Strategies for translating proteomics discoveries into drug discovery for dementia

Tauopathies,diseases characterized by neuropathological aggregates of tau including Alzheimer's disease and subtypes of fro ntotemporal dementia,make up the vast majority of dementia cases.Although there have been recent developments in tauopathy biomarkers and disease-modifying treatments,ongoing progress is required to ensure these are effective,economical,and accessible for the globally ageing population.As such,continued identification of new potential drug targets and biomarkers is critical."Big data"studies,such as proteomics,can generate information on thousands of possible new targets for dementia diagnostics and therapeutics,but currently remain underutilized due to the lack of a clear process by which targets are selected for future drug development.In this review,we discuss current tauopathy biomarkers and therapeutics,and highlight areas in need of improvement,particularly when addressing the needs of frail,comorbid and cognitively impaired populations.We highlight biomarkers which have been developed from proteomic data,and outline possible future directions in this field.We propose new criteria by which potential targets in proteomics studies can be objectively ranked as favorable for drug development,and demonstrate its application to our group's recent tau interactome dataset as an example.

Price Discovery Function of Agricultural Futures Market in China--Based on VECM-PT-IS and DCC-MGARCH-t models

Agricultural futures market plays an important role in financial system,and its function of price discovery and hedging is of great significance to the long-term price stability for agricultural products.However,in China,agricultural futures market is still in construction stage,and scholars have not fully studied its price discovery function.Hence,this study will investigate the price discovery function of China agricultural futures market.The causal relationship,price contribution degree and volatility spillover effect of futures and spot markets are studied by comparing the price discovery function of soybean,yellow corn and soybean oil futures and spot.Taking the average daily settlement price of futures and spot in Dalian Commodity Exchange as study objects,the VECM and PT-IS model is used to investigate the causal relationship and the difference in price contribution between them.Then DDC-MGARCH-t model is used to analyze their volatility spillover effect.The empirical results show that there is obvious mutual guiding relationship between agricultural futures and spot market,and the price contribution of futures is significantly higher than that of spot,proving that agricultural futures have the function of price discovery.Meanwhile,the volatility spillover effect between agricultural futures and spot is bidirectional.The impact of internal fluctuations is often greater than that of external shocks.

Rare earth prices set to see more declines

Prices of rare earths are set to fall further in the next few months as oversupply and lower prices for other commodities hurt offtake, said experts. "Traders are selling their existing stocks as the State Reserve Bureau, which manages China's strategic stockpiles, did not purchase any rare earths in September and dampened expectations of higher prices," said Xu Ruoxu, an analyst with Shenwan Hongyuan Securities.

An Empirical Analysis of the Price Discovery Function of Shanghai Fuel Oil Futures Market

This paper analyzes the role of price discovery of Shanghai fuel oil futures market by using methods, such as unit root test, co-integration test, error correction model, Granger causality test, impulse-response fimction and variance decomposition. The results showed that there exists a strong relationship between the spot price of Huangpu fuel oil spot market and the futures price of Shanghai fuel oil futures market. In addition, the Shanghai fuel oil futures market exhibits a highly effective price discovery function.

The Impact of Restricted Measures on Price Discovery in Stock Index Futures:Evidence from CSI 500 Stock Index Futures

This paper compares the impact of restricted measures on CSI 500 stock index futures market and its underlying spot market.It uses vector error correction(VECM)model and common factor analysis method to study the differences between the two markets before and after the restricted measures was implemented.This paper analyzes the price discovery function through three aspects,i.e.,response to new information,price ratio of new information,and price discovery contribution degree of two markets.Based on empirical results,it is clear that group one in the period of April 17th to September 2nd has an obvious price discovery function.However,group two in the period of September 7th to December 31th does not have.The result shows that stock index futures do have price discovery function to some extent.However,due to the impact of restrictive policies,the spot market price contribution may exceed the futures market in some special time periods,which implies that the price discovery function of CSI 500 stock index futures market is not stable.

