A cerebellar mechanism for learning prior distributions of time intervals

Knowledge about the statistical regularities of the world is essential for cognitive and sensorimotor function. In the domain of timing, prior statistics are crucial for optimal prediction, adaptation and planning. Where and how the nervous system encodes temporal statistics is, however, not known. Based on physiological and anatomical evidence for cerebellar learning, we develop a computational model that demonstrates how the cerebellum could learn prior distributions of time intervals and support Bayesian temporal estimation. The model shows that salient features observed in human Bayesian time interval estimates can be readily captured by learning in the cerebellar cortex and circuit level computations in the cerebellar deep nuclei. We test human behavior in two cerebellar timing tasks and find prior-dependent biases in timing that are consistent with the predictions of the cerebellar model

Antagonistic Effect of Eight Sri Lankan Isolates of Pseudomonas fluorescens on, Meloidogyne incognita in Tomato, Lycopersicon esculentum

The study was conducted to determine the efficacy of Pseudomonas fluorescens isolates collected from eight locations in the Central Province of Sri Lanka against Meloidogyne incognita in tomato. Isolates were tested under laboratory conditions to determine the efficacy on egg hatchability and mortality of second stage juveniles. A planthouse experiment was conducted using potted tomato plants to determine the potential of P. fluorescens isolates and effective application technique. All tested isolates have significantly inhibited egg hatchability and increased the juvenile mortality after 72 hours. P. fluorescens isolate from Kangkung field in Pallekelle (PK) and tomato field in Udispattuwa (UT I) recorded 95% and 95.5% inhibition of egg hatchability after 72 hours. P. fluorescens isolates collected from tomato fields in Bopane (BT II) and Udispattuwa (UT II) and from Kangkung field in Pallekelle recorded the higher mortality of second stage juveniles 93%, 87% and 83.3% respectively. The highest reduction in the root knots (96.8%, 96.3%), egg masses (98.5%, 98.2%) and lower root galling index (1 and 1) were recorded in tomato plants treated as soil drench with UT II and PK isolates respectively.The root dipping technique gave higher reduction in the number of root knots (47.4%), egg masses (44.9%) and lower root galling index (3.75) were recorded from BT II, UT II and tomato fields in Nugethenna (NT) isolates respectively. UT II and PK found to be the most effective isolates and most effective application technique determined as soil drenching ten days after transplanting under plant house conditions

npInv: accurate detection and genotyping of inversions using long read sub-alignment

BACKGROUND: Detection of genomic inversions remains challenging. Many existing methods primarily target inzversions with a non repetitive breakpoint, leaving inverted repeat (IR) mediated non-allelic homologous recombination (NAHR) inversions largely unexplored. RESULT: We present npInv, a novel tool specifically for detecting and genotyping NAHR inversion using long read sub-alignment of long read sequencing data. We benchmark npInv with other tools in both simulation and real data. We use npInv to generate a whole-genome inversion map for NA12878 consisting of 30 NAHR inversions (of which 15 are novel), including all previously known NAHR mediated inversions in NA12878 with flanking IR less than 7kb. Our genotyping accuracy on this dataset was 94%. We used PCR to confirm the presence of two of these novel inversions. We show that there is a near linear relationship between the length of flanking IR and the minimum inversion size, without inverted repeats. CONCLUSION: The application of npInv shows high accuracy in both simulation and real data. The results give deeper insight into understanding inversion

Genomic and protein expression analysis reveals flap endonuclease 1 (FEN1) as a key biomarker in breast and ovarian cancer

