An Efficient Bayesian Inference Framework for Coalescent-Based Nonparametric Phylodynamics

Phylodynamics focuses on the problem of reconstructing past population size dynamics from current genetic samples taken from the population of interest. This technique has been extensively used in many areas of biology, but is particularly useful for studying the spread of quickly evolving infectious diseases agents, e.g.,\ influenza virus. Phylodynamics inference uses a coalescent model that defines a probability density for the genealogy of randomly sampled individuals from the population. When we assume that such a genealogy is known, the coalescent model, equipped with a Gaussian process prior on population size trajectory, allows for nonparametric Bayesian estimation of population size dynamics. While this approach is quite powerful, large data sets collected during infectious disease surveillance challenge the state-of-the-art of Bayesian phylodynamics and demand computationally more efficient inference framework. To satisfy this demand, we provide a computationally efficient Bayesian inference framework based on Hamiltonian Monte Carlo for coalescent process models. Moreover, we show that by splitting the Hamiltonian function we can further improve the efficiency of this approach. Using several simulated and real datasets, we show that our method provides accurate estimates of population size dynamics and is substantially faster than alternative methods based on elliptical slice sampler and Metropolis-adjusted Langevin algorithm

Molecular Tuning of the Magnetic Response in Organic Semiconductors

The tunability of high-mobility organic semi-conductors (OSCs) holds great promise for molecular spintronics. In this study, we show this extreme variability - and therefore potential tunability - of the molecular gyromagnetic coupling ("g-") tensor with respect to the geometric and electronic structure in a much studied class of OSCs. Composed of a structural theme of phenyl- and chalcogenophene (group XVI element containing, five-membered) rings and alkyl functional groups, this class forms the basis of several intensely studied high-mobility polymers and molecular OSCs. We show how in this class the g-tensor shifts, $\Delta g$, are determined by the effective molecular spin-orbit coupling (SOC), defined by the overlap of the atomic spin-density and the heavy atoms in the polymers. We explain the dramatic variations in SOC with molecular geometry, chemical composition, functionalization, and charge life-time using a first-principles theoretical model based on atomic spin populations. Our approach gives a guide to tuning the magnetic response of these OSCs by chemical synthesis

FAN: Focused Attention Networks

Attention networks show promise for both vision and language tasks, by emphasizing relationships between constituent elements through appropriate weighting functions. Such elements could be regions in an image output by a region proposal network, or words in a sentence, represented by word embedding. Thus far, however, the learning of attention weights has been driven solely by the minimization of task specific loss functions. We here introduce a method of learning attention weights to better emphasize informative pair-wise relations between entities. The key idea is to use a novel center-mass cross entropy loss, which can be applied in conjunction with the task specific ones. We then introduce a focused attention backbone to learn these attention weights for general tasks. We demonstrate that the focused attention module leads to a new state-of-the-art for the recovery of relations in a relationship proposal task. Our experiments show that it also boosts performance for diverse vision and language tasks, including object detection, scene categorization and document classification

Optimal experimental design for mathematical models of haematopoiesis.

The haematopoietic system has a highly regulated and complex structure in which cells are organized to successfully create and maintain new blood cells. It is known that feedback regulation is crucial to tightly control this system, but the specific mechanisms by which control is exerted are not completely understood. In this work, we aim to uncover the underlying mechanisms in haematopoiesis by conducting perturbation experiments, where animal subjects are exposed to an external agent in order to observe the system response and evolution. We have developed a novel Bayesian hierarchical framework for optimal design of perturbation experiments and proper analysis of the data collected. We use a deterministic model that accounts for feedback and feedforward regulation on cell division rates and self-renewal probabilities. A significant obstacle is that the experimental data are not longitudinal, rather each data point corresponds to a different animal. We overcome this difficulty by modelling the unobserved cellular levels as latent variables. We then use principles of Bayesian experimental design to optimally distribute time points at which the haematopoietic cells are quantified. We evaluate our approach using synthetic and real experimental data and show that an optimal design can lead to better estimates of model parameters

Chicken faecal microbiota and disturbances induced by single or repeated therapy with tetracycline and streptomycin

