Searching for candidate genes for male infertility

Aim: We describe an approach to search for candidate genes for male infertility using the two human genome databases: the public University of California at Santa Cruz (UCSC) and private Celera databases which list known and predicted gene sequences and provide related information such as gene function, tissue expression, known mutations and single nucleotide polymorphisms (SNPs). Methods and Results: To demonstrate this in silico research, the following male infertility candidate genes were selected: (1) human BOULE, mutations of which may lead to germ cell arrest at the primary spermatocyte stage, (2) mutations of casein kinase 2 alpha genes which may cause globozoospermia, (3) DMR-N9 which is possibly involved in the spermatogenic defect of myotonic dystrophy and (4) several testes expressed genes at or near the breakpoints of a balanced translocation associated with hypospermatogenesis. We indicate how information derived from the human genome databases can be used to confirm these candidate genes may be pathogenic by studying RNA expression in tissue arrays using in situ hybridization and gene sequencing. Conclusion: The paper explains the new approach to discovering genetic causes of male infertility using information about the human genome

Careers of an elite cohort of U.S. basic life science postdoctoral fellows and the influence of their mentor's citation record

<p>Abstract</p> <p>Background</p> <p>There is general agreement that the number of U.S. science PhDs being trained far exceeds the number of future academic positions. One suggested approach to this problem is to significantly reduce the number of PhD positions. A counter argument is that students are aware of the limited academic positions but have chosen a PhD track because it opens other, non-academic, opportunities. The latter view requires that students have objective information about what careers options will be available for them.</p> <p>Methods</p> <p>The scientific careers of the 1992-94 cohort of NIH National Institute of General Medical Sciences (NIGMS) Kirchstein-NRSA F32 postdoctoral fellows (PD) was determined by following their publications (PubMed), grants (NIH and NSF), and faculty and industry positions through 2009. These basic life science PDs receive support through individual grant applications and represent the most successful class of NIH PDs as judged by academic careers and grants. The sex dependence of the career and grant success and the influence of the PD mentor's citation record were also determined</p> <p>Results</p> <p>Of the 439 1992-94 NIGMS F32 fellows, the careers of 417 could be determined. Although females had significantly higher rates of dropping out of science (22% females, 9% males) there was no significant difference in the fraction of females that ended up as associate or full professors at research universities (22.8% females, 29.1% for males). More males then females ended up in industry (34% males, 22% females). Although there was no significant correlation between male grant success and their mentor's publication record (h index, citations, publications), there was a significant correlation for females. Females whose mentor's h index was in the top quartile were nearly 3 times as likely to receive a major grant as those whose mentors were in the bottom quartile (38.7% versus 13.3%).</p> <p>Conclusions</p> <p>Sixteen years after starting their PD, only 9% of males had dropped out of science. More females (28%) have dropped out of science, primarily because fewer went into industry positions. The mentor's publication record does not affect the future grant success of males but it has a dramatic effect on female grant success.</p

Socio-Economic Instability and the Scaling of Energy Use with Population Size

The size of the human population is relevant to the development of a sustainable world, yet the forces setting growth or declines in the human population are poorly understood. Generally, population growth rates depend on whether new individuals compete for the same energy (leading to Malthusian or density-dependent growth) or help to generate new energy (leading to exponential and super-exponential growth). It has been hypothesized that exponential and super-exponential growth in humans has resulted from carrying capacity, which is in part determined by energy availability, keeping pace with or exceeding the rate of population growth. We evaluated the relationship between energy use and population size for countries with long records of both and the world as a whole to assess whether energy yields are consistent with the idea of an increasing carrying capacity. We find that on average energy use has indeed kept pace with population size over long time periods. We also show, however, that the energy-population scaling exponent plummets during, and its temporal variability increases preceding, periods of social, political, technological, and environmental change. We suggest that efforts to increase the reliability of future energy yields may be essential for stabilizing both population growth and the global socio-economic system

Beyond here and now: Evaluating pollution estimation across space and time from street view images with deep learning

