Study on late competence proteins involved in natural transformation of Bacillus subtilis

The following study comprises in vivo and in vitro data on several of the so-called late competence proteins, which are involved in natural transformation of Bacillus subtilis. The gram-positive bacterium B. subtilis belongs to those bacteria, who are able to take up DNA from their environment and incorporate the foreign DNA by homologous recombination into their own chromosome; a feature named competence. This fascinating ability is carried out by only a portion of the bacterial culture, expressing specific proteins, encoded by the late competence operons. If exogenous double-stranded DNA is about to be taken up from the environment, it needs to first cross the thick cell wall of B. subtilis, with a width of ~40 nm. In case of B. subtilis, this first border is crossed by a putative pseudopilus who transfers the DNA inside of the cell. The energy for this particular process is probably provided by the assembly/disassembly ATPase ComGA. The taken-up DNA is then further transferred into the cytosol by the so-called competence complex or competence machinery. The complex consists out of specific competence proteins, which assemble at the membrane, including a DNA-binding transmembrane protein (ComEA) and an aqueous channel protein (ComEC). In the following thesis, the unknown role of the protein ComEB has been further elucidated in the context of competence, and its enzymatic function was analysed in vitro. It was found that the protein carries out deaminase activity, which is not essential for transformation. In case of ComEC, an amino acid, D573, has been identified as essential for transformation. Truncations of the protein, supposed to carry out an exonuclease function, were heterologously expressed and purified as GST-tag fusions, but, probably due to aggregations of the proteins, no enzymatic activity was detected. The intracellular diffusion of fluorophore fusions of several competence proteins, namely ComEB-mV, ComGA-mV, ComEC-mV and mV-ComEA was analysed via single-molecule tracking, in the presence and absence of exogeneous DNA. In case of ComGA, a C-terminal fusion to mVenus was analysed and it was found that the protein becomes more dynamic in the presence of DNA. Further, the localization and diffusion of a fluorescently labeled PCR product inside of competent Bacillus cells was analysed for the first time. The diffusive behaviour and localization of the stained DNA resembles the diffusion of mV-ComEA. This led to the hypothesis that ComEA serves as a reservoir for taken-up DNA, similar to what is already known for orthologues of ComEA from other naturally competent bacteria

Adversarial Distribution Balancing for Counterfactual Reasoning

The development of causal prediction models is challenged by the fact that the outcome is only observable for the applied (factual) intervention and not for its alternatives (the so-called counterfactuals); in medicine we only know patients' survival for the administered drug and not for other therapeutic options. Machine learning approaches for counterfactual reasoning have to deal with both unobserved outcomes and distributional differences due to non-random treatment administration. Unsupervised domain adaptation (UDA) addresses similar issues; one has to deal with unobserved outcomes -- the labels of the target domain -- and distributional differences between source and target domain. We propose Adversarial Distribution Balancing for Counterfactual Reasoning (ADBCR), which directly uses potential outcome estimates of the counterfactuals to remove spurious causal relations. We show that ADBCR outcompetes state-of-the-art methods on three benchmark datasets, and demonstrate that ADBCR's performance can be further improved if unlabeled validation data are included in the training procedure to better adapt the model to the validation domain.Comment: Implementation available at https://github.com/sschrod/ADBC

FACT: Federated Adversarial Cross Training

Federated Learning (FL) facilitates distributed model development to aggregate multiple confidential data sources. The information transfer among clients can be compromised by distributional differences, i.e., by non-i.i.d. data. A particularly challenging scenario is the federated model adaptation to a target client without access to annotated data. We propose Federated Adversarial Cross Training (FACT), which uses the implicit domain differences between source clients to identify domain shifts in the target domain. In each round of FL, FACT cross initializes a pair of source clients to generate domain specialized representations which are then used as a direct adversary to learn a domain invariant data representation. We empirically show that FACT outperforms state-of-the-art federated, non-federated and source-free domain adaptation models on three popular multi-source-single-target benchmarks, and state-of-the-art Unsupervised Domain Adaptation (UDA) models on single-source-single-target experiments. We further study FACT's behavior with respect to communication restrictions and the number of participating clients

