National-scale surveillance of emerging SARS-CoV-2 variants in wastewater

SARS-CoV-2 surveillance is crucial to identify variants with altered epidemiological properties. Wastewater-based epidemiology (WBE) provides an unbiased and complementary approach to sequencing individual cases. Yet, national WBE surveillance programs have not been widely implemented and data analyses remain challenging. We deep-sequenced 2,093 wastewater samples representing 95 municipal catchments, covering >57% of Austria's population, from December 2020 to September 2021. Our Variant Quantification in Sewage pipeline designed for Robustness (VaQuERo) enabled us to deduce variant abundance from complex wastewater samples and delineate the spatiotemporal dynamics of the dominant Alpha and Delta variants as well as regional clusters of other variants of concern. These results were cross validated by epidemiological records of >130,000 individual cases. Finally, we provide a framework to predict emerging variants de novo and infer variant-specific reproduction numbers from wastewater. This study demonstrates the power of national-scale WBE to support public health and promises particular value for countries without dense individual monitoring.

Activity-Based Protein Profiling for the Identification of Novel Carbohydrate-Active Enzymes Involved in Xylan Degradation in the Hyperthermophilic Euryarchaeon Thermococcus sp. Strain 2319x1E

Activity-based protein profiling (ABPP) has so far scarcely been applied in Archaea in general and, especially, in extremophilic organisms. We herein isolated a novel Thermococcus strain designated sp. strain 2319x1E derived from the same enrichment culture as the recently reported Thermococcus sp. strain 2319x1. Both strains are able to grow with xylan as the sole carbon and energy source, and for Thermococcus sp. strain 2319x1E (optimal growth at 85°C, pH 6–7), the induction of xylanolytic activity in the presence of xylan was demonstrated. Since the solely sequence-based identification of xylanolytic enzymes is hardly possible, we established a complementary approach by conducting comparative full proteome analysis in combination with ABPP using α- or β-glycosidase selective probes and subsequent mass spectrometry (MS)-based analysis. This complementary proteomics approach in combination with recombinant protein expression and classical enzyme characterization enabled the identification of a novel bifunctional maltose-forming α-amylase and deacetylase (EGDIFPOO_00674) belonging to the GH57 family and a promiscuous β-glycosidase (EGIDFPOO_00532) with β-xylosidase activity. We thereby further substantiated the general applicability of ABPP in archaea and expanded the ABPP repertoire for the identification of glycoside hydrolases in hyperthermophiles.

Prediction Error-Based Action Policy Learning for Quadcopter Flight Control

Quadcopters are finding their place in everything from transportation, delivery, hospitals, and to homes in almost every part of daily life. In places where human intervention for quadcopter flight control is impossible, it becomes necessary to equip drones with intelligent autopilot systems so that they can make decisions on their own. All previous reinforcement learning (RL)-based efforts for quadcopter flight control in complex, dynamic, and unstructured environments remained unsuccessful during the training phase in avoiding the trend of catastrophic failures by naturally unstable quadcopters. In this work, we propose a complementary approach for quadcopter flight control using prediction error as an effective control policy reward in the sensory space instead of rewards from unstable action spaces alike in conventional RL approaches. The proposed predictive coding biased competition using divisive input modulation (PC/BC-DIM) neural network learns prediction error-based flight control policy without physically actuating quadcopter propellers, which ensures its safety during training. The proposed network learned flight control policy without any physical flights, which reduced the training time to almost zero. The simulation results showed that the trained agent reached the destination accurately. For 20 quadcopter flight trails, the average path deviation from the ground truth was 1.495 and the root mean square (RMS) of the goal reached 1.708.

The semantics of place-related questions

The trend to equip information systems with question-answering capabilities raises the design problem of deciding which questions a system should be able to answer. Typical solutions build on mining human conversations or logs from similar systems for question patterns. For the case of questions about geographic places, we present a complementary approach, showing how to derive possible questions from an ontology of spatial information and a classification of place facets. We argue that such an approach reduces the inherent and substantial data bias of current solutions. At a more general level, we provide a novel understanding of spatial questions and their role in designing and using spatial information systems.

Leveraging Technology in Doctoral Supervision: A Complementary Approach in Kenya’s Institutions of Higher Learning

This paper examines previous empirical studies on adoption of emerging technologies in supervising doctoral students. The conceptual framework highlights the relationship between technology use and enhancing quality of supervision process, borrowing greatly from the theory of change methodology. It highlights the challenges and benefits analysis on the use of technology. The aim of this paper is to examine the efficacies of integration of the technology into the supervision process. As a result, it will provide students, supervisors, colleges of graduate boards, training institutions of higher learning, and regulatory bodies with a framework of incorporating the use of technology, based on needs assessment of respective doctoral supervision process.

