The \u3cem\u3elet-7\u3c/em\u3e MicroRNA Family Members \u3cem\u3emir\u3c/em\u3e-48, \u3cem\u3emir\u3c/em\u3e-84, and mir-241 Function Together to Regulate Developmental Timing in \u3cem\u3eCaenorhabditis elegans\u3c/em\u3e

The microRNA let-7 is a critical regulator of developmental timing events at the larval-to-adult transition in C. elegans. Recently, microRNAs with sequence similarity to let-7 have been identified. We find that doubly mutant animals lacking the let-7 family microRNA genes mir-48 and mir-84 exhibit retarded molting behavior and retarded adult gene expression in the hypodermis. Triply mutant animals lacking mir-48, mir-84, and mir-241 exhibit repetition of L2-stage events in addition to retarded adult-stage events. mir-48, mir-84, and mir-241 function together to control the L2-to-L3 transition, likely by base pairing to complementary sites in the hbl-1 3′ UTR and downregulating hbl-1 activity. Genetic analysis indicates that mir-48, mir-84, and mir-241 specify the timing of the L2-to-L3 transition in parallel to the heterochronic genes lin-28 and lin-46. These results indicate that let-7 family microRNAs function in combination to affect both early and late developmental timing decisions

Most \u3cem\u3eCaenorhabditis elegans\u3c/em\u3e MicroRNAs are Individually Not Essential for Development or Viability

MicroRNAs (miRNAs), a large class of short noncoding RNAs found in many plants and animals, often act to post-transcriptionally inhibit gene expression. We report the generation of deletion mutations in 87 miRNA genes in Caenorhabditis elegans, expanding the number of mutated miRNA genes to 95, or 83% of known C. elegans miRNAs. We find that the majority of miRNAs are not essential for the viability or development of C. elegans, and mutations in most miRNA genes do not result in grossly abnormal phenotypes. These observations are consistent with the hypothesis that there is significant functional redundancy among miRNAs or among gene pathways regulated by miRNAs. This study represents the first comprehensive genetic analysis of miRNA function in any organism and provides a unique, permanent resource for the systematic study of miRNAs

Contextualized analysis of a needs assessment using the Theoretical Domains Framework: a case example in endocrinology

Background: The Theoretical Domains Framework (TDF) is a set of 14 domains of behavior change that provide a framework for the critical issues and factors influencing optimal knowledge translation. Considering that a previous study has identified optimal knowledge translation techniques for each TDF domain, it was hypothesized that the TDF could be used to contextualize and interpret findings from a behavioral and educational needs assessment. To illustrate this hypothesis, findings and recommendations drawn from a 2012 national behavioral and educational needs assessment conducted with healthcare providers who treat and manage Growth and Growth Hormone Disorders, will be discussed using the TDF. Methods: This needs assessment utilized a mixed-methods research approach that included a combination of: [a] data sources (Endocrinologists (n:120), Pediatric Endocrinologists (n:53), Pediatricians (n:52)), [b] data collection methods (focus groups, interviews, online survey), [c] analysis methodologies (qualitative - analyzed through thematic analysis, quantitative - analyzed using frequencies, cross-tabulations, and gap analysis). Triangulation was used to generate trustworthy findings on the clinical practice gaps of endocrinologists, pediatric endocrinologists, and general pediatricians in their provision of care to adult patients with adult growth hormone deficiency or acromegaly, or children/teenagers with pediatric growth disorders. The identified gaps were then broken into key underlying determinants, categorized according to the TDF domains, and linked to optimal behavioral change techniques. Results: The needs assessment identified 13 gaps, each with one or more underlying determinant(s). Overall, these determinants were mapped to 9 of the 14 TDF domains. The Beliefs about Consequences domain was identified as a contributing determinant to 7 of the 13 challenges. Five of the gaps could be related to the Skills domain, while three were linked to the Knowledge domain. Conclusions: The TDF categorization of the needs assessment findings allowed recommendation of appropriate behavior change techniques for each underlying determinant, and facilitated communication and understanding of the identified issues to a broader audience. This approach provides a means for health education researchers to categorize gaps and challenges identified through educational needs assessments, and facilitates the application of these findings by educators and knowledge translators, by linking the gaps to recommended behavioral change techniques

Determination of the Carrier-Envelope Phase of Few-Cycle Laser Pulses with Terahertz-Emission Spectroscopy

The availability of few-cycle optical pulses opens a window to physical phenomena occurring on the attosecond time scale. In order to take full advantage of such pulses, it is crucial to measure and stabilise their carrier-envelope (CE) phase, i.e., the phase difference between the carrier wave and the envelope function. We introduce a novel approach to determine the CE phase by down-conversion of the laser light to the terahertz (THz) frequency range via plasma generation in ambient air, an isotropic medium where optical rectification (down-conversion) in the forward direction is only possible if the inversion symmetry is broken by electrical or optical means. We show that few-cycle pulses directly produce a spatial charge asymmetry in the plasma. The asymmetry, associated with THz emission, depends on the CE phase, which allows for a determination of the phase by measurement of the amplitude and polarity of the THz pulse

