Heteromerization of Arabidopsis Kv channel α-subunits: Data and prospects

Potassium translocation in plants is accomplished by a large variety of transport systems. Most of the available molecular information on these proteins concerns voltage-gated potassium channels (Kv channels). The Arabidopsis genome comprises nine genes encoding α-subunits of Kv channels. Based on knowledge of their animal homologues, and on biochemical investigations, it is broadly admitted that four such polypeptides must assemble to yield a functional Kv channel. The intrinsic functional properties of Kv channel α-subunits have been described by expressing them in suitable heterologous contexts where homo-tetrameric channels could be characterized. However, due to the high similarity of both the polypeptidic sequence and the structural scheme of Kv channel α-subunits, formation of heteromeric Kv channels by at least two types of α-subunits is conceivable. Several examples of such heteromeric plant Kv channels have been studied in heterologous expression systems and evidence that heteromerization actually occurs in planta has now been published. It is therefore challenging to uncover the physiological role of this heteromerization. Fine tuning of Kv channels by heteromerisation could be relevant not only to potassium transport but also to electrical signaling within the plant.Key words: heteromerization, voltage-gated channels, membrane potential     

Towards “A Natural History of Data”: Evolving Practices and Epistemologies of Data in Paleontology, 1800–2000

The fossil record is paleontology’s great resource, telling us virtually everything we know about the past history of life. This record, which has been accumulating since the beginning of paleontology as a professional discipline in the early nineteenth century, is a collection of objects. The fossil record exists literally, in the specimen drawers where fossils are kept, and figuratively, in the illustrations and records of fossils compiled in paleontological atlases and compendia. However, as has become increasingly clear since the later twentieth century, the fossil record is also a record of data. Paleontologists now routinely abstract information from the physical fossil record to construct databases that serve as the basis for quantitative analysis of patterns in the history of life. What is the significance of this distinction? While it is often assumed that the orientation towards treating the fossil record as a record of data is an innovation of the computer age, it turns out that nineteenth century paleontology was substantially “data driven.” This paper traces the evolution of data practices and analyses in paleontology, primarily through examination of the compendia in which the fossil record has been recorded over the past 200 years. I argue that the transition towards conceptualizing the fossil record as a record of data began long before the emergence of the technologies associated with modern databases (such as digital computers and modern statistical methods). I will also argue that this history reveals how new forms of visual representation were associated with the transition from seeing the fossil record as a record of objects to one of data or information, which allowed paleontologists to make new visual arguments about their data. While these practices and techniques have become increasingly sophisticated in recent decades, I will show that their basic methodology was in place over a century ago, and that, in a sense, paleontology has always been a “data driven” science.     

If We Share Data,Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology

Research on practices to share and reuse data will inform the design of infrastructure to support data collection, management, and discovery in the long tail of science and technology. These are research domains in which data tend to be local in character, minimally structured, and minimally documented. We report on a ten-year study of the Center for Embedded Network Sensing (CENS), a National Science Foundation Science and Technology Center. We found that CENS researchers are willing to share their data, but few are asked to do so, and in only a few domain areas do their funders or journals require them to deposit data. Few repositories exist to accept data in CENS research areas.. Data sharing tends to occur only through interpersonal exchanges. CENS researchers obtain data from repositories, and occasionally from registries and individuals, to provide context, calibration, or other forms of background for their studies. Neither CENS researchers nor those who request access to CENS data appear to use external data for primary research questions or for replication of studies. CENS researchers are willing to share data if they receive credit and retain first rights to publish their results. Practices of releasing, sharing, and reusing of data in CENS reaffirm the gift culture of scholarship, in which goods are bartered between trusted colleagues rather than treated as commodities.     

Are We Filling the Data Void? An Assessment of the Amount and Extent of Plant Collection Records and Census Data Available for Tropical South America

Large-scale studies are needed to increase our understanding of how large-scale conservation threats, such as climate change and deforestation, are impacting diverse tropical ecosystems. These types of studies rely fundamentally on access to extensive and representative datasets (i.e., “big data”). In this study, I asses the availability of plant species occurrence records through the Global Biodiversity Information Facility (GBIF) and the distribution of networked vegetation census plots in tropical South America. I analyze how the amount of available data has changed through time and the consequent changes in taxonomic, spatial, habitat, and climatic representativeness. I show that there are large and growing amounts of data available for tropical South America. Specifically, there are almost 2,000,000 unique geo-referenced collection records representing more than 50,000 species of plants in tropical South America and over 1,500 census plots. However, there is still a gaping “data void” such that many species and many habitats remain so poorly represented in either of the databases as to be functionally invisible for most studies. It is important that we support efforts to increase the availability of data, and the representativeness of these data, so that we can better predict and mitigate the impacts of anthropogenic disturbances.     

