Project description:Processing information in relation to the self enhances subsequent item recognition in both young and older adults and further enhances recollection at least in the young. Because older adults experience recollection memory deficits, it is unknown whether self-referencing improves recollection in older adults. We examined recollection benefits from self-referential encoding in older and younger adults and further examined the quality and quantity of episodic details facilitated by self-referencing. We further investigated the influence of valence on recollection, given prior findings of age group differences in emotional memory (i.e., "positivity effects"). Across the two experiments, young and older adults processed positive and negative adjectives either for self-relevance or for semantic meaning. We found that self-referencing, relative to semantic encoding, increased recollection memory in both age groups. In Experiment 1, both groups remembered proportionally more negative than positive items when adjectives were processed semantically; however, when adjectives were processed self-referentially, both groups exhibited evidence of better recollection for the positive items, inconsistent with a positivity effect in aging. In Experiment 2, both groups reported more episodic details associated with recollected items, as measured by a memory characteristic questionnaire, for the self-reference relative to the semantic condition. Overall, these data suggest that self-referencing leads to detail-rich memory representations reflected in higher rates of recollection across age.
Project description:We summarize, in this review, the evidence that genomic balance influences gene expression, quantitative traits, dosage compensation, aneuploid syndromes, population dynamics of copy number variants and differential evolutionary fate of genes after partial or whole-genome duplication. Gene balance effects are hypothesized to result from stoichiometric differences among members of macromolecular complexes, the interactome, and signaling pathways. The implications of gene balance are discussed.
Project description:D-serine is the major D-amino acid in the mammalian central nervous system. As the dominant co-agonist of the endogenous synaptic NMDA receptor, D-serine plays a role in synaptic plasticity, learning, and memory. Alterations in D-serine are linked to neuropsychiatric disorders including schizophrenia. Thus, it is of increasing interest to monitor the concentration of D-serine in vivo as a relevant player in dynamic neuron-glia network activity. Here we present a procedure for amperometric detection of D-serine with self-referencing ceramic-based microelectrode arrays (MEAs) coated with D-amino acid oxidase from the yeast Rhodotorulagracilis (RgDAAO). We demonstrate in vitro D-serine recordings with a mean sensitivity of 8.61 ± 0.83 pA/µM to D-serine, a limit of detection (LOD) of 0.17 ± 0.01 µM, and a selectivity ratio of 80:1 or greater for D-serine over ascorbic acid (mean ± SEM; n = 12) that can be used for freely moving studies.
Project description:Physiological studies require sensitive tools to directly quantify transport kinetics in the cell/tissue spatial domain under physiological conditions. Although biosensors are capable of measuring concentration, their applications in physiological studies are limited due to the relatively low sensitivity, excessive drift/noise, and inability to quantify analyte transport. Nanomaterials significantly improve the electrochemical transduction of microelectrodes, and make the construction of highly sensitive microbiosensors possible. Furthermore, a novel biosensor modality, self-referencing (SR), enables direct measurement of real-time flux and drift/noise subtraction. SR microbiosensors based on nanomaterials have been used to measure the real-time analyte transport in several cell/tissue studies coupled with various stimulators/inhibitors. These studies include: glucose uptake in pancreatic ? cells, cancer cells, muscle tissues, intestinal tissues and P. Aeruginosa biofilms; glutamate flux near neuronal cells; and endogenous indole-3-acetic acid flux near the surface of Zea mays roots. Results from the SR studies provide important insights into cancer, diabetes, nutrition, neurophysiology, environmental and plant physiology studies under dynamic physiological conditions, demonstrating that the SR microbiosensors are an extremely valuable tool for physiology research.
Project description:Strain sensors utilizing mechanoluminescent (ML) materials have garnered significant attention and application due to their advantages, such as self-powering, non-contact operation, and real-time response. However, ML-based strain sensing techniques typically rely on the establishing of a mathematical relationship between ML intensity and mechanical parameters. The absolute ML intensity is vulnerable to environmental factors, which can result in measurement errors. Herein, an color-resolved visualized dynamic ML and self-referencing strain sensing is investigated in Ca9Al(PO4)7: Tb3+, Mn2+. By analyzing the ML performance under various mechanical stimulations and adjustable strain parameters, a relationship between strain and the ML intensity ratio of Tb3+/Mn2+ is aimed to bed established. This will enable the development of a self-referencing and visualized strain sensing technology. Through a comparison of luminescence characteristics under continuous mechanical stimulation (stretching) and continuous X-ray irradiation, it is discovered that the ratiometric dynamic ML is primarily driven by the dynamic filling and continuous release of carriers form traps, which compensates for the ML of Mn2+. Leveraging the self-referencing and color-resolved (from green to red) visualized ML characteristics, an application scenario for monitoring human joint movement is developed. This approach offers new insights into the use of dynamic ML materials in strain sensing and human-machine interaction.
