Browse Articles

Multi-objective optimization design for accelerated degradation test based on game theory

Transitioning to healthy and sustainable diets has higher environmental and affordability trade-offs for emerging and developing economies

Graphene-PbS quantum dot hybrid photodetectors from 200 mm wafer scale processing

Abstract A 200 mm processing platform for the large-scale production of graphene field-effect transistor-quantum dot (GFET-QD) hybrid photodetectors is demonstrated. A comprehensive statistical analysis of the electrical data revealed a high yield (96%) and low variation in the 200 mm scale fabrication. The GFET-QD devices deliver responsivities of 105 to 106 V/W in the wavelength range from 400 to 1800 nm with a response time of 10 ms. The spectral sensitivity compares well to that obtained via similar GFET-QD photodetectors. The device concept enables gate-tunable suppression or enhancement of the photovoltage, which may be exploited for electric shutter operation by toggling between the signal capture and shutter states. The devices show good stability over a wide operation range. Furthermore, an integration solution with complementary metal-oxide-semiconductor technology is presented to realize image-sensor-array chips and a proof-of-concept image system. This work demonstrates the potential for the volume manufacture of infrared photodetectors for a wide range of imaging applications.

Reversible control over the distribution of chemical inhomogeneities in multiferroic BiFeO3

Abstract Despite the appeal of flawless order, semiconductor technology has demonstrated that implanting inhomogeneities into single-crystalline materials is pivotal for modern electronics. However, the influence of the local arrangement of chemical inhomogeneities on the material’s functionalities is underexplored. In this work, we control the distribution of chemical inhomogeneities in La3+-substituted ferroelectric BiFeO3 thin films. By means of a stress- and composition-driven phase transition, we trigger the formation of a lattice of La3+-rich and La3+-poor layers. This ordering correlates with the emergence of an antipolar phase. An electric field restores the original ferroelectric phase and re-randomizes the distribution of the La3+ inhomogeneities. Leveraging these insights, we tune the polar/antipolar phase coexistence to set the net polarization of La0.15Bi0.85FeO3 to any desired value between its saturation limits. Finally, we control the net polarization response in device-compliant capacitor heterostructures to show that inhomogeneity-distribution control is a valuable tool in the design of functional oxide electronics.

Mechanistic insights into proteasome inhibitor MG132 induced apoptosis in melanoma A375 cells

S-nitrosylation of EZH2 alters PRC2 assembly, methyltransferase activity, and EZH2 stability to maintain endothelial homeostasis

Abstract Nitric oxide (NO), a versatile bio-active molecule modulates cellular functions through diverse mechanisms including S-nitrosylation of proteins. Herein, we report S-nitrosylation of selected cysteine residues of EZH2 in endothelial cells, which interplays with its stability and functions. We detect a significant reduction in H3K27me3 upon S-nitrosylation of EZH2 as contributed by the early dissociation of SUZ12 from the PRC2. Moreover, S-nitrosylation of EZH2 causes its cytosolic translocation, ubiquitination, and degradation. Further analysis reveal S-nitrosylation of cysteine 329 induces EZH2 instability, whereas S-nitrosylation of cysteine 700 abrogates its catalytic activity. We further show that S-nitrosylation-dependent regulation of EZH2 maintains endothelial homeostasis in both physiological and pathological settings. Molecular dynamics simulation reveals the inability of SUZ12 to efficiently bind to the SAL domain of EZH2 upon S-nitrosylation. Taken together, our study reports S-nitrosylation-dependent regulation of EZH2 and its associated PRC2 complex, thereby influencing the epigenetics of endothelial homeostasis.

The biosensor using the target urine volatile organic compounds for detecting diabetic kidney disease

Nuclear receptor signaling regulates compartmentalized phosphatidylcholine remodeling to facilitate thermosensitive lipid droplet fusion

Synergistic effects of basalt fiber and volcanic pumice powder in high-strength geopolymer concrete

Improved overall survival in an anti-PD-L1 treated cohort of newly diagnosed glioblastoma patients is associated with distinct immune, mutation, and gut microbiome features: a single arm prospective phase I/II trial

Metagenomics reveals contrasted responses of microbial communities to wheat straw amendment in cropland and grassland soils

Abstract Soil microbial communities respond quickly to natural and/or anthropic-induced changes in environmental conditions. Metagenomics allows studying taxa that are often overlooked in microbiota studies, such as protists or viruses. Here, we employed metagenomics to characterise microbial successions after wheat straw input in a 4-month in-situ field study. We compared microbial successions patterns with those obtained by high throughput amplicon sequencing on the same soil samples to validate metagenomics as a tool for the fine analysis of microbial population dynamics in situ. Taxonomic patterns were concordant between the two methodologies but metagenomics allowed studying all the microbial groups simultaneously. Notably, our results evidenced that each domain displayed a specific dynamic pattern after wheat straw amendment. For instance, viral sequences multiplied in the early phase of straw decomposition, in parallel to copiotrophic bacteria, suggesting a “kill-the-winner” pattern that, to our knowledge, had not been observed before in soil. Altogether, our results highlighted that both inter and intra-domain trophic interactions were impacted by wheat amendment and these patterns depended on the land use history. Our study highlights that top-down regulation by microbial predators or viruses might play a key role in soil microbiota dynamics and structure.

