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Antibodies are fundamental effectors of humoral immunity, and have become a highly successful class of therapeutics. There is increasing evidence that antibodies utilize transient homotypic interactions to enhance function, and elucidation of such interactions can provide insights into their biology and new opportunities for their optimization as drugs. Yet the transitory nature of weak interactions makes them difficult to investigate. Capitalizing on their rich structural data and high conservation, we have characterized all the ways that antibody Fab regions interact crystallographically. This approach led to the discovery of previously unrealized interfaces between antibodies. While diverse interactions exist, β-sheet dimers and variable-constant elbow dimers are recurrent motifs. Disulfide engineering enabled interactions to be trapped and investigated structurally and functionally, providing experimental validation of the interfaces and illustrating their potential for optimization. This work provides first insight into previously undiscovered oligomeric interactions between antibodies, and enables new opportunities for their biotherapeutic optimization.

Whole-genome sequencing (WGS) holds great potential as a diagnostic test. However, the majority of patients currently undergoing WGS lack a molecular diagnosis, largely due to the vast number of undiscovered disease genes and our inability to assess the pathogenicity of most genomic variants. The CAGI SickKids challenges attempted to address this knowledge gap by assessing state-of-the-art methods for clinical phenotype prediction from genomes. CAGI4 and CAGI5 participants were provided with WGS data and clinical descriptions of 25 and 24 undiagnosed patients from the SickKids Genome Clinic Project, respectively. Predictors were asked to identify primary and secondary causal variants. In addition, for CAGI5, groups had to match each genome to one of three disorder categories (neurologic, ophthalmologic, and connective), and separately to each patient. The performance of matching genomes to categories was no better than random but two groups performed significantly better than chance in matching genomes to patients. Two of the ten variants proposed by two groups in CAGI4 were deemed to be diagnostic, and several proposed pathogenic variants in CAGI5 are good candidates for phenotype expansion. We discuss implications for improving in silico assessment of genomic variants and identifying new disease genes.

In the context of global warming, China is facing with increasing climate risks. It is imperative to develop quantitative indices to reflect the climate risks caused by extreme weather/climate events and adverse climatic conditions in association with different industries. Based on the observations at 2288 meteorological stations in China and the meteorological disasters data, a set of indices are developed to measure climate risks due to water-logging, drought, high temperature, cryogenic freezing, and typhoon. A statistical method is then used to construct an overall climate risk index (CRI) for China from these individual indices. There is a good correspondence between these indices and historical climatic conditions. The CRI, the index of water-logging by rain, and the high temperature index increase at a rate of 0.28, 0.37, and 0.65 per decade, respectively, from 1961 to 2016. The cryogenic freezing index is closely related to changes in the consumer price index for food. The high temperature index is correlated with the consumption of energy and electricity. The correlation between the yearly growth in claims on household property insurance and the sum of the water-logging index and the typhoon index in the same year is as high as 0.70. Both the growth rate of claims on agricultural insurance and the annual growth rate of hospital inpatients are positively correlated with the CRI. The year-on-year growth in the number of domestic tourists is significantly negatively correlated with the CRI in the same year. More efforts are needed to develop regional CRIs.

Offshore wind energy development and utilization plays an important role in haze governance and fulfilling the reduction commitments in advance. The reserve estimation of offshore wind resource in China is abundant enough to meet the basic needs from the exploitation of wind power. Recently, more and more attention is paid to the accurate estimation of the offshore extreme winds, which impose an important effect on the wind turbine designs and operations. Different from the abundant study of the typhoon extreme winds, because of lack of the effective methods, it shows a significant deficiency in the study of offshore extreme winds over the mid-high latitude, which is also the key area for the exploitation of wind power. Therefore, based on numerical modeling and spectrum analysis, the paper developed a practical, reliable and robust method for 50-years extreme wind estimation that can be applied for other mid-latitude offshore area in China. Taking into account the complex climatic characteristics of China's mid-latitude offshore area affected by high latitude cold winter wind and tropical cyclone, the 50-yr return wind speeds at 100m over Jiangsu offshore area were estimated by this new method. It was shown that (1) affected by the strong cold winter winds, the 50-yr return wind speed in the northern part of Jiangsu offshore area exceeded 40 m/s; (2) the 50-yr return wind speeds of 40 m/s, or even 44m/s near Xiangshui and Rudong was related to the limited typhoon activities; and (3) the 50-yr return wind speeds over the central Jiangsu offshore area were generally lower than 35 m/s. This study may not only provide a scientific basis of the design and operation of offshore engineering, such as offshore wind farms in the future, but also contribute to future understanding of the extreme climate events in China and the development of atmospheric boundary layer theory as well (This abstract has been published in Acta Meteorologica Sinica, 2019, 77(5): 938-948. doi:10.11671/qxxb2019.054).

