The Web Can Be Suitable for Learning

The authors discuss the advantages and difficulties of Web-based online distance learning. Web-based ODL can and does work for most learners when designed with high levels of interactivity and when cost and access issues can be adequately addressed. However, Web-based ODL requires a fundamental paradigm shift in how we define concepts like education and the classroom

The Web Can Be Suitable for Learning

The authors discuss the advantages and difficulties of Web-based online distance learning. Web-based ODL can and does work for most learners when designed with high levels of interactivity and when cost and access issues can be adequately addressed. However, Web-based ODL requires a fundamental paradigm shift in how we define concepts like education and the classroom

Detection of signal recognition particle (SRP) RNAs in the nuclear ribosomal internal transcribed spacer 1 (ITS1) of three lineages of ectomycorrhizal fungi (Agaricomycetes, Basidiomycota)

During a routine scan for Signal Recognition Particle (SRP) RNAs in eukaryotic sequences, we surprisingly found in silico evidence in GenBank for a 265-base long SRP RNA sequence in the ITS1 region of a total of 11 fully identified species in three ectomycorrhizal genera of the Basidiomycota (Fungi): Astraeus, Russula, and Lactarius. To rule out sequence artifacts, one specimen from a species indicated to have the SRP RNA-containing ITS region in each of these genera was ordered and re-sequenced. Sequences identical to the corresponding GenBank entries were recovered, or in the case of a non-original but conspecific specimen differed by three bases, showing that these species indeed have an SRP RNA sequence incorporated into their ITS1 region. Other than the ribosomal genes, this is the first known case of non-coding RNAs in the eukaryotic ITS region, and it may assist in the examination of other types of insertions in fungal genomes.RHN acknowledges financial support from FORMAS (215-2011- 498) and from Stiftelsen Olle Engkvist Byggmästare. MPM was partially supported by Plan Nacional I+D+i project CGL2012-35559. CW acknowledges a Marie Skłodowska-Curie post doc grant (660122, CRYPTRANS)Peer reviewe

Lung cancer prediction using machine learning on data from a symptom e-questionnaire for never smokers, formers smokers and current smokers

PURPOSE: The aim of the present study was to investigate the predictive ability for lung cancer of symptoms reported in an adaptive e-questionnaire, separately for never smokers, former smokers, and current smokers. PATIENTS AND METHODS: Consecutive patients referred for suspected lung cancer were recruited between September 2014 and November 2015 from the lung clinic at the Karolinska University Hospital, Stockholm, Sweden. A total of 504 patients were later diagnosed with lung cancer (n = 310) or no cancer (n = 194). All participants answered an adaptive e-questionnaire with a maximum of 342 items, covering background variables and symptoms/sensations suspected to be associated with lung cancer. Stochastic gradient boosting, stratified on smoking status, was used to train and test a model for predicting the presence of lung cancer. RESULTS: Among never smokers, 17 predictors contributed to predicting lung cancer with 82% of the patients being correctly classified, compared with 26 predictors with an accuracy of 77% among current smokers and 36 predictors with an accuracy of 63% among former smokers. Age, sex, and education level were the most important predictors in all models. CONCLUSION: Methods or tools to assess the likelihood of lung cancer based on smoking status and to prioritize investigative and treatment measures among all patients seeking care with diffuse symptoms are much needed. Our study presents risk assessment models for patients with different smoking status that may be developed into clinical risk assessment tools that can help clinicians in assessing a patient's risk of having lung cancer

Ordered Arrays of SiGe Islands from Low-Energy PECVD

SiGe islands have been proposed for applications in the fields of microelectronics, optoelectronics and thermoelectrics. Although most of the works in literature are based on MBE, one of the possible advantages of low-energy plasma-enhanced chemical vapor deposition (LEPECVD) is a wider range of deposition rates, which in turn results in the possibility of growing islands with a high Ge concentration. We will show that LEPECVD can be effectively used for the controlled growth of ordered arrays of SiGe islands. In order to control the nucleation of the islands, patterned Si (001) substrates were obtained by e-beam lithography (EBL) and dry etching. We realized periodic circular pits with diameters ranging from 80 to 300 nm and depths from 65 to 75 nm. Subsequently, thin films (0.8–3.2 nm) of pure Ge were deposited by LEPECVD, resulting in regular and uniform arrays of Ge-rich islands. LEPECVD allowed the use of a wide range of growth rates (0.01–0.1 nm s−1) and substrates temperatures (600–750°C), so that the Ge content of the islands could be varied. Island morphology was characterized by AFM, while μ-Raman was used to analyze the Ge content inside the islands and the composition differences between islands on patterned and unpatterned areas of the substrate