A Tight Prediction Interval for False Discovery Proportion under Dependence

The false discovery proportion (FDP) is a useful measure of abundance of false positives when a large number of hypotheses are being tested simultaneously. Methods for controlling the expected value of the FDP, namely the false discovery rate (FDR), have become widely used. It is highly desired to have an accurate prediction interval for the FDP in such applications. Some degree of dependence among test statistics exists in almost all applications involving multiple testing. Methods for constructing tight prediction intervals for the FDP that take account of dependence among test statistics are of great practical importance. This paper derives a formula for the variance of the FDP and uses it to obtain an upper prediction interval for the FDP, under some semi-parametric assumptions on dependence among test statistics. Simulation studies indicate that the proposed formula-based prediction interval has good coverage probability under commonly assumed weak dependence. The prediction interval is generally more accurate than those obtained from existing methods. In addition, a permutation-based upper prediction interval for the FDP is provided, which can be useful when dependence is strong and the number of tests is not too large. The proposed prediction intervals are illustrated using a prostate cancer dataset.

The Essence of Optimism of The Economists and Investors with Regard to the Economy of Russia

The gross domestic product of Russia,expressed in US dollars,indicates problems in the Russian economy associated with the decline in oil prices on the world energy market and the consequences of the sanctions of the United States and the European Union against Russia.The crisis situation of the Russian economy has a negative impact on the income of the population of country,represented mainly by wages.However,an economist or investor may be optimistic about Russian economic development in the medium term.This optimism is related to the economic policy of the United States.The expansion of the United States economy within the global space,based on economic growth,requires maintaining inflation within the target level and weakening the US dollar.These tasks are solved with the help of soft monetary policy of the US Federal Reserve System.The reduction of interest rates by the US Federal Reserve System against the background of inflation of the target level and the devaluation of the US dollar will contribute to economic growth in the United States,because it will lead to the depreciation of public debt,lower consumption of imports,increase in exports and trade balance,growth of production,income,consumption.The economic policy of the United States,which contributes to the devaluation of the US dollar,will also reduce the US dollar against the ruble.The optimistic view of investors-economists on the Russian economy is due to a significant strengthening of the ruble against the US dollar.Consequently,in the medium term,the gross domestic product and wages of citizens of Russia,expressed in US dollars,will significantly increase,and the purchasing power of the national currency of the country will also increase.This growth may continue until the next election of a new President of the United States in november 2020.After the election of the new President of the United States,there is a high probability of sanctions against Russia and of decline in oil prices in the world energy market in accordance with the future economic policy of the United States–two main reasons for the sharp strengthening of the US dollar against the ruble,which will cause a deeper economic crisis in Russia in the medium and long term.

The chaotic behavior among the oil prices, expectation of investors and stock returns: TAR-TR-GARCH copula and TAR-TR-TGARCH copula

This paper has two aims. The first one is to investigate the existence of chaotic structures in the oil prices, expectations of investors and stock returns by combining the Lyapunov exponent and Kolmogorov entropy, and the second one is to analyze the dependence behavior of oil prices, expectations of investors and stock returns from January 02, 1990, to June06, 2017. Lyapunov exponents and Kolmogorov entropy determined that the oil price and the stock return series exhibited chaotic behavior. TAR-TR-GARCH and TAR-TR-TGARCH copula methods were applied to study the co-movement among the selected variables. The results showed significant evidence of nonlinear tail dependence between the volatility of the oil prices, the expectations of investors and the stock returns. Further, upper and lower tail dependence and comovement between the analyzed series could not be rejected. Moreover, the TAR-TR-GARCH and TAR-TR-TGARCH copula methods revealed that the volatility of oil price had crucial effects on the stock returns and on the expectations of investors in the long run.

Theory summary on price discovery function of futures market

Price discovery is the basic function of futures market, and whether the futures market has the function of price discovery is an important research field for scholars both at home and abroad. This paper classifies the test methods and models on a basis of previous research, and introduces the applicable premise of research methods and models as well as the major research achievements of scholars at home and abroad, and also reviews the shortcomings of test methods and models.

