Browse Articles

Targeting a Hormonal Cause of Hypertension

The influence of depth on the global deep-sea plasmidome

Superconductivity in 5.0° twisted bilayer WSe2

Platelet-Targeted Gene Therapy for Hemophilia A with Inhibitor History

Exploration of transfer learning techniques for the prediction of PM10

Abstract Modelling of pollutants provides valuable insights into air quality dynamics, aiding exposure assessment where direct measurements are not viable. Machine learning (ML) models can be employed to explore such dynamics, including the prediction of air pollution concentrations, yet demanding extensive training data. To address this, techniques like transfer learning (TL) leverage knowledge from a model trained on a rich dataset to enhance one trained on a sparse dataset, provided there are similarities in data distribution. In our experimental setup, we utilize meteorological and pollutant data from multiple governmental air quality measurement stations in Graz, Austria, supplemented by data from one station in Zagreb, Croatia to simulate data scarcity. Common ML models such as Random Forests, Multilayer Perceptrons, Long-Short-Term Memory, and Convolutional Neural Networks are explored to predict particulate matter in both cities. Our detailed analysis of PM10 suggests that similarities between the cities and the meteorological features exist and can be further exploited. Hence, TL appears to offer a viable approach to enhance PM10 predictions for the Zagreb station, despite the challenges posed by data scarcity. Our results demonstrate the feasibility of different TL techniques to improve particulate matter prediction on transferring a ML model trained from all stations of Graz and transferred to Zagreb. Through our investigation, we discovered that selectively choosing time spans based on seasonal patterns not only aids in reducing the amount of data needed for successful TL but also significantly improves prediction performance. Specifically, training a Random Forest model using data from all measurement stations in Graz and transferring it with only 20% of the labelled data from Zagreb resulted in a 22% enhancement compared to directly testing the trained model on Zagreb.

Ebola and a Decade of Disparities — Forging a Future for Global Health Equity

Contributing factors to postoperative surgical site infections in pituitary neuroendocrine tumors undergoing endonasal transsphenoidal resection

A Randomized Trial of Drug Route in Out-of-Hospital Cardiac Arrest

Caustic recovery from caustic-containing polyethylene terephthalate (PET) washing wastewater generated during the recycling of plastic bottles

Abstract To prevent water scarcity, wastewater must be discharged to the surface or groundwater after being treated. Another method is to reuse wastewater in some areas after treatment and evaluate it as much as possible. In this study, it is aimed to recover and reuse the caustic (sodium hydroxide, NaOH) used in the recycling of plastic bottles from polyethylene terephthalate (PET) washing wastewater. Chemical substances used in the industry will be significantly reduced with chemical recovery from wastewater. Ultrafiltration (UP150) and nanofiltration (NP010 and NP030) membranes were used for this purpose in our study. Before using nanofiltration membranes, pre-treatment was performed with coagulation-flocculation process to reduce the pollutant accumulation on the membranes. Different coagulants and flocculants were used to find suitable coagulants and flocculants in pre-treatment. The pre-treated wastewater using aluminum oxide, which supplied the highest chemical oxygen demand (COD) removal (76.0%), was used in a dead-end filtration system to be filtered through NP010 and NP030 membranes at different pressures (10–30 bar). In the same filtration system, raw wastewater was filtered through a UP150 membrane. Among these treatment scenarios, the best method that could remove pollutants and provide NaOH recovery was selected. After each treatment, pH, conductivity, COD, and NaOH analyses were performed. The maximum NaOH recovery (98.6%) was obtained with the UP150 membrane at 5 bar.

Chirping chorus rings out from an unexpected part of outer space

FAIRS — A Framework for Evaluating the Inclusion of Sex in Clinical Algorithms

Functional genomic insights into Floricoccus penangensis ML061-4 isolated from leaf surface of Assam tea

