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The contribution of the complement system in the pathophysiology of brain cancers has been recently considered in light of its well-known involvement in carcinogenesis. Complement system represents an important component of the inflammatory response, which acts as a functional bridge between the innate and adaptive immune response. C1q, the first recognition subcomponent of the complement classical pathway, has recently been shown to be involved in a range of pathophysiological functions that are not dependent on complement activation. C1q is expressed in the microenvironment of various types of human tumors, including melanoma, prostate, mesothelioma, and ovarian cancers, where it can exert a protective or a harmful effect on cancer progression. Despite local synthesis of C1q in the central nervous system, the involvement of C1q in glioma pathogenesis has been poorly investigated. We, therefore, performed a bioinformatics analysis, using Oncomine dataset and UALCAN database in order to assess whether the expression of the genes encoding for the three chains of C1q ( , and ) could serve as a potential prognostic marker for gliomas. The obtained results were then validated using an independent glioma cohort from the Chinese Glioma Genome Atlas datasets. Our bioinformatics analysis, coupled with immunohistochemistry and fluorescence microscopy, appears to suggest a positive correlation between higher levels of C1q expression and unfavorable prognosis in a diverse grade of gliomas.

Background: Idiopathic pulmonary fibrosis (IPF) is the most common and severe form of idiopathic interstitial pneumonia, and its prevalence increases with age. In the era of pre-antifibrotic agents, the median survival time of Japanese patients with IPF is 35 months, with a 5-year survival rate in western countries ranging from 20% to 40%. The prevalence of IPF is highest in elderly patients aged ≥75 years; however, the efficacy and safety of long-term use of pirfenidone and/or nintedanib are not fully understood. Objective: This study aimed to determine the efficacy and safety of the sole use of antifibrotic agents (pirfenidone or nintendanib) for IPF in the elderly. Method: We retrospectively reviewed patients with IPF who were diagnosed and treated with either pirfenidone or nintedanib in our hospital between 2008 and 2019. We excluded patients with the subsequent use of both antifibrotic agents. We examined the survival probability and frequency of acute exacerbation, with focus on long-term use (≥1 year), elderly patients (≥75 years of age), and disease severity. Results: We identified 91 patients with IPF (male to female ratio: 63 to 28, age 42 to 90 years). The numbers of patients with disease severity classified by JRS (I/II/III/IV) and GAP stage (I/II/III) were (38/6/17/20) and (39/36/6), respectively. The survival probabilities were comparable between the elderly (n = 46) and non-elderly groups (n = 45, p = 0.877). After the initiation of antifibrotic agents, the cumulative incidence ratio of acute exacerbation of IPF was significantly lower in the early stage (GAP stage I, n = 20) than in the progressive stage of disease (GAP stages II and III, n = 20, p = 0.028). A similar trend was noted in the JRS disease severity classification (I, II vs. III, IV) (n = 27 vs. n = 13, p = 0.072). In the long-term treatment (≥1 year) group (n = 40), the survival probabilities at 2 and 5 years after treatment initiation were 89.0% and 52.4%, respectively, which did not reach the median survival rate. Conclusions: Even in elderly patients (≥75 years of age), antifibrotic agents demonstrated positive effects on survival probability and the frequency of acute exacerbation. These positive effects would be improved for earlier JRS/GAP stages or long-term use.

A standard way to elicit expectations asks for the percentage chance an event will occur. Previous research demonstrates noise in reported percentages. The current research models a bias; a five percentage point change in reported probabilities implies a larger change in beliefs at certain points in the probability distribution. One contribution of my model is that it can parse bias in beliefs from biases in reports. I reconsider age and gender differences in Subjective Survival Probabilities (SSPs). These are generally interpreted as differences in survival beliefs, e.g., that males are more optimistic than females and older respondents are more optimistic than younger respondents. These demographic differences (in the English Longitudinal Study of Ageing) can be entirely explained by reporting bias. Older respondents are no more optimistic than younger respondents and males are no more optimistic than females. Similarly, in forecasting, information is obscured by taking reported percentages at face value. Accounting for reporting bias thus better exploits the private information contained in reports. Relative to a face-value specification, a specification that does this delivers improved forecasts of mortality events, raising the pseudo R-squared from less than 3 percent to over 6 percent.

