Finding emergence in data by maximizing effective information

Quantifying emergence and modeling emergent dynamics in a data-driven manner for complex dynamical systems is challenging due to the lack of direct observations at the micro-level. Thus, it's crucial to develop a framework to identify emergent phenomena and capture emergent dynamics at the macro-level using available data. Inspired by the theory of causal emergence (CE), this paper introduces a machine learning framework to learn macro-dynamics in an emergent latent space and quantify the degree of CE. The framework maximizes effective information, resulting in a macro-dynamics model with enhanced causal effects. Experimental results on simulated and real data demonstrate the effectiveness of the proposed framework. It quantifies degrees of CE effectively under various conditions and reveals distinct influences of different noise types. It can learn a one-dimensional coarse-grained macro-state from fMRI data, to represent complex neural activities during movie clip viewing. Furthermore, improved generalization to different test environments is observed across all simulation data

Exploring the application and challenges of fNIRS technology in early detection of Parkinson’s disease

BackgroundParkinson’s disease (PD) is a prevalent neurodegenerative disorder that significantly benefits from early diagnosis for effective disease management and intervention. Despite advancements in medical technology, there remains a critical gap in the early and non-invasive detection of PD. Current diagnostic methods are often invasive, expensive, or late in identifying the disease, leading to missed opportunities for early intervention.ObjectiveThe goal of this study is to explore the efficiency and accuracy of combining fNIRS technology with machine learning algorithms in diagnosing early-stage PD patients and to evaluate the feasibility of this approach in clinical practice.MethodsUsing an ETG-4000 type near-infrared brain function imaging instrument, data was collected from 120 PD patients and 60 healthy controls. This cross-sectional study employed a multi-channel mode to monitor cerebral blood oxygen changes. The collected data were processed using a general linear model and β values were extracted. Subsequently, four types of machine learning models were developed for analysis: Support vector machine (SVM), K-nearest neighbors (K-NN), random forest (RF), and logistic regression (LR). Additionally, SHapley Additive exPlanations (SHAP) technology was applied to enhance model interpretability.ResultsThe SVM model demonstrated higher accuracy in differentiating between PD patients and control group (accuracy of 85%, f1 score of 0.85, and an area under the ROC curve of 0.95). SHAP analysis identified the four most contributory channels (CH) as CH01, CH04, CH05, and CH08.ConclusionThe model based on the SVM algorithm exhibited good diagnostic performance in the early detection of PD patients. Future early diagnosis of PD should focus on the Frontopolar Cortex (FPC) region

Characteristic Aroma and Molecular Sensory Analysis of Black Teas from Different Regions by Gas Chromatography-Mass Spectrometry and Gas Chromatography-Olfactometry

In order to investigate the differences in the characteristic aroma of black teas from different regions, the volatile aroma compounds of Keemun black tea, Yichang black tea, Dianhong black tea and Yingde black tea were identified by solid phase extraction (SPE) combined with gas chromatography-mass spectrometry (GC-MS) and were evaluated by gas chromatography-olfactory (GC-O). Odor activity value (OAV) calculation and correlation analysis between sensory aroma profile and key aroma-active compounds were performed to analyze the sensory attributes and chemical basis of the characteristic aroma of black tea. The results showed that the four black teas differed in the sensory attributes of seven aroma notes such as floral, sweet and herbal notes. Additionally, 24 differential key aroma compounds were identified (P 1). Geraniol contributed most to black tea aroma with the highest OAV in Keemun black tea (16 581.33), followed by Yichang black tea (7 463.65), Dianhong black tea (2 832.13) and Yingde black tea (467.96). Partial least squares (PLS) regression analysis and Pearson correlation analysis showed that β-ionone, geraniol and indole were responsible for the floral and sweet aroma of Keemun black tea, (Z)-3-hexenol and α-terpineol contributed to the fruity and woody aroma of Dianhong black tea, and 2-heptanol and (Z)-linalooloxide were responsible for the herbal aroma of Yingde black tea. In conclusion, this study has preliminarily clarified the characteristic aroma profiles of black tea from the four regions and their material basis at the molecular level

Hydrogen atom collisions with a semiconductor efficiently promote electrons to the conduction band

