Uncertainties of a window mixing estimator for the noise power spectrum of digital signals

The statistical characteristics of several estimators of the noise power spectrum are analysed in this work. Averaged periodogram, Kim’s large subimage and small subimage methods [1] together with windowed periodogram methods using rectangular and Hamming windows and a new window mixing method are studied to obtain their biasing and standard deviation.Sample means and sample standard deviations of the NPS calculations following the different methods are obtained using synthetic images that simulate noise in digital radiography images. In addition, biasing and variance characteristics of the windowed periodograms and the window mixing methods are derived theoretically.Biasing, characteristic of estimators based in periodograms, is eliminated by modifying the periodogram in such a way that it is obtained as the discrete Fourier transform of the unbiased sampled covariance of the signal. Simulations show that Kim’s methods considerably improve the precision of the averaged periodogram, obtaining an important reduction in the sampled standard deviation. Also, the window mixing method, using a convex combination of windowed periodograms with rectangular and Hamming windows, improves the Kim’s methods in terms of standard deviation and has similar biasing.Finally, it is shown that NPS estimators based in the windowed periodogram and in the window mixing methods are unbiased and mean-square consistent, provided that the support of the autocorrelation function of the system PSF is finite.     

Evolution of specialized microbial cooperation in dynamic fluids

Here, we study the evolution of specialization using realistic computer simulations of bacteria that secrete two public goods in a dynamic fluid. Through this first‐principles approach, we find physical factors such as diffusion, flow patterns and decay rates are as influential as fitness economics in governing the evolution of community structure, to the extent that when mechanical factors are taken into account, (a) generalist communities can resist becoming specialists despite the invasion fitness of specialization; (b) generalist and specialists can both resist cheaters despite the invasion fitness of free‐riding; and (c) multiple community structures can coexist despite the opposing force of competitive exclusion. Our results emphasize the role of spatial assortment and physical forces on niche partitioning and the evolution of diverse community structures.     

Phytochemical profiling,antioxidant and antiproliferation potential of Euphorbia milii var.: Experimental analysis and in-silico validation

This study was aimed to investigate the anticancer potential of Euphorbia milii (E. milii) using an exquisite combination of phytopharmacological and advanced computational techniques. The chloroform fraction (Em-C) of E. milii methanol extract showed the highest antioxidant activity (IC₅₀: 6.41 ± 0.99 µg/ml) among all studied fractions. Likewise, Em-C also showed significant cytotoxicity (IC₅₀: 11.2 ± 0.8 µg/ml) when compared with that of standard compound 5-fluorouracil (5-FU) (IC₅₀: 4.22 ± 0.6 µg/ml) against hepatocarcinoma cell line (HepG2). However, in a human cervical cancer cell line (HeLa), Em-C demonstrated a non-significant difference in cytotoxicity (22.1 ± 0.8 µg/ml) when compared with that of 5-FU (IC₅₀: 6.87 ± 0.5 µg/ml). Furthermore, Western blot and qRT-PCR analysis revealed that the suppression of HepG2 cells was the consequence of a tremendous decrease in CDK2 and E2F1 protein expression. The GC–MS analysis of Em-C revealed the unique presence of cyclobarbital (CBT) and benzodioxole derivative (BAN) as major constituents. Furthermore, molecular docking of compounds BAN, CBT, and MBT into the binding site of different molecular targets i.e. cyclin dependent kinase 2 (CDK2), thymidylate synthase (TS), caspase 3, BCL2 and topoisomerase II was carried out. Compounds BAN and CBT have demonstrated remarkable binding affinity towards CDK2 and thymidylate synthase, respectively. Molecular dynamic simulation studies have further confirmed the finding of docking analysis, suggesting that CDK2 and TS can act as an attractive molecular target for BAN and CBT, respectively. It can be concluded that these E. milii phytoconstituents (BAN and CBT) may likely be responsible for anti-invasive activity against HepG2 cells.     