Advances in computer vision, driven by deep learning, allows for the inference of environmental pollution and its potential sources from images. The spatial and temporal generalisability of image-based pollution models is crucial in their real-world application, but is currently understudied, particularly in low-income countries where infrastructure for measuring the complex patterns of pollution is limited and modelling may therefore provide the most utility. We employed convolutional neural networks (CNNs) for two complementary classification models, in both an end-to-end approach and as an interpretable feature extractor (object detection), to estimate spatially and temporally resolved fine particulate matter (PM2.5) and noise levels in Accra, Ghana. Data used for training the models were from a unique dataset of over 1.6 million images collected over 15 months at 145 representative locations across the city, paired with air and noise measurements. Both end-to-end CNN and object-based approaches surpassed null model benchmarks for predicting PM2.5 and noise at single locations, but performance deteriorated when applied to other locations. Model accuracy diminished when tested on images from locations unseen during training, but improved by sampling a greater number of locations during model training, even if the total quantity of data was reduced. The end-to-end models used characteristics of images associated with atmospheric visibility for predicting PM2.5, and specific objects such as vehicles and people for noise. The results demonstrate the potential and challenges of image-based, spatiotemporal air pollution and noise estimation, and that robust, environmental modelling with images requires integration with traditional sensor networks

Beyond here and now: Evaluating pollution estimation across space and time from street view images with deep learning

Advances in computer vision, driven by deep learning, allows for the inference of environmental pollution and its potential sources from images. The spatial and temporal generalisability of image-based pollution models is crucial in their real-world application, but is currently understudied, particularly in low-income countries where infrastructure for measuring the complex patterns of pollution is limited and modelling may therefore provide the most utility. We employed convolutional neural networks (CNNs) for two complementary classification models, in both an end-to-end approach and as an interpretable feature extractor (object detection), to estimate spatially and temporally resolved fine particulate matter (PM2.5) and noise levels in Accra, Ghana. Data used for training the models were from a unique dataset of over 1.6 million images collected over 15 months at 145 representative locations across the city, paired with air and noise measurements. Both end-to-end CNN and object-based approaches surpassed null model benchmarks for predicting PM2.5 and noise at single locations, but performance deteriorated when applied to other locations. Model accuracy diminished when tested on images from locations unseen during training, but improved by sampling a greater number of locations during model training, even if the total quantity of data was reduced. The end-to-end models used characteristics of images associated with atmospheric visibility for predicting PM2.5, and specific objects such as vehicles and people for noise. The results demonstrate the potential and challenges of image-based, spatiotemporal air pollution and noise estimation, and that robust, environmental modelling with images requires integration with traditional sensor networks

Impact of funding on biomedical research: a retrospective cohort study

BACKGROUND: Public funding is aimed at facilitating the initiation, completion and publication of research study protocols. However, no evaluation is made to investigate the impact of grant success on the conduct of biomedical research. It is therefore of great interest to compare the fate of funded protocols versus not funded: Are they initiated? Are they completed? Did the results confirm the hypothesis? Were they published? The objective was to investigate the fate of protocols submitted for funding, whether they were funded or not. METHODS: Retrospective cohort study of protocols submitted for funding to the Greater Lyon regional scientific committee in 1997. Initial characteristics of protocols (design, study size, investigator status) were abstracted from archives, and follow-up characteristics (initiation, completion and publication) from a mailed questionnaire to the principal investigators. RESULTS: Among the 142 submitted protocols, follow-up information was available for 114 (80%). As a whole, 38% of studies were funded by the Greater Lyon research committee. The rate of initiation varied from 62% for studies with no acknowledged funding to 100% for studies with both committee and other simultaneous funding. When initiated, the rate of completion was 62% for studies with at least one funding and 40% for studies without acknowledged funding. When completed, publication was reached for 77% of studies with either committee or external funding, for 58% of studies without acknowledged funding and for 37% of studies with both committee and external funding. CONCLUSION: Some protocols submitted for funding were initiated and completed without any funding declared. To our understanding this mean that not all protocols submitted really needed funding and also that health care facilities are unaware that they implicitly financially support and pay for biomedical research

Radiative Transfer for Exoplanet Atmospheres

Remote sensing of the atmospheres of distant worlds motivates a firm understanding of radiative transfer. In this review, we provide a pedagogical cookbook that describes the principal ingredients needed to perform a radiative transfer calculation and predict the spectrum of an exoplanet atmosphere, including solving the radiative transfer equation, calculating opacities (and chemistry), iterating for radiative equilibrium (or not), and adapting the output of the calculations to the astronomical observations. A review of the state of the art is performed, focusing on selected milestone papers. Outstanding issues, including the need to understand aerosols or clouds and elucidating the assumptions and caveats behind inversion methods, are discussed. A checklist is provided to assist referees/reviewers in their scrutiny of works involving radiative transfer. A table summarizing the methodology employed by past studies is provided.Comment: 7 pages, no figures, 1 table. Filled in missing information in references, main text unchange

Financial considerations in the conduct of multi-centre randomised controlled trials: evidence from a qualitative study.

