Sod1 Deficiency Reduces Incubation Time in Mouse Models of Prion Disease

Prion infections, causing neurodegenerative conditions such as Creutzfeldt-Jakob disease and kuru in humans, scrapie in sheep and BSE in cattle are characterised by prolonged and variable incubation periods that are faithfully reproduced in mouse models. Incubation time is partly determined by genetic factors including polymorphisms in the prion protein gene. Quantitative trait loci studies in mice and human genome-wide association studies have confirmed that multiple genes are involved. Candidate gene approaches have also been used and identified App, Il1-r1 and Sod1 as affecting incubation times. In this study we looked for an association between App, Il1-r1 and Sod1 representative SNPs and prion disease incubation time in the Northport heterogeneous stock of mice inoculated with the Chandler/RML prion strain. No association was seen with App, however, significant associations were seen with Il1-r1 (P = 0.02) and Sod1 (P<0.0001) suggesting that polymorphisms at these loci contribute to the natural variation observed in incubation time. Furthermore, following challenge with Chandler/RML, ME7 and MRC2 prion strains, Sod1 deficient mice showed highly significant reductions in incubation time of 20, 13 and 24%, respectively. No differences were detected in Sod1 expression or activity. Our data confirm the protective role of endogenous Sod1 in prion disease.     

The Strain-Encoded Relationship between PrPSc Replication,Stability and Processing in Neurons is Predictive of the Incubation Period of Disease

Prion strains are characterized by differences in the outcome of disease, most notably incubation period and neuropathological features. While it is established that the disease specific isoform of the prion protein, PrP^Sc, is an essential component of the infectious agent, the strain-specific relationship between PrP^Sc properties and the biological features of the resulting disease is not clear. To investigate this relationship, we examined the amplification efficiency and conformational stability of PrP^Sc from eight hamster-adapted prion strains and compared it to the resulting incubation period of disease and processing of PrP^Sc in neurons and glia. We found that short incubation period strains were characterized by more efficient PrP^Sc amplification and higher PrP^Sc conformational stabilities compared to long incubation period strains. In the CNS, the short incubation period strains were characterized by the accumulation of N-terminally truncated PrP^Sc in the soma of neurons, astrocytes and microglia in contrast to long incubation period strains where PrP^Sc did not accumulate to detectable levels in the soma of neurons but was detected in glia similar to short incubation period strains. These results are inconsistent with the hypothesis that a decrease in conformational stability results in a corresponding increase in replication efficiency and suggest that glia mediated neurodegeneration results in longer survival times compared to direct replication of PrP^Sc in neurons.     

Heterogeneity,Mixing, and the Spatial Scales of Mosquito-Borne Pathogen Transmission

The Ross-Macdonald model has dominated theory for mosquito-borne pathogen transmission dynamics and control for over a century. The model, like many other basic population models, makes the mathematically convenient assumption that populations are well mixed; i.e., that each mosquito is equally likely to bite any vertebrate host. This assumption raises questions about the validity and utility of current theory because it is in conflict with preponderant empirical evidence that transmission is heterogeneous. Here, we propose a new dynamic framework that is realistic enough to describe biological causes of heterogeneous transmission of mosquito-borne pathogens of humans, yet tractable enough to provide a basis for developing and improving general theory. The framework is based on the ecological context of mosquito blood meals and the fine-scale movements of individual mosquitoes and human hosts that give rise to heterogeneous transmission. Using this framework, we describe pathogen dispersion in terms of individual-level analogues of two classical quantities: vectorial capacity and the basic reproductive number, . Importantly, this framework explicitly accounts for three key components of overall heterogeneity in transmission: heterogeneous exposure, poor mixing, and finite host numbers. Using these tools, we propose two ways of characterizing the spatial scales of transmission—pathogen dispersion kernels and the evenness of mixing across scales of aggregation—and demonstrate the consequences of a model''s choice of spatial scale for epidemic dynamics and for estimation of , both by a priori model formulas and by inference of the force of infection from time-series data.     