The Born-Oppenheimer approximation is the keystone of modern computational chemistry and there is wide interest in understanding under what conditions it remains valid. Hydrogen atom scattering from insulator, semi-metal and metal surfaces has helped provide such information. The approximation is adequate for insulators and for metals it fails, but not severely. Here we present hydrogen atom scattering from a semiconductor surface: Ge(111)c(2 × 8). Experiments show bimodal energy-loss distributions revealing two channels. Molecular dynamics trajectories within the Born-Oppenheimer approximation reproduce one channel quantitatively. The second channel transfers much more energy and is absent in simulations. It grows with hydrogen atom incidence energy and exhibits an energy-loss onset equal to the Ge surface bandgap. This leads us to conclude that hydrogen atom collisions at the surface of a semiconductor are capable of promoting electrons from the valence to the conduction band with high efficiency. Our current understanding fails to explain these observations. [Abstract copyright: © 2022. The Author(s).

Increased recruitment of endogenous stem cells and chondrogenic differentiation by a composite scaffold containing bone marrow homing peptide for cartilage regeneration

Even small cartilage defects could finally degenerate to osteoarthritis if left untreated, owing to the poor self-healing ability of articular cartilage. Stem cell transplantation has been well implemented as a common approach in cartilage tissue engineering but has technical complexity and safety concerns. The stem cell homing-based technique emerged as an alternative promising therapy for cartilage repair to overcome traditional limitations. In this study, we constructed a composite hydrogel scaffold by combining an oriented acellular cartilage matrix (ACM) with a bone marrow homing peptide (BMHP)-functionalized self-assembling peptide (SAP). We hypothesized that increased recruitment of endogenous stem cells by the composite scaffold could enhance cartilage regeneration. Methods: To test our hypothesis, in vitro proliferation, attachment and chondrogenic differentiation of rabbit mesenchymal stem cells (MSCs) were tested to confirm the bioactivities of the functionalized peptide hydrogel. The composite scaffold was then implanted into full-thickness cartilage defects on rabbit knee joints for cartilage repair, in comparison with microfracture or other sample groups. Stem cell recruitment was monitored by dual labeling with CD29 and CD90 under confocal microcopy at 1 week after implantation, followed by chondrogenic differentiation examined by qRT-PCR. Repaired tissue of the cartilage defects was evaluated by histological and immunohistochemistry staining, microcomputed tomography (micro-CT) and magnetic resonance imaging (MRI) at 3 and 6 months post-surgery. Macroscopic and histological scoring was done to evaluate the optimal in vivo repair outcomes of this composite scaffold. Results: The functionalized SAP hydrogels could stimulate rabbit MSC proliferation, attachment and chondrogenic differentiation during in vitro culture. At 7 days after implantation, increased recruitment of MSCs based on CD29(+)/CD90(+) double-positive cells was found in vivo in the composite hydrogel scaffold, as well as upregulation of cartilage-associated genes (aggrecan, Sox9 and type II collagen). After 3 and 6 months post-surgery, the articular cartilage defect in the composite scaffold-treated group was fully covered with cartilage-like tissue with a smooth surface, which was similar to the surrounding native cartilage, according to the results of histological and immunohistochemistry staining, micro-CT and MRI analysis. Macroscopic and histological scoring confirmed that the quality of cartilage repair was significantly improved with implantation of the composite scaffold at each timepoint, in comparison with microfracture or other sample groups. Conclusion: Our findings demonstrated that the composite scaffold could enhance endogenous stem cell homing and chondrogenic differentiation and significantly improve the therapeutic outcome of chondral defects. The present study provides a promising approach for in vivo cartilage repair without cell transplantation. Optimization of this strategy may offer great potential and benefits for clinical application in the future

Comparative genomics reveals insights into avian genome evolution and adaptation

Birds are the most species-rich class of tetrapod vertebrates and have wide relevance across many research fields. We explored bird macroevolution using full genomes from 48 avian species representing all major extant clades. The avian genome is principally characterized by its constrained size, which predominantly arose because of lineage-specific erosion of repetitive elements, large segmental deletions, and gene loss. Avian genomes furthermore show a remarkably high degree of evolutionary stasis at the levels of nucleotide sequence, gene synteny, and chromosomal structure. Despite this pattern of conservation, we detected many non-neutral evolutionary changes in protein-coding genes and noncoding regions. These analyses reveal that pan-avian genomic diversity covaries with adaptations to different lifestyles and convergent evolution of traits

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Carbon Nanotube-based MEMS Energy Storage Devices