Background Phytophthora root rot (PRR) caused by P. medicaginis is a major soil borne disease in chickpea growing regions of Australia. Sources of resistance have been identified in both cultivated and wild Cicer species. However, the molecular basis underlying PRR resistance is not known. Current phenotyping methods rely on mycelium slurry or oospore inoculum. Sensitive and reliable methods are desirable to study variation for PRR resistance in chickpea and allow for a controlled inoculation process to better capture early defence responses following PRR infection. Results In this study, a procedure for P. medicaginis zoospore production was standardized and used as the inoculum to develop a hydroponics based in planta infection method to screen chickpea genotypes with established levels of PRR resistance. The efficiency of the system was both qualitatively validated based on observation of characteristic PRR symptom development, and quantitatively validated based on the amount of pathogen DNA in roots. This system was scaled up to screen two biparental mapping populations previously developed for PRR studies. For each of the screenings, plant survival time was measured after inoculation and used to derive Kaplan–Meier estimates of plant survival (KME-survival). KME-survival and canker length were then selected as phenotypic traits associated with PRR resistance. Genetic analysis of these traits was conducted which identified quantitative trait loci (QTL). Additionally, these hydroponic traits and a set of previously published plant survival traits obtained from multiple PRR field experiments were combined in a model-based correlation analysis. The results suggest that the underlying genetic basis for plant survival during PRR infection within hydroponics and field disease environments is linked. The QTL QRBprrkms03 and QRBprrck03 on chromosome 4 identified for the traits KME-survival and canker length, respectively, correspond to the same region reported for PRR resistance in a field disease experiment. Conclusion A hydroponics based screening system will facilitate reliable and rapid screening in both small- and large-scale experiments to study PRR disease in chickpea. It can be applied in chickpea breeding programs to screen for PRR resistance and classify the virulence of new and existing P. medicaginis isolates.

In the United States, the Bald and Golden Eagle Protection Act prohibits take of golden eagles (Aquila chrysaetos) unless authorized by permit, and stipulates that all permitted take must be sustainable. Golden eagles are unintentionally killed in conjunction with many lawful activities (e.g., electrocution on power poles, collision with wind turbines). Managers who issue permits for incidental take of golden eagles must determine allowable take levels and manage permitted take accordingly. To aid managers in making these decisions in the western United States, we used an integrated population model to obtain estimates of golden eagle vital rates and population size, and then used those estimates in a prescribed take level (PTL) model to estimate the allowable take level. Estimated mean annual survival rates for golden eagles ranged from 0.70 (95% credible interval = 0.66–0.74) for first-year birds to 0.90 (0.88–0.91) for adults. Models suggested a high proportion of adult female golden eagles attempted to breed and breeding pairs fledged a mean of 0.53 (0.39–0.72) young annually. Population size in the coterminous western United States has averaged ~31,800 individuals for several decades, with λ = 1.0 (0.96–1.05). The PTL model estimated a median allowable take limit of ~2227 (708–4182) individuals annually given a management objective of maintaining a stable population. We estimate that take averaged 2572 out of 4373 (59%) deaths annually, based on a representative sample of transmitter-tagged golden eagles. For the subset of golden eagles that were recovered and a cause of death determined, anthropogenic mortality accounted for an average of 74% of deaths after their first year; leading forms of take over all age classes were shooting (~670 per year), collisions (~611), electrocutions (~506), and poisoning (~427). Although observed take overlapped the credible interval of our allowable take estimate and the population overall has been stable, our findings indicate that additional take, unless mitigated for, may not be sustainable. Our analysis demonstrates the utility of the joint application of integrated population and prescribed take level models to management of incidental take of a protected species.

The survival probability for a periodic non-autonomous Ornstein–Uhlenbeck process is calculated analytically using two different methods. The first uses an asymptotic approach. We treat the associated Kolmogorov Backward Equation with an absorbing boundary by dividing the domain into an interior region, centered around the origin, and a “boundary layer” near the absorbing boundary. In each region we determine the leading-order analytical solutions, and construct a uniformly valid solution over the entire domain using asymptotic matching. In the second method we examine the integral relationship between the probability density function and the mean first passage time probability density function. These allow us to determine approximate analytical forms for the exit rate. The validity of the solutions derived from both methods is assessed numerically, and we find the asymptotic method to be superior.