National Coordinating Centre for Research Methodology; Medical Research Council, UK Department of Health; Chief Scientist OfficeNot peer reviewedPublisher PD

Multimodality Imaging of Abnormal Vascular Perfusion and Morphology in Preclinical 9L Gliosarcoma Model

This study demonstrates that a dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI) perfusion parameter may indicate vascular abnormality in a brain tumor model and reflects an effect of dexamethasone treatment. In addition, X-ray computed tomography (CT) measurements of vascular tortuosity and tissue markers of vascular morphology were performed to investigate the underpinnings of tumor response to dexamethasone.One cohort of Fisher 344 rats (N = 13), inoculated intracerebrally with 9L gliosarcoma cells, was treated with dexamethasone (i.p. 3 mg/kg/day) for five consecutive days, and another cohort (N = 11) was treated with equal volume of saline. Longitudinal DSC-MRI studies were performed at the first (baseline), third and fifth day of treatments. Relative cerebral blood volume (rCBV) was significantly reduced on the third day of dexamethasone treatment (0.65 ± .13) as compared to the fifth day during treatment (1.26 ±.19, p < 0.05). In saline treated rats, relative CBV gradually increased during treatment (0.89 ±.13, 1.00 ± .21, 1.13 ± .23) with no significant difference on the third day of treatment (p>0.05). In separate serial studies, microfocal X-ray CT of ex vivo brain specimens (N = 9) and immunohistochemistry for endothelial cell marker anti-CD31 (N = 8) were performed. Vascular morphology of ex vivo rat brains from micro-CT analysis showed hypervascular characteristics in tumors, and both vessel density (41.32 ± 2.34 branches/mm(3), p<0.001) and vessel tortuosity (p<0.05) were significantly reduced in tumors of rats treated with dexamethasone compared to saline (74.29 ± 3.51 branches/mm(3)). The vascular architecture of rat brain tissue was examined with anti-CD31 antibody, and dexamethasone treated tumor regions showed reduced vessel area (16.45 ± 1.36 µm(2)) as compared to saline treated tumor regions (30.83 ± 4.31 µm(2), p<0.001) and non-tumor regions (22.80 ± 1.11 µm(2), p<0.01).Increased vascular density and tortuosity are culprit to abnormal perfusion, which is transiently reduced during dexamethasone treatment

Unexpected High Losses of Anopheles gambiae Larvae Due to Rainfall

Background - Immature stages of the malaria mosquito Anopheles gambiae experience high mortality, but its cause is poorly understood. Here we study the impact of rainfall, one of the abiotic factors to which the immatures are frequently exposed, on their mortality. Methodology/Principal Findings - We show that rainfall significantly affected larval mosquitoes by flushing them out of their aquatic habitat and killing them. Outdoor experiments under natural conditions in Kenya revealed that the additional nightly loss of larvae caused by rainfall was on average 17.5% for the youngest (L1) larvae and 4.8% for the oldest (L4) larvae; an additional 10.5% (increase from 0.9 to 11.4%) of the L1 larvae and 3.3% (from 0.1 to 3.4%) of the L4 larvae were flushed away and larval mortality increased by 6.9% (from 4.6 to 11.5%) and 1.5% (from 4.1 to 5.6%) for L1 and L4 larvae, respectively, compared to nights without rain. On rainy nights, 1.3% and 0.7% of L1 and L4 larvae, respectively, were lost due to ejection from the breeding site. Conclusions/Significance - This study demonstrates that immature populations of malaria mosquitoes suffer high losses during rainfall events. As these populations are likely to experience several rain showers during their lifespan, rainfall will have a profound effect on the productivity of mosquito breeding sites and, as a result, on the transmission of malaria. These findings are discussed in the light of malaria risk and changing rainfall patterns in response to climate chang