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Korean immigrants have migrated to New Zealand over the past three decades in search of a happier and more balanced life. While they anticipated that their children would be integrated into New Zealand society, they have primarily settled in Korean ethnic enclaves. In this context, younger Korean New Zealanders have been exposed to and influenced by New Zealand’s national and Korean ethnic cultures. This study examined success beliefs and well-being among Korean youth in New Zealand with a Third Culture Kid background (TCK K-NZ) in comparison to Korean youth in Korea (K-Korean) and European New Zealand youth (Pākehā). Results indicated that TCK K-NZ youth endorsed extrinsic success similarly to K-Korean youth, but that valuing extrinsic success predicted lowered well-being only for K-Korean youth. Conversely, valuing intrinsic success predicted higher well-being across the three groups. Results also revealed that TCK K-NZ youth's well-being levels were between those of K-Korean and Pākehā youth, potentially influenced by different structural relations between success beliefs and well-being, as well as their position as “third culture kids” in New Zealand. This study contributes to understanding cultures' roles in formulating success beliefs and the relationship between success beliefs and well-being for Korean New Zealander youth.
Background: Patients with cancer have an increased risk of VTE. We compared VTE rates and bleeding complications in 1) cancer patients receiving LMWH or UFH and 2) patients with or without cancer.
Patients with cancer have an increased risk of VTE. We compared VTE rates and bleeding complications in 1) cancer patients receiving LMWH or UFH and 2) patients with or without cancer.
Methods: Acutely-ill, non-surgical patients ≥70 years with (n = 274) or without cancer (n = 2,965) received certoparin 3,000 UaXa o.d. or UFH 5,000 IU t.i.d. for 8-20 days.
Results: 1) Thromboembolic events in cancer patients (proximal DVT, symptomatic non-fatal PE and VTE-related death) occurred at 4.50% with certoparin and 6.03% with UFH (OR 0.73; 95% CI 0.23-2.39). Major bleeding was comparable and minor bleedings (0.75 vs. 5.67%) were nominally less frequent. 7.5% of certoparin and 12.8% of UFH treated patients experienced serious adverse events. 2) Thromboembolic event rates were comparable in patients with or without cancer (5.29 vs. 4.13%) as were bleeding complications. All cause death was increased in cancer (OR 2.68; 95%CI 1.22-5.86). 10.2% of patients with and 5.81% of those without cancer experienced serious adverse events (OR 1.85; 95% CI 1.21-2.81).
Conclusions: Certoparin 3,000 UaXa o.d. and 5,000 IU UFH t.i.d. were equally effective and safe with respect to bleeding complications in patients with cancer. There were no statistically significant differences in the risk of thromboembolic events in patients with or without cancer receiving adequate anticoagulation.
Trial Registration: clinicaltrials.gov, NCT00451412
Highlights
• Protocol for extracting and analyzing pollen grains from fossil insects
• Individual fossil grains can be analyzed using a combined approach
• Simple and fast TEM embedding and sectioning protocol
• Protocol enables a taxonomic assignment of pollen
Summary
This protocol explains how to extract pollen from fossil insects with subsequent descriptions of pollen treatment. We also describe how to document morphological and ultrastructural features with light-microscopy and electron microscopy. It enables a taxonomic assignment of pollen that can be used to interpret flower-insect interactions, foraging and feeding behavior of insects, and the paleoenvironment. The protocol is limited by the state of the fossil, the presence/absence of pollen on fossil specimens, and the availability of extant pollen for comparison.
Highlights
• We present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series.
• The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios.
• The model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33.
Abstract
High-rate Global Navigation Satellite System (HR-GNSS) data can be highly useful for earthquake analysis as it provides continuous high-frequency measurements of ground motion. This data can be used to analyze diverse parameters related to the seismic source and to assess the potential of an earthquake to prompt strong motions at certain distances and even generate tsunamis. In this work, we present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series. The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios. We explored the potential of the model for global application and compared its performance using both synthetic and real data from different seismogenic regions. The performance of our model at this stage was satisfactory in estimating earthquake magnitude from synthetic data with 0.07 ≤ RMS ≤ 0.11. Comparable results were observed in tests using synthetic data from a different region than the training data, with RMS ≤ 0.15. Furthermore, the model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33, provided that the data from a particular group of stations had similar epicentral distance constraints to those used during the model training. The robustness of the DL model can be improved to work independently from the window size of the time series and the number of stations, enabling faster estimation by the model using only near-field data. Overall, this study provides insights for the development of future DL approaches for earthquake magnitude estimation with HR-GNSS data, emphasizing the importance of proper handling and careful data selection for further model improvements.
PolarCAP – A deep learning approach for first motion polarity classification of earthquake waveforms
(2022)
Highlights
• We present PolarCAP, a deep learning model that can classify the polarity of a waveform with a 98% accuracy.
• The first-motion polarity of seismograms is a useful parameter, but its manual determination can be laborious and imprecise.
• We demonstrate that in several cases the model can assign trace polar-ity more accurately than a human analyst.