Retrieval of ice-nucleating particle concentrations from lidar observations and comparison with UAV in situ measurements

Aerosols that are efficient ice-nucleating particles (INPs) are crucial for the formation of cloud ice via heterogeneous nucleation in the atmosphere. The distribution of INPs on a large spatial scale and as a function of height determines their impact on clouds and climate. However, in situ measurements of INPs provide sparse coverage over space and time. A promising approach to address this gap is to retrieve INP concentration profiles by combining particle concentration profiles derived by lidar measurements with INP efficiency parameterizations for different freezing mechanisms (immersion freezing, deposition nucleation). Here, we assess the feasibility of this new method for both ground-based and spaceborne lidar measurements, using in situ observations collected with unmanned aerial vehicles (UAVs) and subsequently analyzed with the FRIDGE (FRankfurt Ice nucleation Deposition freezinG Experiment) INP counter from an experimental campaign at Cyprus in April 2016. Analyzing five case studies we calculated the cloud-relevant particle number concentrations using lidar measurements (n250,dry with an uncertainty of 20 % to 40 % and Sdry with an uncertainty of 30 % to 50 %), and we assessed the suitability of the different INP parameterizations with respect to the temperature range and the type of particles considered. Specifically, our analysis suggests that our calculations using the parameterization of Ullrich et al. (2017) (applicable for the temperature range −50 to −33 ∘C) agree within 1 order of magnitude with the in situ observations of nINP; thus, the parameterization of Ullrich et al. (2017) can efficiently address the deposition nucleation pathway in dust-dominated environments. Additionally, our calculations using the combination of the parameterizations of DeMott et al. (2015, 2010) (applicable for the temperature range −35 to −9 ∘C) agree within 2 orders of magnitude with the in situ observations of INP concentrations (nINP) and can thus efficiently address the immersion/condensation pathway of dust and nondust particles. The same conclusion is derived from the compilation of the parameterizations of DeMott et al. (2015) for dust and Ullrich et al. (2017) for soot.Peer reviewe

The Vertical Distribution of Ice-Nucleating Particles over the North China Plain: A Case of Cold Front Passage

Ice-nucleating particles (INPs) are crucial for cloud freezing processes in the atmosphere. Given the limited knowledge about the vertical distribution of INPs and its relation to aerosols in China, we present two aircraft observations of INPs over the North China Plain on 23 October 2019 and 25 October 2019, before and after a cold front passage. We used a well-established method to identify the INPs on a silicon wafer and then performed single-particle chemical composition analysis using an environmental scanning electron microscope-energy dispersive spectrometer (ESEM-EDS). The INP concentrations range from 0.1 to 9.2 L$^{−1}$ within activation temperatures from −20 to −29 °C. INPs are mostly concentrated within the boundary layer, and their concentration shows a decreasing trend with height (0.5~6 km) before the cold front passage. However, the highest INP concentration always appears at higher altitudes (4~5 km) after the cold front passage. The cold front passage also significantly weakens the correlations between the concentrations of INPs and aerosol particles at different sizes. The activated fraction (AF) of total aerosols increases from 10$^{−6}$ to 10$^{−4}$ with height from near ground to 6 km, reflecting a better nucleating capacity of the aerosols at higher altitudes. There is no obvious variation in AF after the cold front passage. Chemical analysis reveals that the INPs containing mineral dust components comprise the majority of total INPs during both flights. The proportion of pure mineral dust declines from 52.2% to 43.5% after the cold front passage while the proportion of mixed mineral dust increases from 23.9% to 45.7%, suggesting that an increased probability of aging or coating of INPs is introduced by the cold front during their long-distance transport. In addition, 88% of INPs have a diameter larger than 1 μm. This indicates that larger aerosols (>1 μm) are the major contributors to INPs at high altitudes despite their relatively low abundance. Our results demonstrate a significant impact of transport events on the sources and vertical distribution of INPs in the atmosphere

