Your search

Anthropogenic noise of variable temporal patterns is increasing in aquatic environments, causing physiological stress and sensory impairment. However, scarce information exists on exposure effects to continuous versus intermittent disturbances, which is critical for noise sustainable management. We tested the effects of different noise regimes on the auditory system and behaviour in the zebrafish (Danio rerio). Adult zebrafish were exposed for 24 h to either white noise (150 ± 10 dB re 1 μPa) or silent control. Acoustic playbacks varied in temporal patterns—continuous, fast and slow regular intermittent, and irregular intermittent. Auditory sensitivity was assessed with Auditory Evoked Potential recordings, revealing hearing loss and increased response latency in all noise-treated groups. The highest mean threshold shifts (c. 13 dB) were registered in continuous and fast intermittent treatments, and no differences were found between regular and irregular regimes. Inner ear saccule did not reveal significant hair cell loss but showed a decrease in presynaptic Ribeye b protein especially after continuous exposure. Behavioural assessment using the standardized Novel Tank Diving assay showed that all noise-treated fish spent > 98% time in the bottom within the first minute compared to 82% in control, indicating noise-induced anxiety/stress. We provide first data on how different noise time regimes impact a reference fish model, suggesting that overall acoustic energy is more important than regularity when predicting noise effects.

Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.

Ligand peptides that have high affinity for ion channels are critical for regulating ion flux across the plasma membrane. These peptides are now being considered as potential drug candidates for many diseases, such as cardiovascular disease and cancers. In this work, we developed Multi-Branch-CNN, a CNN method with multiple input branches for identifying three types of ion channel peptide binders (sodium, potassium, and calcium) from intra- and inter-feature types. As for its real-world applications, prediction models that are able to recognize novel sequences having high or low similarities to training sequences are required. To this end, we tested our models on two test sets: a general test set including sequences spanning different similarity levels to those of the training set, and a novel-test set consisting of only sequences that bear little resemblance to sequences from the training set. Our experiments showed that the Multi-Branch-CNN method performs better than thirteen traditional ML algorithms (TML13), yielding an improvement in accuracy of 3.2%, 1.2%, and 2.3% on the test sets as well as 8.8%, 14.3%, and 14.6% on the novel-test sets for sodium, potassium, and calcium ion channels, respectively. We confirmed the effectiveness of Multi-Branch-CNN by comparing it to the standard CNN method with one input branch (Single-Branch-CNN) and an ensemble method (TML13-Stack). The data sets, script files to reproduce the experiments, and the final predictive models are freely available at https://github.com/jieluyan/Multi-Branch-CNN.

Hydrothermal activities on ultraslow-spreading ridges exhibit diverse characteristics, long histories with multiple participants, and might form large-scale, high-grade sulfide deposits. The Duanqiao hydrothermal field (DHF) is located at the segment with the thickest oceanic crust and a large axial magma chamber on the Southwest Indian Ridge, providing unique perspective of sulfide metallogenesis on ultraslow-spreading ridges. Previous studies revealed that DHF sulfide exhibits distinct features of enrichment of ore-forming elements in comparison with those of hydrothermal fields on sediment-starved mid-ocean ridges. However, the genesis and processes responsible for such differences remain poorly constrained. In this study, mineralogical, geochemical and S and Pb isotopic analyses were performed on relict sulfide mound samples to characterize DHF formation. The samples show clear concentric mineral zonation from the interior to the exterior wall. Assemblages of chalcopyrite, sphalerite, and pyrite are distributed mainly in the interior wall, whereas pyrite and marcasite are distributed mainly in the exterior wall. The low Cu content and Pb isotopic composition of the sulfide indicate that the metals are derived mainly from basement basalts. The δ34S values exhibit positive values distributed over a reasonably narrow range (2.42‰–7.97‰), which suggests approximately 62.1%–88.5% of S with basaltic origin. Compared with most hydrothermal fields along the sediment starved mid-ocean ridges, the DHF sulfide shows particularly high contents of Pb (263–2630 ppm), As (234–726 ppm), Sb (7.32–44.3 ppm), and Ag (35.2 to >100 ppm). The δ34S values exhibit an increasing tendency from the sample exterior to the interior. We propose that these features probably reflect the existence of a subsurface zone refining process. Our results provide new insight into the sulfide formation process and contribute to understanding the metallogenic mechanism of hydrothermal sulfides on ultraslow-spreading ridges.