Tropical cyclones caused significant national economic losses in China in recent decades. For alleviating these hydro-meteorological disasters, the spatiotemporal distributions of losses per economic sector and the associated rainfall and wind patterns of such influential tropical cyclones (ITCs) were estimated for China. Based on multiple reference databases and socio-economic data, ITCs losses for major economic sectors have been deduced. The obtained information plays an important role in the projection of sector losses of future ITCs affecting China. The total economic losses have been split into five major sector loss categories, comprising agriculture, industry, construction, transport, and business & others. About 37% of the total loss occurred in the industrial sector, occupying the largest proportion. High values were primarily distributed in the urban agglomerations of the Pearl River and Yangtze River deltas and along the mainland coast of the Taiwan Strait, with over 1 billion CNY per year. Significant upward trends are found in all of sectors except in agriculture, which shows a decreasing tendency for the period of 1984–2014. The years with the highest losses occurred in the mid-1990s, mid-2000s, and early 2010s, consistently affecting all sectors in all regions along the mainland coast. The southern part of China was most frequently affected by ITCs, with increasing trends in sector losses for most areas. The relationships between wind speed, rainfall and sector losses can be presented in the form of an exponential function. The results show that rainfall is more positively correlated with ITCs losses than wind speed. Here, the industry sector shows the largest correlation coefficient and highest growth rate, whereas the construction sector shows the lowest. In future, research should pay more attention on impacts of heavy rainfall triggered by ITCs in China.

The existence and magnitude of the recently suggested global warming hiatus, or slowdown, have been strongly debated1–3. Although various physical processes4–8 have been examined to elucidate this phenomenon, the accuracy and completeness of observational data that comprise global average surface air temperature (SAT) datasets is a concern9,10. In particular, these datasets lack either complete geographic coverage or in situ observations over the Arctic, owing to the sparse observational network in this area 9 . As a consequence, the contribution of Arctic warming to global SAT changes may have been underestimated, leading to an uncertainty in the hiatus debate. Here, we constructed a new Arctic SAT dataset using the most recently updated global SATs 2 and a drifting buoys based Arctic SAT dataset 11 through employing the ‘data interpolating empirical orthogonal functions’ method 12 . Our estimate of global SAT rate of increase is around 0.112 °C per decade, instead of 0.05 °C per decade from IPCC AR5 1 , for 1998–2012. Analysis of this dataset shows that the amplified Arctic warming over the past decade has significantly contributed to a continual global warming trend, rather than a hiatus or slowdown. The Arctic is under-represented in surface temperature datasets and this could affect estimates of global warming. A new dataset with greater coverage of the Arctic shows a higher warming rate of 0.112 °C per decade compared to 0.005 °C from IPCC AR5.

Understanding the basis of complex trait disease is a fundamental problem in human genetics. The CAGI Crohn’s Exome challenges are providing insight into the adequacy of current disease models by requiring participants to identify which of a set of individuals has been diagnosed with the disease, given exome data. For the CAGI4 round, we developed a method that used the genotypes from exome sequencing data only to impute the status of Genome Wide Association Studies (GWAS) marker single nucleotide polymorphisms (SNPs). We then used the imputed genotypes as input to several machine learning methods that had been trained to predict disease status from marker SNP information. We achieved the best performance using Naïve Bayes and with a consensus machine learning method, obtaining an area under the curve (AUC) of 0.72, larger than other methods used in CAGI4. We also developed a model that incorporated the contribution from rare missense variants in the exome data, but this performed less well. Future progress is expected to come from the use of whole genome data rather than exomes.

