Infectious Disease in a Warming World: How Weather Influenced West Nile Virus in the United States (2001–2005)

Background: The effects of weather on West Nile virus (WNV) mosquito populations in the United States have been widely reported, but few studies assess their overall impact on transmission to humans. Objectives: We investigated meteorologic conditions associated with reported human WNV cases in the United States. Methods: We conducted a case–crossover study to assess 16,298 human WNV cases reported to the Centers for Disease Control and Prevention from 2001 to 2005. The primary outcome measures were the incidence rate ratio of disease occurrence associated with mean weekly maximum temperature, cumulative weekly temperature, mean weekly dew point temperature, cumulative weekly precipitation, and the presence of ≥ 1 day of heavy rainfall (≥ 50 mm) during the month prior to symptom onset. Results: Increasing weekly maximum temperature and weekly cumulative temperature were similarly and significantly associated with a 35–83% higher incidence of reported WNV infection over the next month. An increase in mean weekly dew point temperature was significantly associated with a 9–38% higher incidence over the subsequent 3 weeks. The presence of at least 1 day of heavy rainfall within a week was associated with a 29–66% higher incidence during the same week and over the subsequent 2 weeks. A 20-mm increase in cumulative weekly precipitation was significantly associated with a 4–8% increase in incidence of reported WNV infection over the subsequent 2 weeks. Conclusions: Warmer temperatures, elevated humidity, and heavy precipitation increased the rate of human WNV infection in the United States independent of season and each others’ effects

Contrasting responses of mean and extreme snowfall to climate change

Snowfall is an important element of the climate system, and one that is expected to change in a warming climate. Both mean snowfall and the intensity distribution of snowfall are important, with heavy snowfall events having particularly large economic and human impacts. Simulations with climate models indicate that annual mean snowfall declines with warming in most regions but increases in regions with very low surface temperatures. The response of heavy snowfall events to a changing climate, however, is unclear. Here I show that in simulations with climate models under a scenario of high emissions of greenhouse gases, by the late twenty-first century there are smaller fractional changes in the intensities of daily snowfall extremes than in mean snowfall over many Northern Hemisphere land regions. For example, for monthly climatological temperatures just below freezing and surface elevations below 1,000 metres, the 99.99th percentile of daily snowfall decreases by 8% in the multimodel median, compared to a 65% reduction in mean snowfall. Both mean and extreme snowfall must decrease for a sufficiently large warming, but the climatological temperature above which snowfall extremes decrease with warming in the simulations is as high as −9 °C, compared to −14 °C for mean snowfall. These results are supported by a physically based theory that is consistent with the observed rain–snow transition. According to the theory, snowfall extremes occur near an optimal temperature that is insensitive to climate warming, and this results in smaller fractional changes for higher percentiles of daily snowfall. The simulated changes in snowfall that I find would influence surface snow and its hazards; these changes also suggest that it may be difficult to detect a regional climate-change signal in snowfall extremes.National Science Foundation (U.S.) (Grant AGS-1148594)United States. National Aeronautics and Space Administration (ROSES Grant 09-IDS09-0049

Problems with studying wolf predation on small prey in summer via global positioning system collars

We attempted to study predation on various-sized prey by a male and female wolf (Canis lupus) with global positioning system (GPS) collars programmed to acquire locations every 10 min in the Superior National Forest of Minnesota. During May to August 2007, we investigated 147 clusters of locations (31% of the total) and found evidence of predation on a white-tailed deer (Odocoileus virginianus) fawn and yearling, a beaver (Castor canadensis), ruffed grouse (Bonasa umbellus), and fisher (Martes pennanti) and scavenging on a road-killed deer and other carrion. However, we missed finding many prey items and discuss the problems associated with trying to conduct such a study

Sexual Priming, Gender Stereotyping, and Likelihood to Sexually Harass: Examining the Cognitive Effects of Playing a Sexually-Explicit Video Game

The present study examines the short-term cognitive effects of playing a sexually explicit video game with female “objectification” content on male players. Seventy-four male students from a university in California, U.S. participated in a laboratory experiment. They were randomly assigned to play either a sexually-explicit game or one of two control games. Participants’ cognitive accessibility to sexual and sexually objectifying thoughts was measured in a lexical decision task. A likelihood-to-sexually-harass scale was also administered. Results show that playing a video game with the theme of female “objectification” may prime thoughts related to sex, encourage men to view women as sex objects, and lead to self-reported tendencies to behave inappropriately towards women in social situations

Enhanced resistance to bacterial and fungal pathogens by overexpression of a human cathelicidin antimicrobial peptide (hCAP18/LL-37) in Chinese cabbage