Identification and comparative analysis of components from the signal recognition particle in protozoa and fungi

BACKGROUND: The signal recognition particle (SRP) is a ribonucleoprotein complex responsible for targeting proteins to the ER membrane. The SRP of metazoans is well characterized and composed of an RNA molecule and six polypeptides. The particle is organized into the S and Alu domains. The Alu domain has a translational arrest function and consists of the SRP9 and SRP14 proteins bound to the terminal regions of the SRP RNA. So far, our understanding of the SRP and its evolution in lower eukaryotes such as protozoa and yeasts has been limited. However, genome sequences of such organisms have recently become available, and we have now analyzed this information with respect to genes encoding SRP components. RESULTS: A number of SRP RNA and SRP protein genes were identified by an analysis of genomes of protozoa and fungi. The sequences and secondary structures of the Alu portion of the RNA were found to be highly variable. Furthermore, proteins SRP9/14 appeared to be absent in certain species. Comparative analysis of the SRP RNAs from different Saccharomyces species resulted in models which contain features shared between all SRP RNAs, but also a new secondary structure element in SRP RNA helix 5. Protein SRP21, previously thought to be present only in Saccharomyces, was shown to be a constituent of additional fungal genomes. Furthermore, SRP21 was found to be related to metazoan and plant SRP9, suggesting that the two proteins are functionally related. CONCLUSIONS: Analysis of a number of not previously annotated SRP components show that the SRP Alu domain is subject to a more rapid evolution than the other parts of the molecule. For instance, the RNA portion is highly variable and the protein SRP9 seems to have evolved into the SRP21 protein in fungi. In addition, we identified a secondary structure element in the Sacccharomyces RNA that has been inserted close to the Alu region. Together, these results provide important clues as to the structure, function and evolution of SRP

Defect Characterization in SiGe/SOI Epitaxial Semiconductors by Positron Annihilation

The potential of positron annihilation spectroscopy (PAS) for defect characterization at the atomic scale in semiconductors has been demonstrated in thin multilayer structures of SiGe (50 nm) grown on UTB (ultra-thin body) SOI (silicon-on-insulator). A slow positron beam was used to probe the defect profile. The SiO2/Si interface in the UTB-SOI was well characterized, and a good estimation of its depth has been obtained. The chemical analysis indicates that the interface does not contain defects, but only strongly localized charged centers. In order to promote the relaxation, the samples have been submitted to a post-growth annealing treatment in vacuum. After this treatment, it was possible to observe the modifications of the defect structure of the relaxed film. Chemical analysis of the SiGe layers suggests a prevalent trapping site surrounded by germanium atoms, presumably Si vacancies associated with misfit dislocations and threading dislocations in the SiGe films

Predicting RNA secondary structure by the comparative approach: how to select the homologous sequences

<p>Abstract</p> <p>Background</p> <p>The secondary structure of an RNA must be known before the relationship between its structure and function can be determined. One way to predict the secondary structure of an RNA is to identify covarying residues that maintain the pairings (Watson-Crick, Wobble and non-canonical pairings). This "comparative approach" consists of identifying mutations from homologous sequence alignments. The sequences must covary enough for compensatory mutations to be revealed, but comparison is difficult if they are too different. Thus the choice of homologous sequences is critical. While many possible combinations of homologous sequences may be used for prediction, only a few will give good structure predictions. This can be due to poor quality alignment in stems or to the variability of certain sequences. This problem of sequence selection is currently unsolved.</p> <p>Results</p> <p>This paper describes an algorithm, <it>SSCA</it>, which measures the suitability of sequences for the comparative approach. It is based on evolutionary models with structure constraints, particularly those on sequence variations and stem alignment. We propose three models, based on different constraints on sequence alignments. We show the results of the <it>SSCA </it>algorithm for predicting the secondary structure of several RNAs. <it>SSCA </it>enabled us to choose sets of homologous sequences that gave better predictions than arbitrarily chosen sets of homologous sequences.</p> <p>Conclusion</p> <p><it>SSCA </it>is an algorithm for selecting combinations of RNA homologous sequences suitable for secondary structure predictions with the comparative approach.</p