FEN1 has key roles in Okazaki fragment maturation during replication, long patch base excision repair, rescue of stalled replication forks, maintenance of telomere stability and apoptosis. FEN1 may be dysregulated in breast and ovarian cancers and have clinicopathological significance in patients. We comprehensively investigated FEN1 mRNA expression in multiple cohorts of breast cancer [training set (128), test set (249), external validation (1952)]. FEN1 protein expression was evaluated in 568 oestrogen receptor (ER) negative breast cancers, 894 ER positive breast cancers and 156 ovarian epithelial cancers. FEN1 mRNA overexpression was highly significantly associated with high grade (p= 4.89 x 10 - 57) , high mitotic index (p= 5.25 x 10 - 28), pleomorphism (p= 6.31 x 10-19), ER negative (p= 9.02 x 10-35 ), PR negative (p= 9.24 x 10-24 ), triple negative phenotype (p= 6.67 x 10-21) , PAM50.Her2 (p=5.19 x 10-13 ), PAM50.Basal (p=2.7 x 10-41), PAM50.LumB (p=1.56 x 10-26), integrative molecular cluster 1 (intClust.1) ( p=7.47 x 10-12), intClust.5 (p=4.05 x 10-12) and intClust. 10 (p=7.59 x 10-38 ) breast cancers. FEN1 mRNA overexpression is associated with poor breast cancer specific survival in univariate (p=4.4 x 10-16) and multivariate analysis (p=9.19 x 10-7). At the protein level, in ER positive tumours , FEN1 overexpression remains significantly linked to high grade, high mitotic index and pleomorphism (ps< 0.01). In ER negative tumours, high FEN1 is significantly associated with pleomorphism, tumour type, lymphovascular invasion, triple negative phenotype, EGFR and HER2 expression (ps<0.05). In ER positive as well as in ER negative tumours, FEN1 protein over expression is associated with poor survival in univariate and multivariate analysis (ps<0.01). In ovarian epithelial cancers , similarly, FEN1 overexpression is associated with high grade, high stage and poor survival (ps<0.05). We conclude that FEN1 is a promising biomarker in breast and ovarian epithelial cancer

Sensorimotor priors in non-stationary environments

In the course of its interaction with the world, the human nervous system must constantly estimate various variables in the surrounding environment. Past research indicates that environmental variables may be represented as probabilistic distributions of a priori information (priors). Priors for environmental variables that do not change much over time have been widely studied. Little is known however, about how priors develop in environments with non-stationary statistics. We examine whether humans change their reliance on the prior based on recent changes in environmental variance. Through experimentation, we obtain an online estimate of the human sensorimotor prior (prediction) and then compare it to similar online predictions made by various non-adaptive and adaptive models. Simulations show that models that rapidly adapt to non-stationary components in the environments predict the stimuli better than models that do not take the changing statistics of the environment into consideration. We found that adaptive models best predict participants' responses in most cases. However, we find no support for the idea that this is a consequence of increased reliance on recent experience just after the occurrence of a systematic change in the environment

Adverse prognostic and predictive significance of low DNA-dependent protein kinase catalytic subunit (DNA-PKcs) expression in early-stage breast cancers

Background: DNA-dependent protein kinase catalytic subunit (DNA-PKcs), a serine threonine kinase belonging to the PIKK family (phosphoinositide 3-kinase-like-family of protein kinase), is a critical component of the non-homologous end joining (NHEJ) pathway required for the repair of DNA double strand breaks. DNA-PKcs may be involved in breast cancer pathogenesis. Methods: We evaluated clinicopathological significance of DNA-PKcs protein expression in 1161 tumours and DNA-PKcs mRNA expression in 1950 tumours. We correlated DNA-PKcs to other markers of aggressive phenotypes, DNA repair, apoptosis and cell cycle regulation. Results: Low DNA-PKcs protein expression was associated with higher tumour grade, higher mitotic index, tumour de-differentiation and tumour type (ps<0.05). Absence of BRCA1, low XRCC1/SMUG1/APE1/Polβ were also more likely in low DNA-PKcs expressing tumours (ps<0.05). Low DNA-PKcs protein expression was significantly associated with worse breast cancer specific survival (BCCS) in univariate and multivariate analysis (ps<0.01). At the mRNA level, low DNA-PKcs was associated with PAM50.Her2 and PAM50.LumA molecular phenotypes (ps<0.01) and poor BCSS. In patients with ER positive tumours who received endocrine therapy, low DNA-PKcs (protein and mRNA) was associated with poor survival. In ER negative patients, low DNA-PKcs mRNA remains significantly associated with adverse outcome. Conclusions: Our study suggests that low DNA-PKcs expression may have prognostic and predictive significance in breast cancers