Causal relationship between circulating vitamin C and 25-hydroxyvitamin D concentrations and common mental disorders-a Mendelian randomization study

Mental disorders seriously affect people’s health and social stability.This Mendelian randomization(MR)study was designed to investigate the causal relationship between circulating vitamin C(VC)or 25-hydroxyvitamin D(25(OH)D)levels and mental disorders.The data used for the MR analysis were derived from the summary genome-wide association studies(GWAS)database for VC and 25(OH)D and from the Finn Gen consortium for fourteen mental disorders.Based on the inverse variance weighted(IVW)method,we found a potential causal association between circulating VC and anxiety disorders(IVW:OR=1.139,95%CI:1.023-1.269,P=0.018).However,no causal association was found between VC or 25(OH)D and other mental disorders(P>0.05).In the reverse MR analysis,individuals with Alzheimer’s disease was causally associated with higher concentrations of circulating VC(P=0.012),while individuals with anxiety disorders had a negative association between the concentrations of 25(OH)D(P=0.012).However,the current evidence does not support a causal relationship between VC or 25(OH)D and other mental disorders.In addition,there was no causal association between circulating VC and 25(OH)D(P>0.05).Future studies are needed to confirm these findings and to elucidate the mechanisms of potential causality.

Causal association between 25-hydroxyvitamin D status and cataract development:A two-sample Mendelian randomization study

BACKGROUND Vitamin deficiencies are linked to various eye diseases,and the influence of vitamin D on cataract formation has been noted in prior research.However,detailed investigations into the causal relationship between 25-(OH)D status and cataract development remain scarce.AIM To explore a possible causal link between cataracts and vitamin D.METHODS In this study,we explored the causal link between 25-(OH)D levels and cataract development using Mendelian randomization.Our analytical approach included inverse-variance weighting(IVW),MR-Egger,weighted median,simple mode,and weighted mode methods.The primary analyses utilized IVW with random effects,supplemented by sensitivity and heterogeneity tests using both IVW and MR-Egger.MR-Egger was also applied for pleiotropy testing.Additionally,a leave-one-out analysis helped identify potentially impactful single-nucleotide polymorphisms.RESULTS The analysis revealed a positive association between 25-(OH)D levels and the risk of developing cataracts(OR=1.11,95%CI:1.00-1.22;P=0.032).The heterogeneity test revealed that our IVW analysis exhibited minimal heterogeneity(P>0.05),and the pleiotropy test findings confirmed the absence of pleiotropy within our IVW analysis(P>0.05).Furthermore,a search of the human genotype-phenotype association database failed to identify any potentially relevant risk-factor single nucleotide polymorphisms.CONCLUSION There is a potential causal link between 25-(OH)D levels and the development of cataracts,suggesting that greater 25-(OH)D levels may be a contributing risk factor for cataract formation.Further experimental research is required to confirm these findings.

SOME LIMIT THEOREMS FOR SEQUENCES OF PAIRWISE NQD RANDOM VARIABLES

In this article, the authors study some limit properties for sequences of pairwise NQD random variables, which are not necessarily identically distributed. They obtain Baum and Katz complete convergence and the strong stability of Jamison＇s weighted sums for pairwise NQD random variables, which may have different distributions. Some wellknown results are improved and extended.

Complete Convergence Properties of the Sums for -mixing Sequences of Random Variables

This paper discusses complete convergence properties of the sums of -mixing random sequences.As a result,we improve the corresponding results of Wu Qunying(2001). And extended the Baum and Katz complete convergence to the case of -mixing random sequences by moment inequality and truncating without necessarily adding any extra conditions.

Evolutionary Random Sequence Generators Based on LFSR

Random numbers play a crucial role in modern security schemes. Couple to the rapid development of cryptography, the strength of security protocols and encryption algorithms consumingly relies on the quality of random number. With simple architecture and faster speed, linear feedback shift register often is selected in many applications. However, the random sequence generated by LFSR can not meet the demand of unpredictability for secure mechanism. Genetic algorithm improves the linear property of LFSR and constructs a novel random sequence generator with longer period and complex architecture.

