Enabling Automatic Detection of Anomalies in Wastewater: A Highly Simplified Approach to Defining “Normal” in Complex Chemical Mixtures

Wastewater treatment demands management of influent conditions to stabilize biological processes. Generally wastewater collection systems lack advance warning of approaching water parcels with anomalous characteristics, which could then be diverted for testing or pre-treatment. A major challenge in achieving this goal is identifying anomalies against the complex chemical background of wastewaters. This work evaluates unsupervised clustering methods to characterize “normal” wastewater characteristics, using >17 months of 10-min resolution absorbance spectrometry data collected at an operating wastewater treatment facility. Comparison of results using K-means, GMM, Hierarchical, and DBSCAN clustering shows minimal intra-cluster variability achieved using K-means. The four K-means clusters include three representing 99% of samples, with the remaining cluster (<0.3% of samples) representing atypical measurements, demonstrating utility in identifying both underlying modalities of wastewater characteristics and outliers. K-means clustering provides a better separation than grouping based on factors such as month, precipitation, or flow (with 25% overlap at 1-σ level, compared to 93, 93, and 83%, respectively) and enables identification of patterns that are not visible in factor-driven grouping, e.g., shows that summer and November months have a characteristic type of behavior. When evaluated with respect to wastewater influent changes occurring during the SARS-CoV-2 pandemic, the K-means approach shows a distinct change in strength of diurnal patterns when compared to non-pandemic periods during the same season. This method may therefore be useful both as a tool for fast anomaly detection in wastewaters, contributing to improved infrastructure resilience, as well for providing overall analysis of temporal patterns in wastewater characteristics.

Ecological constraints on highly evolvable olfactory receptor genes and morphology

While evolvability of genes and traits may promote specialization during species diversification, how ecology subsequently restricts such variation remains unclear. Chemosensation requires animals to decipher a complex chemical background to locate fitness-related resources, and thus the underlying genomic architecture and morphology must cope with constant exposure to a changing odorant landscape; detecting adaptation amidst extensive chemosensory diversity is an open challenge. Phyllostomid bats, an ecologically diverse clade that evolved plant-visiting from an insectivorous ancestor, suggests the evolution of novel food detection mechanisms is a key innovation: phyllostomids behaviorally rely strongly on olfaction, while echolocation is supplemental. If this is true, exceptional variation of underlying olfactory genes and phenotypes may have preceded dietary diversification. We compared olfactory receptor (OR) genes sequenced from olfactory epithelium transcriptomes and olfactory epithelium surface area of bats with differing diets. Surprisingly, although OR evolution rates were quite variable and generally high, they are largely independent of feeding ecology. olfactory epithelial surface area, however, is greater in plant-visiting bats and there is an inverse relationship between OR evolution rates and surface area. Larger surface areas suggest greater reliance on olfactory detection and stronger ecological constraint on maintaining an already diverse OR repertoire. Instead of the typical case in which specialization and elaboration is coupled with rapid diversification of associated genes, here the relevant genes are already evolving so quickly that increased reliance on smell has led to stabilizing selection, presumably to maintain the ability to consistently discriminate among specific odorants - an ecological constraint on sensory evolution.

Cyclodextrins AS Dominant Chiral Selective Agents in the Capillary Separation Techniques

This review paper shows the dominant role of the cyclodextrins in the chiral separations using capillary columns (GC, SFC, CE). The cyclodextrins (CDs) have extremely broad chiral selectivity spectra because they have several different chiral recognition sites in various arrangements and various interaction modes. Their chiral selectivity features can further improve with their various substitutions. Their selectivities are moderate therefore they need high efficiency separations (capillary columns) for good chiral resolutions. The shape selectivity of cyclodextrins is also shown with non-chiral isomers too. The utility of the cyclodextrins is demonstrated with several examples based on the personal observations of authors and critical review of literature. The theoretical backgrounds of their chiral recognitions (e.g. H-bond interaction, inclusion, induced fit) are discussed in depth. This paper is not application oriented but is dealing with mostly on the physical and chemical background of separations using CDs.

Predicting drug-target affinity based on recurrent neural networks and graph convolutional neural networks

Background: Drug development requires a lot of money and time, and the outcome of the challenge is unknown. So, there is an urgent need for researchers to find a new approach that can reduce costs. Therefore, the identification of drug-target interactions (DTIs) has been a critical step in the early stages of drug discovery. These computational methods aim to narrow the search space for novel DTIs and to elucidate the functional background of drugs. Most of the methods developed so far use binary classification to predict the presence or absence of interactions between the drug and the target. However, it is more informative, but also more challenging, to predict the strength of the binding between a drug and its target. If the strength is not strong enough, such a DTI may not be useful. Hence, the development of methods to predict drug-target affinity (DTA) is of significant importance. Method: We have improved the Graph DTA model from a dual-channel model to a triple-channel model. We interpreted the target/protein sequences as time series and extracted their features using the LSTM network. For the drug, we considered both the molecular structure and the local chemical background, retaining the four variant networks used in Graph DTA to extract the topological features of the drug and capturing the local chemical background of the atoms in the drug by using BiGRU. Thus, we obtained the latent features of the target and two latent features of the drug. The connection of these three feature vectors is then input into a 2-layer FC network, and a valuable binding affinity is output. Result: We use the Davis and Kiba datasets, using 80% of the data for training and 20% of the data for validation. Our model shows better performance by comparing it with the experimental results of Graph DTA. Conclusion: In this paper, we altered the Graph DTA model to predict drug-target affinity. It represents the drug as a graph, and extracts the two-dimensional drug information using a graph convolutional neural network. Simultaneously, the drug and protein targets are represented as a word vector, and the convolutional neural network is used to extract the time series information of the drug and the target. We demonstrate that our improved method has better performance than the original method. In particular, our model has better performance in the evaluation of benchmark databases.

