Search

Background: The kidney-brain crosstalk has been involved in Alzheimer's disease (AD) with the mechanism remaining unclear. The anti-aging factor Klotho was reported to attenuate both kidney injury and AD pathologies. Objective: To investigate whether plasma Klotho participated in kidney-brain crosstalk in AD. Methods: We enrolled 33 PiB-PET-positive AD patients and 33 amyloid-β (Aβ)-negative age- and sex-matched cognitively normal (CN) controls from the Chongqing Ageing & Dementia Study (CADS). The levels of plasma Klotho, Aβ, and tau in the cerebrospinal fluid (CSF) were measured by enzyme-linked immunosorbent assay. Results: We found higher plasma Klotho and lower estimated glomerular filtration rate (eGFR) levels in AD patients compared with CN. The eGFR was positively associated with Aβ42, Aβ40 levels in CSF and negatively associated with CSF T-tau levels. Plasma Klotho levels were both negatively correlated with CSF Aβ42 and eGFR. Mediation analysis showed that plasma Klotho mediated 24.96% of the association between eGFR and CSF Aβ42. Conclusion: Renal function impacts brain Aβ metabolism via the kidney-brain crosstalk, in which the plasma Klotho may be involved as a mediator. Targeting Klotho to regulate the kidney-brain crosstalk provides potential therapeutic approaches for AD. [ABSTRACT FROM AUTHOR]

Introduction and Aims: Alzheimer's disease (AD) is the most common form of dementia with a complex genetic background. The cause of sporadic AD (sAD) remains largely unknown. Increasing evidence shows that genetic variations play a crucial role in sAD. P75 neurotrophin receptor (p75NTR, encoded by NGFR) plays a critical role in the pathogenesis of AD. Yet, the relationship between NGFR gene polymorphisms and AD was less studied. This study aims to analyze the relationship of NGFR gene polymorphism with the risk of AD in the Chinese Han population and amyloid‐β deposition in the ADNI cohort. Methods: This case–control association study was conducted in a Chinese Han cohort consisting of 366 sporadic AD (sAD) patients and 390 age‐ and sex‐matched controls. Twelve tag‐SNPs were selected and genotyped with a multiplex polymerase chain reaction‐ligase detection reaction (PCR‐LDR) method. The associations between tag‐SNPs and the risk of AD were analyzed by logistic regression. Moreover, another cohort from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database was included to examine the association of one tag‐SNP (rs2072446) with indicators of amyloid deposition. Kaplan–Meier survival analysis and Cox proportional hazards models were used to test the predictive abilities of rs2072446 genotypes for AD progression. The mediation effects of Aβ deposition on this association were subsequently tested by mediation analyses. Results: After multiple testing corrections, one tag‐SNP, rs2072446, was associated with an increased risk of sAD (additive model, OR = 1.79, Padjustment = 0.0144). Analyses of the ADNI cohort showed that the minor allele (T) of rs2072446 was significantly associated with the heavier Aβ burden, which further contributed to an increased risk of AD progression in APOE ε4 non‐carrier. Conclusion: Our study found that rs2072446 in NGFR is associated with both the risk of sAD in the Chinese Han population and the amyloid burden in the ADNI cohort, which reveals the role of p75NTR in AD from a genetic perspective. [ABSTRACT FROM AUTHOR]

Obtaining accurate monthly irrigation water consumption is of great significance for exploring the temporal and spatial variation of the irrigation water consumption in a river basin. A BPNN based on the TensorFlow architecture is constructed to calculate the monthly irrigation volume taking a case study over 24 irrigation districts in the Yellow River Basin. The data of crop types, rainfall, weather, and measured irrigation quotas in 24 irrigation districts are collected to train the model, verify the accuracy of the model, and analyze the temporal and spatial variation of the monthly irrigation water consumption in the Yellow River Basin in 2018. The results show that the irrigation water consumption of wheat and corn in the Yellow River Basin is the largest, accounting for 26.35% and 37.98% of the total irrigation water consumption. The irrigation water consumption of other crops is in the order of vegetable>oil crops>potatoes>rice>soybean>cotton. Irrigation water consumption shows an increasing trend first and then a decreasing one over time, reaching a peak in June. The spatial distribution of irrigation water shows a trend of gradual decrease from the northwest to the middle, and then to the east and west. [ABSTRACT FROM AUTHOR]

To solve the issue that the traditional BP neural network predicts the amount of agricultural irrigation water, fall into the local minimum and select the appropriate learning rate with difficulty, this paper proposes an agricultural irrigation amount prediction model based on the genetic algorithm and the Adam algorithm to optimize the BP neural network in parallel. This model uses the global search characteristics of genetic algorithm to prescreen the BP neural network with initial weights and thresholds, and then uses Adam algorithm to realize the learning rate adaptation to the constant update of the parameter gradient. The meteorological data of 7 typical irrigation areas in the Long- zhong Area of the Yellow River Basin and the measured irrigation data of corn are collected to train the model. At the same time, it is compared with the network model under the traditional GD method, GA method, and Adam method. The results show that the GA-Adam model reaches the preset accuracy only when the training times are 67 times and the training duration is 0.403 s; and the RMSE and MAE of the predicted value and expected value of the GA-Adam model are the smallest: 54.73 and 47.76 respectively. The coefficient of determination R is 0.81. In a word, the overall prediction effect of GA-Adam model is the best. [ABSTRACT FROM AUTHOR]