LIVE BIRTH CERTIFICATES—Evaluation of Medical and Health Data in California

In a study of 1,609 single live births occurring in San Francisco County, the information on the birth certificate was compared with that on the hospital record to determine completeness and accuracy of the items reported on the certificate.Items such as color or race of mother, age of mother, birth weight and birth length of child were well recorded on the certificate and agreed with information found in the hospital record.Medical conditions were grossly underreported on the birth certificate. Conditions relating to the mother were more frequently recorded than those relating to the infant, but the birth certificates recorded less than one-fifth of all medical conditions of both mother and infant that were entered in the hospital records.Methods suggested for improving the quality of maternal and newborn morbidity information include revision of the medical section of the present certificates of live birth and fetal death and use of a precoded hospital record.     

Is Bagging Effective in the Classification of Small-Sample Genomic and Proteomic Data?

There has been considerable interest recently in the application of bagging in the classification of both gene-expression data and protein-abundance mass spectrometry data. The approach is often justified by the improvement it produces on the performance of unstable, overfitting classification rules under small-sample situations. However, the question of real practical interest is whether the ensemble scheme will improve performance of those classifiers sufficiently to beat the performance of single stable, nonoverfitting classifiers, in the case of small-sample genomic and proteomic data sets. To investigate that question, we conducted a detailed empirical study, using publicly-available data sets from published genomic and proteomic studies. We observed that, under t-test and RELIEF filter-based feature selection, bagging generally does a good job of improving the performance of unstable, overfitting classifiers, such as CART decision trees and neural networks, but that improvement was not sufficient to beat the performance of single stable, nonoverfitting classifiers, such as diagonal and plain linear discriminant analysis, or 3-nearest neighbors. Furthermore, as expected, the ensemble method did not improve the performance of these classifiers significantly. Representative experimental results are presented and discussed in this work.     

Impacts of Land Cover Data Selection and Trait Parameterisation on Dynamic Modelling of Species’ Range Expansion

Dynamic models for range expansion provide a promising tool for assessing species’ capacity to respond to climate change by shifting their ranges to new areas. However, these models include a number of uncertainties which may affect how successfully they can be applied to climate change oriented conservation planning. We used RangeShifter, a novel dynamic and individual-based modelling platform, to study two potential sources of such uncertainties: the selection of land cover data and the parameterization of key life-history traits. As an example, we modelled the range expansion dynamics of two butterfly species, one habitat specialist (Maniola jurtina) and one generalist (Issoria lathonia). Our results show that projections of total population size, number of occupied grid cells and the mean maximal latitudinal range shift were all clearly dependent on the choice made between using CORINE land cover data vs. using more detailed grassland data from three alternative national databases. Range expansion was also sensitive to the parameterization of the four considered life-history traits (magnitude and probability of long-distance dispersal events, population growth rate and carrying capacity), with carrying capacity and magnitude of long-distance dispersal showing the strongest effect. Our results highlight the sensitivity of dynamic species population models to the selection of existing land cover data and to uncertainty in the model parameters and indicate that these need to be carefully evaluated before the models are applied to conservation planning.     

CHOLEDOCHOLITHIASIS—Correlation of Preoperative with Operative and Postoperative Data to Enhance Diagnostic Insight

One hundred cases of common bile duct explorations were reviewed in an attempt to obtain information that might give insight into the diagnosis and definitive treatment of choledocholithiasis. Fifty of the hundred patients had common duct stones. Correlations were made between the incidence of choledocholithiasis as proved at operation, and the following factors: Kind and number of choledochal exploratory criteria used, the clinical diagnosis of common duct stones, and the pathologic features of gallbladders removed.The incidence of stones was statistically related to aging.The most frequent choledochal exploratory criteria were common duct dilatation or thickening (63 cases) and history of jaundice (50 cases).The most reliable single criterion in “diagnosing” common duct stones was palpable common or hepatic duct stones, the diagnosis having been correct in 15 of 17 such cases.The most reliable combination of criteria was a history of jaundice, plus palpable stones, with correct diagnosis in all such cases.The clinical diagnosis of choledocholithiasis was correct in only 17 per cent of cases.The correlation of the incidence of common duct stones with the degree of gallbladder disease—that is, acute or chronic—did not provide information that might be helpful in diagnosing choledocholithiasis.The incidence of proven retained common duct stones was 3 per cent, the non-fatal postoperative complication rate was 21 per cent and operative mortality was 1 per cent.     

