The majority of biological processes are mediated via protein–protein interactions. Determination of residues participating in such interactions improves our understanding of molecular mechanisms and facilitates the development of therapeutics. Experimental approaches to identifying interacting residues, such as mutagenesis, are costly and time-consuming and thus, computational methods for this purpose could streamline conventional pipelines. Here we review the field of computational protein interface prediction. We make a distinction between methods which address proteins in general and those targeted at antibodies, owing to the radically different binding mechanism of antibodies. We organize the multitude of currently available methods hierarchically based on required input and prediction principles to provide an overview of the field.

Video-based recognition of activities of daily living (ADLs) is being used in ambient assisted living systems in order to support the independent living of older people. However, current systems based on cameras located in the environment present a number of problems, such as occlusions and a limited field of view. Recently, wearable cameras have begun to be exploited. This paper presents a review of the state of the art of egocentric vision systems for the recognition of ADLs following a hierarchical structure: motion, action and activity levels, where each level provides higher semantic information and involves a longer time frame. The current egocentric vision literature suggests that ADLs recognition is mainly driven by the objects present in the scene, especially those associated with specific tasks. However, although object-based approaches have proven popular, object recognition remains a challenge due to the intra-class variations found in unconstrained scenarios. As a consequence, the performance of current systems is far from satisfactory.

Water soluble quinones are a group of cytotoxic anti-bacterial compounds that are secreted by many species of plants, invertebrates, fungi and bacteria. Studies in a number of species have shown the importance of quinones in response to pathogenic bacteria of the genus Pseudomonas. Two electron reduction is an important mechanism of quinone detoxification as it generates the less toxic quinol. In most organisms this reaction is carried out by a group of flavoenzymes known as NAD(P)H quinone oxidoreductases. Azoreductases have previously been separate from this group, however using azoreductases from Pseudomonas aeruginosa we show that they can rapidly reduce quinones. Azoreductases from the same organism are also shown to have distinct substrate specificity profiles allowing them to reduce a wide range of quinones. The azoreductase family is also shown to be more extensive than originally thought, due to the large sequence divergence amongst its members. As both NAD(P)H quinone oxidoreductases and azoreductases have related reaction mechanisms it is proposed that they form an enzyme superfamily. The ubiquitous and diverse nature of azoreductases alongside their broad substrate specificity, indicates they play a wide role in cellular survival under adverse conditions.

Driven by continuous advancements in artificial intelligence, especially deep learning, the level of realism associated with deepfake technology continues to improve year after year, which poses unprecedented challenges to the field of deepfake detection. The boundary between what we as humans can detect as real or fake becomes evermore blurred as new generations of algorithms such as Dall-E 3 and Stable Diffusion are released. This paper provides a comprehensive study into the landscape of deepfake detection, exploring in-depth the key challenges, recognising recent successes, and suggesting promising avenues for future research. A meta-literature review is conducted to identify the current challenges and future directions, which form the foundation of this work. They are investigated by analysing state-of-the-art research with a focus on the key components that are crucial to the design of a deepfake detector, i.e., the architecture, detection methods and datasets. A major challenge identified by this study is the lack of dataset diversity leading to unfair attribute representation. This must be addressed by improving standardisation on dataset ethics and privacy. This is one of the main reasons for the insufficient generalisation capability of current deepfake detectors as demonstrated by their unsatisfactory performance when faced with unseen data or data in the wild. This literature review provides deepfake detection researchers and practitioners with the latest information that will serve as a vital resource for their continued and important activity, now and in the future.

Pungent chemical compounds originating from decaying tissue are strong drivers of animal behavior. Two of the best-characterized death smell components are putrescine (PUT) and cadaverine (CAD), foul-smelling molecules produced by decarboxylation of amino acids during decomposition. These volatile polyamines act as ‘necromones’, triggering avoidance or attractive responses, which are fundamental for the survival of a wide range of species. The few studies that have attempted to identify the cognate receptors for these molecules have suggested the involvement of the seven-helix trace amine-associated receptors (TAARs), localized in the olfactory epithelium. However, very little is known about the precise chemosensory receptors that sense these compounds in the majority of organisms and the molecular basis of their interactions. In this work, we have used computational strategies to characterize the binding between PUT and CAD with the TAAR6 and TAAR8 human receptors. Sequence analysis, homology modeling, docking and molecular dynamics studies suggest a tandem of negatively charged aspartates in the binding pocket of these receptors which are likely to be involved in the recognition of these small biogenic diamines. The distinctive dead smell comes largely from molecules like cadaverine and putrescine that are produced during decomposition of organic tissues. These volatile compounds act as powerful chemical signals important for the survival of a wide range of species. Previous studies have identified the trace amine-associated receptor 13c (or TAAR13c) in zebrafish as the cognate receptor of cadaverine in bony fishes. In this work, we employed computational strategies to disclose the human TAAR6 and TAAR8 receptors as sensors of the putrescine and cadaverine molecules. Our results indicate that several negatively charged residues in the ligand binding pocket of these receptors constitute the molecular basis for recognition of these necromones in humans.