The human cathelicidin antimicrobial protein hCAP18, which includes the C-terminal peptide LL-37, is a multifunctional protein. As a possible approach to enhancing the resistance to plant disease, a DNA fragment coding for hCAP18/LL-37 was fused at the C-terminal end of the leader sequence of endopolygalacturonase-inhibiting protein under the control of the cauliflower mosaic virus 35S promoter region. The construct was then introduced into Brassica rapa. LL-37 expression was confirmed in transgenic plants by reverse transcription-polymerase chain reaction and western blot analysis. Transgenic plants exhibited varying levels of resistance to bacterial and fungal pathogens. The average size of disease lesions in the transgenic plants was reduced to less than half of that in wild-type plants. Our results suggest that the antimicrobial LL-37 peptide is involved in wide-spectrum resistance to bacterial and fungal pathogen infection

Energetic signatures of single base bulges: thermodynamic consequences and biological implications

DNA bulges are biologically consequential defects that can arise from template-primer misalignments during replication and pose challenges to the cellular DNA repair machinery. Calorimetric and spectroscopic characterizations of defect-containing duplexes reveal systematic patterns of sequence-context dependent bulge-induced destabilizations. These distinguishing energetic signatures are manifest in three coupled characteristics, namely: the magnitude of the bulge-induced duplex destabilization (ΔΔGBulge); the thermodynamic origins of ΔΔGBulge (i.e. enthalpic versus entropic); and, the cooperativity of the duplex melting transition (i.e. two-state versus non-two state). We find moderately destabilized duplexes undergo two-state dissociation and exhibit ΔΔGBulge values consistent with localized, nearest neighbor perturbations arising from unfavorable entropic contributions. Conversely, strongly destabilized duplexes melt in a non-two-state manner and exhibit ΔΔGBulge values consistent with perturbations exceeding nearest-neighbor expectations that are enthalpic in origin. Significantly, our data reveal an intriguing correlation in which the energetic impact of a single bulge base centered in one strand portends the impact of the corresponding complementary bulge base embedded in the opposite strand. We discuss potential correlations between these bulge-specific differential energetic profiles and their overall biological implications in terms of DNA recognition, repair and replication

Familial hypercholesterolemia in St.-Petersburg: the known and novel mutations found in the low density lipoprotein receptor gene in Russia

BACKGROUND: Familial hypercholesterolemia is a human monogenic disease caused by population-specific mutations in the low density lipoprotein (LDL) receptor gene. Despite thirteen different mutations of the LDL receptor gene were reported from Russia prior to 2003, the whole spectrum of disease-causing gene alterations in this country is poorly known and requires further investigation provided by the current study. METHODS: Forty-five patients with clinical diagnosis of FH were tested for the apolipoprotein B (apoB) mutation R3500Q by restriction fragment length analysis. After exclusion of R3500Q mutation high-sensitive fluorescent single-strand conformation polymorphism (SSCP) analysis and automatic DNA sequencing were used to search for mutations in the LDL receptor gene. RESULTS: We found twenty one rare sequence variations of the LDL receptor gene. Nineteen were probably pathogenic mutations, and two (P518P, T705I) were considered as neutral ones. Among the mutations likely to be pathogenic, eight were novel (c.670-671insG, C249X, c.936-940del5, c.1291-1331del41, W422X, c.1855-1856insA, D601N, C646S), and eleven (Q12X, IVS3+1G>A, c.651-653del3, E207X, c.925-931del7, C308Y, L380H, c.1302delG, IVS9+1G>A, V776M, V806I) have already been described in other populations. None of the patients had the R3500Q mutation in the apoB gene. CONCLUSIONS: Nineteen pathogenic mutations in the LDL receptor gene in 23 probands were identified. Two mutations c.925-931del7 and L380H are shared by St.-Petersburg population with neighbouring Finland and several other mutations with Norway, Sweden or Denmark, i.e. countries from the Baltic Sea region. Only four mutations (c.313+1G>A, c.651-653del3, C308Y and W422X) were recurrent as all those were found in two unrelated families. By this study the number of known mutations in the LDL receptor gene in St.-Petersburg area was increased nearly threefold. Analysis of all 34 low density lipoprotein receptor gene mutations found in St.-Petersburg argues against strong founder effect in Russian familial hypercholesterolemia

Predicting Worst-Case Execution Time Trends in Long-Lived Real-Time Systems

In some long-lived real-time systems, it is not uncommon to see that the execution times of some tasks may exhibit trends. For hard and firm real-time systems, it is important to ensure these trends will not jeopardize the system. In this paper, we first introduce the notion of dynamic worst-case execution time (dWCET), which forms a new perspective that could help a system to predict potential timing failures and optimize resource allocations. We then have a comprehensive review of trend prediction methods. In the evaluation, we make a comparative study of dWCET trend prediction. Four prediction methods, combined with three data selection processes, are applied in an evaluation framework. The result shows the importance of applying data preprocessing and suggests that non-parametric estimators perform better than parametric methods