Precise identification of causative variants from whole-genome sequencing data, including both coding and non-coding variants, is challenging. The CAGI5 SickKids clinical genome challenge provided an opportunity to assess our ability to extract such information. Participants in the challenge were required to match each of 24 whole-genome sequences to the correct phenotypic profile and to identify the disease class of each genome. These are all rare disease cases that have resisted genetic diagnosis in a state-of-the-art pipeline. The patients have a range of eye, neurological, and connective-tissue disorders. We used a gene-centric approach to address this problem, assigning each gene a multi-phenotype-matching score. Mutations in the top scoring genes for each phenotype profile were ranked on a six-point scale of pathogenicity probability, resulting in an approximately equal number of top ranked coding and non-coding candidate variants overall. We were able to assign the correct disease class for 12 cases and the correct genome to a clinical profile for five cases. The challenge assessor found genes in three of these five cases as likely appropriate. In the post-submission phase, after careful screening of the genes in the correct genome we identified additional potential diagnostic variants, a high proportion of which are non-coding.

The Critical Assessment of Genome Interpretation-5 intellectual disability challenge asked to use computational methods to predict patient clinical phenotypes and the causal variant(s) based on an analysis of their gene panel sequence data. Sequence data for 74 genes associated with intellectual disability (ID) and/or autism spectrum disorders (ASD) from a cohort of 150 patients with a range of neurodevelopmental manifestations (i.e. ID, autism, epilepsy, microcephaly, macrocephaly, hypotonia, ataxia) have been made available for this challenge. For each patient, predictors had to report the causative variants and which of the seven phenotypes were present. Since neurodevelopmental disorders are characterized by strong comorbidity, tested individuals often present more than one pathological condition. Considering the overall clinical manifestation of each patient, the correct phenotype has been predicted by at least one group for 93 individuals (62%). ID and ASD were the best predicted among the seven phenotypic traits. Also, causative or potentially pathogenic variants were predicted correctly by at least one group. However, the prediction of the correct causative variant seems to be insufficient to predict the correct phenotype. In some cases, the correct prediction has been supported by rare or common variants in genes different from the causative one.

Thermodynamic stability is a fundamental property shared by all proteins. Changes in stability due to mutation are a widespread molecular mechanism in genetic diseases. Methods for the prediction of mutation-induced stability change have typically been developed and evaluated on incomplete and/or biased data sets. As part of the Critical Assessment of Genome Interpretation (CAGI), we explored the utility of high-throughput variant stability profiling (VSP) assay data as an alternative for the assessment of computational methods and evaluated state-of-the-art predictors against over 7,000 non-synonymous variants from two proteins. We found that predictions were modestly correlated with actual experimental values. Predictors fared better when evaluated as classifiers of extreme stability effects. While different methods emerged as top-performers depending on the metric, it is non-trivial to draw conclusions on their adoption or improvement. Our analyses revealed that only 16% of all variants in VSP assays could be confidently defined as stability-affecting. Furthermore, it is unclear to what extent VSP abundance scores were reasonable proxies for the stability-related quantities that participating methods were designed to predict. Overall, our observations underscore the need for clearly defined objectives when developing and using both computational and experimental methods in the context of measuring variant impact.

Endolysins are bacteriophage-derived peptidoglycan hydrolases that represent an emerging class of proteinaceous therapeutics. While the streptococcal endolysin PlyC has been validated in vitro and in vivo for its therapeutic efficacy, the inherent thermosusceptible structure of the enzyme correlates to transient long-term stability, thereby hindering the feasibility of developing the enzyme as an antimicrobial. Here, we thermostabilized the cysteine, histidine-dependent amidohydrolase/peptidase (CHAP) domain of the PlyCA catalytic subunit of PlyC using a FoldX-driven computational protein engineering approach. Using a combination of FoldX and Rosetta algorithms, as well as visual inspection, a final list of PlyC point mutant candidates with predicted stabilizing ΔΔG values was assembled and thermally characterized. Five of the eight point mutations were found experimentally to be destabilizing, a result most likely attributable to computationally modeling a complex and dynamic nine-subunit holoenzyme with a corresponding 3.3-Å X-ray crystal structure. However, one of the mutants, PlyC (PlyCA) T406R, was shown experimentally to increase the thermal denaturation temperature by ∼2.2°C and kinetic stability 16-fold over wild type. This mutation is expected to introduce a thermally advantageous hydrogen bond between the Q106 side chain of the N-terminal glycosyl hydrolase domain and the R406 side chain of the C-terminal CHAP domain.