Strong Law of Large Numbers and Complete Convergence for Sequences of -Mixing Random Variables

We give some theorems of strong law of large numbers and complete convergence for sequences of φ-mixing random variables. In particular, Wittmann＇s strong law of large numbers and Teicher＇s strong law of large nnumbers for independent random variables are generalized to the case of φ -minxing random variables.

SOME SMALL DEVIATION THEOREMS FOR ARBITRARY CONTINUOUS RANDOM SEQUENCE

Let （Xn）n∈EN be a sequence of arbitrary continuous random variables, by the notion of relative entropy hμ^μ（w） as a measure of dissimilarity between probability measure # and reference measure μ, the explicit, general bounds for the partial sums of arbitrary continuous random variables under suitable conditions are developed. The argument uses the known and elementary lcmma of convergence for likelihood ratio.

On Strong Law of Large Numbers for Random Sequence

This note is devoted to introduce a new concept of conditionally dominated random variables.Under suitable restrict conditions,a general strong law of large numbers for arbitrary continuous random variables is obtained.

A RANDOM SEQUENCE GENERATOR BASED ON CHAOTIC CIRCUITS

In this paper, a random sequence generator based on chaotic circuits is presented.Fundamental principle and experimental circuit have been carried out in case of Chua's circuit.The statistical results are in good agreement with probability characteristics of random sequence.

USING ERROR-CORRECTING ENCODERS TO DESIGN LOCAL-RANDOM SEQUENCE GENERATORS

This paper proved the statement that a good linear block encoder is in fact a good local-random sequence generator. Furthermore, this statement discovers the deep relationship between the error-correcting coding theory and the modern cryptography.

ON ALMOST SURE CONVERGENCE OF WEIGHTED SUMS OF RANDOM ELEMENT SEQUENCES

We mainly study the almost sure limiting behavior of weighted sums of the form ∑ni=1 aiXi/bn , where {Xn, n ≥ 1} is an arbitrary Banach space valued random element sequence or Banach space valued martingale difference sequence and {an, n ≥ 1} and {bn,n ≥ 1} are two sequences of positive constants. Some new strong laws of large numbers for such weighted sums are proved under mild conditions.

Hájek-Rényi-type Inequality for a Class of Random Variable Sequences and Its Applications

In this paper, we obtain the Hejek-Renyi-type inequality for a class of random variable sequences and give some applications for associated random variable sequences, strongly positive dependent stochastic sequences and martingale difference sequences which generalize and improve the results of Prakasa Rao and Soo published in Statist. Probab. Lett., 57（2002） and 78（2008）. Using this result, we get the integrability of supremum and the strong law of large numbers for a class of random variable sequences.

Pseudo-Random Sequences Generator Based on Discrete Hyperchaotic Systems

We first design a discrete hyperchaotic system via piece-wise linear state feedback. The states of the closed loop system are locally expanding in two directions but absolutely bounded on the whole, which implies hyperchaos. Then, we use three suchlike hyperchaotic systems with different feedback gain matrices to design a pseudo-random sequence generator (PRSG). Through a threshold function, three sub-sequences generated from the output of piecewise linear functions are changed into 0-1 sequences. Then, followed by XOR operation, an unpredictable pseudo-random sequence (PRS) is ultimately obtained. The analysis and simulation results indicate that the PRS, generated with hyperchaotic systems, has desirable statistical features.

Permutation and Complementary Algorithm to Generate Random Sequences for Binary Logic

Randomness number generation plays a key role in network, information security and IT applications. In this paper, a permutation and complementary algorithm is proposed to use vector complementary and permuta-tion operations to extend n-variable Logic function space from 22n functions to 22n * 2n! configurations for variant logic framework. Each configuration contains 2n functions can be shown in a 22n-1*22n-1 matrix. A set of visual results can be represented by their symmetric properties in W, F and C codes respec-tively to provide the essential support on the variant logic framework.

