at 31°C. When the protoplasts were grown in the regeneration medium containing yeast extract (1.5%), 1 I of Mandel's salts, pH 5.6, and glucose (0.8 M), a high frequency of regeneration of the protoplast was obseved: 66% forT. koningii and 45% forT. harzianum. Two patterns of regeneration were observed. First, the hyphae arose directly from the regenerated protoplast mother cell. Second, a chain of bud cells developed from the protoplast and subsequently generating hyphae generally protruded from the terminal bud cells.