Artificial intelligence as a tool in drug discovery and development

The rapidly advancing field of artificial intelligence(AI)has garnered substantial attention for its potential application in drug discovery and development.This opinion review critically examined the feasibility and prospects of integrating AI as a transformative tool in the pharmaceutical industry.AI,encompassing machine learning algorithms,deep learning,and data analytics,offers unprecedented opportunities to streamline and enhance various stages of drug development.This opinion review delved into the current landscape of AI-driven approaches,discussing their utilization in target identification,lead optimization,and predictive modeling of pharmacokinetics and toxicity.We aimed to scrutinize the integration of large-scale omics data,electronic health records,and chemical informatics,highlighting the power of AI in uncovering novel therapeutic targets and accelerating drug repurposing strategies.Despite the considerable potential of AI,the review also addressed inherent challenges,including data privacy concerns,interpretability of AI models,and the need for robust validation in realworld clinical settings.Additionally,we explored ethical considerations surrounding AI-driven decision-making in drug development.This opinion review provided a nuanced perspective on the transformative role of AI in drug discovery by discussing the existing literature and emerging trends,presenting critical insights and addressing potential hurdles.In conclusion,this study aimed to stimulate discourse within the scientific community and guide future endeavors to harness the full potential of AI in drug development.

Relative Performance Evaluation of Competing Crude Oil Prices’ Volatility Forecasting Models: A Slacks-Based Super-Efficiency DEA Model

With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the literature revealed that most studies tend to use several performance criteria to evaluate the performance of competing forecasting models;however, models are compared to each other using a single criterion at a time, which often leads to different rankings for different criteria—A situation where one cannot make an informed decision as to which model performs best when taking all criteria into account. In order to overcome this methodological problem, Xu and Ouenniche [1] proposed a multidimensional framework based on an input-oriented radial super-efficiency Data Envelopment Analysis (DEA) model to rank order competing forecasting models of crude oil prices’ volatility. However, their approach suffers from a number of issues. In this paper, we overcome such issues by proposing an alternative framework.

Nonlinear Differential Equation of Macroeconomic Dynamics for Long-Term Forecasting of Economic Development

In this article we derive a general differential equation that describes long-term economic growth in terms of cyclical and trend components. Equation is based on the model of non-linear accelerator of induced investment. A scheme is proposed for obtaining approximate solutions of nonlinear differential equation by splitting solution into the rapidly oscillating business cycles and slowly varying trend using Krylov-Bogoliubov-Mitropolsky averaging. Simplest modes of the economic system are described. Characteristics of the bifurcation point are found and bifurcation phenomenon is interpreted as loss of stability making the economic system available to structural change and accepting innovations. System being in a nonequilibrium state has a dynamics with self-sustained undamped oscillations. The model is verified with economic development of the US during the fifth Kondratieff cycle (1982-2010). Model adequately describes real process of economic growth in both quantitative and qualitative aspects. It is one of major results that the model gives a rough estimation of critical points of system stability loss and falling into a crisis recession. The model is used to forecast the macroeconomic dynamics of the US during the sixth Kondratieff cycle (2018-2050). For this forecast we use fixed production capital functional dependence on a long-term Kondratieff cycle and medium-term Juglar and Kuznets cycles. More accurate estimations of the time of crisis and recession are based on the model of accelerating log-periodic oscillations. The explosive growth of the prices of highly liquid commodities such as gold and oil is taken as real predictors of the global financial crisis. The second wave of crisis is expected to come in June 2011.

Effects of Financial Market Variables on Stock Prices:A Review of the Literature

Stock market plays a pivotal role in firms’expansion and turns economic growth.In the literature,because of the importance of stock markets to the real economy,the smooth and risk-free operation of the stock market has attracted significant attention.The finance literature contains a large number of studies that examine the stock price behaviour with some emphasis on the determinants of the relationship between the equity prices and the financial market activities.The present study reviews the previous works of the effect of financial market variables and stock price.Five selected financial market variables,market capitalization,earnings per share,price earnings multiples,dividend yield,and trading volume are reviewed in this study.In the past literature,there are the opinions of the positive significant relationship between market capitalization and stock price.To find the relationship between dividend yield and stock price,there are two broad schools of thoughts.Both of the relevance and irrelevance theory of Gordon and Modigliani have the strong evidence in the current literature that keeps on the dilemma and provides the scopes for future research.Price-earnings multiples are analyzed in the past literature by using different variables.Based on that,it is evidenced that price-earnings multiples have a negative significant effect on stock price.The reviewed studies state the cointegrating relationship between the stock price and the trading volume as the trading volume is a source of risk.