Characterizing the Transmission Dynamics and Control of Ebola Virus Disease

Carefully calibrated transmission models have the potential to guide public health officials on the nature and scale of the interventions required to control epidemics. In the context of the ongoing Ebola virus disease (EVD) epidemic in Liberia, Drake and colleagues, in this issue of PLOS Biology, employed an elegant modeling approach to capture the distributions of the number of secondary cases that arise in the community and health care settings in the context of changing population behaviors and increasing hospital capacity. Their findings underscore the role of increasing the rate of safe burials and the fractions of infectious individuals who seek hospitalization together with hospital capacity to achieve epidemic control. However, further modeling efforts of EVD transmission and control in West Africa should utilize the spatial-temporal patterns of spread in the region by incorporating spatial heterogeneity in the transmission process. Detailed datasets are urgently needed to characterize temporal changes in population behaviors, contact networks at different spatial scales, population mobility patterns, adherence to infection control measures in hospital settings, and hospitalization and reporting rates.Ebola virus disease (EVD) is caused by an RNA virus of the family Filoviridae and genus Ebolavirus. Five different Ebolavirus strains have been identified, namely Zaire ebolavirus (EBOV), Sudan ebolavirus (SUDV), Tai Forest ebolavirus (TAFV), Bundibugyo ebolavirus (BDBV), and Reston ebolavirus (RESTV). The great majority of past Ebola outbreaks in humans have been linked to three Ebola strains: EBOV, SUDV, and BDBV [1]. The Ebola virus ([EBOV] formerly designated Zaire ebolavirus) derived its name from the Ebola River, located near the epicenter of the first outbreak identified in 1976 in Zaire (now the Democratic Republic of Congo). EVD outbreaks among humans have been associated with direct human exposure to fruit bats—the most likely reservoir of the virus—or through contact with intermediate infected hosts, which include gorillas, chimpanzees, and monkeys. Outbreaks have been reported on average every 1.5 years [2]. Past EVD outbreaks have occurred in relatively isolated areas and have been limited in size and duration (Fig. 1). It has been recently estimated that about 22 million people living in areas of Central and West Africa are at risk of EVD [3].Open in a separate windowFigure 1Time series of the temporal progression of four past EVD outbreaks in Congo (1976, 1995, 2014) [4–6] and Uganda (2000) [7].An epidemic of EVD (EBOV) has been spreading in West Africa since December 2013 in Guinea, Liberia, and Sierra Leone [8]. A total of 18,603 cases, with 6,915 deaths, have been reported to the World Health Organization as of December 17, 2014 [9]. While the causative strain associated with this epidemic is closely related to that of past outbreaks in Central Africa [10], three key factors have contributed disproportionately to this unprecedented epidemic: (1) substantial delays in detection and implementation of control efforts in a region characterized by porous borders; (2) limited public health infrastructure including epidemiological surveillance systems and diagnostic testing [11], which are necessary for the timely diagnosis of symptomatic individuals, effective isolation of infectious individuals, contact tracing to rapidly identify new cases, and providing supportive care to increase the chances of survival to EVD infection; and (3) cultural practices that involve touching the body of the deceased and the association of illness with witchcraft or conspiracy theories.EBOV is transmitted by direct human-to-human contact via body fluids or indirect contact with contaminated surfaces, but it is not spread through the airborne route. Individuals become symptomatic after an average incubation period of 10 days (range 2–21 days) [12], and infectiousness is increased during the later stages of disease [13]. The characteristic symptoms of EVD are nonspecific and include sudden onset of fever, weakness, vomiting, diarrhea, headache, and a sore throat, while only a fraction of the symptomatic individuals present with hemorrhagic manifestations [14]. The case fatality risk (CFR), calculated as the proportion of deaths among the total number of EVD cases with known outcomes, has been estimated from data of the first 9 months of the epidemic in West Africa at 70.8% (95% CI 68.6–72.8), in broad agreement with estimates from past outbreaks [12].Two important quantities to understand in the transmission dynamics of EVD are the serial interval and the basic reproduction number. The serial interval is defined as the time from illness onset in a primary case to illness onset in a secondary case [15] and has been estimated at 15 days on average for the ongoing epidemic [12]. The basic reproduction number, R ₀, quantifies transmission potential at the beginning of an epidemic and is defined as the average number of secondary cases generated by a typical infected individual during the early phase of an epidemic, before interventions are put in place [16]. If R ₀ < 1, transmission is not sufficient to generate a major epidemic. In contrast, a major epidemic is likely to occur whenever R ₀ > 1. When transmission potential is measured over time t, the effective reproduction number R_t, can be helpful to quantify the time-dependent transmission potential resulting from the effect of control interventions and behavior changes [17]. Estimates of R ₀ for the ongoing epidemic in West Africa have fluctuated around 2 with some uncertainty (e.g., [12, 18–22]), which are in good agreement with estimates from past EVD outbreaks [23]. R ₀ could also vary across regions as a function of the local public health infrastructure (e.g., availability of health care settings and infection control protocols), such that an outbreak may be very unlikely to unfold in developed countries simply as a result of baseline infection control measures in place (i.e., R ₀ < 1) while poor countries with extremely weak or absent public health systems may be unable to control an Ebola outbreak (i.e., R ₀ > 1).Mathematical models of disease transmission have proved to be useful tools to characterize the transmission dynamics of infectious diseases and evaluate the effects of control intervention strategies in order to inform public health policy [16, 24, 25]. There are a limited number of mathematical models for the transmission and control of EVD, but a number of efforts are underway in the context of the epidemic in West Africa. The transmission dynamics of EVD have been modeled on the basis of the simple compartmental susceptible-exposed-infectious-removed (SEIR) model that assumes a homogenously mixed population [23]. The modeled population can be structured according to the contributions of community, hospital, and unsafe burials to transmission as EVD transmission has been amplified in health care settings with ineffective infection control measures and during unsafe burials [23]. A schematic representation of the main transmission pathways of EVD is shown in Fig. 2.Open in a separate windowFigure 2Schematic representation of the transmission dynamics of Ebola virus disease.A recent study published in PLOS Biology by Drake and colleagues [26] presents an interesting and flexible modeling framework