Browse Articles

Controlling intermolecular base pairing in Drosophila germ granules by mRNA folding and its implications in fly development

Engineered spermidine-secreting Saccharomyces boulardii ameliorates colitis and colon cancer in mice

Cultivating an equity-oriented data sharing culture for African health research initiatives

Risk, reward and loss in addictive behavior: a six-year cross-lagged panel study

Abstract Decision making in the context of addiction is characterized by altered values regarding risk and reward, but the long-term and reciprocal relationship between value-based decision-making and addictive behavior remains poorly understood. In this study, 338 individuals (19–27 years, 59% female) from a community sample participated in a baseline assessment, including clinical interviews on addictive behaviors (quantity of use, frequency of use, DSM-5 criteria) and a task battery to assess four facets of value-based decision-making (delay aversion, risk-seeking for gains and losses, and loss aversion). Follow-up assessments were conducted after 3 and 6 years, with 75% and 71% of participants retained, respectively. Using random-intercept cross-lagged panel models (RI-CLPM) controlled for age, gender, IQ, and the baseline addiction level, we examined the bidirectional relationships between value-based decision-making and addictive behavior. Our findings demonstrate temporal stability in both decision-making and addictive behavior, but provide limited evidence for predictive cross-lagged relationships. While prior research indicates that altered value-based decision-making is a predictor of addictive behavior, our findings suggest that its impact may be more limited or context-dependent, highlighting the importance of multifaceted approaches to understanding addiction.

Cardiomyocyte mitochondrial mono-ADP-ribosylation dictates cardiac tolerance to sepsis by configuring bioenergetic reserve in male mice

Identification and experimental validation of biomarkers related to mitochondrial and programmed cell death in obsessive-compulsive disorder

Deep intra-slab rupture and mechanism transition of the 2024 Mw 7.4 Calama earthquake

A comprehensive well-to-wake climate impact assessment of sustainable aviation fuel

Abstract The aviation industry and policymakers are advocating Sustainable Aviation Fuels (SAF) as one of the main pillars for making the aviation industry sustainable. However, regulatory frameworks like CORSIA and the EU Renewable Energy Directive often exclude the climate impact from in-flight non-CO2 emissions (e.g., NOx, H2O, and soot emissions), which is important in determining the effect of SAF in reducing the climate impact of aviation. To bridge this gap, we evaluate the total global warming effects of SAF from a well-to-wake analysis, which includes the climate effects from CO2 emissions of the well-to-wake combined with the non-CO2 emissions of the pump-to-wake (i.e., inflight). We quantify the climate impact of NOx, H2O and contrails and convert them to a CO2 equivalence (CO2e) factor based on a climate metric, for instance, the Average Temperature Response over a given time horizon (i.e., 20, 50 and 100 years). The resulting well-to-wake CO2e values for SAF vary from about 150 to 250 g/MJ, depending on the specific fuel pathways. Our analysis shows that the maximum reduction in CO2e emissions when using SAF is less than 50% compared to conventional jet fuel, mainly due to the inflight NOx and contrail effects.

Single-component-based multicolor emissions enabled by symmetry breaking

The effect of developmental electronic performance monitoring on employee innovative behavior

Metformin alters mitochondria-related metabolism and enhances human oligodendrocyte function

Abstract Metformin rejuvenates adult rat oligodendrocyte progenitor cells (OPCs) allowing more efficient differentiation into oligodendrocytes and improved remyelination, and therefore is of interest as a therapeutic in demyelinating diseases such as multiple sclerosis (MS). Here, we test whether metformin has a similar effect in human stem cell derived-OPCs. We assess how well human monoculture, organoid and chimera model culture systems simulate in vivo adult human oligodendrocytes, finding most close resemblance in the chimera model. Metformin increases myelin proteins and/or sheaths in all models even when human cells remain fetal-like. In the chimera model, metformin leads to increased mitochondrial area both in the human transplanted cells and in the mouse axons with associated increase of mitochondrial function/metabolism transcripts. Human oligodendrocytes from MS brain donors treated pre-mortem with metformin also express similar transcripts. Metformin’s brain effect is thus not cell-specific, alters metabolism in part through mitochondrial changes and leads to more myelin production. This bodes well for clinical trials testing metformin for neuroprotection.

A lightweight multi round confusion-diffusion cryptosystem for securing images using a modified 5D chaotic system

Abstract In recent years, technological advancements have made the transmission of confidential information spooky. This research proposes a modified 5D chaotic map and a new image encryption algorithm based on an integrated chaotic system developed with SHA-512 hashing and a confusion-diffusion architecture. The modified 5D chaotic map provides randomness, and its performance is evaluated through a bifurcation diagram and Lyapunov exponent. The randomness of chaotic sequences is validated through the NIST test. The multi-round diffusion and permutation incorporating the proposed chaotic sequences significantly enhances security by destroying pixel correlation among pixels. The encryption algorithm is validated through performance metric analysis, yielding NPCR of 99.6069%, UACI of 33.4284%, and entropy of 7.99442. These values depict advanced security features needed for various multimedia, medical, and military applications. Therefore, this approach reveals the extent to which chaotic encryption systems provide digital image protection in high-risk communication environments.

Silicon-rhodamine-enabled identification for near-infrared light controlled proximity labeling in vitro and in vivo

Three-dimensional characterization of caves within the Grand Canyon’s deep karst aquifer

Deep indel mutagenesis reveals the regulatory and modulatory architecture of alternative exon splicing

Molecular identification of wild caught phlebotomine sand flies (Diptera, Psychodidae) by mitochondrial DNA barcoding in India

Single photon γ-ray imaging with high energy and spatial resolution perovskite semiconductor for nuclear medicine

Prevalence of eye diseases, refractive errors and visual impairments in the Debre Markos referral hospital

Ultrafast dynamics of ferroelectric polarization of NbOI2 captured with femtosecond electron diffraction

Machine learning algorithms for voltage stability assessment in electrical distribution systems

Abstract Voltage instability poses a significant challenge by limiting power system operation and transmission capacity. Rapid detection and effective corrective actions are essential to prevent voltage collapse. However, traditional methods for assessing voltage security margins are computationally intensive and often impractical for real-time applications. This study addresses voltage stability assessment in power systems using machine learning (ML) to overcome the computational limitations of traditional methods. By employing Linear Regression (LR), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM), we predict Fast Voltage Stability Indices (FVSI) at nominal load as well as under varying loads (10–150%) in 15 kV Ethiopian distribution networks: a 35-bus Bata feeder system and a 53-bus Papyrus feeder system. RF and GB models achieved superior accuracy with R² values of 0.999 and 0.9998 respectively, significantly outperforming LR and SVM which exhibited substantial deviations. The GB model achieves the highest accuracy, with RMSE values of 0.0002 (53-bus) and 2.419e-05 (35-bus), while RF yields RMSE values of 0.0039 (53-bus) and 0.00120 (35-bus), demonstrating strong predictive performance. The FVSI threshold analysis revealed critical stability limits, with values approaching 1.0 indicating proximity to voltage collapse. The analysis identified buses 36, 32, and 21 in the 53-bus system (FVSI values: 0.087, 0.082, and 0.080) and buses 27 and 16 in the 35-bus system (FVSI values: 0.085 and 0.082) as critical instability risk points requiring immediate monitoring. These findings underscore the efficacy of ensemble methods for rapid voltage stability assessment and emphasize the need for targeted interventions in high-risk areas to bolster grid resilience in Ethiopian distribution networks.