Multi-input distributed classifiers for synthetic genetic circuits

For practical construction of complex synthetic genetic networks able to perform elaborate functions it is important to have a pool of relatively simple "bio-bricks" with different functionality which can be compounded together. To complement engineering of very different existing synthetic genetic devices such as switches, oscillators or logical gates, we propose and develop here a design of synthetic multiple input distributed classifier with learning ability. Proposed classifier will be able to separate multi-input data, which are inseparable for single input classifiers. Additionally, the data classes could potentially occupy the area of any shape in the space of inputs. We study two approaches to classification, including hard and soft classification and confirm the schemes of genetic networks by analytical and numerical results

Peculiarities of piRNA-mediated post-transcriptional silencing of Stellate repeats in testes of Drosophila melanogaster

Silencing of Stellate genes in Drosophila melanogaster testes is caused by antisense piRNAs produced as a result of transcription of homologous Suppressor of Stellate (Su(Ste)) repeats. Mechanism of piRNA-dependent Stellate repression remains poorly understood. Here, we show that deletion of Su(Ste) suppressors causes accumulation of spliced, but not nonspliced Stellate transcripts both in the nucleus and cytoplasm, revealing post-transcriptional degradation of Stellate RNA as the predominant mechanism of silencing. We found a significant amount of Su(Ste) piRNAs and piRNA-interacting protein Aubergine (Aub) in the nuclear fraction. Immunostaining of isolated nuclei revealed co-localization of a portion of cellular Aub with the nuclear lamina. We suggest that the piRNA–Aub complex is potentially able to perform Stellate silencing in the cell nucleus. Also, we revealed that the level of the Stellate protein in Su(Ste)-deficient testes is increased much more dramatically than the Stellate mRNA level. Similarly, Su(Ste) repeats deletion exerts an insignificant effect on mRNA abundance of the Ste-lacZ reporter, but causes a drastic increase of β-gal activity. In cell culture, exogenous Su(Ste) dsRNA dramatically decreases β-gal activity of hsp70-Ste-lacZ construct, but not its mRNA level. We suggest that piRNAs, similarly to siRNAs, degrade only unmasked transcripts, which are accessible for translation

Omecamtiv mecarbil in chronic heart failure with reduced ejection fraction, GALACTIC‐HF: baseline characteristics and comparison with contemporary clinical trials

Aims: The safety and efficacy of the novel selective cardiac myosin activator, omecamtiv mecarbil, in patients with heart failure with reduced ejection fraction (HFrEF) is tested in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTIC‐HF) trial. Here we describe the baseline characteristics of participants in GALACTIC‐HF and how these compare with other contemporary trials. Methods and Results: Adults with established HFrEF, New York Heart Association functional class (NYHA) ≥ II, EF ≤35%, elevated natriuretic peptides and either current hospitalization for HF or history of hospitalization/ emergency department visit for HF within a year were randomized to either placebo or omecamtiv mecarbil (pharmacokinetic‐guided dosing: 25, 37.5 or 50 mg bid). 8256 patients [male (79%), non‐white (22%), mean age 65 years] were enrolled with a mean EF 27%, ischemic etiology in 54%, NYHA II 53% and III/IV 47%, and median NT‐proBNP 1971 pg/mL. HF therapies at baseline were among the most effectively employed in contemporary HF trials. GALACTIC‐HF randomized patients representative of recent HF registries and trials with substantial numbers of patients also having characteristics understudied in previous trials including more from North America (n = 1386), enrolled as inpatients (n = 2084), systolic blood pressure < 100 mmHg (n = 1127), estimated glomerular filtration rate < 30 mL/min/1.73 m2 (n = 528), and treated with sacubitril‐valsartan at baseline (n = 1594). Conclusions: GALACTIC‐HF enrolled a well‐treated, high‐risk population from both inpatient and outpatient settings, which will provide a definitive evaluation of the efficacy and safety of this novel therapy, as well as informing its potential future implementation

Distribution and habitat preference of the Ortolan Bunting in the Czech Republic

The Ortolan Bunting (Emberiza hortulana) is a farmland bird species, whose population size has declined very sharply in recent decades, especia ll y in Western and Central European countries. The aim of our study in 2015 was to record where the last populations of this critically endangered species in the Czech Republic are located and also what habitat the species associated with. We examined nine areas (925 km2 in total). Two main areas of occurrence (surface mines in northern Bohemia and farmland landscape of Silesia) and two small isolated populations in central Bohemia were registered for this species. In contrast, observations in some traditional areas of its occurence (České středohoří in northern Bohemia, Hovorany-Čejkovice region in southe r n Moravia and Javoricko region in Silesia) were negative. Altogether, we counted 75-79 singing males. Our estimation of the size of the Czech population in 2015 is 75-100 singing males, which indicates further population decline compared to the last mapping in 2001-2003. Furthermore, habitat associat io ns were investigated at two spatial scales and we made habitat compar is o n between farmland and post-mining landscape. Our research highlights a high degree of flexibility in habitat selection of Ortolan Bunting and also positive association with high..

Hard classification technique.

<p><i>P</i>₁, <i>P</i>₂, <i>P</i>₃—positive classes of individual linear classifiers, <i>D</i>—negative class of the collective classifier.</p

Scheme of a two-input linear classifier circuit.

<p><i>x</i>₁, <i>x</i>₂—inputs inducing the corresponding promoters, RBS_A1 and RBS_A2—ribosome binding sites determining the strengths of the input branches, A—intermediate transcription factor (same in both input branches), GFP—reporter gene.</p

Scheme of a two-input classifier circuit with a bell-shaped response.

<p><i>x</i>₁, <i>x</i>₂—inputs inducing the corresponding promoters, RBS_U1 and RBS_U2—ribosome binding sites determining the strengths of the input branches, U₁, U₂—intermediate repressor/activator factors, Z₁, Z₂—outputs of the individual branches, GFP—reporter gene.</p

Soft learning algorithm.

<p>Soft learning algorithm.</p

Training a distributed classifier with a linear target border.

<p>(A) Target classes: <i>P</i>—positive, <i>D</i>—negative. (B) Trained ensemble region on the plane of parameters: hatched area.</p