for the transmission and control of EVD in Liberia. Their framework is based on a multi-type branching process model in which “multi-type” refers to the consideration of two types of settings where transmission can occur, while “branching process” is the mathematical term to specify a probabilistic model. For instance, in the case of a single-type branching process, the transmission dynamics are simply described using a single reproduction number, i.e., the average number of secondary cases produced by a single primary case. However, when two types of hosts are considered in the transmission process, two reproduction numbers are needed to characterize within-group mixing (e.g., within-hospital and within-community transmission) and two reproduction numbers characterize transmission between groups (e.g., transmission from hospital to community and vice versa).Drake and colleague’s elegant modeling approach describes EVD transmission according to infection generations by calculating probability distributions of the number of secondary cases that arise in the community via nursing care or during unsafe burials and in health care settings via infections to health care workers and visitors. The model explicitly accounts for the hospitalization rate—the fraction of infectious individuals in the community seeking hospitalization (estimated in this study at 60%). However, the number of effectively isolated infectious individuals is constrained by the number of available beds in treatment centers—which are assumed in this study to operate at twice their regular capacity. It is important to note that the number of beds available to treat EVD patients was severely limited in Liberia prior to mid August 2014 (Fig. 1 in [26]). Moreover, the rate of safe burials that reduces the force of infection is included in their model as an increasing function of time. The model was calibrated by tuning six parameters to fit the trajectories of the number of reported cases in the community and among health care workers during the period 4 July to 2 September 2014 for a total of four infection generations during which the effective reproduction number was estimated to decline on average from about 2.8 to 1.4. The model was able to effectively capture heterogeneity in transmission of EVD in both the community and hospital settings.Drake and colleagues [26] employed their calibrated model to forecast the epidemic trajectory in Liberia from 3 September to 31 December 2014 under different scenarios that account for an increasing fraction of cases seeking hospitalization and a surge in the number of beds available to isolate and treat EVD patients. Their results indicate that allocating 1,700 additional beds (100 new beds every 4 days) in new Ebola treatment centers committed by US aid reduces the mean epidemic size to ~51,000 (60% reduction with respect to the baseline scenario), while epidemic control by mid-March is only plausible through a 4-fold increase in the number of beds committed by US aid and enhancing the hospitalization rate from 60% to 99% for a final epidemic size of 12,285. Moreover, an additional epidemic forecast incorporating data up to 1 December 2014 indicated that containment could be achieved between March and June 2015.Other interventions were not explicitly incorporated in their model because it is difficult to parameterize them in the absence of datasets that permit statistical estimation of their impact on the transmission dynamics. These additional interventions include the use of household protection kits, designed to reduce transmission in the community; improvements in infection control protocols in health care settings that reduce transmission among health care workers; and the impact of rapid diagnostic kits in Ebola treatment centers, which reduce the time to isolation for infectious individuals seeking hospitalization. Increasing awareness and education of the population about the disease could have also yielded further reductions in case incidence by reducing the size of the at-risk susceptible population (Fig. 3) [27]. Nevertheless, some of these effects could have been indirectly captured implicitly by the time-dependent safe burial rate parameter in their model.Open in a separate windowFigure 3Contrasting epidemic growth in the presence and absence of behavior changes that reduce the transmission rate.Importantly, prior models of EVD transmission [23, 28, 29] and the model by Drake and colleagues have not incorporated spatial heterogeneity in the transmission dynamics. In particular, the EVD epidemic in West Africa can be characterized as a set of asynchronous local (e.g., district) epidemics that exhibit sub-exponential growth, which could be driven by a highly clustered underlying contact network or population behavior changes induced by the accumulation of morbidity and mortality rates (see Fig. 4 and [30]). EVD contagiousness is most pronounced in the later and more severe stages of Ebola infection when infectious individuals are confined at home or health care settings and mostly exposed to caregivers (e.g., health care workers, family members) [30]. This characterization would lead to EVD transmission over a network of contacts that is highly clustered (e.g., individuals are likely to share a significant fraction of their contacts), which is associated with significantly slower spread relative to the common random mixing assumption as illustrated in Fig. 5. The development of transmission models that incorporate spatial heterogeneity (e.g., by modeling spatial coupling or human migration) is currently limited by the shortage of detailed datasets from the EVD-affected areas about the geographic distribution of households, health care settings, reporting and hospitalization rates across urban and rural areas, and patterns of population mobility in the region. Some of these limitations may be overcome in the near future. For instance, cell phone data could provide a basis to characterize population mobility in the region at a refined spatial scale.Open in a separate windowFigure 4Representative time series of the cumulative number of EVD cases (in log scale) at the district level in Guinea, Sierra Leone, and Liberia.Open in a separate windowFigure 5Epidemic growth in two populations characterized by two different underlying contact networks.The ongoing epidemic in West Africa offers a unique opportunity to improve our current understanding of the transmission characteristics of EVD in humans. To achieve this goal, it is crucial to collect spatial-temporal data on population behaviors, contact networks, social distancing measures, and education campaigns. Datasets comprising detailed demographic, socio-economic, contact rates, and population mobility estimates in the region (e.g., commuting networks, air traffic) need to be integrated and made publicly available in order to develop highly resolved transmission models, which could guide control strategies with greater precision in the context of the EVD epidemic in West Africa. Although recent data from Liberia indicates that the epidemic is on track for eventual control, the epidemic in Sierra Leone continues an increasing trend, and in Guinea, case incidence roughly follows a steady trend. The potential impact of vaccines should also be incorporated in future modeling efforts as these pharmaceutical interventions are expected to become available in the upcoming months.     