© 2017 Wiley Periodicals, Inc. The CAGI-4 Hopkins clinical panel challenge was an attempt to assess state-of-the-art methods for clinical phenotype prediction from DNA sequence. Participants were provided with exonic sequences of 83 genes for 106 patients from the Johns Hopkins DNA Diagnostic Laboratory. Five groups participated in the challenge, predicting both the probability that each patient had each of the 14 possible classes of disease, as well as one or more causal variants. In cases where the Hopkins laboratory reported a variant, at least one predictor correctly identified the disease class in 36 of the 43 patients (84%). Even in cases where the Hopkins laboratory did not find a variant, at least one predictor correctly identified the class in 39 of the 63 patients (62%). Each prediction group correctly diagnosed at least one patient that was not successfully diagnosed by any other group. We discuss the causal variant predictions by different groups and their implications for further development of methods to assess variants of unknown significance. Our results suggest that clinically relevant variants may be missed when physicians order small panels targeted on a specific phenotype. We also quantify the false-positive rate of DNA-guided analysis in the absence of prior phenotypic indication.

Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype–phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype–phenotype relationships.

Correct phenotypic interpretation of variants of unknown significance for cancer-associated genes is a diagnostic challenge as genetic screenings gain in popularity in the next-generation sequencing era. The Critical Assessment of Genome Interpretation (CAGI) experiment aims to test and define the state of the art of genotype-phenotype interpretation. Here, we present the assessment of the CAGI p16INK4a challenge. Participants were asked to predict the effect on cellular proliferation of ten variants for the p16INK4a tumor suppressor, a cyclin-dependent kinase inhibitor encoded by the CDKN2A gene. Twenty-two pathogenicity predictors were assessed with a variety of accuracy measures for reliability in a medical context. Different assessment measures were combined in an overall ranking to provide more robust results. The R scripts used for assessment are publicly available from a GitHub repository for future use in similar assessment exercises. Despite a limited test-set size, our findings show a variety of results, with some methods performing significantly better. Methods combining different strategies frequently outperform simpler approaches. The best predictor, Yang&Zhou lab, uses a machine learning method combining an empirical energy function measuring protein stability with an evolutionary conservation term. The p16INK4a challenge highlights how subtle structural effects can neutralize otherwise deleterious variants. This article is protected by copyright. All rights reserved.

CAGI (Critical Assessment of Genome Interpretation) conducts community experiments to determine the state of the art in relating genotype to phenotype. Here we report results obtained using newly-developed ensemble methods to address two CAGI4 challenges: enzyme activity for population missense variants found in NAGLU (Human N-acetyl-glucosaminidase) and random missense mutations in Human UBE2I (Human SUMO E2 ligase), assayed in a high throughput competitive yeast complementation procedure. The ensemble methods are effective, ranked 2nd for SUMO-ligase and 3rd for NAGLU, according to the CAGI independent assessors. However, in common with other methods used in CAGI, there are large discrepancies between predicted and experimental activities for a subset of variants. Analysis of the structural context provides some insight into these. Post-challenge analysis shows the ensemble methods are also effective at assigning pathogenicity for the NAGLU variants. In the clinic, providing an estimate of the reliability of pathogenic assignments is key. We have also used the NAGLU dataset to show that ensemble methods have considerable potential for this task, and are already reliable enough for use with a subset of mutations. This article is protected by copyright. All rights reserved

Afforestation activities in the Kubuqi Desert, Inner Mongolia, China, have substantially increased tree and shrub coverage in this region. In this study, the response of the surface temperature to afforestation is simulated with the Weather Research and Forecasting model. The surface temperature changes are decomposed into contributions from the intrinsic surface biophysical effect and atmospheric feedback, using the theory of intrinsic biophysical mechanism. The effect of afforestation on the surface temperature is 1.34 K, &minus, 0.48 K, 2.09 K and 0.22 K for the summer daytime, the summer nighttime, the winter daytime and the winter nighttime, respectively, for the grid cells that have experienced conversion from bare soil to shrub. The corresponding domain mean values are 0.15 K, &minus, 0.2 K, 0.67 K, and 0.06 K. The seasonal variation of surface temperature change is mainly caused by changes in roughness and Bowen ratio. In the daytime, the surface temperature changes are dominated by the biophysical effect, with albedo change being the main biophysical factor. In the nighttime, the biophysical effect (mainly associated with roughness change) and the atmospheric feedback (mainly associated with change in the background air temperature) contribute similar amounts to the surface temperature changes. We conclude that the atmospheric feedback can amplify the influence of the surface biophysical effect, especially in the nighttime.

