L-NAME Administration Enhances Diabetic Kidney Disease Development in an STZ/NAD Rat Model

One of the most important risk factors for developing chronic kidney disease (CKD) is diabetes. To assess the safety and efficacy of potential drug candidates, reliable animal models that mimic human diseases are crucial. However, a suitable model of diabetic kidney disease (DKD) is currently not available. The aim of this study is to develop a rat model of DKD by combining streptozotocin and nicotinamide (STZ/NAD) with oral N(ω)-Nitro-L-Arginine Methyl Ester (L-NAME) administration. Diabetes was induced in male Wistar rats by intravenous injection of 65 mg/kg STZ, 15 min after intraperitoneal injection of 230 mg/kg NAD. Rats were assigned to different groups receiving L-NAME (100 mg/kg/day) (STZ/NAD/L-NAME) or vehicle (STZ/NAD) for a period of 9 or 12 weeks by daily oral gavage. All rats developed hyperglycemia. Hyperfiltration was observed at the start of the study, whereas increased serum creatinine, albumin-to-creatinine ratio, and evolving hypofiltration were detected at the end of the study. Daily L-NAME administration caused a rapid rise in blood pressure. Histopathological evaluation revealed heterogeneous renal injury patterns, which were most severe in the STZ/NAD/L-NAME rats. L-NAME-induced NO-deficiency in STZ/NAD-induced diabetic rats leads to multiple characteristic features of human DKD and may represent a novel rat model of DKD.

Research on Prediction Method of Hydraulic Pump Remaining Useful Life Based on KPCA and JITL

Hydraulic pumps are commonly used; however, it is difficult to predict their remaining useful life (RUL) effectively. A new method based on kernel principal component analysis (KPCA) and the just-in-time learning (JITL) method was proposed to solve this problem. First, as the research object, the non-substitute time tac-tail life experiment pressure signals of gear pumps were collected. Following the removal and denoising of the DC component of the pressure signals by the wavelet packet method, multiple characteristic indices were extracted. Subsequently, the KPCA method was used to calculate the weighted fusion of the selected feature indices. Then the state evaluation indices were extracted to characterize the performance degradation of the gear pumps. Finally, an RUL prediction method based on the k-vector nearest neighbor (k-VNN) and JITL methods was proposed. The k-VNN method refers to both the Euclidean distance and angle relationship between two vectors as the basis for modeling. The prediction results verified the feasibility and effectiveness of the proposed method. Compared to the traditional JITL RUL prediction method based on the k-nearest neighbor algorithm, the proposed prediction model of the RUL of a gear pump presents a higher prediction accuracy. The method proposed in this paper is expected to be applied to the RUL prediction and condition monitoring and has broad application prospects and wide applicability.

Gamma-ray blazar variability: new statistical methods of time-flux distributions

ABSTRACT Variable γ-ray emission from blazars, one of the most powerful classes of astronomical sources featuring relativistic jets, is a widely discussed topic. In this work, we present the results of a variability study of a sample of 20 blazars using γ-ray (0.1–300 GeV) observations from Fermi/LAT telescope. Using maximum likelihood estimation (MLE) methods, we find that the probability density functions that best describe the γ-ray blazar flux distributions use the stable distribution family, which generalizes the Gaussian distribution. The results suggest that the average behaviour of the γ-ray flux variability over this period can be characterized by log-stable distributions. For most of the sample sources, this estimate leads to standard lognormal distribution (α = 2). However, a few sources clearly display heavy tail distributions (MLE leads to α < 2), suggesting underlying multiplicative processes of infinite variance. Furthermore, the light curves were analysed by employing novel non-stationarity and autocorrelation analyses. The former analysis allowed us to quantitatively evaluate non-stationarity in each source – finding the forgetting rate (corresponding to decay time) maximizing the log-likelihood for the modelled evolution of the probability density functions. Additionally, evaluation of local variability allows us to detect local anomalies, suggesting a transient nature of some of the statistical properties of the light curves. With the autocorrelation analysis, we examined the lag dependence of the statistical behaviour of all the {(yt, yt + l)} points, described by various mixed moments, allowing us to quantitatively evaluate multiple characteristic time scales and implying possible hidden periodic processes.

Why Women Earn High Marks: Examining the Role of Partisanship and Gender in Political Evaluations

We present the results of a randomized survey experiment demonstrating that the public evaluates women politicians more highly than men across multiple characteristic assessments. This finding is consistent with a recent wave of research indicating greater preferences for women politicians. Which respondents rate women politicians more highly, and why? We find that women and younger voters do not account for the greater marks given to women politicians. Instead, respondent partisanship and the presumed partisanship of the politician account for a great deal of our findings, with gender playing a complicating role. Democratic and Republican respondents are apt to project their own partisanship onto politicians, and across both parties, we find higher assessments for co-partisan politicians and for women politicians. On the whole, women politicians are evaluated on par with or significantly higher than men politicians across six characteristics, scoring especially well relative to men when politicians are presumed to be members of the opposing party and when traditionally feminine characteristics are assessed.

Recent Advancements in Studies on Chemosensory Mechanisms Underlying Detection of Semiochemicals in Dacini Fruit Flies of Economic Importance (Diptera: Tephritidae)

Dacini fruit flies mainly contain two genera, Bactrocera and Zeugodacus, and include many important pests of fruits and vegetables. Their life cycle is affected by various environmental cues. Among them, multiple characteristic semiochemicals have remarkable effects on their reproductive and host-finding behaviors. Notably, floral fragrances released from so-called fruit fly orchids strongly attract males of several Dacini fruit fly species. Focusing on the strong attraction of male flies to particular chemicals, natural and synthetic lures have been used for pest management. Thus, the perception of semiochemicals is important to understand environmental adaptation in Dacini fruit flies. Since next-generation sequencers are available, a large number of chemosensory-related genes have been identified in Dacini fruit flies, as well as other insects. Furthermore, recent studies have succeeded in the functional analyses of olfactory receptors in response to semiochemicals. Thus, characterization of molecular components required for chemoreception is under way. However, the mechanisms underlying chemoreception remain largely unknown. This paper reviews recent findings on peripheral mechanisms in the perception of odors in Dacini fruit flies, describing related studies in other dipteran species, mainly the model insect Drosophilamelanogaster. Based on the review, important themes for future research have also been discussed.

Ramie Yield Estimation Based on UAV RGB Images

Timely and accurate crop growth monitoring and yield estimation are important for field management. The traditional sampling method used for estimation of ramie yield is destructive. Thus, this study proposed a new method for estimating ramie yield based on field phenotypic data obtained from unmanned aerial vehicle (UAV) images. A UAV platform carrying RGB cameras was employed to collect ramie canopy images during the whole growth period. The vegetation indices (VIs), plant number, and plant height were extracted from UAV-based images, and then, these data were incorporated to establish yield estimation model. Among all of the UAV-based image data, we found that the structure features (plant number and plant height) could better reflect the ramie yield than the spectral features, and in structure features, the plant number was found to be the most useful index to monitor the yield, with a correlation coefficient of 0.6. By fusing multiple characteristic parameters, the yield estimation model based on the multiple linear regression was obviously more accurate than the stepwise linear regression model, with a determination coefficient of 0.66 and a relative root mean square error of 1.592 kg. Our study reveals that it is feasible to monitor crop growth based on UAV images and that the fusion of phenotypic data can improve the accuracy of yield estimations.