Project description:Miniaturized reconstructive spectrometers play a pivotal role in on-chip and portable devices, offering high-resolution spectral measurement through precalibrated spectral responses and AI-driven reconstruction. However, two key challenges persist for practical applications: artificial intervention in algorithm parameters and compatibility with complementary metal-oxide-semiconductor (CMOS) manufacturing. We present a cutting-edge miniaturized reconstructive spectrometer that incorporates a self-adaptive algorithm referenced with Fabry-Perot resonators, delivering precise spectral tests across the visible range. The spectrometers are fabricated with CMOS technology at the wafer scale, achieving a resolution of ~2.5 nm, an average wavelength deviation of ~0.27 nm, and a resolution-to-bandwidth ratio of ~0.46%. Our approach provides a path toward versatile and robust reconstructive miniaturized spectrometers and facilitates their commercialization.
Project description:To improve the ability of an external cavity laser (ECL) biosensor to more easily distinguish true signals caused by biomolecular binding from a variety of sources of background noise, two photonic crystal (PC) resonant reflectors were incorporated into a single flow cell, with one of the PCs performing the detection function and the other one serving as a reference sensor. The ECL-based sensor system simultaneously emits at two distinct wavelengths corresponding to two different longitudinal cavity modes selected by the sensing and reference PC reflectors. The surface of the sensing PC filter was functionalized by a biomolecule recognition layer and exhibited narrowband reflection with the peak reflection wavelength at 856 nm. The reference PC was untreated and had the peak reflection wavelength at 859 nm. The PCs were bond to the upper and lower surfaces of a thin chamber frame, forming a flow cell. Utilizing the reference external cavity mode, the dual-mode ECL sensor system eliminated common-mode noise sources, including thermal drift, refractive index variations of the analyte solution, and nonspecific biomolecule binding.
Project description:Frequency microcombs, alternative to mode-locked laser and fiber combs, enable miniature rulers of light for applications including precision metrology, molecular fingerprinting and exoplanet discoveries. To enable frequency ruling functions, microcombs must be stabilized by locking their carrier-envelope offset frequency. So far, the microcomb stabilization remains compounded by the elaborate optics external to the chip, thus evading its scaling benefit. To address this challenge, here we demonstrate a nanophotonic chip solution based on aluminum nitride thin films, which simultaneously offer optical Kerr nonlinearity for generating octave soliton combs and quadratic nonlinearity for enabling heterodyne detection of the offset frequency. The agile dispersion control of crystalline aluminum nitride photonics permits high-fidelity generation of solitons with features including 1.5-octave spectral span, dual dispersive waves, and sub-terahertz repetition rates down to 220 gigahertz. These attractive characteristics, aided by on-chip phase-matched aluminum nitride waveguides, allow the full determination of the offset frequency. Our proof-of-principle demonstration represents an important milestone towards fully integrated self-locked microcombs for portable optical atomic clocks and frequency synthesizers.
Project description:BackgroundMyalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease which involves multiple body systems (e.g., immune, nervous, digestive, circulatory) and research domains (e.g., immunology, metabolomics, the gut microbiome, genomics, neurology). Despite several decades of research, there are no established ME/CFS biomarkers available to diagnose and treat ME/CFS. Sharing data and integrating findings across these domains is essential to advance understanding of this complex disease by revealing diagnostic biomarkers and facilitating discovery of novel effective therapies.MethodsThe National Institutes of Health funded the development of a data sharing portal to support collaborative efforts among an initial group of three funded research centers. This was subsequently expanded to include the global ME/CFS research community. Using the open-source comprehensive knowledge archive network (CKAN) framework as the base, the ME/CFS Data Management and Coordinating Center developed an online portal with metadata collection, smart search capabilities, and domain-agnostic data integration to support data findability and reusability while reducing the barriers to sustainable data sharing.ResultsWe designed the mapMECFS data portal to facilitate data sharing and integration by allowing ME/CFS researchers to browse, share, compare, and download molecular datasets from within one data repository. At the time of publication, mapMECFS contains data curated from public data repositories, peer-reviewed publications, and current ME/CFS Research Network members.ConclusionsmapMECFS is a disease-specific data portal to improve data sharing and collaboration among ME/CFS researchers around the world. mapMECFS is accessible to the broader research community with registration. Further development is ongoing to include novel systems biology and data integration methods.
Project description:This study conducts a meta-analysis of over 1000 abstracts to examine the use and consistency of the terminology in biomimetics, bioinspiration, biomimicry, and bionics, focusing on how these terms impact biological study design. Despite the increasing research in these areas, the ambiguous definitions of key terms complicate study design and interdisciplinary collaboration. The primary aim of this work is to analyse how biological studies in these fields are conceptualised and evaluated, particularly concerning the inconsistent use of terminology. By identifying discrepancies in term usage, we offer refined definitions and practical examples to improve the clarity of study design and research methodologies. Our findings underscore the importance of standardised terminology for ensuring that biological research is accurately designed and executed, leading to more rigorous experimental frameworks and better alignment across disciplines. This meta-analysis reveals how clearer, more consistent terminology can enhance study design in biologically inspired research fields.