Identification of a negative regulatory role for Spi-C in the murine B cell lineage

Spi-C is an E26 transformation-specific family transcription factor that is highly related to PU.1 and Spi-B. Spi-C is expressed in developing B cells, but its function in B cell development and function is not well characterized. To determine whether Spi-C functions as a negative regulator of Spi-B (encoded by Spib), mice were generated that were germline knockout for Spib and heterozygous for Spic (Spib-/-Spic+/-). Interestingly, loss of one Spic allele substantially rescued B cell frequencies and absolute numbers in Spib-/- mouse spleens. Spib-/-Spic+/- B cells had restored proliferation compared with Spib-/- B cells in response to anti-IgM or LPS stimulation. Investigation of a potential mechanism for the Spib-/-Spic+/- phenotype revealed that steady-state levels of Nfkb1, encoding p50, were elevated in Spib-/-Spic+/- B cells compared with Spib-/- B cells. Spi-B was shown to directly activate the Nfkb1 gene, whereas Spi-C was shown to repress this gene. These results indicate a novel role for Spi-C as a negative regulator of B cell development and function

The "ram effect": new insights into neural modulation of the gonadotropic axis by male odors and socio-sexual interactions

Reproduction in mammals is controlled by the hypothalamo-pituitary-gonadal (HPG) axis under the influence of external and internal factors such as photoperiod, stress, nutrition, and social interactions. Sheep are seasonal breeders and stop mating when day length is increasing (anestrus). However, interactions with a sexually active ram during this period can override the steroid negative feedback responsible for the anoestrus state, stimulate LH secretion and eventually reinstate cyclicity. This is known as the ram effect and research into the mechanisms underlying it is shedding new light on HPG axis regulation. The first step in the ram effect is increased LH pulsatile secretion in anestrus ewes exposed to a sexually active male or only to its fleece, the latter finding indicating a pheromone-like effect. Estradiol secretion increases in all ewes and this eventually induces a LH surge and ovulation, just as during the breeding season. An exception is a minority of ewes that exhibit a precocious LH surge (within 4h) with no prior increase in estradiol. The main olfactory system and the cortical nucleus of the amygdala are critical brain structures in mediating the ram effect since it is blocked by their inactivation. Sexual experience is also important since activation (increased c-fos expression) in these and other regions is greatly reduced in sexually naïve ewes. In adult ewes kisspeptin neurons in both arcuate and preoptic regions and some preoptic GnRH neurons are activated 2h after exposure to a ram. Exposure to rams also activates noradrenergic neurons in the locus coeruleus and A1 nucleus and increased noradrenalin release occurs in the posterior preoptic area. Pharmacological modulation of this system modifies LH secretion in response to the male or his odor. Together these results show that the ram effect can be a fruitful model to promote both a better understanding of the neural and hormonal regulation of the HPG axis in general and also the spe

Exploring the impact of cumulative testing on academic performance of undergraduate students in Spain

