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Contains fulltext : 292738.pdf (Publisher’s version ) (Open Access), Chronic lymphocytic leukemia (CLL) is the most common leukemia among adults worldwide. Although genome-wide association studies (GWAS) have uncovered the germline genetic component underlying CLL susceptibility, the potential use of GWAS-identified risk variants to predict disease progression and patient survival remains unexplored. Here, we evaluated whether 41 GWAS-identified risk variants for CLL could influence overall survival (OS) and disease progression, defined as time to first treatment (TTFT) in a cohort of 1039 CLL cases ascertained through the CRuCIAL consortium. Although this is the largest study assessing the effect of GWAS-identified susceptibility variants for CLL on OS, we only found a weak association of ten single nucleotide polymorphisms (SNPs) with OS (p < 0.05) that did not remain significant after correction for multiple testing. In line with these results, polygenic risk scores (PRSs) built with these SNPs in the CRuCIAL cohort showed a modest association with OS and a low capacity to predict patient survival, with an area under the receiver operating characteristic curve (AUROC) of 0.57. Similarly, seven SNPs were associated with TTFT (p < 0.05); however, these did not reach the multiple testing significance threshold, and the meta-analysis with previous published data did not confirm any of the associations. As expected, PRSs built with these SNPs showed reduced accuracy in prediction of disease progression (AUROC = 0.62). These results suggest that susceptibility variants for CLL do not impact overall survival and disease progression in CLL patients.

Feeding trials were conducted to evaluate the effects of dietary pine pollen from Pinus tabulaeformis Carr. on masculinization, growth, food conversion efficiency, and survival of Clarias gariepinus. Experimental diets were prepared using a commercial feed for fry as basal diet with 5 levels of pine pollen supplementation: 0 (control, T1), 80 (T2), 160 (T3), 320 (T4), and 640 (T5) mg/kg diet; two positive control diets were also included, namely 17--methyltestosterone, and testosterone at 60 mg/kg diet. Two feeding trials were conducted, the first; larvae to fry for 72 days (Phase 1) and the second; fry to fingerling for an additional 45 days (Phase 2). Results showed that the percentage of males was significantly lower in the untreated diet (50.4%) and significantly higher in all of the pine pollen groups although there was no significant difference between the pine pollen groups. The male percentage was significantly highest in the 17--methyltestosterone and testosterone groups with no significant difference between the two groups. Phase 1 feeding showed that pine pollen did not affect growth, feed intake, food conversion efficiency, and survival of catfish. In Phase 2 feeding, the final average body weight (FABW), weight gain (WG), feed intake (FI), and protein efficiency ratio (PER) in all pine pollen groups similarly increased, but specific growth rate (SGR) and food conversion ratio (FCR) did not. 17--methyltestosterone treatment significantly increased WG and SGR, but enhancement was significantly inferior to the pine pollen groups (P<0.05). Testosterone did not have any effect on any of the response parameters. FIs were significantly higher in the pine pollen groups than either the control, 17--methyltestosterone, and testosterone groups. There was no effect on survival rate in any of the treatments All rates were statistically similar (P>0.05). Conclusion: pine pollen resulted in slightly but significantly inferior androgenic effect, but anabolic effect

Biometrics aims at reliable and robust identification of humans from their personal traits, mainly for security and authentication purposes, but also for identifying and tracking the users of smarter applications. Frequently considered modalities are fingerprint, face, iris, palmprint and voice, but there are many other possible biometrics, including gait, ear image, retina, DNA, and even behaviours. This chapter presents a survey of machine learning methods used for biometrics applications, and identifies relevant research issues. We focus on three areas of interest: offline methods for biometric template construction and recognition, information fusion methods for integrating multiple biometrics to obtain robust results, and methods for dealing with temporal information. By introducing exemplary and influential machine learning approaches in the context of specific biometrics applications, we hope to provide the reader with the means to create novel machine learning solutions to challenging biometrics problems.