Abstract: Quantitative PCR (qPCR) is broadly used as the gold standard to quantify microbial community fractions in environmental microbiology and biotechnology. Benchmarking efforts to ensure the comparability of qPCR data for environmental bioprocesses are still scarce. Also, for partial nitritation/anammox (PN/A) systems systematic investigations are still missing, rendering meta-analysis of reported trends and generic insights potentially precarious. We report a baseline investigation of the variability of qPCR-based analyses for microbial communities applied to PN/A systems. Round-robin testing was performed for three PN/A biomass samples in six laboratories, using the respective in-house DNA extraction and qPCR protocols. The concentration of extracted DNA was significantly different between labs, ranged between 2.7 and 328 ng mg−1 wet biomass. The variability among the qPCR abundance data of different labs was very high (1−7 log fold) but differed for different target microbial guilds. DNA extraction caused maximum variation (3–7 log fold), followed by the primers (1–3 log fold). These insights will guide environmental scientists and engineers as well as treatment plant operators in the interpretation of qPCR data.

Abstract: Water quality standards (WQS) set the legal definition for safe and desirable water. WQS impose regulatory concentration limits to act as a jurisdiction-specific legislative risk-management tool. Despite its importance in shaping a universal definition of safe, clean water, little information exists with respect to (dis)similarity of chemical WQS worldwide. Therefore, this paper compares chemical WQS for drinking and surface water matrices in eight jurisdictions representing a global geographic distribution: Australia, Brazil, Canada, China, the European Union, the region of Flanders in Belgium, the United States of America, and South Africa. The World Health Organization's list is used as a reference for drinking water standards. Sørensen–Dice indices (SDI) showed little qualitative similarity in the compounds that are regulated in drinking water (median SDI = 40%) and surface water (median SDI = 33%), indicating that the heterogeneity within a matrix is substantial at the level of the standard. Quantitative similarity for matching standards was higher than the qualitative per Kendall correlation (median = 0.73 and 0.58 for drinking water and surface water respectively), yet variance observed within standards remained inexplicably high for organic compounds. Variations in WQS were more pronounced for organic compounds. Most differences cannot be easily explained from a toxicological or risk-based point-of-view. Historical development, ease of measurement, and (toxicological) knowledge gaps on the risk of a vast number of organic compounds are theorized to be the drivers. Therefore, this study argues for a more tailored, risk-based approach in which standards incorporated into water safety plans are dynamically set for compounds that are persistent and could pose a risk for human health and/or aquatic ecosystems. Global variations in WQS should therefore not necessarily be avoided but rather globally harmonized with enough flexibility to ensure a global, up-to-date definition of safe and desirable water everywhere.

Abstract: Bridging the gap between complex signal data output and clear interpretation by non-expert end-users is a major challenge many scientists face when converting their scientific technology into a real-life application. Currently, pattern recognition algorithms are the most frequently encountered signal data interpretation algorithms to close this gap, not in the least because of their straight-forward implementation via convenient software packages. Paradoxically, just because their implementation is so straight-forward, it becomes cumbersome to integrate the expert's domain-specific knowledge. In this work, a novel signal data interpretation approach is presented that uses this domain-specific knowledge as its fundament, thereby fully exploiting the unique expertise of the scientist. The new approach applies data preprocessing in an innovative way that transcends its usual purpose and is easy to translate into a software application. Multiple case studies illustrate the straight-forward application of the novel approach. Ultimately, the approach is highly suited for integration in various (bio)analytical applications that require interpretation of signal data.

Abstract: Perfluorooctanoic acid (PFOA) is a toxic compound that is absorbed and distributed throughout the body by noncovalent binding to serum proteins such as human serum albumin (hSA). Though the interaction between PFOA and hSA has been already assessed using various analytical techniques, a high resolution and detailed analysis of the binding mode is still lacking. We report here the crystal structure of hSA in complex with PFOA and a medium-chain saturated fatty acid (FA). A total of eight distinct binding sites, four occupied by PFOAs and four by FAs, have been identified. In solution binding studies confirmed the 4:1 PFOA-hSA stoichiometry and revealed the presence of one high and three low affinity binding sites. Competition experiments with known hSA-binding drugs allowed locating the high affinity binding site in sub-domain IIIA. The elucidation of the molecular basis of the interaction between PFOA and hSA might provide not only a better assessment of the absorption and elimination mechanisms of these compounds in vivo but also have implications for the development of novel molecular receptors for diagnostic and biotechnological applications.

Abstract: The wearable revolution is already present in society through numerous gadgets. However, the contest remains in fully deployable wearable (bio)chemical sensing. Its use is constrained by the energy consumption which is provided by miniaturized batteries, limiting the autonomy of the device. Hence, the combination of materials and engineering efforts to develop sustainable energy management is paramount in the next generation of wearable self-powered electrochemical devices (WeSPEDs). In this direction, this review highlights for the first time the incorporation of innovative energy harvesting technologies with top-notch wearable self-powered sensors and low-powered electrochemical sensors toward battery-free and self-sustainable devices for health and wellbeing management. First, current elements such as wearable designs, electrochemical sensors, energy harvesters and storage, and user interfaces that conform WeSPEDs are depicted. Importantly, the bottlenecks in the development of WeSPEDs from an analytical perspective, product side, and power needs are carefully addressed. Subsequently, energy harvesting opportunities to power wearable electrochemical sensors are discussed. Finally, key findings that will enable the next generation of wearable devices are proposed. Overall, this review aims to bring new strategies for an energy-balanced deployment of WeSPEDs for successful monitoring of (bio)chemical parameters of the body toward personalized, predictive, and importantly, preventive healthcare.