Mass Spectrometry Imaging of Neurotransmitters

Mass spectrometry imaging (MSI) is a powerful analytical method for the simultaneous analysis of hundreds of compounds within a biological sample. Despite the broad applicability of this technique, there is a critical need for advancements in methods for small molecule detection. Some molecular classes of small molecules are more difficult than others to ionize, e.g., neurotransmitters (NTs). The chemical structure of NTs (i.e., primary, secondary, and tertiary amines) affects ionization and has been a noted difficulty in the literature. In order to achieve detection of NTs using MSI, strategies must focus on either changing the chemistry of target molecules to aid in detection or focus on new methods of ionization. Additionally, even with new strategies, the issues of delocalization, chemical background noise, and ability to achieve high throughput (HTP) must be considered. This chapter will explore previous and up-and-coming techniques for maximizing the detection of NTs.

Hallelujah Moments

This work provides eleven stories of drug discovery and features the scientists who made them. The outcome of the discovery work has provided novel therapies in cancer, cardiovascular medicine, antibacterial and antiviral infectious diseases, parasitic diseases, metabolic diseases, and weight control. Each story begins with the basic biomedical science that revealed the pathway to effective drug therapy and continues with the step-by-step process that leads from insight to a product in clinical practice meeting a defined medical need. The tales feature creative problem-solving by clever and dedicated scientists as they overcame roadblocks to success, hallelujah moments. Each drug discovery story reflects the interface between basic science, medicine, and drug discovery. Embedded in these tales are the societal and medical environments in which drug discovery takes place, the discovery of agents to treat HIV/AIDS, for example. Finally, a series of appendices provides basic chemical background for non-scientists.

Experimental oxygen concentration influences rates of mitochondrial hydrogen peroxide release from cardiac and skeletal muscle preparations

Mitochondria utilize the majority of oxygen (O2) consumed by aerobic organisms as the final electron acceptor for oxidative phosphorylation (OXPHOS) but also to generate reactive oxygen species (mtROS) that participate in cell signaling, physiological hormesis, and disease pathogenesis. Simultaneous monitoring of mtROS production and oxygen consumption ( Jo2) from tissue mitochondrial preparations is an attractive investigative approach, but it introduces dynamic changes in media O2 concentration ([O2]) that can confound experimental results and interpretation. We utilized high-resolution fluorespirometry to evaluate Jo2 and hydrogen peroxide release ( Jh2o2) from isolated mitochondria (Mt), permeabilized fibers (Pf), and tissue homogenates (Hm) prepared from murine heart and skeletal muscle across a range of experimental [O2]s typically encountered during respirometry protocols (400–50 µM). Results demonstrate notable variations in Jh2o2 across tissues and sample preparations during nonphosphorylating (LEAK) and OXPHOS-linked respiration states at 250 µM [O2] but a linear decline in Jh2o2 of 5–15% per 50-µM decrease in chamber [O2] in all samples. Jo2 was generally stable in Mt and Hm across [O2]s above 50 µM but tended to decline below 250 µM in Pf, leading to wide variations in assayed rates of Jh2o2/O2 across chamber [O2]s and sample preparations. Development of chemical background fluorescence from the H2O2 probe (Amplex Red) was also O2 sensitive, emphasizing relevant calibration considerations. This study highlights the importance of monitoring and reporting the chamber [O2] at which Jo2 and Jh2o2 are recorded during fluorespirometry experiments and provides a basis for selecting sample preparations for studies addressing the role of mtROS in physiology and disease.

Implementation of a chemical background method for atmospheric OH measurements by laser-induced fluorescence: characterisation and observations from the UK and China

Hydroxyl (OH) and hydroperoxy (HO2) radicals are central to the understanding of atmospheric chemistry. Owing to their short lifetimes, these species are frequently used to test the accuracy of model predictions and their underlying chemical mechanisms. In forested environments, laser-induced fluorescence–fluorescence assay by gas expansion (LIF–FAGE) measurements of OH have often shown substantial disagreement with model predictions, suggesting the presence of unknown OH sources in such environments. However, it is also possible that the measurements have been affected by instrumental artefacts, due to the presence of interfering species that cannot be discriminated using the traditional method of obtaining background signals via modulation of the laser excitation wavelength (OHwave). The interference hypothesis can be tested by using an alternative method to determine the OH background signal, via the addition of a chemical scavenging prior to sampling of ambient air (OHchem). In this work, the Leeds FAGE instrument was modified to include such a system to facilitate measurements of OHchem, in which propane was used to selectively remove OH from ambient air using an inlet pre-injector (IPI). The IPI system was characterised in detail, and it was found that the system did not reduce the instrument sensitivity towards OH ( 99 %) without the removal of OH formed inside the fluorescence cell (