BACKGROUND: In this study, we characterised the microbiota present in the faeces of 15- and 46-week-old egg laying hens before and after tetracycline or streptomycin therapy. In the first experiment, the layers were subjected to 7 days of therapy. In the second experiment, the hens were subjected to two days of therapy, which was repeated for an additional two days after 12 days of antibiotic withdrawal. This enabled us to characterise dynamics of the changes after antibiotic administration and withdrawal, and to identify genera repeatedly resistant to tetracycline and streptomycin. RESULTS: Real-time PCRs specific for Enterobacteriales, Lactobacillales, Clostridiales and Bifidobacteriales showed that changes in the microbiota in response to antibiotic therapy and antibiotic withdrawal were quite rapid and could be observed within 24 hours after the change in therapy status. Pyrosequencing of PCR amplified V3/V4 variable regions of 16S rRNA genes showed that representatives of the orders Clostridiales, Lactobacillales, Bacteroidales, Bifidobacteriales, Enterobacteriales, Erysipelotrichales, Coriobacteriales, Desulfovibrionales, Burkholderiales, Campylobacterales and Actinomycetales were detected in the faeces of hens prior to the antibiotic therapy. Tetracycline and streptomycin therapies decreased the prevalence of Bifidobacteriales, Bacteroidales, Clostridiales, Desulfovibrionales, Burkholderiales and Campylobacterales in faecal samples in both experiments. On the other hand, Enterobacteriales and Lactobacillales always increased in prevalence in response to both therapies. Within the latter two orders, Escherichia and Enterococcus were the genera prevalence of which increased after all the antibiotic treatments. CONCLUSIONS: The changes in microbiota composition induced by the antibiotic therapy were rapid and quite dramatic and only representatives of the genera Enterococcus and Escherichia increased in response to the therapy with both antibiotics in both experiments

New organoruthenium complexes with dipyrido[3,2-a:2’,3’- c]phenazine based ligands

Ruthenium complexes with dipyrido[3,2-a:2’,3’-c]phenazine (dppz) ligands have been extensively investigated as potential anticancer agents due to possibility of modulation of their intracellular accumulation and respective anticancer mechanism of action [1,2]. In recent years we have explored the anticancer activity of a variety of ruthenium(II)-arene complexes with dppz based ligands and some of them demonstrated remarkable cytotoxic activity [3]. Following these studies here we present a series of Ru(II)-arene complexes with the general formula [(η6-arene)Ru(dppz-R)Cl]PF6, where arene fragment was benzene, toluene or pcymene and R was -NO2, -Me or -COOMe with aim to study influence of both of half-sandwich Ru(II)-arene fragments and the variation of dppz ligands on improvement of the therapeutic potential of those complexes. All compounds were fully characterized by physico-chemical methods. The anticancer activity of dppz ligands and respective Ru complexes was assessed against MDA-MB-231, HCT116 and CT26 cancer cell lines and healthy MRC5 lung fibroblasts. In vivo efficacy of lead Ru-dppz complex revealed significantly reduction of tumor burden in mice with colorectal cancers without inducing liver and kidney toxicity. Thus, all the results indicated that introducing appropriate dppz into ruthenium-arene scaffold was a promising strategy for developing potent antitumor agents

New Water-Soluble Copper(II) Complexes with Morpholine-Thiosemicarbazone Hybrids: Insights into the Anticancer and Antibacterial Mode of Action

Six morpholine-(iso)­thiosemicarbazone hybrids HL1–HL6 and their Cu­(II) complexes with good-to-moderate solubility and stability in water were synthesized and characterized. Cu­(II) complexes [Cu­(L1–6)­Cl] (1–6) formed weak dimeric associates in the solid state, which did not remain intact in solution as evidenced by ESI-MS. The lead proligands and Cu­(II) complexes displayed higher antiproliferative activity in cancer cells than triapine. In addition, complexes 2–5 were found to specifically inhibit the growth of Gram-positive bacteria Staphylococcus aureus with MIC50 values at 2–5 μg/mL. Insights into the processes controlling intracellular accumulation and mechanism of action were investigated for 2 and 5, including the role of ribonucleotide reductase (RNR) inhibition, endoplasmic reticulum stress induction, and regulation of other cancer signaling pathways. Their ability to moderately inhibit R2 RNR protein in the presence of dithiothreitol is likely related to Fe chelating properties of the proligands liberated upon reduction

New Water-Soluble Copper(II) Complexes with Morpholine-Thiosemicarbazone Hybrids: Insights into the Anticancer and Antibacterial Mode of Action

Six morpholine-(iso)thiosemicarbazone hybrids HL1-HL6 and their Cu(II) complexes with good-to-moderate solubility and stability in water were synthesized and characterized. Cu(II) complexes [Cu(L1-6)Cl] (1-6) formed weak dimeric associates in the solid state, which did not remain intact in solution as evidenced by ESI-MS. The lead proligands and Cu(II) complexes displayed higher antiproliferative activity in cancer cells than triapine. In addition, complexes 2-5 were found to specifically inhibit the growth of Gram-positive bacteria Staphylococcus aureus with MIC50 values at 2-5 μg/mL. Insights into the processes controlling intracellular accumulation and mechanism of action were investigated for 2 and 5, including the role of ribonucleotide reductase (RNR) inhibition, endoplasmic reticulum stress induction, and regulation of other cancer signaling pathways. Their ability to moderately inhibit R2 RNR protein in the presence of dithiothreitol is likely related to Fe chelating properties of the proligands liberated upon reduction