Confirmation and variability of the Allee effect in Dictyostelium discoideum cell populations,possible role of chemical signaling within cell clusters

In studies of the unicellular eukaryote Dictyostelium discoideum, many have anecdotally observed that cell dilution below a certain "threshold density” causes cells to undergo a period of slow growth (lag). However, little is documented about the slow growth phase and the reason for different growth dynamics below and above this threshold density. In this paper, we extend and correct our earlier work to report an extensive set of experiments, including the use of new cell counting technology, that set this slow-to-fast growth transition on a much firmer biological basis. We show that dilution below a certain density (around 10E4 cells/ml) causes cells to grow slower on average and exhibit a large degree of variability: sometimes a sample does not lag at all, while sometimes it takes many moderate density cell cycle times to recover back to fast growth. We perform conditioned media experiments to demonstrate that a chemical signal mediates this endogenous phenomenon. Finally, we argue that while simple models involving fluid transport of signal molecules or cluster-based signaling explain typical behavior, they do not capture the high degree of variability between samples but nevertheless favor an intra-cluster mechanism.

Parthenogenesis in weevils of the tribe Naupactini (Coleoptera, Curculionidae): a Wolbachia-density dependent trait?

The intracellular bacteria Wolbachia pipientis can manipulate host reproduction to enhance their vertical transmission. It has been reported an association between parthenogenesis and Wolbachia infection in weevils from the tribe Naupactini. A curing experiment suggested that a threshold density of Wolbachia is required for parthenogenesis to occur. The aim of this study was to analyze Wolbachia infection status in the bisexual species Naupactus xanthographus and Naupactus dissimulator.Wolbachia infection was detected in both species from some geographic locations, not being fixed. In all positive cases, faint PCR bands were observed. Quantification through real time PCR confirmed that Wolbachia loads in bisexual species were significantly lower than in parthenogenetic ones; this strengthens the hypothesis of a threshold level. Strain typing showed that both species carry wNau1, the most frequent in parthenogenetic Naupactini weevils. These infections seem to be recently acquired by horizontal transfer. Wolbachia was located throughout the whole body, which reinforce the idea of recent transmission. Moreover, we demonstrated that this strain carries the WO phage.Finally, the analysis of eubacterial 16S rRNA gene showed intense PCR bands for both bisexual species, suggesting –the presence of additional bacteria. Interspecific competition might explain why the parthenogenetic phenotype is not triggered.

On the design of precision nanomedicines

<div> <div> <p>Tight control on the selectivity of nanoparticles' interaction with biological systems is paramount for the development of targeted therapies. However, the large number of synthetically tunable parameters makes it difficult to identify optimal design ``sweet spots'' without rational guiding principles. Here we address this problem combining super-selectivity theory (SST) with analytical models from soft matter and polymer physics into a unified theoretical framework. Starting from an archetypal system, a polymersome functionalized with targeting ligands, we use our model to identify the most selective combination of parameters in terms of particle size, brush polymerization degree and grafting density, as well as tether length, binding affinity and ligands number. We further show how to combine multivalent interactions into multiplexed systems which act holistically as a function of the density of more than one receptor type, so as to achieve binding only when multiple receptors are expressed above a threshold density. We show that theory can be used to effectively fit experimental data and, hence confirming its suitability. We thus propose the design of “bar-coding" targeting approach that can be tailor-made to unique cell populations enabling personalized therapies. </p> </div> </div>

On the design of precision nanomedicines

<div> <div> <p>Tight control on the selectivity of nanoparticles' interaction with biological systems is paramount for the development of targeted therapies. However, the large number of synthetically tunable parameters makes it difficult to identify optimal design ``sweet spots'' without rational guiding principles. Here we address this problem combining super-selectivity theory (SST) with analytical models from soft matter and polymer physics into a unified theoretical framework. Starting from an archetypal system, a polymersome functionalized with targeting ligands, we use our model to identify the most selective combination of parameters in terms of particle size, brush polymerization degree and grafting density, as well as tether length, binding affinity and ligands number. We further show how to combine multivalent interactions into multiplexed systems which act holistically as a function of the density of more than one receptor type, so as to achieve binding only when multiple receptors are expressed above a threshold density. We show that theory can be used to effectively fit experimental data and, hence confirming its suitability. We thus propose the design of “bar-coding" targeting approach that can be tailor-made to unique cell populations enabling personalized therapies. </p> </div> </div>

Assessment and forecast of Siberian moth mass propagation risks in the Krasnoyarsk Krai forests