The final publication is available at Springer via http://dx.doi.org/10.1007/s11092-014-9208-zFrequent testing provides opportunities for students to receive regular feedback and to increase their motivation. It also provides the instructor with valuable information on how course progresses, thus making it possible to solve the problems encountered before it is too late. Frequent tests with noncumulative contents have been widely analysed in the literature with inconclusive results. However, cumulative testing methods have hardly been reported in higher education courses. This paper analyses the effect of applying an assessment method based on frequent and cumulative tests on student performance. Our results show that, when applied to a microeconomics course, students who were assessed by a frequent, cumulative testing approach largely outperformed those assessed with a single final exam.Doménech I De Soria, J.; Blázquez Soriano, MD.; De La Poza, E.; Muñoz Miquel, A. (2015). Exploring the impact of cumulative testing on academic performance of undergraduate students in Spain. Educational Assessment, Evaluation and Accountability. 27(2):153-169. https://doi.org/10.1007/s11092-014-9208-zS153169272Adelman, HS, & Taylor, L. (1990). Intrinsic motivation and school misbehaviour some intervention implications. Journal of Learning Disabilities, 23, 541–550.Biggs, J, & Tang, C. (2007). Teaching for quality learning at university 3rd edn. Open University Press.Boston, C. (2002). The concept of formative assessment. Practical Assessment Research & Evaluation 8.Brown, GA, Bull, J, Pendlebury, M. (1997). Assessing Student Learning in Higher Education, 1st edn. Routledge.Cano, MD. (2011). Students’ involvement in continuous assessment methodologies: a case study for a distributed information systems course. IEEE Transactions on Education, 54, 442–451.Casem, ML (2006). Active learning is not enough. Journal of College Science Teaching, 35.Chen, J, & Lin, TF. (2008). Class attendance and exam performance a randomized experiment. The Journal of Economic Education, 39, 213–227.Chickering, AW, & Gamson, ZF. (1987). Seven principles for good practice in undergraduate education. American Association for Higher Education Bulletin, 39, 3–7.Crooks, TJ. (1988). The impact of classroom evaluation practices on students. Review of Educational Research, 58, 438–481.De Paola, M, & Scoppa, V. (2011). Frequency of examinations and student achievement in a randomized experiment. Economics of Education Review, 30, 1416–1429.Deck, W. (1998). The effects of frequency of testing on college students in a principles of marketing course, PhD thesis, Virginia Polytechnic Institute and State University. Virginia: Blacksburg.Dempster, FN. (1991). Synthesis of research on reviews and tests. Educational Leadership, 48, 71–76.Dochy, F. (2008). The Edumetric Quality of New Modes of Assessment: Some Issues and Prospects. Assessment, Learning and Judgement in Higher Education. Dordrecht: Springer Netherlands.Eikner, AE, & Montondon, L. (2001). Evidence on factors associated with success in intermediate accounting I. Accounting Educators’ Journal 13.Emerson, TLN, & Mencken, KD. (2011). Homework to require or not? online graded homework and student achievement Perspectives on Economic Education Research 7.Fulkerson, F, & Martin, G. (1981). Effects of exam frequency on student performance, evaluations of instructor, and test anxiety. Teaching of Psychology, 8, 90–93.Furnham, A, & Chamorro-Premuzic, T. (2005). Individual differences and beliefs concerning preference for university assessment methods. Journal of Applied Social Psychology, 35, 1968–1994.Gibbs, G, & Simpson, C. (2005). Conditions under which assessment supports students’ learning Learning and Teaching in Higher Education 1 (August 5, 2011)3–31.Haberyan, KA. (2003). Do weekly quizzes improve student performance on general biology exams?. The American Biology Teacher, 65, 110–114.Kling, N, McCorkle, D, Miller, C, Reardon, J. (2005). The impact of testing frequency on student performance in a marketing course. Journal of Education for Business, 81, 67–72.Kuh, GD (2003). What we’re learning about student engagement from NSSE Change 35.Kuo, T, & Simon, A. (2009). How many tests do we really need. College Teaching, 57, 156–160.Leeming, FC. (2002). The exam-a-day procedure improves performance in psychology classes. Teaching of Psychology, 29, 210–212.Lumsden, KG, Scott, A, Becker, WE. (1987). The economics student reexamined Male-female differences in comprehension. Journal of Economic Education, 18, 365–375.Marriott, P. (2009). Students’ evaluation of the use of online summative assessment on an undergraduate financial accounting module. British Journal of Educational Technology, 40, 237–254.Marriott, P, & Lau, A. (2008). The use of on-line summative assessment in an undergraduate financial accounting course. 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Plants Modify Biological Processes to Ensure Survival following Carbon Depletion: A Lolium perenne Model