Abstract
The polarity of first P-wave arrivals plays a significant role in the effective determination of focal mechanisms specially for smaller earthquakes. Manual estimation of polarities is not only time-consuming but also prone to human errors. This warrants a need for an automated algorithm for first motion polarity determination. We present a deep learning model - PolarCAP that uses an autoencoder architecture to identify first-motion polarities of earth-quake waveforms. PolarCAP is trained in a supervised fashion using more than 130,000 labelled traces from the Italian seismic dataset (INSTANCE) and is cross-validated on 22,000 traces to choose the most optimal set of hyperparameters. We obtain an accuracy of 0.98 on a completely unseen test dataset of almost 33,000 traces. Furthermore, we check the model generalizability by testing it on the datasets provided by previous works and show that our model achieves a higher recall on both positive and negative polarities.
Hypoxia inhibits ferritinophagy, increases mitochondrial ferritin, and protects from ferroptosis
(2020)
Highlights
• Hypoxia decreases NCOA4 transcription in primary human macrophages.
• NCOA4 mRNA is a target of miR-6862-5p.
• Lowering NCOA4 increases FTMT abundance under hypoxia.
• FTMT and FTH protect from ferroptosis.
• Tumor cells lack the hypoxic decrease of NCOA4 and fail to stabilize FTMT.
Abstract
Cellular iron, at the physiological level, is essential to maintain several metabolic pathways, while an excess of free iron may cause oxidative damage and/or provoke cell death. Consequently, iron homeostasis has to be tightly controlled. Under hypoxia these regulatory mechanisms for human macrophages are not well understood. Hypoxic primary human macrophages reduced intracellular free iron and increased ferritin expression, including mitochondrial ferritin (FTMT), to store iron. In parallel, nuclear receptor coactivator 4 (NCOA4), a master regulator of ferritinophagy, decreased and was proven to directly regulate FTMT expression. Reduced NCOA4 expression resulted from a lower rate of hypoxic NCOA4 transcription combined with a micro RNA 6862-5p-dependent degradation of NCOA4 mRNA, the latter being regulated by c-jun N-terminal kinase (JNK). Pharmacological inhibition of JNK under hypoxia increased NCOA4 and prevented FTMT induction. FTMT and ferritin heavy chain (FTH) cooperated to protect macrophages from RSL-3-induced ferroptosis under hypoxia as this form of cell death is linked to iron metabolism. In contrast, in HT1080 fibrosarcome cells, which are sensitive to ferroptosis, NCOA4 and FTMT are not regulated. Our study helps to understand mechanisms of hypoxic FTMT regulation and to link ferritinophagy and macrophage sensitivity to ferroptosis.
Despite the recent popularity of predictive processing models of brain function, the term prediction is often instantiated very differently across studies. These differences in definition can substantially change the type of cognitive or neural operation hypothesised and thus have critical implications for the corresponding behavioural and neural correlates during visual perception. Here, we propose a five-dimensional scheme to characterise different parameters of prediction. Namely, flow of information, mnemonic origin, specificity, complexity, and temporal precision. We describe these dimensions and provide examples of their application to previous work. Such a characterisation not only facilitates the integration of findings across studies, but also helps stimulate new research questions.
The tremendous diversity of life in the ocean has proven to be a rich source of inspiration for drug discovery, with success rates for marine natural products up to 4 times higher than other naturally derived compounds. Yet the marine biodiscovery pipeline is characterized by chronic underfunding, bottlenecks and, ultimately, untapped potential. For instance, a lack of taxonomic capacity means that, on average, 20 years pass between the discovery of new organisms and the formal publication of scientific names, a prerequisite to proceed with detecting and isolating promising bioactive metabolites. The need for “edge” research that can spur novel lines of discovery and lengthy high-risk drug discovery processes, are poorly matched with research grant cycles. Here we propose five concrete pathways to broaden the biodiscovery pipeline and open the social and economic potential of the ocean genome for global benefit: (1) investing in fundamental research, even when the links to industry are not immediately apparent; (2) cultivating equitable collaborations between academia and industry that share both risks and benefits for these foundational research stages; (3) providing new opportunities for early-career researchers and under-represented groups to engage in high-risk research without risking their careers; (4) sharing data with global networks; and (5) protecting genetic diversity at its source through strong conservation efforts. The treasures of the ocean have provided fundamental breakthroughs in human health and still remain under-utilised for human benefit, yet that potential may be lost if we allow the biodiscovery pipeline to become blocked in a search for quick-fix solutions.
Recent lattice QCD results, comparing to a hadron resonance gas model, have shown the need for hundreds of particles in hadronic models. These extra particles influence both the equation of state and hadronic interactions within hadron transport models. Here, we introduce the PDG21+ particle list, which contains the most up-to-date database of particles and their properties. We then convert all particles decays into 2 body decays so that they are compatible with SMASH in order to produce a more consistent description of a heavy-ion collision.
The transporter associated with antigen processing (TAP) is an essential machine of the adaptive immune system that translocates antigenic peptides from the cytosol into the endoplasmic reticulum lumen for loading of major histocompatibility class I molecules. To examine this ABC transport complex in mechanistic detail, we have established, after extensive screening and optimization, the solubilization, purification, and reconstitution for TAP to preserve its function in each step. This allowed us to determine the substrate-binding stoichiometry of the TAP complex by fluorescence cross-correlation spectroscopy. In addition, the TAP complex shows strict coupling between peptide binding and ATP hydrolysis, revealing no basal ATPase activity in the absence of peptides. These results represent an optimal starting point for detailed mechanistic studies of the transport cycle of TAP by single molecule experiments to analyze single steps of peptide translocation and the stoichiometry between peptide transport and ATP hydrolysis.