A miRNA-Target Prediction Case Study

Giansanti, V., Castelli, M., Beretta, S., & Merelli, I. (2019). Comparing Deep and Machine Learning Approaches in Bioinformatics: A miRNA-Target Prediction Case Study. In V. V. Krzhizhanovskaya, M. H. Lees, P. M. A. Sloot, J. J. Dongarra, J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, ... R. Lam (Eds.), Computational Science – ICCS 2019: 19th International Conference, Faro, Portugal, June 12–14, 2019, Proceedings, Part III (pp. 31-44). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11538 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-22744-9_3MicroRNAs (miRNAs) are small non-coding RNAs with a key role in the post-transcriptional gene expression regularization, thanks to their ability to link with the target mRNA through the complementary base pairing mechanism. Given their role, it is important to identify their targets and, to this purpose, different tools were proposed to solve this problem. However, their results can be very different, so the community is now moving toward the deployment of integration tools, which should be able to perform better than the single ones. As Machine and Deep Learning algorithms are now in their popular years, we developed different classifiers from both areas to verify their ability to recognize possible miRNA-mRNA interactions and evaluated their performance, showing the potentialities and the limits that those algorithms have in this field. Here, we apply two deep learning classifiers and three different machine learning models to two different miRNA-mRNA datasets, of predictions from 3 different tools: TargetScan, miRanda, and RNAhybrid. Although an experimental validation of the results is needed to better confirm the predictions, deep learning techniques achieved the best performance when the evaluation scores are taken into account.authorsversionpublishe

Chromosome 10q26-driven age-related macular degeneration is associated with reduced levels of HTRA1 in human retinal pigment epithelium

Genome-wide association studies have identified the chromosome 10q26 (Chr10) locus, which contains the age-related maculopathy susceptibility 2 (ARMS2) and high temperature requirement A serine peptidase 1 (HTRA1) genes, as the strongest genetic risk factor for age-related macular degeneration (AMD) [L.G. Fritsche et al., Annu. Rev. Genomics Hum. Genet. 15, 151–171, (2014)]. To date, it has been difficult to assign causality to any specific single nucleotide polymorphism (SNP), haplotype, or gene within this region because of high linkage disequilibrium among the disease-associated variants [J. Jakobsdottir et al. Am. J. Hum. Genet. 77, 389–407 (2005); A. Rivera et al. Hum. Mol. Genet. 14, 3227–3236 (2005)]. Here, we show that HTRA1 messenger RNA (mRNA) is reduced in retinal pigment epithelium (RPE) but not in neural retina or choroid tissues derived from human donors with homozygous risk at the 10q26 locus. This tissue-specific decrease is mediated by the presence of a noncoding, cis-regulatory element overlapping the ARMS2 intron, which contains a potential Lhx2 transcription factor binding site that is disrupted by risk variant rs36212733. HtrA1 protein increases with age in the RPE–Bruch’s membrane (BM) interface in Chr10 nonrisk donors but fails to increase in donors with homozygous risk at the 10q26 locus. We propose that HtrA1, an extracellular chaperone and serine protease, functions to maintain the optimal integrity of the RPE–BM interface during the aging process and that reduced expression of HTRA1 mRNA and protein in Chr10 risk donors impairs this protective function, leading to increased risk of AMD pathogenesis. HtrA1 augmentation, not inhibition, in high-risk patients should be considered as a potential therapy for AMD

Neural Network Parameterizations of Electromagnetic Nucleon Form Factors

The electromagnetic nucleon form-factors data are studied with artificial feed forward neural networks. As a result the unbiased model-independent form-factor parametrizations are evaluated together with uncertainties. The Bayesian approach for the neural networks is adapted for chi2 error-like function and applied to the data analysis. The sequence of the feed forward neural networks with one hidden layer of units is considered. The given neural network represents a particular form-factor parametrization. The so-called evidence (the measure of how much the data favor given statistical model) is computed with the Bayesian framework and it is used to determine the best form factor parametrization.Comment: The revised version is divided into 4 sections. The discussion of the prior assumptions is added. The manuscript contains 4 new figures and 2 new tables (32 pages, 15 figures, 2 tables

MAGIC observations of very high energy gamma-rays from HESS J1813-178

Recently, the HESS collaboration has reported the detection of gamma-ray emission above a few hundred GeV from eight new sources located close to the Galactic Plane. The source HESS J1813-178 has sparked particular interest, as subsequent radio observations imply an association with SNR G12.82-0.02. Triggered by the detection in VHE gamma-rays, a positionally coincident source has also been found in INTEGRAL and ASCA data. In this Letter we present MAGIC observations of HESS J1813-178, resulting in the detection of a differential gamma-ray flux consistent with a hard-slope power law, described as dN/(dA dt dE) = (3.3+/-0.5)*10^{-12} (E/TeV)^{-2.1+/-0.2} cm^(-2)s^(-1)TeV^(-1). We briefly discuss the observational technique used, the procedure implemented for the data analysis, and put this detection in the perspective of multifrequency observations.Comment: Accepted by ApJ Letter