Mosquito Surveillance Revealed Lagged Effects of Mosquito Abundance on Mosquito-Borne Disease Transmission: A Retrospective Study in Zhejiang,China

Mosquito-borne diseases (MBDs) are still threats to public health in Zhejiang. In this study, the associations between the time-lagged mosquito capture data and MBDs incidence over five years were used to examine the potential effects of mosquito abundance on patterns of MBDs epidemiology in Zhejiang during 2008–2012. Light traps were used to collect adult mosquitoes at 11 cities. Correlation tests with and without time lag were performed to investigate the correlations between MBDs incidence rates and mosquito abundance by month. Selected MBDs consisted of Japanese encephalitis (JE), dengue fever (DF) and malaria. A Poisson regression analysis was performed by using a generalized estimating equations (GEE) approach, and the most parsimonious model was selected based on the quasi-likelihood based information criterion (QICu). We identified five mosquito species and the constituent ratio of Culex pipiens pallens, Culex tritaeniorhynchus, Aedes albopictus, Anopheles sinensis and Armigeres subalbatus was 66.73%, 21.47%, 6.72%, 2.83% and 2.25%, respectively. The correlation analysis without and with time lag showed that Culex mosquito abundance at a lag of 0 or 1 month was positively correlated with JE incidence during 2008–2012, Ae. albopictus abundance at a lag of 1 month was positively correlated with DF incidence in 2009, and An. sinensis abundance at a lag of 0–2 months was positively correlated with malaria incidence during 2008–2010. The Poisson regression analysis showed each 0.1 rise of monthly mosquito abundance corresponded to a positive increase of MBD cases for the period of 2008–2012. The rise of mosquito abundance with a lag of 0–2 months increased the risk of human MBDs infection in Zhejiang. Our study provides evidence that mosquito monitoring could be a useful early warning tool for the occurrence and transmission of MBDs.     