PFP-RFSM: Protein fold prediction by using random forests and sequence motifs

Protein tertiary structure is indispensible in revealing the biological functions of proteins. De novo perdition of protein tertiary structure is dependent on protein fold recognition. This study proposes a novel method for prediction of protein fold types which takes primary sequence as input. The proposed method, PFP-RFSM, employs a random forest classifier and a comprehensive feature representation, including both sequence and predicted structure descriptors. Particularly, we propose a method for generation of features based on sequence motifs and those features are firstly employed in protein fold prediction. PFP-RFSM and ten representative protein fold predictors are validated in a benchmark dataset consisting of 27 fold types. Experiments demonstrate that PFP-RFSM outperforms all existing protein fold predictors and improves the success rates by 2%-14%. The results suggest sequence motifs are effective in classification and analysis of protein sequences.

Complete Convergence and Weak Law of Large Numbers for <i><span style="text-decoration:overline;">ρ</span></i>-Mixing Sequences of Random Variables

In this paper, the complete convergence and weak law of large numbers are established for ρ-mixing sequences of random variables. Our results extend and improve the Baum and Katz complete convergence theorem and the classical weak law of large numbers, etc. from independent sequences of random variables to ρ-mixing sequences of random variables without necessarily adding any extra conditions.

Limiting Distribution of Extreme Values for FGM Random Sequences

This paper mainly study extreme values of FGM random sequences.We prove a technique theorem by the dependence structure of FGM sequences,and further obtain the limiting distributions of maxima and k-th largest for stationary FGM random sequences.

An Invariance Principle for ρ^--Mixing Random Sequences

In this paper,we establish an invariance principle for ρ^--mixing random sequences under some moment condition.The result improve and extend the relevant result of Wu（2003）.

Some Strong Deviation Theorems for Dependent Continuous Random Sequence

Let {Xn,n ≥ 1} be a sequence of arbitrary continuous random variables,we introduce the notion of limit asymptotic logarithm likelihood ratio r（ω）,as a measure of dissimilarity between probability measure P and reference measure Q.We get some strong deviation theorems for the partial sums of arbitrary continuous random variables under Chung-Teicher＇s type conditions[6-7].

Structural plane recognition from three-dimensional laser scanning points using an improved region-growing algorithm based on the robust randomized Hough transform

The staggered distribution of joints and fissures in space constitutes the weak part of any rock mass.The identification of rock mass structural planes and the extraction of characteristic parameters are the basis of rock-mass integrity evaluation,which is very important for analysis of slope stability.The laser scanning technique can be used to acquire the coordinate information pertaining to each point of the structural plane,but large amount of point cloud data,uneven density distribution,and noise point interference make the identification efficiency and accuracy of different types of structural planes limited by point cloud data analysis technology.A new point cloud identification and segmentation algorithm for rock mass structural surfaces is proposed.Based on the distribution states of the original point cloud in different neighborhoods in space,the point clouds are characterized by multi-dimensional eigenvalues and calculated by the robust randomized Hough transform(RRHT).The normal vector difference and the final eigenvalue are proposed for characteristic distinction,and the identification of rock mass structural surfaces is completed through regional growth,which strengthens the difference expression of point clouds.In addition,nearest Voxel downsampling is also introduced in the RRHT calculation,which further reduces the number of sources of neighborhood noises,thereby improving the accuracy and stability of the calculation.The advantages of the method have been verified by laboratory models.The results showed that the proposed method can better achieve the segmentation and statistics of structural planes with interfaces and sharp boundaries.The method works well in the identification of joints,fissures,and other structural planes on Mangshezhai slope in the Three Gorges Reservoir area,China.It can provide a stable and effective technique for the identification and segmentation of rock mass structural planes,which is beneficial in engineering practice.