Trends in medication use during the COVID-19 pandemic in Quebec, Canada

A surprise induced by a visual-haptic illusion in virtual reality can lead to motor improvement

Social vulnerability index enhances FRAX prediction of hip fractures in fall patients

Direct brain recordings reveal implicit encoding of structure in random auditory streams

Abstract The brain excels at processing sensory input, even in rich or chaotic environments. Mounting evidence attributes this to sophisticated internal models of the environment that draw on statistical structures in the unfolding sensory input. Understanding how and where such modeling proceeds is a core question in statistical learning and predictive processing. In this context, we address the role of transitional probabilities as an implicit structure supporting the encoding of the temporal structure of a random auditory stream. Leveraging information-theoretical principles and the high spatiotemporal resolution of intracranial electroencephalography, we analyzed the trial-by-trial high-frequency activity representation of transitional probabilities. This unique approach enabled us to demonstrate how the brain automatically and continuously encodes structure in random stimuli and revealed the involvement of a network outside of the auditory system, including hippocampal, frontal, and temporal regions. Our work provides a comprehensive picture of the neural correlates of automatic encoding of implicit structure that can be the crucial substrate for the swift detection of patterns and unexpected events in the environment.

Alcohol-induced gut permeability defect through dysbiosis and enterocytic mitochondrial interference causing pro-inflammatory macrophages in a dose dependent manner

A comparison of statistical methods for deriving occupancy estimates from machine learning outputs

Abstract The combination of autonomous recording units (ARUs) and machine learning enables scalable biodiversity monitoring. These data are often analysed using occupancy models, yet methods for integrating machine learning outputs with these models are rarely compared. Using the Yucatán black howler monkey as a case study, we evaluated four approaches for integrating ARU data and machine learning outputs into occupancy models: (i) standard occupancy models with verified data, and false-positive occupancy models using (ii) presence-absence data, (iii) counts of detections, and (iv) continuous classifier scores. We assessed estimator accuracy and the effects of decision threshold, temporal subsampling, and verification strategies. We found that classifier-guided listening with a standard occupancy model provided an accurate estimate with minimal verification effort. The false-positive models yielded similarly accurate estimates under specific conditions, but were sensitive to subjective choices including decision threshold. The inability to determine stable parameter choices a priori, coupled with the increased computational complexity of several models (i.e. the detection-count and continuous-score models), limits the practical application of false-positive models. In the case of a high-performance classifier and a readily detectable species, classifier-guided listening paired with a standard occupancy model provides a practical and efficient approach for accurately estimating occupancy.

Failure of multistorey parking site in hilly region and its forensic geotechnical investigation

Disproportionality analysis of European safety reports on autoimmune and rheumatic diseases following COVID-19 vaccination

Abstract The safety profile of COVID-19 vaccines is well-established, yet the widespread immunization campaign has led to an increase in reported cases of Immune-Mediated and Rheumatic Diseases (IMDRs). This study aimed to assess the reporting of Adverse Events Following Immunization (AEFIs) related to IMDRs after COVID-19 vaccination. We analyzed all individual case safety reports (ICSRs) related to COVID-19 vaccines authorized in the European Union (i.e., tozinameran, elasomeran, ChAdOx1-S NCoV-19, and Ad26.Cov2.S) registered in the EudraVigilance (EV) database from January 1, 2021, to October 23, 2023. Our analysis identified ICSRs with events indicative of IMDRs and conducted disproportionality analysis (i.e., Reporting Odds Ratio (ROR) with 95% CI) to examine the frequency of different IMDR types linked to each vaccine. In total, 45,352 ICSRs reported at least one AEFI associated with rheumatic or autoimmune conditions, with 54% of them implicating tozinameran as the suspected vaccine. More than half of the reported AEFIs were classified as serious, with approximately 45% remaining unresolved. The most frequently reported conditions were other immune-mediated diseases, followed by arthritis, vasculitis, systemic lupus erythematosus, and tendinopathies. Our disproportionality analysis suggested that mRNA vaccines may be more frequently associated with new autoimmune rheumatic diseases. Stratified analysis revealed significant associations for ChAd, particularly in vasculitis and tendinopathies, only when compared to Ad26.Cov2.S. Real-world pharmacovigilance data suggest that autoimmune and rheumatic diseases may be under-reported following COVID-19 vaccination, highlighting the need for further research to better understand the underlying mechanisms. The findings from this disproportionality analysis suggest the need for further studies to investigate these results in greater depth.

Towards developing an operational Indian ocean dipole warning system for Southeast Asia

Abstract Two strong positive Indian Ocean Dipole (IOD) events in 2019 and 2023 led to multiple disasters over Southeast Asia, highlighting the need for warnings of IOD events. This paper presents a stock-take of the current criteria for IOD monitoring and prediction and describes the development of an IOD warning system for Southeast Asia. We examined how subjective choices such as observational datasets, baseline periods, and time averaging affect IOD event identification. Our findings indicate that the choice of sea-surface temperature dataset and time averaging (monthly vs. 3-monthly mean) lead to marked differences in the Dipole Mode Index (DMI), the index used for the monitoring and prediction of IOD events, and hence between various centers on IOD state. The southern Maritime Continent can experience the impact of the IOD on rainfall even when the IOD has not met the current operational criterion, suggesting a need for an impact-based threshold for the IOD. We assess the skill of models in capturing the strength and phase of the IOD and report errors in IOD predictions. While most models are skillful in capturing the active phase of the IOD, many models have an overactive IOD strength. Calibration of DMI-based monitoring products is therefore recommended for the most skilful IOD predictions. Finally, we describe an objective standard operating procedure to assist climate forecasters in issuing timely alerts of IOD events.