Abstract Floricoccus penangensis ML061-4 was originally isolated from the leaf surface of an Assam tea plant (Camellia sinensis var. assamica) from Northern Thailand. To assess the functions encoded by the F. penangensis ML061-4 genome, gene identification and annotation were undertaken by in silico analysis. The complete genome of F. penangensis ML061-4 consists of single chromosome of 2,159,127 base pairs, containing a GC content of 33.2% and encompassing 2049 predicted protein-encoding genes. A total of 1195 genes (58.0%) in the F. penangensis ML061-4 genome have assignable functions based on BlastKOALA analysis. Furthermore, 1235 genes (59.9%) were classified into six KEGG functional categories with 187 associated pathways, while 1419 genes (68.8%) were assigned a putative function by the Clusters of Orthologous Groups (COGs) database. The ML061-4 genome was evaluated for genes associated with complex carbohydrate metabolism, bacterial adhesion, virulence factors, pathogenicity, bacteriophages, antiviral defence systems as well as toxin- and antibiotic-resistance associated genes, and genes involved in toxin production, secondary metabolite biosynthesis and xenobiotics biodegradation. The obtained results support the notion of F. penangensis ML061-4 being safe for biotechnological and food industry purposes. This is the first report outlining functional genomic insights regarding a member of the genus Floricoccus.

Intraosseous or Intravenous Vascular Access for Out-of-Hospital Cardiac Arrest

Droplets impact on sparse microgrooved non-wetting surfaces

Evaluation of contralateral arterial flow compensation using transcranial Doppler in acute internal carotid artery occlusion and implications for neurological outcome

Analysis of tire contact stresses and asphalt pavement rutting under gradient temperature and typical driving conditions

Real-time detection and monitoring of public littering behavior using deep learning for a sustainable environment

Tracing the relationship between the upper plate earthquake cycle and megathrust slip, the Atacama fault system in Northern Chile

Associations of inferior frontal sulcal hyperintensities on brain MRI with cerebral small vessel disease, cognitive function, and depression symptoms

Abstract Inferior frontal sulcal hyperintensities (IFSH) observed on fluid-attenuated inversion recovery (FLAIR) MRI have been proposed as indicators of elevated cerebrospinal fluid waste accumulation in cerebral small vessel disease (CSVD). However, to validate IFSH as a reliable imaging biomarker, further replication studies are required. The objective of this study was to investigate associations between IFSH and CSVD, and their potential repercussions, i.e., cognitive impairment and depression. We prospectively recruited 47 patients with CSVD and 29 cognitively normal controls (NC). IFSH were rated visually based on FLAIR MRI. Using different regression models, we explored the relationship between IFSH, group status (CSVD vs. NC), CSVD severity assessed with MRI, cognitive function, and symptoms of depression. Patients with CSVD were more likely to have higher IFSH scores compared to NC (OR 5.64, 95% CI 1.91–16.60), and greater CSVD severity on MRI predicted more severe IFSH (OR 1.47, 95% CI 1.14–1.88). Higher IFSH scores were associated with lower cognitive function (-0.96, 95% CI -1.81 to -0.10), and higher levels of depression (0.33, 95% CI 0.01–0.65). CSVD and IFSH may be tightly linked to each other, and the accumulation of waste products, indicated by IFSH, could have detrimental effects on cognitive function and symptoms of depression.

Patient-derived xenografts from circulating cancer stem cells as a preclinical model for personalized pancreatic cancer research

Abstract Patient-derived xenografts (PDXs) provide biologically relevant models and potential platforms for the development of treatment strategies for precision medicine in pancreatic cancer. Furthermore, circulating epithelial tumor cells (CETCs/CTCs) are released into the bloodstream by solid tumors and a rare subpopulation—circulating cancer stem cells (cCSCs) – is considered to be responsible for recurrence and plays a key role in metastasis. For the identification of cCSCs, an innovative in vitro assay to generate tumorspheres was established in this study. The number of tumorspheres and CETCs/CTCs was analyzed perioperatively in 25 pancreatic cancer patients. Additionally, an individual in vivo chorioallantoic membrane (CAM) culture system was used to generate PDXs from these tumorspheres. While overall correlations of CETCs/CTCs with clinicopathological parameters did not reach statistical significance, a significant difference in the number of tumorspheres was observed between patient subgroups with lower and higher UICC stages. This finding underscores their potential as biomarkers, providing valuable insights into clinical decision-making and tumor progression. The application of tumorspheres on the CAM successfully established PDXs within 7 days. These xenografts closely resembled the histological features of the primary tumor. Hence, this model represents a novel and fast option for individualized testing of new therapies for PDAC.