Knowledge Discovery from Dynamic Data on a Nonlinear System

A method is presented for performing knowledge discovery on the dynamic data of a nonlinear system. In the proposed approach, a synchronized phasor measurement technique is used to acquire the dynamic data of the nonlinear system and a hyper-rectangular type neural network (HRTNN) is then applied to extract crisp and fuzzy rules with which to estimate the system stability. The effectiveness of the proposed methodology is verified using the dynamic data of a typical real-world nonlinear system, namely an AEP-14 bus, and the extracted rules are relating to the knowledge discovery of the stability levels for the nonlinear system. The discovered relationships among the dynamic data (i.e., the operating state), the extracted rules, and the system stability are confirmed by means of a two-stage confirmatory factor analysis.

Analysis of Influence of Price to Customer Satisfaction Based on the Prediction Models

For the purpose of improvement of the sales, confirming the influence of price to the sales and customer satisfaction of the product is important. The most suitable price should be determined from the view point of customers, and it is extremely important to implement a high quality product corresponding to the real need of customers. It may have close relationship between cost and an expense to implement the individual inherent attribute of system product. Also, it may have close relationship between production cost and price of product. For the purpose of improvement of the customer satisfaction for quality of system product, the method of quantitative quality requirement and evaluation based on the ISO/IEC9126 quality model that includes six quality characteristics is widely recognized. In the precedent study, I have introduced the requirements definition method for the quality of system product based on the system quality model defined in ISO/IEC9126 and proposed the effectiveness of it statistically. In the previous study, I have also confirmed the relationship between inherent attributes of the product and quantitative result of the measured value of total customer satisfaction from the view point of six quality characteristics statistically. I performed the development of the prediction model to estimate the total customer satisfaction for the system product from the view point of inherent attribute of the product. And, I have proposed the effectiveness of application of the estimated prediction model and possibility of improvement of the total customer satisfaction of a system product. Based on the result of previous study, in this paper, I propose the result of investigation of influence of price to customer satisfaction, and the possibility of application of estimated prediction model for improvement of the total customer satisfaction of system product based on the price of product. Also, based on the result of investigation of relationship among price and inherent attributes of product, I propose the possibility of application of estimated prediction model and improvement of the price of system product from the view point of inherent attributes of product.

丹麦养猪业考察报告

介绍了丹麦猪品种、生产、疾病防治、价格、饲料与服务组织的一些概况。原种猪主要是兰德瑞斯(LL)、大约克夏(YY)、杜洛克(DD)与汉普夏(HH),兰德瑞斯与大约克夏正反交生产的优质二元杂交母猪作为种母猪用,以杜洛克、汉普夏或大约克夏公猪为父本与杜洛克为母本生产的优质一元或二元杂交公猪作生产中的终端父本,三元杂交猪或四元杂交猪是主要的生产性内猪。露天猪场多用杜洛克为母本,兰德瑞斯或大约克夏为父本,杂交生产的 LD 或 YD 作为内猪。用购买的精液或自行采精稀释成的备用精液人工授精在猪生产中应用普遍。用接种疫苗法与抗生素防治法防止猪场疾病的发生。依据市场情况,猪价每周变动一次。猪场的饲料有购买与自制两种方式。农业服务体系较为发达。

Critical Exercise Price for American Floating Strike Lookback Option in a Mixed Jump-Diffusion Model

This paper studies the critical exercise price of American floating strike lookback options under the mixed jump-diffusion model. By using It formula and Wick-It-Skorohod integral, a new market pricing model established under the environment of mixed jumpdiffusion fractional Brownian motion. The fundamental solutions of stochastic parabolic partial differential equations are estimated under the condition of Merton assumptions. The explicit integral representation of early exercise premium and the critical exercise price are also given, then the American floating strike lookback options factorization formula is obtained, the results is generalized the classical Black-Scholes market pricing model.