Understanding evolutionary consequences of intermittent breeding (nonbreeding in individuals that previously bred) requires investigation of the relationships between adult breeding state and two demographic parameters: survival probability and subsequent breeding probability. One major difficulty raised by comparing the demographic features of breeders and nonbreeders as estimated from capture-recapture data is that breeding state is often suspected to influence recapture or resighting probability. We used multistate capture-recapture models to test the hypothesis of equal recapture probabilities for breeding and nonbreeding Kittiwakes and found no evidence of an effect of breeding state on this parameter. The same method was used to test whether reproductive state affects survival probability. Nonbreeding individuals have lower survival rates than breeders. Moreover, nonbreeders have a higher probability of being nonbreeders the following year than do breeders. State-specific survival rates and transition probabilities vary from year to year, but temporal variations of survival and transition probabilities of breeders and nonbreeders are in parallel (on a logit scale). These inferences led us to conclude that nonbreeders tend to be lower quality individuals. The effect of sex was also investigated: males and females do not differ with respect to survival probabilities when reproductive state is taken into account. Similarly, there is no effect of sex on transition probabilities between reproductive states.

In this paper an efficient formulation of the Path integral (PI) approach is developed for determining the response probability density functions (PDFs) and first-passage statistics of nonlinear oscillators subject to stationary and time-modulated external Gaussian white noise excitations. Specifically, the evolution of the response PDF is obtained in short time steps, by using a discrete version of the Chapman-Kolmogorov equation and assuming a Gaussian form for the conditional response PDF. Next, the technique involves proceeding to treating the problem via an analytical asymptotic expansion procedure, namely the Laplace’s method of integration. In this manner, the repetitive double integrals involved in the standard implementation of the PI approach are evaluated in a closed form, while the response and first-passage PDFs are obtained by mundane step-by-step application of the derived approximate analytical expression. It is shown that the herein proposed formulation can drastically decrease the associated computational cost by several orders of magnitude, as compared to both the standard PI technique and Monte Carlo solution (MCS) approach. A number of nonlinear oscillators are considered in the numerical examples. Notably, for these systems both response PDFs and first-passage probabilities are presented, whereas comparisons with pertinent MCS data demonstrate the efficiency and accuracy of the technique.

Due to rapidly developing irrigation demand, the water allocation needs of the farming community have begun to intensify, leading to an equally significant policy shift in cropping structure and resource prioritization. This study aims to encourage the adoption of analytical model-supported planting structure alternatives in the sunflower production system and provide the empirical basis of evidence regarding the economic scenario trade-offs. The objectives of this study are to examine the interaction between water-saving priorities, cash-crop incentives and profitability, risk exposure of sunflower cultivation and survival probability of farm income in irrigated areas. With this objective in mind, a field-based economic scenario assessment through multi-criteria evaluation with an eight-week survey period was carried out with medium-scale farmers in the districts of Bukhara and Navoi provinces under the supervision of the regional irrigation authority at Bukhara State University, Bukhara, Uzbekistan. Samples from water-saving plots and cash-crop oriented farms were purposively selected in the irrigation command area through the use of analytic hierarchy process of determining relative weights of a given decision criterion. Findings indicated the relative efficiency and economic resilience of sunflower alternatives with emphasis on water productivity, which confirmed the established assumptions of priority weighting analysis (AHP) as well as requirements in the irrigation planning framework. Most importantly, it is hoped that this model would be a reference for a transition in the planting structure towards a more efficient and climate-resilient irrigation strategy for all stakeholders.

A treatment regime is a deterministic function that dictates personalized treatment based on patients’ individual prognostic information. There is increasing interest in finding optimal treatment regimes, which determine treatment at one or more treatment decision points to maximize expected long-term clinical outcomes, where larger outcomes are preferred. For chronic diseases such as cancer or human immunodeficiency virus infection, survival time is often the outcome of interest, and the goal is to select treatment to maximize survival probability. We propose two non-parametric estimators for the survival function of patients following a given treatment regime involving one or more decisions, i.e. the so-called value. On the basis of data from a clinical or observational study, we estimate an optimal regime by maximizing these estimators for the value over a prespecified class of regimes. Because the value function is very jagged, we introduce kernel smoothing within the estimator to improve performance. Asymptotic properties of the proposed estimators of value functions are established under suitable regularity conditions, and simulation studies evaluate the finite sample performance of the regime estimators. The methods are illustrated by application to data from an acquired immune deficiency syndrome clinical trial.