Maternal and offspring intelligence in relation to BMI across childhood and adolescence

Objective: The present study tested the association between both mothers’ and offspring’s intelligence and offspring’s body mass index (BMI) in youth. Method: Participants were members of the National Longitudinal Survey of Youth 1979 (NLSY-79) Children and Young Adults cohort (n = 11,512) and their biological mothers who were members of the NLSY-79 (n = 4932). Offspring’s IQ was measured with the Peabody Individual Achievement Test (PIAT). Mothers’ IQ was measured with the Armed Forces Qualification Test (AFQT). A series of regression analyses tested the association between IQ and offspring’s BMI by age group, while adjusting for pre-pregnancy BMI and family SES. The analyses were stratified by sex and ethnicity (non-Black and non-Hispanic, Black, and Hispanic). Results: The following associations were observed in the fully adjusted analyses. For the non-Blacks and non-Hispanics, a SD increment in mothers’ IQ was negatively associated with daughters’ BMI across all age-groups, ranging from β = −0.12 (95% CI −0.22 to −0.02, p = 0.021) in late childhood, to β = −0.17 (95% C.I. −0.27 to −0.07, p = 0001), in early adolescence and a SD increment in boys’ IQ was positively associated with their BMI in early adolescence β = 0.09 (95% CI 0.01–0.18, p = 0.031). For Blacks, there was a non-linear relationship between mothers’ IQ and daughters’ BMI across childhood and between girls’ IQ and BMI across adolescence. There was a positive association between mothers’ IQ and sons’ BMI in early adolescence (β = 0.17, 95% CI 0.02–0.32, p = 0.030). For Hispanic boys, there was a positive IQ-BMI association in late childhood (β = 0.19, 95% CI 0.05–0.33, p = 0.008) and early adolescence (β = 0.17, 95% CI 0.04–0.31, p = 0.014). Conclusion: Mothers’ IQ and offspring’s IQ were associated with offspring’s BMI. The relationships varied in direction and strength across ethnicity, age group and sex. Obesity interventions may benefit from acknowledging the heterogeneous influence that intelligence has on childhood BMI

GAMETOPHYTE DEFECTIVE 1, a Putative Subunit of RNases P/MRP, Is Essential for Female Gametogenesis and Male Competence in Arabidopsis

RNA biogenesis, including biosynthesis and maturation of rRNA, tRNA and mRNA, is a fundamental process that is critical for cell growth, division and differentiation. Previous studies showed that mutations in components involved in RNA biogenesis resulted in abnormalities in gametophyte and leaf development in Arabidopsis. In eukaryotes, RNases P/MRP (RNase mitochondrial RNA processing) are important ribonucleases that are responsible for processing of tRNA, and transcription of small non-coding RNAs. Here we report that Gametophyte Defective 1 (GAF1), a gene encoding a predicted protein subunit of RNases P/MRP, AtRPP30, plays a role in female gametophyte development and male competence. Embryo sacs were arrested at stages ranging from FG1 to FG7 in gaf1 mutant, suggesting that the progression of the gametophytic division during female gametogenesis was impaired in gaf1 mutant. In contrast, pollen development was not affected in gaf1. However, the fitness of the mutant pollen tube was weaker than that of the wild-type, leading to reduced transmission through the male gametes. GAF1 is featured as a typical RPP30 domain protein and interacts physically with AtPOP5, a homologue of RNases P/MRP subunit POP5 of yeast. Together, our data suggest that components of the RNases P/MRP family, such as RPP30, play important roles in gametophyte development and function in plants