Прогноз угрозы массовых размножений сибирского шелкопряда Dendrolimus sibiricus Tschetverikov (Lepidoptera: Lasiocampidae) осуществляется на основе оценки их периодичности (частоты), пороговой плотности популяции и благоприятности (степени засушливости) погоды. Приведены оценки этих показателей по литературным данным и материалам лесопатологического мониторинга для лесов Красноярского края. Проведен их анализ и предложены методы, модели и алгоритмы прогнозирования угрозы массовых размножений. Наиболее информативными являются многолетние данные мониторинга численности насекомого (фазовый портрет динамики популяции), которые позволяют оценивать степень угрозы по текущему положению плотности популяции и коэффициента размножения относительно пороговой численности (5 гусениц/дерево). Использованы данные и фазовая траектория динамики численности сибирского шелкопряда в 1955 1972 гг. При отсутствии фазовых портретов угрозу следует прогнозировать по ретроспективным данным динамики площадей очагов сибирского шелкопряда (1962 2017 гг.). Сопоставляя среднюю периодичность образования очагов (11 лет в наиболее благоприятных для насекомого условиях) и продолжительность межочагового периода (5 лет) с текущими оценками этих показателей, можно определить время (год) появления угрозы. Уровень угрозы устанавливается по степени превышения показателями средних оценок. Для начала массового размножения кроме достижения пороговой численности необходимо наличие засушливой погоды. Вероятность такой погоды в темнохвойных южнотаежных лесах Красноярского края 20, а в период развития двух смежных поколений шелкопряда около 9. Очаги образуются в основном на пике или сразу после пика солнечной активности. Полученные результаты позволяют обеспечивать более точный и заблаговременный прогноз угрозы массового размножения за счет более полного использования и интеграции информации. Forecast of Siberian moth (Dendrolimus sibiricus Tschetverikov (Lepidoptera: Lasiocampidae) mass reproduction risk is based on its recurrence (frequency), population threshold density, and favourable weather conditions (drought rate). The paper presents an assessment of these indicators based on literature data and forest pathology monitoring of the Krasnoyarsk Krai forests. The analysis resulted in proposed procedures, models, and algorithms to forecast the risk of mass reproduction. The most comprehensive are the multiyear data on insect population monitoring (population dynamics phase pattern) that enable risk rate assessment based on available population density and reproduction coefficient in relation to the threshold density (5 caterpillars per tree). Such data and population stage curve of Siberian moth during 1955 1972 were used for the analysis. With the lack of the phase patterns, risk prediction should be based on retrospective data (1962 2017) on Siberian moth outbreak area dynamics. Comparison of average frequency of outbreak development (11 years provided the most favourable conditions for the insect) and the duration of the period between outbreaks (5 years) with the current data on these indicators enables identification of risk occurrence timing (year). Risk rate is based on the indicators excess over the mean assessment values. For the mass outbreak start, in addition to the threshold population density, dry weather is essential. The chance of such weather conditions in dark coniferous south taiga forests of the Krasnoyarsk Krai is 20, and during the development of two succeeding moth generations is around 9. Outbreaks mostly develop at the peak of solar activity or right afterwards. Our results enable to ensure the most accurate and timely mass reproduction forecast due to comprehensive application and integration of information.

Determining Star Formation Thresholds from Observations

Most gas in giant molecular clouds is relatively low-density and forms star inefficiently, converting only a small fraction of its mass to stars per dynamical time. However, star formation models generally predict the existence of a threshold density above which the process is efficient and most mass collapses to stars on a dynamical timescale. A number of authors have proposed observational techniques to search for a threshold density above which star formation is efficient, but it is unclear which of these techniques, if any, are reliable. In this paper we use detailed simulations of turbulent, magnetised star-forming clouds, including stellar radiation and outflow feedback, to investigate whether it is possible to recover star formation thresholds using current observational techniques. Using mock observations of the simulations at realistic resolutions, we show that plots of projected star formation efficiency per free-fall time εff can detect the presence of a threshold, but that the resolutions typical of current dust emission or absorption surveys are insufficient to determine its value. In contrast, proposed alternative diagnostics based on a change in the slope of the gas surface density versus star formation rate surface density (Kennicutt-Schmidt relation) or on the correlation between young stellar object counts and gas mass as a function of density are ineffective at detecting thresholds even when they are present. The signatures in these diagnostics sometimes taken as indicative of a threshold in observations, which we generally reproduce in our mock observations, do not prove to correspond to real physical features in the 3D gas distribution.

On the design of precision nanomedicines

<div> <div> <p>Tight control on the selectivity of nanoparticles' interaction with biological systems is paramount for the development of targeted therapies. However, the large number of synthetically tunable parameters makes it difficult to identify optimal design ``sweet spots'' without rational guiding principles. Here we address this problem combining super-selectivity theory (SST) with analytical models from soft matter and polymer physics into a unified theoretical framework. Starting from an archetypal system, a polymersome functionalized with targeting ligands, we use our model to identify the most selective combination of parameters in terms of particle size, brush polymerization degree and grafting density, as well as tether length, binding affinity and ligands number. We further show how to combine multivalent interactions into multiplexed systems which act holistically as a function of the density of more than one receptor type, so as to achieve binding only when multiple receptors are expressed above a threshold density. We show that theory can be used to effectively fit experimental data and, hence confirming its suitability. We thus propose the design of “bar-coding" targeting approach that can be tailor-made to unique cell populations enabling personalized therapies. </p> </div> </div>