A new composite index called the yearly tropical cyclone potential impact (YTCPI) is introduced. The relationship between YTCPI and activities of tropical cyclones (TCs) in China, disaster loss, and main ambient fields are investigated to show the potential of YTCPI as a new tool for short-term climate prediction of TCs. YTCPI can indicate TC activity and potential disaster loss. As correlation coefficients between YTCPI and frequency of landfalling TCs, the frequency of TCs traversing or forming inside a 24 h warning line in China from 1971 to 2010 are 0.58 and 0.56, respectively (both are at a statistically significant level, above α = 0.001). Furthermore, three simple indexes are used to compare with YTCPI. They all have very close relationships with it, with correlation coefficients 0.75, 0.82 and 0.78. For economic loss and YTCPI, the correlation coefficient is 0.57 for 1994–2009. Information on principal ambient fields (sea surface temperature, 850 and 500 hPa geopotential heights) during the previous winter is reflected in the relationship with YTCPI. Spatial and temporal variabilities of ambient fields are extracted through empirical orthogonal function (EOF) analysis. Spatial distributions of correlation coefficient between YTCPI and ambient fields match the EOF main mode. Correlation coefficients between YTCPI and the EOF time array for the three ambient fields are 0.46, 0.44 and 0.4, respectively, all statistically significant, above α = 0.01. The YTCPI has the overall potential to be an improved prediction tool.

Purpose To investigate the effect of resolvin D1 (RvD1) on the Nod-like receptor family pyrin domain-containing (NLRP3) inflammasome and the nuclear factor-kappa beta (NF-κB) pathway in streptozotocin (STZ)-induced diabetic retinopathy in rats. Methods Ninety-six male rats were divided into four groups: control, STZ, RvD1, and vehicle. The rats with diabetic retinopathy induced by STZ in the RvD1 and vehicle groups were given an intravitreal injection of RvD1 (1,000 ng/kg) or the same dosage of vehicle, respectively. All rats were euthanized 7 days following treatment. Hematoxylin and eosin staining was used to observe the pathological changes in the retinal tissues. The location and expression of the NLRP3 inflammasome components, including NLRP3, caspase-associated recruitment domain (ASC), and caspase-1, in the retinas were detected using immunohistochemistry, real-time PCR, and western blot, respectively. Retinal homogenate of rats were collected for the detection of the downstream molecules interleukin 1 beta (IL-1β) and IL-18 of the NLRP3 inflammasome with enzyme-linked immunosorbent assay kits. Results The levels of NLRP3, ASC, cleaved caspase-1, IL-1β, and IL-18 were upregulated in the retinas of the STZ-induced diabetic rats; however, these changes were partially inhibited by the RvD1 treatment. Furthermore, the administration of RvD1 suppressed activation of NF-kB, which was upregulated in STZ-induced diabetic retinopathy. Conclusions RvD1 plays a protective role in STZ-induced diabetic retinopathy by inhibiting the level of activation of the NLRP3 inflammasome and associated cytokine production, suggesting targeting of this pathway might be an effective strategy in treatment of diabetic retinopathy.

The use of gene panel sequence for diagnostic and prognostic testing is now widespread, but there are so far few objective tests of methods to interpret these data. We describe the design and implementation of a gene panel sequencing data analysis pipeline (VarP) and its assessment in a CAGI4 community experiment. The method was applied to clinical gene panel sequencing data of 106 patients, with the goal of determining which of 14 disease classes each patient has and the corresponding causative variant(s). The disease class was correctly identified for 36 cases, including 10 where the original clinical pipeline did not find causative variants. For a further seven cases, we found strong evidence of an alternative disease to that tested. Many of the potentially causative variants are missense, with no previous association with disease, and these proved the hardest to correctly assign pathogenicity or otherwise. Post analysis showed that three-dimensional structure data could have helped for up to half of these cases. Over-reliance on HGMD annotation led to a number of incorrect disease assignments. We used a largely ad hoc method to assign probabilities of pathogenicity for each variant, and there is much work still to be done in this area.

Compared with earlier more restricted sequencing technologies, identification of rare disease variants using whole-genome sequence has the possibility of finding all causative variants, but issues of data quality and an overwhelming level of background variants complicate the analysis. The CAGI4 SickKids clinical genome challenge provided an opportunity to assess the landscape of variants found in a difficult set of 25 unsolved rare disease cases. To address the challenge, we developed a three-stage pipeline, first carefully analyzing data quality, then classifying high-quality gene-specific variants into seven categories, and finally examining each candidate variant for compatibility with the often complex phenotypes of these patients for final prioritization. Variants consistent with the phenotypes were found in 24 out of the 25 cases, and in a number of these, there are prioritized variants in multiple genes. Data quality analysis suggests that some of the selected variants are likely incorrect calls, complicating interpretation. The data providers followed up on three suggested variants with Sanger sequencing, and in one case, a prioritized variant was confirmed as likely causative by the referring physician, providing a diagnosis in a previously intractable case.