大杜鹃和东方大苇莺卵的人工孵卵期和孵化率比较

大杜鹃(Cuculus canorus)是一种专性巢寄生鸟类,进化出了一系列适应对策,如雏鸟普遍出壳较早等,以更好适应寄生生活。本研究使用恒温自动孵化箱对25枚大杜鹃卵和20枚其宿主东方大苇莺(Acrocephalus orientalis)卵进行人工孵化,并对孵卵期的卵重进行连续测量。结果表明,在人工孵化条件下,大杜鹃卵的孵化率(76%)极显著高于东方大苇莺(40%)(χ~2=25.144,df=1,P0.01)。尽管大杜鹃的卵鲜重(t=7.447,df=43,P0.01)和卵体积(t=8.817,df=43,P0.01)均极显著大于东方大苇莺,但两种鸟卵的孵卵期不存在显著性差异(t=1.006,df=16,P0.05)。     

马鞍山遗址动物群的死亡年龄研究

本文根据牙齿的萌出和磨蚀情况确定了马鞍山遗址动物群的死亡年龄,采用三角图法展现了它们的死亡年龄分布,并对上、下文化层动物群的死亡年龄分布成因进行了对比和分析,认为:马鞍山遗址早期和晚期原始居民猎杀水牛、中国犀和水鹿时可能都采取了潜伏式狩猎或陷阱狩猎的策略,所以死亡年龄分布点均位于灾难型死亡年龄分布区;猕猴的死亡年龄分布点落入壮年居优型的分布区可能是晚期原始居民追求脂肪的高回馈率,而忽视幼年个体的结果;东方剑齿象的死亡年龄分布点落入幼年居优型的分布区,可能是原始居民面对生命危险和狩猎机会时,做出折衷之选的结果,即放弃体型巨大的成年个体而猎杀攻击性较弱的幼年个体,但是(参照民族学材料后发现)也有可能是基于可食用性的考虑而放弃口感较差、难以食用并且攻击性较强的成年个体,而捕猎肉较鲜嫩而且危险性较小的幼象的结果。     

Seasonally Dependent Relationships between Indicators of Malaria Transmission and Disease Provided by Mathematical Model Simulations

Evaluating the effectiveness of malaria control interventions on the basis of their impact on transmission as well as impact on morbidity and mortality is becoming increasingly important as countries consider pre-elimination and elimination as well as disease control. Data on prevalence and transmission are traditionally obtained through resource-intensive epidemiological and entomological surveys that become difficult as transmission decreases. This work employs mathematical modeling to examine the relationships between malaria indicators allowing more easily measured data, such as routine health systems data on case incidence, to be translated into measures of transmission and other malaria indicators. Simulations of scenarios with different levels of malaria transmission, patterns of seasonality and access to treatment were run with an ensemble of models of malaria epidemiology and within-host dynamics, as part of the OpenMalaria modeling platform. For a given seasonality profile, regression analysis mapped simulation results of malaria indicators, such as annual average entomological inoculation rate, prevalence, incidence of uncomplicated and severe episodes, and mortality, to an expected range of values of any of the other indicators. Results were validated by comparing simulated relationships between indicators with previously published data on these same indicators as observed in malaria endemic areas. These results allow for direct comparisons of malaria transmission intensity estimates made using data collected with different methods on different indicators. They also address key concerns with traditional methods of quantifying transmission in areas of differing transmission intensity and sparse data. Although seasonality of transmission is often ignored in data compilations, the models suggest it can be critically important in determining the relationship between transmission and disease. Application of these models could help public health officials detect changes of disease dynamics in a population and plan and assess the impact of malaria control interventions.     