Understanding the Relationship Between Shrinking Cities and Land Prices:Spatial Pattern,Effectiveness,and Policy Implications

Urban shrinkage has emerged as a widespread phenomenon globally and has a significant impact on land,particularly in terms of land use and price.This study focuses on 2851 county-level cities in China in 2005–2018(excluding Hong Kong,Macao,Taiwan,and‘no data’areas in Qinhai-Tibet Plateau)as the fundamental units of analysis.By employing nighttime light(NTL)data to identify shrinking cities,the propensity score matching(PSM)model was used to quantitatively examine the impact of shrinking cities on land prices,and evaluate the magnitude of this influence.The findings demonstrate the following:1)there were 613 shrinking cities in China,with moderate shrinkage being the most prevalent and severe shrinkage being the least.2)Regional disparities are evident in the spatial distribution of shrinking cities,especially in areas with diverse terrain.3)The spatial pattern of land price exhibits a significant correlated to the economic and administrative levels.4)Shrinking cities significantly negatively impact on the overall land price(ATT=–0.1241,P<0.05).However,the extent of the effect varies significantly among different spatial regions.This study contributes novel insights into the investigation of land prices and shrinking cities,ultimately serving as a foundation for government efforts to promote the sustainable development of urban areas.

Bioinformatics analyses of differentially expressed genes associated with spinal cord injury:a microarray-based analysis in a mouse model

Gene spectrum analysis has shown that gene expression and signaling pathways change dramatically after spinal cord injury,which may affect the microenvironment of the damaged site.Microarray analysis provides a new opportunity for investigating diagnosis,treatment,and prognosis of spinal cord injury.However,differentially expressed genes are not consistent among studies,and many key genes and signaling pathways have not yet been accurately studied.GSE5296 was retrieved from the Gene Expression Omnibus DataSet.Differentially expressed genes were obtained using R/Bioconductor software(expression changed at least two-fold;P < 0.05).Database for Annotation,Visualization and Integrated Discovery was used for functional annotation of differentially expressed genes and Animal Transcription Factor Database for predicting potential transcription factors.The resulting transcription regulatory protein interaction network was mapped to screen representative genes and investigate their diagnostic and therapeutic value for disease.In total,this study identified 109 genes that were upregulated and 30 that were downregulated at 0.5,4,and 24 hours,and 3,7,and 28 days after spinal cord injury.The number of downregulated genes was smaller than the number of upregulated genes at each time point.Database for Annotation,Visualization and Integrated Discovery analysis found that many inflammation-related pathways were upregulated in injured spinal cord.Additionally,expression levels of these inflammation-related genes were maintained for at least 28 days.Moreover,399 regulation modes and 77 nodes were shown in the protein-protein interaction network of upregulated differentially expressed genes.Among the 10 upregulated differentially expressed genes with the highest degrees of distribution,six genes were transcription factors.Among these transcription factors,ATF3 showed the greatest change.ATF3 was upregulated within 30 minutes,and its expression levels remained high at28 days after spinal cord injury.These key genes screened by bioinformatics tools can be used as biological markers to diagnose diseases and provide a reference for identifying therapeutic targets.

Floating Waste Discovery by Request via Object-Centric Learning

Discovering floating wastes,especially bottles on water,is a crucial research problem in environmental hygiene.Nevertheless,real-world applications often face challenges such as interference from irrelevant objects and the high cost associated with data collection.Consequently,devising algorithms capable of accurately localizing specific objects within a scene in scenarios where annotated data is limited remains a formidable challenge.To solve this problem,this paper proposes an object discovery by request problem setting and a corresponding algorithmic framework.The proposed problem setting aims to identify specified objects in scenes,and the associated algorithmic framework comprises pseudo data generation and object discovery by request network.Pseudo-data generation generates images resembling natural scenes through various data augmentation rules,using a small number of object samples and scene images.The network structure of object discovery by request utilizes the pre-trained Vision Transformer(ViT)model as the backbone,employs object-centric methods to learn the latent representations of foreground objects,and applies patch-level reconstruction constraints to the model.During the validation phase,we use the generated pseudo datasets as training sets and evaluate the performance of our model on the original test sets.Experiments have proved that our method achieves state-of-the-art performance on Unmanned Aerial Vehicles-Bottle Detection(UAV-BD)dataset and self-constructed dataset Bottle,especially in multi-object scenarios.