The advent of next-generation sequencing has dramatically decreased the cost for whole-genome sequencing and increased the viability for its application in research and clinical care. The Personal Genome Project (PGP) provides unrestricted access to genomes of individuals and their associated phenotypes. This resource enabled the Critical Assessment of Genome Interpretation (CAGI) to create a community challenge to assess the bioinformatics community's ability to predict traits from whole genomes. In the CAGI PGP challenge, researchers were asked to predict whether an individual had a particular trait or profile based on their whole genome. Several approaches were used to assess submissions, including ROC AUC (area under receiver operating characteristic curve), probability rankings, the number of correct predictions, and statistical significance simulations. Overall, we found that prediction of individual traits is difficult, relying on a strong knowledge of trait frequency within the general population, whereas matching genomes to trait profiles relies heavily upon a small number of common traits including ancestry, blood type, and eye color. When a rare genetic disorder is present, profiles can be matched when one or more pathogenic variants are identified. Prediction accuracy has improved substantially over the last 6 years due to improved methodology and a better understanding of features.

PlyC, a bacteriophage-encoded endolysin, lyses Streptococcus pyogenes (Spy) on contact. Here, we demonstrate that PlyC is a potent agent for controlling intracellular Spy that often underlies refractory infections. We show that the PlyC holoenzyme, mediated by its PlyCB subunit, crosses epithelial cell membranes and clears intracellular Spy in a dose-dependent manner. Quantitative studies using model membranes establish that PlyCB interacts strongly with phosphatidylserine (PS), whereas its interaction with other lipids is weak, suggesting specificity for PS as its cellular receptor. Neutron reflection further substantiates that PlyC penetrates bilayers above a PS threshold concentration. Crystallography and docking studies identify key residues that mediate PlyCB–PS interactions, which are validated by site-directed mutagenesis. This is the first report that a native endolysin can traverse epithelial membranes, thus substantiating the potential of PlyC as an antimicrobial for Spy in the extracellular and intracellular milieu and as a scaffold for engineering other functionalities. DOI: http://dx.doi.org/10.7554/eLife.13152.001, eLife digest Streptococcus pyogenes is the bacterium that causes throat infections and other serious infections in humans. Antibiotics such as penicillin are used to treat active infections, but so-called “strep throat infections” often return after treatment. This is because S. pyogenes can enter the cells that line the throat and hide from the antibiotics, which cannot enter the throat cells. Endolysins are enzymes produced by viruses that attack bacteria, and these enzymes target and destroy the bacterial cell wall. A previous study revealed that an endolysin known as PlyC could destroy S. pyogenes bacteria on contact. PlyC and other endolysins have the potential to act as alternatives to common antibiotics, but before these enzymes can be developed as therapeutics, it is important to understand how they interact with human host cells. Like antibiotics, the PlyC endolysin was not expected to enter throat cells. However, Shen, Barros et al. have now discovered that not only can PlyC enter throat cells, it can essentially chase down and kill S. pyogenes that are hiding inside. Other similar enzymes could not act in this way, and further studies confirmed that PlyC could move around inside a throat cell without causing it damage. Shen, Barros et al. also determined that PlyC has a pocket on its surface that binds with a specific component of the throat cell membrane, a molecule called phosphatidylserine. This interaction – which is a bit like a lock and key – grants PlyC access into the cell. While it is clear that PlyC eventually kills S. pyogenes hiding inside throat cells, future experiments will aim to determine how PlyC moves around once inside an infected throat cell. Together, an understanding of how an endolysin enters cells and destroys hiding S. pyogenes will contribute to the development of endolysins with broader activity, which can be used as alternatives to common antibiotics. DOI: http://dx.doi.org/10.7554/eLife.13152.002