Published and Perished? The Influence of the Searched Protein Database on the Long-Term Storage of Proteomics Data

In proteomics, protein identifications are reported and stored using an unstable reference system: protein identifiers. These proprietary identifiers are created individually by every protein database and can change or may even be deleted over time. To estimate the effect of the searched protein sequence database on the long-term storage of proteomics data we analyzed the changes of reported protein identifiers from all public experiments in the Proteomics Identifications (PRIDE) database by November 2010. To map the submitted protein identifier to a currently active entry, two distinct approaches were used. The first approach used the Protein Identifier Cross Referencing (PICR) service at the EBI, which maps protein identifiers based on 100% sequence identity. The second one (called logical mapping algorithm) accessed the source databases and retrieved the current status of the reported identifier. Our analysis showed the differences between the main protein databases (International Protein Index (IPI), UniProt Knowledgebase (UniProtKB), National Center for Biotechnological Information nr database (NCBI nr), and Ensembl) in respect to identifier stability. For example, whereas 20% of submitted IPI entries were deleted after two years, virtually all UniProtKB entries remained either active or replaced. Furthermore, the two mapping algorithms produced markedly different results. For example, the PICR service reported 10% more IPI entries deleted compared with the logical mapping algorithm. We found several cases where experiments contained more than 10% deleted identifiers already at the time of publication. We also assessed the proportion of peptide identifications in these data sets that still fitted the originally identified protein sequences. Finally, we performed the same overall analysis on all records from IPI, Ensembl, and UniProtKB: two releases per year were used, from 2005. This analysis showed for the first time the true effect of changing protein identifiers on proteomics data. Based on these findings, UniProtKB seems the best database for applications that rely on the long-term storage of proteomics data.     

Multivariate Data Analysis for Neuroimaging Data: Overview and Application to Alzheimer’s Disease

As clinical and cognitive neuroscience mature, the need for sophisticated neuroimaging analysis becomes more apparent. Multivariate analysis techniques have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address functional connectivity in the brain. The covariance approach can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent, and often overly conservative, corrections for voxel-wise multiple comparisons. Multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The following article attempts to provide a basic introduction with sample applications to simulated and real-world data sets.     

Characterisation of Clostridium difficile Hospital Ward–Based Transmission Using Extensive Epidemiological Data and Molecular Typing

Background

Clostridium difficile infection (CDI) is a leading cause of antibiotic-associated diarrhoea and is endemic in hospitals, hindering the identification of sources and routes of transmission based on shared time and space alone. This may compromise rational control despite costly prevention strategies. This study aimed to investigate ward-based transmission of C. difficile, by subdividing outbreaks into distinct lineages defined by multi-locus sequence typing (MLST).

Methods and Findings

All C. difficile toxin enzyme-immunoassay-positive and culture-positive samples over 2.5 y from a geographically defined population of ∼600,000 persons underwent MLST. Sequence types (STs) were combined with admission and ward movement data from an integrated comprehensive healthcare system incorporating three hospitals (1,700 beds) providing all acute care for the defined geographical population. Networks of cases and potential transmission events were constructed for each ST. Potential infection sources for each case and transmission timescales were defined by prior ward-based contact with other cases sharing the same ST. From 1 September 2007 to 31 March 2010, there were means of 102 tests and 9.4 CDIs per 10,000 overnight stays in inpatients, and 238 tests and 15.7 CDIs per month in outpatients/primary care. In total, 1,276 C. difficile isolates of 69 STs were studied. From MLST, no more than 25% of cases could be linked to a potential ward-based inpatient source, ranging from 37% in renal/transplant, 29% in haematology/oncology, and 28% in acute/elderly medicine to 6% in specialist surgery. Most of the putative transmissions identified occurred shortly (≤1 wk) after the onset of symptoms (141/218, 65%), with few >8 wk (21/218, 10%). Most incubation periods were ≤4 wk (132/218, 61%), with few >12 wk (28/218, 13%). Allowing for persistent ward contamination following ward discharge of a CDI case did not increase the proportion of linked cases after allowing for random meeting of matched controls.