BACKGROUND: Plants, due to their immobility, have evolved mechanisms allowing them to adapt to multiple environmental and management conditions. Short-term undesirable conditions (e.g. moisture deficit, cold temperatures) generally reduce photosynthetic carbon supply while increasing soluble carbohydrate accumulation. It is not known, however, what strategies plants may use in the long-term to adapt to situations resulting in net carbon depletion (i.e. reduced photosynthetic carbon supply and carbohydrate accumulation). In addition, many transcriptomic experiments have typically been undertaken under laboratory conditions; therefore, long-term acclimation strategies that plants use in natural environments are not well understood. METHODOLOGY/PRINCIPAL FINDINGS: Perennial ryegrass (Lolium perenne L.) was used as a model plant to define whether plants adapt to repetitive carbon depletion and to further elucidate their long-term acclimation mechanisms. Transcriptome changes in both lamina and stubble tissues of field-grown plants with depleted carbon reserves were characterised using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The RT-qPCR data for select key genes indicated that plants reduced fructan degradation, and increased photosynthesis and fructan synthesis capacities following carbon depletion. This acclimatory response was not sufficient to prevent a reduction (P<0.001) in net biomass accumulation, but ensured that the plant survived. CONCLUSIONS: Adaptations of plants with depleted carbon reserves resulted in reduced post-defoliation carbon mobilization and earlier replenishment of carbon reserves, thereby ensuring survival and continued growth. These findings will help pave the way to improve plant biomass production, for either grazing livestock or biofuel purposes

Degree correlations in directed scale-free networks

Scale-free networks, in which the distribution of the degrees obeys a power-law, are ubiquitous in the study of complex systems. One basic network property that relates to the structure of the links found is the degree assortativity, which is a measure of the correlation between the degrees of the nodes at the end of the links. Degree correlations are known to affect both the structure of a network and the dynamics of the processes supported thereon, including the resilience to damage, the spread of information and epidemics, and the efficiency of defence mechanisms. Nonetheless, while many studies focus on undirected scale-free networks, the interactions in real-world systems often have a directionality. Here, we investigate the dependence of the degree correlations on the power-law exponents in directed scale-free networks. To perform our study, we consider the problem of building directed networks with a prescribed degree distribution, providing a method for proper generation of power-law-distributed directed degree sequences. Applying this new method, we perform extensive numerical simulations, generating ensembles of directed scale-free networks with exponents between~2 and~3, and measuring ensemble averages of the Pearson correlation coefficients. Our results show that scale-free networks are on average uncorrelated across directed links for three of the four possible degree-degree correlations, namely in-degree to in-degree, in-degree to out-degree, and out-degree to out-degree. However, they exhibit anticorrelation between the number of outgoing connections and the number of incoming ones. The findings are consistent with an entropic origin for the observed disassortativity in biological and technological networks.Comment: 10 pages, 5 figure

Adolescent smoking: The relationship between cigarette consumption and BMI

Background:Studies relating cigarette smoking and body weight yield conflicting results. Weight-lowering effects in women and men have been associated with smoking, however, no effects on weight have been proven.This study examined the association between cigarette smoking and relative weight in adolescent males and females as they age into young adults.Methods:Data from the National Longitudinal Survey of Youth—a nationally representative survey conducted annually—was used for this analysis. The sample consists of 4225 males and females observed annually from1997 at age 12 to 17 through 2011 at age 27 to 31. Hierarchical generalized models (HGM) assess the impact of smoking on the likelihood of having higher BMI controlling for demographic, household and environmental impacts. The second estimation considers the possibility that smoking is endogenous and utilizes a multinomial instrument (IV) for smoking level. Results:HGM models reveal a negative association between cigarette smoking and BMI for both males and females. Individuals who smoke more have lower BMI compared to infrequent or non-smokers. General health rating, region of residence and income were used instrument for smoking in a linear two-stage IV specification. The instrument is highly correlated with BMI and results mirror the HGM. Finally, models run on early, middle and advanced adolescents show that the relationship diminishes over time. The relationship between BMI and smoking decreases as females age but increases for males.Conclusions:Empirical models confirm an association cigarette consumption and BMI in both males and females.This negative relationship varies with age. It is important to identify health risks—obesity—and modifiable risk factors—smoking—that contribute to health disparities among adolescents. However, the increase in one risky behavior leading to the decrease in the prevalence of the other, complicates the issue. The higher prevalence of frequent cigarette uses among both adolescents and young adults of lower BMI suggest that smoking could be used curb or suppress appetite