Advancing Aptamers as Molecular Probes for Cancer Theranostic Applications—The Role of Molecular Dynamics Simulation

Theranostics cover emerging technologies for cell biomarking for disease diagnosis and targeted introduction of drug ingredients to specific malignant sites. Theranostics development has become a significant biomedical research endeavor for effective diagnosis and treatment of diseases, especially cancer. An efficient biomarking and targeted delivery strategy for theranostic applications requires effective molecular coupling of binding ligands with high affinities to specific receptors on the cancer cell surface. Bioaffinity offers a unique mechanism to bind specific target and receptor molecules from a range of non‐targets. The binding efficacy depends on the specificity of the affinity ligand toward the target molecule even at low concentrations. Aptamers are fragments of genetic materials, peptides, or oligonucleotides which possess enhanced specificity in targeting desired cell surface receptor molecules. Aptamer–target binding results from several inter‐molecular interactions including hydrogen bond formation, aromatic stacking of flat moieties, hydrophobic interaction, electrostatic, and van der Waals interactions. Advancements in Systematic Evolution of Ligands by Exponential Enrichment (SELEX) assay has created the opportunity to artificially generate aptamers that specifically bind to desired cancer and tumor surface receptors with high affinities. This article discusses the potential application of molecular dynamics (MD) simulation to advance aptamer‐mediated receptor targeting in targeted cancer therapy. MD simulation offers real‐time analysis of the molecular drivers of the aptamer‐receptor binding and generate optimal receptor binding conditions for theranostic applications. The article also provides an overview of different cancer types with focus on receptor biomarking and targeted treatment approaches, conventional molecular probes, and aptamers that have been explored for cancer cells targeting.     

Discovery of a potent and selective adenylyl cyclase type 8 agonist by docking-based virtual screening

Adenylyl cyclases (ACs), which are responsible for catalyzing the conversion of adenosine triphosphate (ATP) into the second messenger cyclic adenosine monophosphate (cAMP), play a critical role in cell signal transduction. In this study, a combined approach involving docking-based virtual screening, with the combination of homology modeling followed by an in-vitro, and cell-based biological assay have been performed for discovering a class of novel potent and selective isoform adenylyl cyclase type 8 (AC8) agonist. The computer-aided virtual screening was used to identify fourteen virtual cluster compounds as potential hits which were further subjected to rigorous bioassays. A novel hit compound VHC-7 (ethyl 3-(2,4-dichlorobenzyl)-2-oxoindoline-3-carboxylate) was identified as a highly potent selective AC8 agonist with EC₅₀ value of 0.1052 ± 0.038 µM. Remarkably, the molecule herein reported can be explored further to discover greater number of hit compounds with better pharmacokinetic properties as well as to serve as a promising novel hit agonist of AC8 for the treatment of various central nervous system disorders and its associated diseases.     

面向森林碳汇供给的企业减排路径选择机理与政策模拟

采用我国碳交易试点省市中钢铁、火电、化工3个碳排放密集型行业89家企业2759个减排单位为样本,通过构建企业减排路径选择模型,模拟分析面向森林碳汇供给的企业在不同减排政策影响下的减排路径及其行业差异。研究表明：（1）企业购买森林碳汇、技术减排和购买配额的成本分别为210元/t、319元/t和158元/t,因边际减排成本不同,行业间企业减排路径选择受减排政策的影响存在明显差异;（2）为激励企业购买森林碳汇以抵消碳排放,应对不同行业企业采取有差别的政策,综合考虑补贴投入和激励效果,应分别对火电企业实施102元/t的技术减排补贴,对化工企业给予技术减排补贴和购买森林碳汇补贴各83元/t,对钢铁企业购买森林碳汇进行168元/t的补贴;（3）在以上分行业施策的影响下,3个行业企业经不同减排路径成本比较后,均将有约50%的减排单位选择购买森林碳汇。结合国家应对气候变化战略目标和各行业发展规律,研究结果为积极引导企业节能减排、充分挖掘森林碳汇市场潜力、减小企业减排成本压力等提供了科学依据。     