Density strips: visualisation of uncertainty in clinical data summaries and research findings

The disproportionate focus on statistical significance in reporting and interpreting clinical research studies contributes to publication bias and encourages selective reporting. This highlights a need for alternative approaches that clearly communicate the uncertainty in the data, enabling researchers to provide a more nuanced interpretation of clinical research findings.Our purpose in this article is to introduce the density strip method as one potential approach that might act as a bridge between data visualisation for descriptive purposes and formal statistical inference. We build on existing theory, translating it to the applied research context to illustrate its utility to clinical researchers.We achieve this by considering an exemplar clinical trial, Multiple Sclerosis-Secondary Progressive Multi-Arm Randomisation Trial (MS-SMART). MS-SMART was a multiarm randomised placebo-controlled trial of three potentially neuroprotective drugs in secondary progressive MS. We illustrate through MS-SMART the potential of the density strip as an effective visualisation of the distribution of clinical trial outcomes and as a complementary approach to aid the interpretation of formal, inferential, statistical analysis.We conclude by summarising the advantages and disadvantages of the density strip methodology and provide suggestions for its potential extensions and possible further uses.

Spatio-temporal variations of water sources and mixing spots in a riparian zone

Riparian zones are known to modulate water quality in stream-corridors. They can act as buffers for groundwater borne solutes before they enter the stream at harmful, high concentrations, or facilitate solute turnover and attenuation in zones where stream water (SW) and groundwater (GW) mix. This natural attenuation capacity is strongly controlled by the dynamic exchange of water and solutes between the stream and the adjoining aquifer, creating potential for mixing-dependent reactions to take place. Here, we couple a previously calibrated transient and fully-integrated 3D surface-subsurface, numerical flow model with a Hydraulic Mixing Cell (HMC) method to map the source composition of water along a reach of the 4th-order Selke stream and track its spatio-temporal evolution. This allows us to define zones in the aquifer with similar fractions of surface- and groundwater per aquifer volume (called “mixing hot-spots”), which have a high potential to facilitate mixing-dependent reactions and in turn enhance solute turnover. We further evaluated the HMC results against hydrochemical monitoring data. Our results show that on average about 50 % of the water in the aquifer consists of infiltrating SW. Within about 200 m around the stream the aquifer is almost entirely made up of infiltrated SW with nearly no other water sources mixed in. On average, about 9 % of the aquifer volume could be characterized as “mixing hot-spots”, but this percentage could rise to values nearly 1.5 times higher following large discharge events. Moreover, event intensity (magnitude of peak flow) was found to be more important for the increase of mixing than event duration. Our modelling results further suggest that discharge events more significantly increase mixing potential at greater distances from the stream. In contrast near the stream, the rapid increase of SW influx shifts the ratio between the water fractions to SW, reducing the potential for mixing and the associated reactions. With this easy-to-transfer framework we seek to show the applicability of the HMC method as a complementary approach for the identification of mixing hot-spots in stream corridors, while showing the spatio-temporal controls of the SW-GW mixing process and the implications for riparian biogeochemistry and mixing-dependent turnover processes.

Utilizing high-p⊥ theory and data to constrain the initial stages of quark-gluon plasma

The scarce knowledge of the initial stages of quark-gluon plasma before the thermalization is mostly inferred through the low-[Formula: see text] sector. We propose a complementary approach in this report — the use of high-[Formula: see text] probes’ energy loss. We study the effects of four commonly assumed initial stages, whose temperature profiles differ only before the thermalization, on high-[Formula: see text][Formula: see text] and [Formula: see text] predictions. The predictions are based on our Dynamical Radiative and Elastic ENergy-loss Approach (DREENA) framework. We report insensitivity of [Formula: see text] to the initial stages, making it unable to distinguish between different cases. [Formula: see text] displays sensitivity to the presumed initial stages, but current experimental precision does not allow resolution between these cases. We further revise the commonly accepted procedure of fitting the energy loss parameters, for each individual initial stage, to the measured [Formula: see text]. We show that the sensitivity of [Formula: see text] to various initial stages obtained through such procedure is mostly a consequence of fitting procedure, which may obscure the physical interpretations. Overall, the simultaneous study of high-[Formula: see text] observables, with unchanged energy loss parametrization and restrained temperature profiles, is crucial for future constraints on initial stages.

Air delay propagation patterns in Europe from 2015 to 2018: an information processing perspective

The characterisation of delay propagation is one of the major topics of research in air transport management, due to its negative effects on the cost-efficiency, safety and environmental impact of this transportation mode. While most research works have naturally framed it as a transportation process, the successful application of network theory in neuroscience suggests a complementary approach, based on describing delay propagation as a form of information processing. This allows reconstructing propagation patterns from the dynamics of the individual elements, i.e. from the evolution observed at individual airports, without the need of additional a priori information. We here apply this framework to the analysis of delay propagation in the European airspace between 2015 and 2018, describe the evolution of the observed structure, and identify the role of individual airports in it. We further use this analysis to illustrate the limitations and challenges associated to this approach, and to sketch a roadmap of future research in this evolving topic.