Conclusions

In an endemic setting with well-implemented infection control measures, ward-based contact with symptomatic enzyme-immunoassay-positive patients cannot account for most new CDI cases. Please see later in the article for the Editors'' Summary     

The Paleolithic of northwestern Zagros. 2012–2019 MAFGS Survey: Data and preliminary results

Between 2012 and 2019, the surveys conducted by the French archaeological mission of the Soulaimanih Governorate in the Rania and Peshadar plains of Iraqi Kurdistan, previously devoid of Paleolithic records, have yielded numerous lithic assemblages from open-air sites, caves and rock-shelters. We present here six lithic assemblages from the three districts surveyed, Rania, Peshdar and Dukan. Despite the absence of chronostratigraphic contexts and the still fragmentary data, the first typo-technological analyses carried out allow us to propose a first techno-cultural attribution to the Paleolithic and, more specifically for some assemblages, to the Middle Paleolithic. Among the sites uncovered, it is certainly the Paleolithic shelter of Sarsyan-Rostam Agha that was the most distinctive and which represents, with the Shanidar cave, one of the rare Paleolithic sites preserved in the northwestern Zagros. Its preserved Paleolithic sequence, rich in archaeological remains, and explored in a test-pit conducted in 2018, shows clear affinities with the Mousterian Zagros industries, a well-defined entity based on sites such as Shanidar, Bisitun, Warwasi and Mar Tarik.     

Why Do Women Deliver at Home? Multilevel Modeling of Ethiopian National Demographic and Health Survey Data

Background

Despite of the existing intensive efforts to improve maternal health in Ethiopia, the proportion of birth delivered at home remains high and is still the top priority among the national health threats.

Objective

The study aimed to examine effects of individual women and community-level factors of women’s decision on place of delivery in Ethiopia.

Methods

Data were obtained from the nationally representative 2011 Ethiopian Demographic and Health Survey (EDHS) which used a two-stage cluster sampling design with rural-urban and regions as strata. The EDHS collected data from a big sample size but our study focused on a sample of 7,908 women whose most recent birth was within five years preceding 2011 and 576 communities in which the women were living in. The data were analyzed using a two-level mixed-effects logistic regression to determine fixed-effects of individual- and community-level factors and random-intercept of between-cluster characteristics.

Results

In the current study, 6980 out of 7908 deliveries (88.3%) took place at home. Lower educational levels (OR=2.74, 95%CI:1.84,4.70; p<0.0001), making no or only a limited number of ANC visits (OR=3.72,95%CI:2.85, 4.83; p<0.0001), non-exposure to media (OR=1.51, 95%CI 1.13, 2.01; p=0.004), higher parity (OR=2.68, 95%CI:1.96,3.68; p<0.0001), and perceived distance problem to reach health facilities (OR=1.29, 95%CI:1.03,1.62; p=0.022) were positively associated with home delivery. About 75% of the total variance in the odds of giving birth at home was accounted for the between-community differences of characteristics (ICC=0.75, p<0.0001). With regard to community-level characteristics, rural communities (OR=4.67, 95%CI:3.06,7.11; p<0.0001), pastoralist communities (OR=4.53, 95%CI:2.81,7.28; p<0.0001), communities with higher poverty levels (OR=1.49 95%CI:1.08,2.22; p=0.048), with lower levels of ANC utilization (OR=2.01, 95%CI:1.42,2.85; p<0.0001) and problem of distance to a health facility (OR=1.29, 95%CI:1.03,1.62; p=0.004) had a positive influence on women to give birth at home.

Conclusions

Not only individual characteristics of women, but also community-level factors determine women’s decision to deliver at home.     

Does Multimorbidity Influence the Occurrence Rates of Chronic Conditions? A Claims Data Based Comparison of Expected and Observed Prevalence Rates

Objective

Multimorbidity is a complex phenomenon with an almost endless number of possible disease combinations with unclear implications. One important aspect in analyzing the clustering of diseases is to distinguish between random coexistence and statistical dependency. We developed a model to account for random coexistence based on stochastic distribution. We analyzed if the number of diseases of the patients influences the occurrence rates of chronic conditions.