This paper addresses the interdecadal variation of the West African summer monsoon (WASM) along with its background of atmospheric circulation and possible physical mechanism over the past 32 years (1979–2010). It is indicated that the WASM starts to strengthen from 1998 as the rainfall begins to increase over western West Africa on the whole, which shows a new interdecadal variation. In this interdecadal variation, the strengthened ascending motion corresponding to enhanced divergence (convergence) movement on the upper (lower) troposphere is prone to develop the local circulation of the monsoon. Moreover, the strengthened southwestern (eastern) wind on the lower (upper) level leads to more moisture from the Atlantic and the Gulf of Guinea transported to the West African continent. In addition, the summer subtropical high over the north Atlantic and western West Africa is strong and northward, and the tropical east wind is also strong. Statistically, the weaker (stronger) the spring North Atlantic Oscillation (NAO) is, the stronger (weaker) the tropical easterly is, and then the WASM is also stronger. But the effect of the NAO on the decadal variation of the WASM is not so significant from the north Atlantic anomaly sensitivity simulation with a single model. This is also an indication that the relationship between the WASM and NAO is complicated in an interdecadal time scale and is needed further study. In terms of sea surface temperature (SST) variation, the tendency is toward warming in the subtropical north Pacific, the south Pacific and north Atlantic. Numerical simulation experiments and data analysis show that the SST variation in the north Pacific plays an important role in the latest interdecadal strengthening of the WASM during the past 32 years, while the influences of the south Pacific and the north Atlantic SST anomalies are not so significant to the associated atmospheric circulation changes.

Most of China’s coastline is economically developing, densely populated, and frequently hit by landfalling Tropical Cyclones (TCs). Economic losses below province level have not been systematically analyzed due to lack of data. The objective of this paper is to conduct a TC loss assessment on county level in Fujian Province, coastal SE China, and to break down economic losses from province-scale to smaller administrative units. A unique loss database established by the China Meteorological Administration (CMA) and National Climate Center is used, and four entries at county-level are derived: location of losses due to TC, total economic losses per TC, start time of TC, and end time of TC. Tracks and intensities of TCs from 1949–2008 are derived from the CMA and Shanghai Typhoon Center annual typhoon yearbooks. Physical and economic data are overlaid in a Geographical information System. The landfall spots of TCs on China’s mainland coastline from 1949–2008 are determined by generating segments of 1° length. The result shows that Guangdong Province (South China) and Fujian Province (South-East China) host distinct hot spots where TCs make frequent landfall. All TCs making landfall in Central-North Fujian originated from the West North Pacific and none from the South China Sea. Direct economic losses in 25 counties along Fujian’s coastline due to TCs from 1984–2007 are analyzed and corrected with the Consumer Price Index. Some counties report losses frequently at low magnitude but seldom at high magnitude. Others like urban Fuzhou experience losses above 10 million CNY per TC, the most costly one 70 times higher. Other rural counties might experience economic losses far below 10 million CNY when being hit. Landfalling TCs in average cause economic losses of minimum 10 million CNY in 21 of 25 counties. In roughly one third of the counties, economic losses account for more than 30 million CNY when a TC has impact. The average economic losses per TC are 0.2–0.5% of the counties’ GDP (South and North of Fujian’s coastline) and even more than 0.5% of the GDP in North-East Fujian. The economic impacts of TCs are higher along the southern and northern coastline of Fujian Province where TC landfall activity is higher, but higher economic losses are triggered by higher GDP in these counties. The most costly TCs in Fujian had intensities of Severe Tropical Storm or Typhoon and occurred mostly in the 2000’s after making landfall in July.

Tropical cyclone, a high energy destructive meteorological system with heavy rainfall and gale triggered massive landslides and windstorms, poses a significant threat to coastal areas. In this paper we have developed a Tropical Cyclone Potential Impact Index (TCPI) based on the air mass trajectories, disaster information, intensity, duration, and frequency of tropical cyclones. We analyzed the spatial pattern and interannual variation of the TCPI over the period 1949–2009, and taking the Super Typhoon Saomai as an example have examined the relationship between the TCPI and direct economic losses, total rainfall, and maximum wind speed. The results reveal that China’s TCPI appears to be a weak decreasing trend over the period, which is not significant overall, but significant in some periods. Over the past 20 years, the TCPI decreased in the southern China coastal provinces of Hainan, Guangdong and Guangxi, while it increased in the southeastern coastal provinces of Zhejiang, Fujian and Taiwan. The highest values of TCPI are mainly observed in Taiwan, Hainan, the coastal areas of Guangdong and Fujian and Zhejiang’s southern coast. The TCPI has a good correlation (P=0.01) with direct economic loss, rainfall, and maximum wind speed.

