Browse Articles

Splicing accuracy varies across human introns, tissues, age and disease

Abstract Alternative splicing impacts most multi-exonic human genes. Inaccuracies during this process may have an important role in ageing and disease. Here, we investigate splicing accuracy using RNA-sequencing data from >14k control samples and 40 human body sites, focusing on split reads partially mapping to known transcripts in annotation. We show that splicing inaccuracies occur at different rates across introns and tissues and are affected by the abundance of core components of the spliceosome assembly and its regulators. We find that age is positively correlated with a global decline in splicing fidelity, mostly affecting genes implicated in neurodegenerative diseases. We find support for the latter by observing a genome-wide increase in splicing inaccuracies in samples affected with Alzheimer’s disease as compared to neurologically normal individuals. In this work, we provide an in-depth characterisation of splicing accuracy, with implications for our understanding of the role of inaccuracies in ageing and neurodegenerative disorders.

Integrating mitochondrial and lysosomal gene analysis for breast cancer prognosis using machine learning

Triboelectric sensor gloves for real-time behavior identification and takeover time adjustment in conditionally automated vehicles

Aspect category sentiment analysis based on pre-trained BiLSTM and syntax-aware graph attention network

STAIG: Spatial transcriptomics analysis via image-aided graph contrastive learning for domain exploration and alignment-free integration

A photodetector for red and green with balanced negative and positive photocurrent for imaging is realized

Simultaneous aerobic and anaerobic respiration in hot spring chemolithotrophic bacteria

Long-term surgical outcomes of esotropic duane retraction syndrome type 1

GPX modulation promotes regenerative axonal fusion and functional recovery after injury through PSR-1 condensation

Clinical characteristics and prognosis of patients treated as invasive pulmonary aspergillosis outside of severe immunosuppression

Tailorable biosensors for real-time monitoring of stress distribution in soft biomaterials and living tissues

Varying effects of Vicia sativa and Vicia villosa on bacterial composition and enzyme activities in nutrient-deficient sugarcane soils under greenhouse conditions

Flow chemistry-enabled asymmetric synthesis of cyproterone acetate in a chemo-biocatalytic approach

Fe3O4@HA-Cu(OAc)2 nanocomposite as a nanomagnetic water-compatible catalyst for efficient synthesis of 1,2,3-triazoles in water

Reproducibility and transparency: what’s going on and how can we help

Hybrid clustering strategies for effective oversampling and undersampling in multiclass classification

Abstract Multiclass imbalance is a challenging problem in real-world datasets, where certain classes may have a low number of samples because they correspond to rare occurrences. To address the challenge of multiclass imbalance, this paper introduces a novel hybrid cluster-based oversampling and undersampling (HCBOU) technique. By clustering and separating classes into majority and minority categories, this algorithm retains the most information during undersampling while generating efficient data in the minority class. The classification is carried out using one-vs-one and one-vs-all decomposition schemes. Extensive experimentation was carried out on 30 datasets to evaluate the proposed algorithm's performance. The results were subsequently compared with those of several state-of-the-art algorithms. Based on the results, the proposed algorithm outperforms the competing algorithms under different scenarios. Finally, The HCBOU algorithm demonstrated robust performance across varying class imbalance levels, highlighting its effectiveness in handling imbalanced datasets.

Penicillin allergy as an instrumental variable for estimating antibiotic effects on resistance

Dual-energy CT-derived virtual noncalcium imaging to assess bone marrow lesions in patients with knee osteoarthritis

Automatic speech recognition predicts contemporaneous earthquake fault displacement

Abstract Significant progress has been made in probing the state of an earthquake fault by applying machine learning to continuous seismic waveforms. The breakthroughs were originally obtained from laboratory shear experiments and numerical simulations of fault shear, then successfully extended to slow-slipping faults. Here we apply the Wav2Vec-2.0 self-supervised framework for automatic speech recognition to continuous seismic signals emanating from a sequence of moderate magnitude earthquakes during the 2018 caldera collapse at the Kīlauea volcano on the island of Hawai’i. We pre-train the Wav2Vec-2.0 model using caldera seismic waveforms and augment the model architecture to predict contemporaneous surface displacement during the caldera collapse sequence, a proxy for fault displacement. We find the model displacement predictions to be excellent. The model is adapted for near-future prediction information and found hints of prediction capability, but the results are not robust. The results demonstrate that earthquake faults emit seismic signatures in a similar manner to laboratory and numerical simulation faults, and artificial intelligence models developed for encoding audio of speech may have important applications in studying active fault zones.

The lean body mass to visceral fat mass ratio is negatively associated with cardiometabolic disorders: a cross-sectional study