Convergence of Y Chromosome STR Haplotypes from Different SNP Haplogroups Compromises Accuracy of Haplogroup Prediction

The paternally inherited Y chromosome has been widely used in forensics for personal identification, in anthropology and population genetics to understand origin and migration of human populations, and also in medical and clinical studies （Wang and Li, 2013; Wang et al., 2014）. There are two kinds of extremely useful markers in Y chromosome, single nucle- otide polymorphism （SNP） and short tandem repeats （STRs）. With a very low mutation rate on the order of 3.0 x 10-8 mutations/nucleotide/generation （Xue et al., 2009）, SNP markers have been used in constructing a robust phylogeny tree linking all the Y chromosome lineages from world pop- ulations （Karafet et al., 2008）. Those lineages determined by the pattern of SNPs are called haplogroups. That is to say, we have to genotype an appropriate number of SNPs in order to assign a given Y chromosome to a haplogroup. Compared with SNPs, the mutation rates of STR markers are about four to five orders of magnitude higher （Gusmgo et al., 2005; Ballantyne et al., 2010）. Typing STR has advantages of saving time and cost compared with typing SNPs in phylogenetic assignment of a Y chromosome （Wang et al., 2010）. A set of STR values for an individual is called a haplotype. Because of the disparity in mutation rates between SNP and STR, one SNP haplogroup could actually comprise many STR haplotypes （Wang et al., 2010）. It is most interesting that STR variability is clustered more by haplogroups than by populations （Bosch et al., 1999; Behar et al., 2004）, which indicates that STR haplotypes could be used to infer the haplogroup information of a given Y chromosome. There has been increasing interest in this cost- effective strategy for predicting the haplogroup from a given STR haplotype when SNP data are unavailable. For instance, Vadim Urasin＇s YPredictor （http：//predictor.ydna.ru/）, Whit Atheys＇ haplogroup predictor （http：//www.hprg.com/hapest5/） （Athey, 2005, 2006）, and haplogroup classifier of Arizona University （Schlecht et al., 2008） have been widely employed in previous studies for haplogroup prediction （Larmuseau et al., 2010; Bembea et al., 2011; Larmuseau et al., 2012; Tarlykov et al., 2013）.

Gray Matter-Based Age Prediction Characterizes Different Regional Patterns

Dear Editor,The brain experiences ongoing changes across different ages to support brain development and functional reorganization.During the span of adulthood,although the brain has matured from a neurobiological perspective,it is still continuously shaped by external factors such as habits,the family setting,socioeconomic status,and the work environment [1].In contrast to chronological age (CA),brain(or biological) age (BA) is conceptualized as an important index for characterizing the aging process and neuropsychological state,as well as individual cognitiveperformance.Growing evidence indicates that BA can be assessed by neuroimaging techniques,including MRI [2].

Enhancement of IoT device security using an Improved Elliptic Curve Cryptography algorithm and malware detection utilizing deep LSTM

Internet of things(IoT)has become more popular due to the development and potential of smart technology aspects.Security concerns against IoT infrastructure,applications,and devices have grown along with the need for IoT technologies.Enhanced system security protocols are difficult due to the diverse capabilities of IoT devices and the dynamic,ever-changing environment,and simply applying basic security requirements is dangerous.Therefore,this proposed work designs a malware detection and prevention approach for secure data transmission among IoT gadgets.The malware detection approach is designed with the aid of a deep learning approach.The initial process is identifying attack nodes from normal nodes through a trust value using contextual features.After discovering attack nodes,these are considered for predicting different kinds of attacks present in the network,while some preprocessing and feature extraction strategies are applied for effective classification.The Deep LSTM classifier is applied for this malware detection approach.Once completed malware detection,prevention is performed with the help of the Improved Elliptic Curve Cryptography(IECC)algorithm.A hybrid MA-BW optimization is adopted for selecting the optimal key during transmission.Python 3.8 software is used to test the performance of the proposed approach,and several existing techniques are considered to evaluate its performance.The proposed approach obtained 95%of accuracy,5%of error value and 92%of precision.In addition,the improved ECC algorithm is also compared with some existing algorithm which takes 6.02 s of execution time.Compared to the other methods,the proposed approach provides better security to IoT gadgets during data transmission.