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Anti-tumor efficacy and safety of AEV01 in preclinical glioblastoma and hepatocellular carcinoma models
Helix-Guarded Molecular Clips for Cell-Free DNA Scavenging and Treatment of Systemic Lupus Erythematosus
Revealing the confluences of workplace bullying, suicidality, and their association with depression
Quad-band split ring resonator-based sensor for microwave sensing application
Epidemiological studies on the incidence of papaya ringspot disease under Indian sub-continent conditions
Fecal metabolomics to understand intestinal dysfunction in male dairy beef calves at arrival to the rearing farm
Pentraxin3 exacerbates acute pancreatitis injury by inhibiting oxidative phosphorylation pathway
Molecular mechanisms of CAND2 in regulating SCF ubiquitin ligases
SmartAPM framework for adaptive power management in wearable devices using deep reinforcement learning
Thermokarst lake drainage halves the temperature sensitivity of CH4 release on the Qinghai-Tibet Plateau
Abstract Thermokarst lakes as hot spots of methane (CH4) release are crucial for predicting permafrost carbon feedback to global warming. These lakes are suffering from serious drainage events, however, the impacts of lake drainage on CH4 release remain unclear. Here, synthesizing field drilling, incubation experiments, and carbon composition and microbial communities, we reveal the temperature sensitivities (Q10) and drivers of CH4 release from drainage-affected lakes on the Qinghai-Tibet Plateau. We find that cumulative CH4 release decreases with depth, where 0–30 cm-depth sediment accounts for 97% of the whole release. The Q10 of surface sediment is 2 to 4 times higher than deep layers, but roughly 56% lower than the non-drainage lakes. The response of CH4 release to warming is mainly driven by microbial communities (49.3%) and substrate availability (30.3%). Our study implies that drainage mitigates CH4 release from thermokarst lakes and sheds light on crucial processes for understanding permafrost carbon projections.
Power enhancement of PV arrays in different configurations under different partial shaded condition
A reconfigurable non-linear active metasurface for coherent wave down-conversion
Evaluation the toxic effects of Cobalt-Zinc Ferrite nanoparticles in experimental mice
Abstract Cobalt Zinc ferrite nanoparticles (NPs) were synthesized utilizing the auto-combustion flash method, with the general formula Co1 − xZnxFe2O4 (x = 0,0.35). This study aimed to evaluate the hepato-renal and systemic toxicity of Cobalt Zinc Ferrite nanoparticles (CZF NPs). A total of eighty female mice were utilized to ascertain the median lethal dose (LD50) of CF NPs (100 mg/kg) and CZF NPs (100 mg/kg). Thirty female CD1 mice were placed into three groups, each containing ten animals. In Group 1 (Gp1), mice were administered a 200 µl injection of sterile saline intraperitoneally (i.p.). During a 6-day period, Gp2 and Gp3 received injections of CF NPs and CFZ NPs. On day 14 after injection, hematological, biochemical, and histopathological data were measured. CZF NPs were characterized using X-ray Diffraction Analysis (XRD), Transmission Electron Microscope (TEM) and Vibrating Sample Magnetometer (VSM). There was a significant alteration in the overall body weight of mice injected with CZF NPs. Injections of CF NPs did not significantly alter red blood cells (RBC) counts, hemoglobin concentration (Hb), hematocrit percentage (Hct%), total white blood cells (WBCs), and platelets. However, injections of CZF NPs resulted in an increase in WBC count and a decrease in platelet count. Furthermore, injection of CZF NPs altered the differential leukocyte percentages. The liver and kidney functions in mice injected with CF NPs did not show any notable changes. However, mice treated with CZF NPs had considerable increases in liver and kidney bio-markers. The administration of CF NPS did not modify the histological structure of hepatic and renal tissues; however, the hepatic and renal structures were disrupted in animals injected with CZF NPs. Overall, the findings indicated high toxicity of CZF NPs in the mice used for the experiment.
Unraveling the Stoichiometric Interactions and Synergism between Ligand-Protected Gold Nanoparticles and Proteins
Dysregulation of mitochondrial α-ketoglutarate dehydrogenase leads to elevated lipid peroxidation in CHCHD2-linked Parkinson’s disease models
Root growth, yield and stress tolerance of soybean to transient waterlogging under different climatic regimes
Interpolating numerically exact many-body wave functions for accelerated molecular dynamics
Abstract While there have been many developments in computational probes of both strongly-correlated molecular systems and machine-learning accelerated molecular dynamics, there remains a significant gap in capabilities in simulating accurate non-local electronic structure over timescales on which atoms move. We develop an approach to bridge these fields with a practical interpolation scheme for the correlated many-electron state through the space of atomic configurations, whilst avoiding the exponential complexity of these underlying electronic states. With a small number of accurate correlated wave functions as a training set, we demonstrate provable convergence to near-exact potential energy surfaces for subsequent dynamics with propagation of a valid many-body wave function and inference of its variational energy whilst retaining a mean-field computational scaling. This represents a profoundly different paradigm to the direct interpolation of potential energy surfaces in established machine-learning approaches. We combine this with modern electronic structure approaches to systematically resolve molecular dynamics trajectories and converge thermodynamic quantities with a high-throughput of several million interpolated wave functions with explicit validation of their accuracy from only a few numerically exact quantum chemical calculations. We also highlight the comparison to traditional machine-learned potentials or dynamics on mean-field surfaces.
Aversive learning reduces aversive-reinforcer sensitivity in honey bees
Abstract Research on associative learning typically focuses on behavioral and neural changes in response to learned stimuli. In Pavlovian conditioning, changes in responsiveness to conditioned stimuli are crucial for demonstrating learning. A less explored, but equally important, question is whether learning can induce changes not only in the processing of conditioned stimuli but also in the processing of unconditioned stimuli. In this study, we addressed this question by combining reinforcer-sensitivity assays with Pavlovian conditioning in honey bees. We focused on aversive shock responsiveness, measuring the sting extension response to electric shocks of increasing voltage, and examined the effect of aversive olfactory conditioning—where bees learn to associate an odor with shock—on shock responsiveness. After experiencing electric shocks during conditioning, the bees showed a persistent decrease in responsiveness to lower voltages, observable three days after conditioning, indicating reduced shock sensitivity. This effect was specific to electric shock, as appetitive conditioning involving a sucrose reinforcer did not alter shock responsiveness, leaving shock sensitivity unchanged. These findings highlight a previously unexplored effect of associative learning on reinforcer sensitivity, demonstrating a lasting decrease of responsiveness to reinforcer intensities perceived as less relevant than that encountered during conditioning.