Browse Articles

The efficacy of disease-modifying therapies in patients with clinically isolated syndrome: a systematic review and network meta-analysis

Homocentric Chirality and Magnetism Enable Strong NIR Magneto-Optical Activity in a Chiral DyFe ₃ Cluster

Comparative analysis of flower volatiles from four Jasminum species growing in Egypt using multivariate analysis

Abstract The genus Jasminum is a valuable oil-bearing shrub in the Oleaceae family that produces concrete and absolute jasmine scents. Jasmine is an important floral ingredient in fragrances, soaps, cosmetics, and toiletries. As an export commodity, it is important to establish effective analytical procedures for authenticating jasmine species and detecting adulterants. In the current investigation, the volatile constituents of the concrete and absolute of Jasminum sambac (L.) Aiton, J. azoricum L., J. grandiflorum L., J. multiflorum (Burm. f.) Andrews harvested in August, in addition to J. grandiflorum factory products were analyzed using Gas Chromatography-Mass Spectrometry (GC-MS). Furthermore, Headspace-GC-MS (HS-GC-MS) was used to investigate variations in the volatile oil composition of the flowers of the four Jasminum species throughout June to August. A total of 157 volatile components were identified belonging to various classes including monoterpene hydrocarbons, oxygenated monoterpenes, sesquiterpene hydrocarbons, oxygenated sesquiterpenes, diterpenes, triterpenes, phenylpropanoids/benzenoids, fatty-acid derivatives, aliphatic hydrocarbons, and nitrogenous compounds. Unsupervised multivariate data analysis tools, including Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), were used to distinguish between the oil complexity within parameters such as species and seasonal variation. Moreover, the inhibitory effects of the concrete and absolute on monoamine oxidase A (MAO-A) were evaluated in vitro. Concrete and absolute showed a potent MAO-A inhibitory activity with half-maximal inhibitory concentrations (IC 50 ) ranging from 4 to 35.39 µg/mL and 0.46 to 15.82 µg/mL, respectively. This study showed that jasmine blooms′ volatiles displayed promising in vitro MAO-A inhibitory activity, which establishing a biochemical basis for future mechanistic investigations.

Co-Translational Incorporation of <i>(R)</i> - and <i>(S)</i> -β ² -Hydroxy Acids <i>In Vitro</i> : A Structural and Biochemical Study on the <i>E. coli</i> Ribosome

Upper limb lymphatic mapping and quantitative functional analysis in normal cynomolgus monkeys using indocyanine green near-infrared fluorescence lymphography

Synergistic optoelectronic and thermoelectric performance in Rb2AsAuBr6 and Rb2AsAuCl6 double perovskites for multifunctional energy conversion

Dynamic Covalent Boronate Chemistry for <i>In Situ</i> Formation, Interfacial Stabilization, and Cytomimetic Optimization of Coacervates

Optimizing multi-level shuttle-based puzzle storage systems with horizontal and vertical dynamics using integer programming and ALNS-IP

Surface Polarons for Synergistic Kinetic and Thermodynamic C–H Bond Oxidation

CD44v6 is associated with tumor aggressiveness and chemoresistance in bladder cancer

Orthogonal Biosynthesis of Pyrrolamides through Dehydration by a Pathway-Specific Glycoside Hydrolase-Like Enzyme in Gram-Positive Bacteria

Correcting model error bias in estimations of neuronal dynamics from time series observations

Abstract Neuron models built from experimental data have successfully predicted observed voltage oscillations within and beyond training range. A tantalising prospect is the possibility of estimating the unobserved dynamics of ion channels, which is largely inaccessible to experiment, from membrane voltage recordings. The main roadblock here is our lack of knowledge of the equations governing biological neurons which forces us to rely on surrogate models and parameter estimates biassed by model error. Error correction algorithms are therefore needed to infer both observed and unobserved dynamics, and ultimately the actual parameters of a biological neuron. Here we use a recurrent neural network to correct the outputs of a surrogate Hodgkin-Huxley (HH) model. The reservoir-surrogate HH model hybrid was trained on the voltage oscillations of a reference HH model and its driving current waveform. Out of the six reservoir-surrogate model architectures investigated, we identify one that most accurately recovers the reference membrane voltage and ion channel dynamics. The reservoir was thus effective in correcting model error in an externally driven nonlinear oscillator and in reconstructing the dynamics of both observed and unobserved state variables from the reference model mimicking an actual neuron.

A Universal Platform for One-Pot Synthesis of Block Copolymers via Organoborane-Mediated Aerobic Tandem Polymerization

Design of a portable machine for picking chamomile flowers

Abstract This research details the design, fabrication, and performance evaluation of a novel, portable machine for mechanized harvesting of chamomile flowers. The machine incorporates a specialized picking mechanism with a variable-length comb, a reciprocating cutting blade, and a collecting brush, powered by independent lithium-ion battery units for field portability. A systematic experimental investigation was conducted to determine the effects of key operational parameters on productivity, including comb length, comb gap width, and cutting blade speed. The study identified optimal conditions that yielded the highest productivity of 31.74 kg/h. This was achieved with a 100 mm comb length, a 5 mm comb gap, a 5.15 m/min blade speed, and a 200 rpm brush rotation. These findings provide crucial insights for the optimal design and operational parameters of chamomile harvesting machinery, facilitating efficient mechanization.

Understanding the Performance Gap between Polycrystalline and Single-Crystal Nickel-Rich Layered Oxide Cathodes

Energy-dissipative adaptive-step L1 discretisation for the Caputo time-fractional incompressible magnetohydrodynamic system

Enzyme-Catalyzed Intramolecular C–C Coupling Transformation of Nitriles

Parametric assessment of rainfall-related slope stability through SRM modeling and orthogonal experimental design: insights from the Zhuquedong slope, China

Beyond the Multicomponent Debus–Radziszewski Route: Two-Component Cyclocondensation Constructing a 12 + 3-Connected aea Topology Three-Dimensional Imidazole-Linked COF for Sustainable Wastewater Treatment

Rapid Inversion of Singleton Distractor Representations Underlies Learned Attentional Suppression

In visually complex and dynamically changing environments, humans must often filter out salient but task-irrelevant stimuli. Prior work shows that with repeated exposure to color singleton distractors, individuals can learn to divert attention away from these salient items. However, the neural mechanisms supporting such attentional suppression remain unclear. The present study examined the temporal trajectories of singleton distractor representations during visual search to address this gap. Using multivariate pattern analyses of EEG data in human subjects ( N  = 40, 30 females, 10 males), we identified two clusters of decodable singleton distractor representations: an early cluster from 100 to 200 ms and a later cluster from 200 to 400 ms. Temporal generalization analyses showed that the later representations were inverted versions of the early ones. Importantly, stronger late but not early representations predicted faster search responses, suggesting that the later signals support distractor suppression. This representational inversion facilitates suppressing singleton distractors in the spatial priority map. Comparing decoding evidence across locations revealed that singleton distractor locations were suppressed relative to nonsingleton distractors. Moreover, comparing the neural coding of locations revealed that the spatial organization in the singleton distractor neural space was inverted relative to that in the target neural space. Together, these findings reveal a rapid representational inversion underlying salient distractor suppression at the onset of visual search. This inversion of singleton distractor signals was likely driven by top–down control mechanisms that transform bottom–up saliency signals, producing an inverted arrangement of target and distractor information within a shared neural space.