Methods

We analyzed claims data of 121,389 persons aged 65+ using a list of 46 chronic conditions. Expected prevalences were simulated by drawing without replacement from all observed diseases using observed overall prevalences as initial probability weights. To determine if a disease occurs more or less frequently than expected by chance we calculated observed-minus-expected deltas for each disease. We defined clinical relevance as |delta| ≥ 5.0%. 18 conditions were excluded because of a prevalence < 5.0%.

Results

We found that (1) two chronic conditions (e.g. hypertension) were more frequent than expected in patients with a low number of comorbidities; (2) four conditions (e.g. renal insufficiency) were more frequent in patients with many comorbidities; (3) six conditions (e.g. cancer) were less frequent with many comorbidities; and (4) 16 conditions had an average course of prevalences.

Conclusion

A growing extent of multimorbidity goes along with a rapid growth of prevalences. This is for the largest part merely a stochastic effect. If we account for this effect we find that only few diseases deviate from the expected prevalence curves. Causes for these deviations are discussed. Our approach also has methodological implications: Naive analyses of multimorbidity might easily be affected by bias, because the prevalence of all chronic conditions necessarily increases with a growing extent of multimorbidity. We should therefore always examine and discuss the stochastic interrelations between the chronic conditions we analyze.     

Identification of Zinc-ligated Cysteine Residues Based on 13Cα and 13Cβ Chemical Shift Data

Although a significant number of proteins include bound metals as part of their structure, the identification of amino acid residues coordinated to non-paramagnetic metals by NMR remains a challenge. Metal ligands can stabilize the native structure and/or play critical catalytic roles in the underlying biochemistry. An atom’s chemical shift is exquisitely sensitive to its electronic environment. Chemical shift data can provide valuable insights into structural features, including metal ligation. In this study, we demonstrate that overlapped ¹³Cβ chemical shift distributions of Zn-ligated and non-metal-ligated cysteine residues are largely resolved by the inclusion of the corresponding ¹³Cα chemical shift information, together with secondary structural information. We demonstrate this with a bivariate distribution plot, and statistically with a multivariate analysis of variance (MANOVA) and hierarchical logistic regression analysis. Using 287 ¹³Cα/¹³Cβ shift pairs from 79 proteins with known three-dimensional structures, including 86 ¹³Cα and¹³Cβ shifts for 43 Zn-ligated cysteine residues, along with corresponding oxidation state and secondary structure information, we have built a logistic regression model that distinguishes between oxidized cystines, reduced (non-metal ligated) cysteines, and Zn-ligated cysteines. Classifying cysteines/cystines with a statisical model incorporating all three phenomena resulted in a predictor of Zn ligation with a recall, precision and F-measure of 83.7%, and an accuracy of 95.1%. This model was applied in the analysis of Bacillus subtilis IscU, a protein involved in iron–sulfur cluster assembly. The model predicts that all three cysteines of IscU are metal ligands. We confirmed these results by (i) examining the effect of metal chelation on the NMR spectrum of IscU, and (ii) inductively coupled plasma mass spectrometry analysis. To gain further insight into the frequency of occurrence of non-cysteine Zn ligands, we analyzed the Protein Data Bank and found that 78% of the Zn ligands are histidine and cysteine (with nearly identical frequencies), and 18% are acidic residues aspartate and glutamate. Electronic Supplementary Material Supplementary material is available for this article at and is accessible for authorized users.     

Special Issue on “Big Data and Precision Medicine”

正The journal Genomics ProteomicsBioinformatics(GPB)is now inviting submissions for a special issue on the topic of‘‘Big Data and Precision Medicine’’to be published in the fall of 2016.For many complex diseases,the traditional‘‘one drug for one disease’’scenario may soon become history.The new concept of‘‘precision medicine’’will heavily rely on the collection of large amount of data from the population as well as patients of specific diseases.     

Special Issue on “Big Data and Precision Medicine”

正The journal Genomics ProteomicsBioinformatics(GPB)is now inviting submissions for a special issue on the topic of‘‘Big Data and Precision Medicine’’to be published in the fall of 2016.For many complex diseases,the traditional‘‘one drug for one disease’’scenario may