The Influence of the Incubation Period on Qualitative Aspects of the Spread of AIDS

Based on a simple stochastic model the influence of the incubation period of the HIV-infection on some qualitative aspects of the spread of AIDS is studied. A critical ‘nfection rat’ is calculated in dependence on parameters of the incubation period density function.     

火斑鸠孵卵期和育雏期伴巢行为

2002年5～11月对7巢火斑鸠的伴巢行为进行了预观察;2003年4～9月采用所有事件取样法(Alloccurrence recording)和焦点动物取样法(Focal animalsampling)对其3巢的孵卵期和育雏期伴巢行为进行了系统研究。结果表明：其伴巢行为时间长,雌雄差异大。孵卵期内伴巢行为变化小;而育雏期则较复杂,行为特征和时间变化大,根据行为不同可分3个时期：暖雏期、守护期、巢周育雏期。     

The Important Role of Lipid Peroxidation Processes in Aging and Age Dependent Diseases

Any change in the cell membrane structure activates lipoxygenases (LOX). LOX transform polyunsaturated fatty acids (PUFAs) to lipidhydroperoxide molecules (LOOHs). When cells are severely wounded, this physiological process switches to a non-enzymatic lipid peroxidation (LPO) process producing LOO^· radicals. These oxidize nearly all-biological molecules such as lipids, sugars, and proteins. The LOO^· induced degradations proceed by transfer of the radicals from cell to cell like an infection. The chemical reactions induced by LO^· and LOO^· radicals seem to be responsible for aging and induction of age dependent diseases. Alternatively, LO^· and LOO^· radicals are generated by frying of fats and involve cholesterol-PUFA esters and thus induce atherogenesis. Plants and algae are exposed to LOO^· radicals generating radiation. In order to remove LOO^· radicals, plants and algae transform PUFAs to furan fatty acids, which are incorporated after consumption of vegetables into mammalian tissues where they act as excellent scavengers of LOO^· and LO^· radicals. Figure 6 of this article is reprinted from the paper of G. Spiteller: “Peroxyl radicals: Inductors of neurodegenerative and other inflammatory diseases. Their origin and how they transform cholesterol, phospholipids, plasmalogens, polyunsaturated fatty acids, sugars and proteins into deleterious products” published in Free Radic. Biol. Med. 41, 362–387 (2006) Elsevier, 2006 by permission from Elsevier.     

Effects of Fertility Control on Behavior and Disease Transmission in Brushtail Possums

Abstract: Fertility control is currently under development for the control of brushtail possums (Trichosurus vulpecula), one of New Zealand's most serious vertebrate pests. Despite intensive research into various methods for achieving infertility, including immunocon-traception and disrupting endocrine control of reproduction, researchers know little about the potential effects of these methods on the behavior of wild possums. We assessed the effects of surgically imposed sterility, either to block fertilization (tubal ligation) or to disrupt endocrine control of fertility (gonadectomy), by using radiotelemetry on the movement patterns and site fidelity of wild brushtail possums. In addition, we assessed the effect of gonadectomy on the transmission rate of a commonly occurring, directly transmitted pathogen in possums, Leptospira interrogans serovar balcanica (hereafter L. balcanica), to determine the effect of any behavioral changes on possum contact rates. Both tubal ligation and gonadectomy of females did not appear to have any appreciable effect on behavior, with sterilized females having space-use patterns and fidelity to seasonal breeding ranges similar to those of fertile females. However, gonadectomy of male possums resulted in a significant reduction of 42% and 47% in the 95% and 70% isopleth seasonal breeding ranges, respectively. Furthermore, the transmission rate of L. balcanica in gonadectomized male and female possums was reduced by 88% and 63%, respectively, compared with that in fertile male and female possums. Overall, these results suggest that fertility control, either by blocking fertilization (e.g., immunocontraception) or by disrupting endocrine control of reproduction (e.g., gonadotropin-releasing hormone vaccines), is unlikely to have an impact on social organization and behavior of brushtail possums in ways that may compromise the efficacy of fertility control for reducing population density. However, the reduction in the transmission rate of L. balcanica indicates that fertility control that interferes with endocrine control of reproduction is likely to reduce the contact rate between possums. This could have implications for the control of other wildlife diseases requiring direct contact for transmission.