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In the Arctic, under-ice primary production is limited to summer months and is restricted not only by ice thickness and snow cover but also by the stratification of the water column, which constrains nutrient supply for algal growth. Research Vessel Polarstern visited the ice-covered eastern-central basins between 82° to 89°N and 30° to 130°E in summer 2012, when Arctic sea ice declined to a record minimum. During this cruise, we observed a widespread deposition of ice algal biomass of on average 9 grams of carbon per square meter to the deep-sea floor of the central Arctic basins. Data from this cruise will contribute to assessing the effect of current climate change on Arctic productivity, biodiversity, and ecological function.

Matricaria chamomilla L. (chamomile) is a medicinal plant that is widely used for treating skin infections and respiratory ailments. Chamomile belongs to the Asteraceae family of flowering plants and is a primarily outcrossing species with a heterozygous genome. Despite its extensive use, no reference genome has been available for chamomile until now. We present a chromosome-level genome sequence for chamomile which was assembled with TRITEX pipeline from PacBio accurate long reads and chromosome conformation capture sequencing data. The assembled pseudo-haploid genome has a total size of 2.75 Gb, organized into 9 chromosomes with a scaffold N50 of 285 Mb. This high-quality reference genome has a BUSCO value of 98.8% and includes 47,820 functional genes. Additionally, we assembled a haplotype-resolved genome, taking advantage of the high heterozygosity of chamomile. The haplotype assemblies have total sizes of 2.28 Gb and 2.34 Gb and cover 87.6% and 89.8% of the pseudo-haploid reference genome, respectively. Our assemblies provide a valuable resource for genetics and genomics works for chamomile and related members of the Asteraceae.

With the growing share of renewable energy sources, the uncertainty in power supply is increasing. In addition to the inherent fluctuations in the renewables, this is due to the threat of deliberate malicious attacks, which may become more prevalent with a growing number of distributed generation units. Also in other safety-critical technology sectors, control systems are becoming more and more decentralized, causing the targets for attackers and thus the risk of attacks to increase. It is thus essential that distributed controllers are robust toward these uncertainties and able to react quickly to disturbances of any kind. To this end, we present novel methods for model-based identification of attacks and combine them with distributed model predictive control to obtain a resilient framework for adaptively robust control. The methodology is specially designed for distributed setups with limited local information due to privacy and security reasons. To demonstrate the efficiency of the method, we introduce a mathematical model for physically coupled microgrids under the uncertain influence of renewable generation and adversarial attacks, and perform numerical experiments, applying the proposed method for microgrid control.

Visceral leishmaniasis causes considerable mortality and morbidity in many parts of the world. There is an urgent need for the development of new, effective treatments for this disease. Here we describe the development of an anti-leishmanial drug-like chemical series based on a pyrazolopyrimidine scaffold. The leading compound from this series (7, DDD853651/GSK3186899) is efficacious in a mouse model of visceral leishmaniasis, has suitable physicochemical, pharmacokinetic and toxicological properties for further development, and has been declared a preclinical candidate. Detailed mode-of-action studies indicate that compounds from this series act principally by inhibiting the parasite cdc-2-related kinase 12 (CRK12), thus defining a druggable target for visceral leishmaniasis. A series of compounds are discovered for the treatment of visceral leishmaniasis, and cdc2-related kinase 12 (CRK12) is identified as the probable primary drug target.

We present a class of methods for distributed optimization with event-triggered communication. To this end, we extend Nesterov’s first order scheme to use event-triggered communication in a networked environment. We then apply this approach to generalize the proximal center algorithm (PCA) for separable convex programs by Necoara and Suykens. Our method uses dual decomposition and applies the developed event-triggered version of Nesterov’s scheme to update the dual multipliers. The approach is shown to be well suited for solving the active optimal power flow (DC-OPF) problem in parallel with event-triggered and local communication. Numerical results for the IEEE 57 bus and IEEE 118 bus test cases confirm that approximate solutions can be obtained with significantly less communication while satisfying the same accuracy estimates as solutions computed without event-triggered communication.

Visceral leishmaniasis (VL) causes significant mortality and morbidity in many parts of the world. There is an urgent need for the development of new, effective treatments for this disease. We describe the development of a novel anti-leishmanial drug-like chemical series based on a pyrazolopyrimidine scaffold. The leading compound from this series (7, DDD853651/GSK3186899) is efficacious in a mouse model of VL, has suitable physicochemical, pharmacokinetic and toxicological properties for further development and has been declared a preclinical candidate. Detailed mode of action studies indicate that compounds from this series act principally by inhibiting the parasite cdc-2-related kinase 12 (CRK12), thus defining a novel, druggable, target for VL.

Synopsis Interatomic Coulombic Decay in mixed NeKr dimers has been measured time-resolved and the nuclear dynamics of the decay have been investigated.

Efficient planning of assembly motions is a long standing challenge in the field of robotics that has been primarily tackled with reinforcement learning and sampling-based methods by using extensive physics simulations. This paper proposes a sample-efficient robust optimal control approach for the determination of assembly motions, which requires significantly less physics simulation steps during planning through the efficient use of derivative information. To this end, a differentiable physics simulation is constructed that provides second-order analytic derivatives to the numerical solver and allows one to traverse seamlessly from informative derivatives to accurate contact simulation. The solution of the physics simulation problem is made differentiable by using smoothing inspired by interior-point methods applied to both the collision detection as well as the contact resolution problem. We propose a modified variant of an optimization-based formulation of collision detection formulated as a linear program and present an efficient implementation for the nominal evaluation and corresponding first- and second-order derivatives. Moreover, a multi-scenario-based trajectory optimization problem that ensures robustness with respect to sim-to-real mismatches is derived. The capability of the considered formulation is illustrated by results where over 99\\% successful executions are achieved in real-world experiments. Thereby, we carefully investigate the effect of smooth approximations of the contact dynamics and robust modeling on the success rates. Furthermore, the method's capability is tested on different peg-in-hole problems in simulation to show the benefit of using exact Hessians over commonly used Hessian approximations.

With the growing share of renewable energy sources, the uncertainty in power supply is increasing. In addition to the inherent fluctuations in the renewables, this is due to the threat of deliberate malicious attacks, which may become more revalent with a growing number of distributed generation units. Also in other safety-critical technology sectors, control systems are becoming more and more decentralized, causing the targets for attackers and thus the risk of attacks to increase. It is thus essential that distributed controllers are robust toward these uncertainties and able to react quickly to disturbances of any kind. To this end, we present novel methods for model-based identification of attacks and combine them with distributed model predictive control to obtain a resilient framework for adaptively robust control. The methodology is specially designed for distributed setups with limited local information due to privacy and security reasons. To demonstrate the efficiency of the method, we introduce a mathematical model for physically coupled microgrids under the uncertain influence of renewable generation and adversarial attacks, and perform numerical experiments, applying the proposed method for microgrid control.