Tropical cyclones (TCs) are historically documented along the coastline of China. They can have negative impacts on society and damages are reported annually. This paper examines to what extent TCs are responsible for heavy rain days (precipitation > 100 mm/d) in coastal Fujian Province. The average deviation of TCs making landfall in China is 25%, and they occur more reliably than TCs in the west North Pacific. A distinct typhoon season between July and September/October was detected and can be explained by low interannual variability during these months. The annual number of TCs is below average (above average) during El Nino (La Nina) years, but the intensity of TCs is higher during El Nino years. From 1960 to 2007, 41% of heavy rain days (precipitation > 100 mm/d) at all 66 weather stations in Fujian Province were triggered by TCs. The number of heavy rain days is higher during El Nino years, although the number of TCs affecting Fujian in general is higher during La Nina years. For the coastal weather stations in east Fujian, 57% (45%) of the heavy rain days are triggered by TCs during El Nino (La Nina) years. In the research area, more than 50% of annual heavy rain days can be explained by TCs, and the ratio reaches 85% in the northern part bordering Zhejiang Province. More TCs impact Fujian during La Nina years, but more TCs cause heavy precipitation days during El Nino years. The number of heavy rain days fluctuates in line with the interannual variability of TCs making landfall.

In the version of this Letter originally published, the increments on the y axis of Fig. 3 were incorrectly labelled as ‘0.0; 0.2; 0.2; 0.3’; they should have read ‘0.0; 0.1; 0.2; 0.3’. This has now been corrected in all versions of the Letter.

Four different DNA extraction methods were used to extract genomic DNA of the medicinal mushroom Lingzhi from its developing stage materials, such as mycelium, dry fruiting body, or sliced and spore powder or sporoderm-broken spore powder. The DNA samples were analyzed using agarose gel electrophoresis, UV spectrophotometer, and PCR amplification. According to the average yields and purity of DNA, high salt concentrations and low pH methods were the best for DNA extraction. The mycelia and sporoderm-broken spore powder yielded higher and purer DNA. The method developed could effectively eliminate the influence of the secondary metabolites to DNA extraction. The DNA samples extracted from the developed method could be successfully used for PCR applications.

The objective of this paper is to review the natural products and the pharmacological functions of Ganodermataceae family. Presently, studies on the bioactive components of Lingzhi are focused on polysaccharides and triterpenes/triterpenoids compounds. New Ganoderma polysaccharides, including their molecular weights, glycosyl residue compositions, glycosyl linkage and branches, are summarized in this paper. Also presented are new types of triterpenes and their characteristics from Lingzhi. Taking Ganoderma lucidum as an example, we reviewed its pharmacological functions in anti-tumor and immune-modulating activities for treating hypoglycemosis, hepatoprotection, and the effect on blood vessel system. Based on the advances in Lingzhi research in the past few decades, both G. lucidum and G. sinense are considered as the representative species of medicinal mushroom Lingzhi in China. Until 2001, G. tsugae was only advised to be used as the materials of the health products. The biologically-active components related to pharmacological functions of these three species were studied more than other Ganodermataceae family species; however, which have been used in less modern folk medicine.

From the middle of the 20th century, Chinese scientists have been actively involved in biotechnology. However, biotechnology education in China is a relatively recent phenomenon. This subject has not been addressed at the undergraduate level in a serious way until recently. In the last decade, biotechnology education developed rapidly and reached a new level in Chinese universities. The Chinese scientific establishment is very much aware of the importance of biotechnology and has identified this subject as one of the priority areas. Some universities are taking positive steps toward enhancing biotechnology education. This article focuses on the emergence, as well as the problems and prospects, of biotechnology education in China.

A novel course, ‘‘Participation in Research Program (PRP)’’ in life sciences is open for 1st to 3rd year undergraduates. PRP introduces the principles of a variety of biological methods and techniques and also offers an opportunity to explore some specific knowledge in more detail prior to thesis research. In addition, the PRP introduces some methodologies that have been proven to be successful at each institution to participants. Through disciplines crossing, students were trained theoretically and practically about modern techniques, facilitating the efficient commutation of general laboratory skills and modern laboratory skills, and the possession of higher research ability. Therefore, during some basic training (e.g., usage and maintenance of equipments, designing and completing experiments, analyzing data and reporting results, etc.), a series of capabilities are strengthened, such as basic experimental skills, searching appropriate methods, explaining unknown biological phenomena, and the capacity of solving problems. To determine the efficiency of these strategies, we carefully examined students’ performance and demonstrated the progress in students’ basic abilities of scientific research in their training.