Carbon nanotube (CNT) forests have been utilized as electrodes in supercapacitors in this work for energy storage applications. High surface area to volume ratio, good electrical conductivity, and low contact resistance to a bottom metal electrode make CNT forests attractive as electrodes in supercapacitors. Several approaches have been investigated to improve the performances such as configurations, power and energy density of CNT-based supercapacitors, including the single layer architecture by utilizing interdigitated finger electrodes, pseudo capacitors based on electroplated nickel nanoparticles, ultra-long and densified CNT forests electrodes. Vertically aligned CNT forests have been synthesized using the thermal CVD process and their sheet and contact resistances have been characterized with four distinct methods: (1) the transfer length method (TLM), (2) the contact chain method, (3) the Kelvin method, and (4) the four point probe method. Experimental results show that CNT forests of 100µm in height and 100µm in width have a sheet resistance of about 100ohm/sq;. The specific contact resistance to a current collector is 5×10E4 ohm.µm2. Consistent results from these methods have been observed and less than 0.9% resistance deviations were measured after two months of open-air storage.In the first development stage, planar supercapacitors based on CNT forests electrodes with interdigitated finger shapes have been fabricated using a combination of Mo/Al/Fe metal stack layers to achieve dense growth of CNT with low resistance. The specific capacitances of the prototype electrodes were measured to be about 1000 times higher than those made of flat metal electrodes. Furthermore, charging/discharging experiments show over 92% energy storage efficiency and robust cycling stability.In the second development stage, CNT forests with embedded nickel nanoparticles have been used as electrodes for pseudo supercapacitors. A vacuum infiltration process is used in the electroplating process for the uniform deposition of 30-200nm in diameter nickel nanoparticles within the 80µm-high CNT forests. The measured specific capacitances are up to one order of magnitude higher than those CNT forests electrodes without nickel nanoparticles. No visual morphologic change was observed on nanoparticles after 1000 cycles of cyclic voltammetry tests.In the third development stage, ultra long CNT forests were synthesized using a water-assisted CVD process. Experimental results confirmed the capacitance increments were linearly proportion to the height increase of CNT forests with good long term stability.In the fourth development stage, a two-stage, self-aligned liquid densification process has applied on CNT forest to shrink the volume of CNT forests electrodes. By combining both mechanical bending and liquid densification, the height of CNT forest shrunk from 320µm to 21µm. Experimental results show self-aligned and continuous CNT films with preserved bottom contacts to the conductive metal layer. These densified CNT forests electrodes had similar total capacitances before and after the densification process while the volumetric specific capacitance increased from 1.07F/cm3 to 10.7F/cm3 because of the volume reductions

Numerical Study of Particle Separation through Integrated Multi-Stage Surface Acoustic Waves and Modulated Driving Signals

The manipulation of biomedical particles, such as separating circulating tumor cells from blood, based on standing surface acoustic wave (SSAW) has been widely used due to its advantages of label-free approaches and good biocompatibility. However, most of the existing SSAW-based separation technologies are dedicated to isolate bioparticles in only two different sizes. It is still challenging to fractionate various particles in more than two different sizes with high efficiency and accuracy. In this work, to tackle the problems of low efficiency for multiple cell particle separation, integrated multi-stage SSAW devices with different wavelengths driven by modulated signals were designed and studied. A three-dimensional microfluidic device model was proposed and analyzed using the finite element method (FEM). In addition, the effect of the slanted angle, acoustic pressure, and the resonant frequency of the SAW device on the particle separation were systemically studied. From the theoretical results, the separation efficiency of three different size particles based on the multi-stage SSAW devices reached 99%, which was significantly improved compared with conventional single-stage SSAW devices

The Impact of Color Rendering on Visual Fatigue in Interior Zone of Tunnel

Judicious use of lamps is of profound significance to improve the internal traffic safety of tunnels. This study evaluated the effect of LED color on human visual fatigue under mesopic vision category. According to the difference of human eyes’ response to different wavelengths of light radiation, the mesopic vision spectral luminous efficiency curve is applied to the visual fatigue evaluation methods. Taking the critical fusion frequency as the physiological index, the detection experiment of human visual fatigue was carried out in the simulated tunnel environment. The results show that spectrum with high color rendering index has a positive effect on alleviating drivers’ visual fatigue, and is more suitable for tunnel interior lighting