Browse Articles

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

Capacitance enhancement by ion-laminated borophene-like layered materials

A novel label-free method to determine equilibrium dissociation constants of antibodies binding to cell surface proteins

Author Correction: Multi-omic and single-cell profiling of chromothriptic medulloblastoma reveals genomic and transcriptomic consequences of genome instability

Determinants maintaining healthcare personnel’s motivation during COVID-19 pandemic in Uganda

The transcriptional response of cortical neurons to concussion reveals divergent fates after injury

Abstract Traumatic brain injury (TBI) is a risk factor for neurodegeneration, however little is known about how this kind of injury alters neuron subtypes. In this study, we follow neuronal populations over time after a single mild TBI (mTBI) to assess long ranging consequences of injury at the level of single, transcriptionally defined neuronal classes. We find that the stress-responsive Activating Transcription Factor 3 (ATF3) defines a population of cortical neurons after mTBI. Using an inducible reporter linked to ATF3, we genetically mark these damaged cells to track them over time. We find that a population in layer V undergoes cell death acutely after injury, while another in layer II/III survives long term and remains electrically active. To investigate the mechanism controlling layer V neuron death, we genetically silenced candidate stress response pathways. We found that the axon injury responsive dual leucine zipper kinase (DLK) is required for the layer V neuron death. This work provides a rationale for targeting the DLK signaling pathway as a therapeutic intervention for traumatic brain injury. Beyond this, our approach to track neurons after a mild, subclinical injury can inform our understanding of neuronal susceptibility to repeated impacts.

Pulsed laser deposition assisted epitaxial growth of cesium telluride photocathodes for high brightness electron sources

Investigating the Antiscale Magnetic Treatment Controversy: Insights from the Model Calcium Carbonate Scalant

Abstract The antiscale magnetic treatment (ASMT) claims to utilize magnetic field to combat scaling. However, its underlying mechanism, effectiveness, and reliability remain controversial. To address these contentious aspects, we analyze the influence of a magnetic field on the different stages of typical scale formation, using $${\text{CaCO}}_{3}$$ as a model scale. For simplification, we consider the working fluid, such as in domestic and industrial settings, as a homogeneous mixture of a supersaturated, multi-ionic solution and a suspension of neutral multiphase contaminants, a fraction of which is magnetic. We argue that the combined effects of pH variation and catalytic role of magnetic contaminants are crucial factors affecting the properties of the resultant scale. Based on these considerations, we clarify the controversy by showing that each side holds a valid piece of the overall picture of the ASMT process. Indeed, the two viewpoints on magnetic field’s influence on scaling can be explained along the following scenarios: (i) Within a non-contaminated, supersaturated solution, there is no significant field influence because, under typical laboratory conditions, the Lorentz force does not practically affect the scaling process. (ii) Within a high-pH, magnetically-contaminated, supersaturated solution, the field does have an influence: Here, gradient-force-driven agglomerated particulates can act as templates for heterogeneous nucleation and growth.