Detection of exons in eukaryotic DNA sequences is a challenging research area in computational biology. In this work, a Starfish Optimization Algorithm (SFOA) based hybrid deep learning architecture is proposed for the efficient detection of exons, which is the amalgamation of squeeze and excitation residual network (SeResNet), bidirectional long short-term memory (BiLSTM), and a customized attention (CA) mechanism. Firstly, SeResNet retrieves the deeply hidden features by performing recalibration and interdependencies between the channels. After that, BiLSTM is utilized to extract temporal information for a better understanding of the input data. Later, a CA mechanism is employed to select the salient features by redistributing the feature weights obtained from the SeResNet-BiLSTM to enhance the detection accuracy. Finally, the SFOA is applied to automatically optimize the hyperparameters of the SeResNet-BiLSTM-CA framework to improve the feature extraction. Before applying the deep learning framework, the modified Gabor wavelet transform (MGWT) is used to extract the periodic feature information. The efficacy of the proposed model is validated using two benchmark datasets, namely the HMR195 and the Genscan training set. To verify the efficiency of the proposed method, its performance is compared with several pre-trained networks and various existing techniques using evaluation metrics. The simulation outcomes confirmed that the proposed SFOA-based hybrid optimized SeResNet-BiLSTM-CA model achieves superior performance than its counterparts.

Spatial transcriptomics (ST) has become a powerful tool for studying gene expression at the spatial level, as the position of a given spot provides crucial information about its phenotype. One key application of downstream analysis on these ST datasets is identifying spatial domains within tissue regions. However, the accuracy of spatial domain detection can be hindered by technical noise and low resolution due to technological limitations. Some ST technologies now provide high-resolution histology images of the original tissue sample prior to sequencing, offering complementary information. Incorporating these histology images presents a promising approach to addressing these limitations. However, there is a scarcity of reviews on recent methods that use histology images for spatial domain detection tasks. Also, it remains unclear how much improvement is gained by these methods when integrating histology image with ST data over using ST data alone.

When oxygen supply to the kidneys is restricted, it may trigger a series of acid-base imbalances and pathological states, subsequently inducing hypoxic adaptive mechanisms in the kidneys. However, the protein-metabolism interaction mechanisms underlying this process remain unclear. Therefore, this study conducted proteomic and metabolomic analyses on Tibetan sheep kidneys collected at three distinct altitudes (2500 m, 3500 m and 4500 m) to elucidate the protein-metabolism interaction mechanisms during hypoxic adaptation. The results demonstrate a synergistic relationship between proteins and metabolism, revealing dynamic expression patterns. Key DEPs (ALDH2, COX5A, GLUD1, NDUFS2, NDUFS8 and SLC17A1) and key DEMs (Wedelolactone, Cytidine, and 3-hydroxybenzoic acid) primarily participated in the antioxidant defense, anti-inflammatory, and energy supply functions of Tibetan sheep kidney hypoxia adaptation through signaling pathways such as the citrate cycle (TCA cycle) and oxidative phosphorylation. In summary, the adaptive mechanisms to renal hypoxia are not influenced by a single protein or metabolite, but rather are related to the interactions between proteins and metabolites. These interactions may collectively influence the adaptive mechanisms, ensuring that the kidneys maintain normal function in high-altitude environments.

We present here a draft genome of the photosynthetic sacoglossan sea slug, Elysia crispata (ecotype crispata) generated with a reference guided approach using Illumina short reads and long PacBio HiFi reads. The genome size is approximately 671 Mb with an N50 of 460 kb. BUSCO genome completeness was 98.2%, comprising 97.5% single-copy complete and 0.7% duplicated complete metazoan core genes, with an additional 0.7% partial core genes. This genome provided interesting observations such as the possible remnants of an endogenous retrovirus and the presence of polyketide synthesis proteins thought to protect the slug from photosynthetic damage, based on homology with characterized sacoglossan PKS proteins. Few genomes of photosynthetic sacoglossans are available and this draft genome of the E. crispata sea slug will serve as a valuable dataset to study sacoglossan biology, genetics, evolution, and photosymbiosis. In addition, our assembly strategy may serve as a guide to other researchers who also do not have Hi-C data to assemble their genomes, though this approach relies on the availability of a closely related reference genome for scaffolding.