Synucleins Have Multiple Effects on Presynaptic Architecture

Synucleins ( a , b , g -synuclein) are abundant presynaptic proteins, with a -synuclein linked to the pathogenesis of Parkinson’s disease. Vargas et al. investigate the effects of deleting synucleins and overexpressing mutated a -synuclein on synapse architecture using electron microscopy. They find that synucleins regulate presynaptic terminal size and synaptic vesicle distribution

Sources and nature of ice-nucleating particles in the free troposphere at Jungfraujoch in winter 2017

Primary ice formation in mixed-phase clouds is initiated by a minute subset of the ambient aerosol population, called ice-nucleating particles (INPs). The knowledge about their atmospheric concentration, composition, and source in cloud-relevant environments is still limited. During the 2017 joint INUIT/CLACE (Ice Nuclei research UnIT/CLoud–Aerosol Characterization Experiment) field campaign, observations of INPs as well as of aerosol physical and chemical properties were performed, complemented by source region modeling. This aimed at investigating the nature and sources of INPs. The campaign took place at the High-Altitude Research Station Jungfraujoch (JFJ), a location where mixed-phase clouds frequently occur. Due to its altitude of 3580 m a.s.l., the station is usually located in the lower free troposphere, but it can also receive air masses from terrestrial and marine sources via long-range transport. INP concentrations were quasi-continuously detected with the Horizontal Ice Nucleation Chamber (HINC) under conditions representing the formation of mixed-phase clouds at −31 ∘C. The INP measurements were performed in parallel to aerosol measurements from two single-particle mass spectrometers, the Aircraft-based Laser ABlation Aerosol MAss Spectrometer (ALABAMA) and the laser ablation aerosol particle time-of-flight mass spectrometer (LAAPTOF). The chemical identity of INPs is inferred by correlating the time series of ion signals measured by the mass spectrometers with the time series of INP measurements. Moreover, our results are complemented by the direct analysis of ice particle residuals (IPRs) by using an ice-selective inlet (Ice-CVI) coupled with the ALABAMA. Mineral dust particles and aged sea spray particles showed the highest correlations with the INP time series. Their role as INPs is further supported by source emission sensitivity analysis using atmospheric transport modeling, which confirmed that air masses were advected from the Sahara and marine environments during times of elevated INP concentrations and ice-active surface site densities. Indeed, the IPR analysis showed that, by number, mineral dust particles dominated the IPR composition (∼58 %), and biological and metallic particles are also found to a smaller extent (∼10 % each). Sea spray particles are also found as IPRs (17 %), and their fraction in the IPRs strongly varied according to the increased presence of small IPRs, which is likely due to an impact from secondary ice crystal formation. This study shows the capability of combining INP concentration measurements with chemical characterization of aerosol particles using single-particle mass spectrometry, source region modeling, and analysis of ice residuals in an environment directly relevant for mixed-phase cloud formation.</p

BITES: Balanced Individual Treatment Effect for Survival data

Estimating the effects of interventions on patient outcome is one of the key aspects of personalized medicine. Their inference is often challenged by the fact that the training data comprises only the outcome for the administered treatment, and not for alternative treatments (the so-called counterfactual outcomes). Several methods were suggested for this scenario based on observational data, i.e.~data where the intervention was not applied randomly, for both continuous and binary outcome variables. However, patient outcome is often recorded in terms of time-to-event data, comprising right-censored event times if an event does not occur within the observation period. Albeit their enormous importance, time-to-event data is rarely used for treatment optimization. We suggest an approach named BITES (Balanced Individual Treatment Effect for Survival data), which combines a treatment-specific semi-parametric Cox loss with a treatment-balanced deep neural network; i.e.~we regularize differences between treated and non-treated patients using Integral Probability Metrics (IPM). We show in simulation studies that this approach outperforms the state of the art. Further, we demonstrate in an application to a cohort of breast cancer patients that hormone treatment can be optimized based on six routine parameters. We successfully validated this finding in an independent cohort. BITES is provided as an easy-to-use python implementation