The use and misuse of regression models in landscape genetic analyses

The field of landscape genetics has been rapidly evolving, adopting and adapting analytical frameworks to address research questions. Current studies are increasingly using regression‐based frameworks to infer the individual contributions of landscape and habitat variables on genetic differentiation. This paper outlines appropriate and inappropriate uses of multiple regression for these purposes, and demonstrates through simulation the limitations of different analytical frameworks for making correct inference. Of particular concern are recent studies seeking to explain genetic differences by fitting regression models with effective distance variables calculated independently on separate landscape resistance surfaces. When moving across the landscape, organisms cannot respond independently and uniquely to habitat and landscape features. Analyses seeking to understand how landscape features affect gene flow should model a single conductance or resistance surface as a parameterized function of relevant spatial covariates, and estimate the values of these parameters by linking a single set of resistance distances to observed genetic dissimilarity via a loss function. While this loss function may involve a regression‐like step, the associated nuisance parameters are not interpretable in terms of organismal movement and should not be conflated with what is actually of interest: the mapping between spatial covariates and conductance/resistance. The growth and evolution of landscape genetics as a field has been rapid and exciting. It is the goal of this paper to highlight past missteps and demonstrate limitations of current approaches to ensure that future use of regression models will appropriately consider the process being modeled, which will provide clarity to model interpretation.     

Modeling Cumulative Effects of Climate and Development on Moose,Wolf, and Caribou Populations

Wildlife models focused solely on a single strong influence (e.g., habitat components, wildlife harvest) are limited in their ability to detect key mechanisms influencing population change. Instead, we propose integrated modeling in the context of cumulative effects assessment using multispecies population dynamics models linked to landscape-climate simulation at large spatial and temporal scales. We developed an integrated landscape and population simulation model using ALCES Online as the model-building platform, and the model accounted for key ecological components and relationships among moose (Alces alces), grey wolves (Canis lupus nubilus), and woodland caribou (Rangifer tarandus caribou) in northern Ontario, Canada. We simulated multiple scenarios over 5 decades (beginning 2020) to explore sensitivity to climate change and land use and assessed effects at multiple scales. The magnitude of effect and the relative importance of key factors (climate change, roads, and habitat) differed depending on the scale of assessment. Across the full extent of the study area (654,311km² [ecozonal scale]), the caribou population declined by 26% largely because of climate change and associated predator-prey response, which led to caribou range recession in the southern part of the study area. At the caribou range scale (108,378 km²), which focused on 2 herds in the northern part of the study area, climate change led to a 10% decline in the population and development led to an additional 7% decline. At the project scale (8,331 km²), which was focused more narrowly on the landscape surrounding 4 proposed mines, the caribou population declined by 29% largely in response to simulated development. Given that observed caribou population dynamics were sensitive to the cumulative effects of climate change, land use, interspecific interactions, and scale, insights from the analysis might not emerge under a less complex model. Our integrated modeling framework provides valuable support for broader regional assessments, including estimation of risk to caribou and Indigenous food security, and for developing and evaluating potential caribou recovery strategies. © 2021 The Authors. The Journal of Wildlife Management published by Wiley Periodicals LLC on behalf of The Wildlife Society.     

利用APSIM模型分析不同播期和耕作方式对旱地小麦籽粒干物质积累的影响

黄土丘陵区旱地小麦籽粒干物质积累的准确模拟可为调控小麦生产提供重要的技术支持。本研究利用甘肃省定西市安定区1971—2017年的气象资料和甘肃省定西市安定区凤翔镇安家沟村2016—2017年的大田试验数据,基于APSIM模型对旱地小麦籽粒干物质积累与分配进行模拟,并在模型验证的基础上,定量分析了播期和耕作方式对小麦籽粒干物质积累的影响。结果表明: 3个播期(早播、正常播、晚播)和4种耕作方式(传统耕作、传统耕作+覆盖、免耕、免耕+覆盖)下,籽粒干物质模拟值与实测值间的均方根误差(RMSE)为57.5～143.1 kg·hm^-2,归一化均方根误差(NRMSE)为1.4%～9.9%,模型模拟精度较高。不同播期下,耕作方式对籽粒干物质积累的促进效果排序均表现为: 免耕+覆盖>传统耕作+覆盖>免耕>传统耕作,免耕+覆盖最有利于小麦籽粒干物质积累,而免耕与传统耕作差异不显著。不同耕作方式下,小麦干物质积累过程均表现为早播好于正常播和晚播,晚播对干物质积累的影响较大,积累过程最不理想。