STW-MD: a novel spatio-temporal weighting and multi-step decision tree method for considering spatial heterogeneity in brain gene expression data.

Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal. Previous studies have mainly focused on individual brain regions or a certain developmental stage. Our motivation is to address this gap by incorporating spatio-temporal information to gain a more complete understanding of brain development or abnormal brain development, such as Alzheimer's disease (AD), and to identify potential determinants of response. In this study, we propose a novel two-step framework based on spatial-temporal information weighting and multi-step decision trees. This framework can effectively exploit the spatial similarity and temporal dependence between different stages and different brain regions, and facilitate differential gene analysis in brain regions with high heterogeneity. We focus on two datasets: the AD dataset, which includes gene expression data from early, middle and late stages, and the brain development dataset, spanning fetal development to adulthood. Our findings highlight the advantages of the proposed framework in discovering gene classes and elucidating their impact on brain development and AD progression across diverse brain regions and stages. These findings align with existing studies and provide insights into the processes of normal and abnormal brain development.

A Segmented Cross-Correlation Algorithm for Dynamic North Finding Using Fiber Optic Gyroscopes.

Fiber optic gyroscope (FOG)-based north finding is extensively applied in navigation, positioning, and various fields. In dynamic north finding, an accelerated turntable speed shortens the time required for north finding, resulting in a rapid north-finding response. However, with an increase in turntable speed, the turntable's jitter contributes to signal contamination in the FOG, leading to a deterioration in north-finding accuracy. This paper introduces a divide-and-conquer algorithm, the segmented cross-correlation algorithm, designed to mitigate the impact of turntable speed jitter. A model for north-finding error is established and analyzed, incorporating FOG's self-noise and the turntable's speed jitter. To validate the feasibility of our method, we implemented the algorithm on a FOG. The simulation and experimental results exhibited a strong concordance, affirming the validity of our proposed north-finding error model. The experimental findings indicate that, at a turntable speed of 180°/s, the north-finding bias error within a 360 s duration is 0.052°, representing a 64% improvement over the traditional algorithm. These results indicate the effectiveness of the proposed algorithm in mitigating the impact of unstable turntable speeds, offering a solution for north finding with both prompt response and enhanced accuracy.

CM-YOLOv8: Lightweight YOLO for Coal Mine Fully Mechanized Mining Face.

With the continuous development of deep learning, the application of object detection based on deep neural networks in the coal mine has been expanding. Simultaneously, as the production applications demand higher recognition accuracy, most research chooses to enlarge the depth and parameters of the network to improve accuracy. However, due to the limited computing resources in the coal mining face, it is challenging to meet the computation demands of a large number of hardware resources. Therefore, this paper proposes a lightweight object detection algorithm designed specifically for the coal mining face, referred to as CM-YOLOv8. The algorithm introduces adaptive predefined anchor boxes tailored to the coal mining face dataset to enhance the detection performance of various targets. Simultaneously, a pruning method based on the L1 norm is designed, significantly compressing the model's computation and parameter volume without compromising accuracy. The proposed algorithm is validated on the coal mining dataset DsLMF+, achieving a compression rate of 40% on the model volume with less than a 1% drop in accuracy. Comparative analysis with other existing algorithms demonstrates its efficiency and practicality in coal mining scenarios. The experiments confirm that CM-YOLOv8 significantly reduces the model's computational requirements and volume while maintaining high accuracy.

A Coal Mine Tunnel Deformation Detection Method Using Point Cloud Data.

In recent years, the deformation detection technology for underground tunnels has played a crucial role in coal mine safety management. Currently, traditional methods such as the cross method and those employing the roof abscission layer monitoring instrument are primarily used for tunnel deformation detection in coal mines. With the advancement of photogrammetric methods, three-dimensional laser scanners have gradually become the primary method for deformation detection of coal mine tunnels. However, due to the high-risk confined spaces and distant distribution of coal mine tunnels, stationary three-dimensional laser scanning technology requires a significant amount of labor and time, posing certain operational risks. Currently, mobile laser scanning has become a popular method for coal mine tunnel deformation detection. This paper proposes a method for detecting point cloud deformation of underground coal mine tunnels based on a handheld three-dimensional laser scanner. This method utilizes SLAM laser radar to obtain complete point cloud information of the entire tunnel, while projecting the three-dimensional point cloud onto different planes to obtain the coordinates of the tunnel centerline. By using the calculated tunnel centerline, the three-dimensional point cloud data collected at different times are matched to the same coordinate system, and then the tunnel deformation parameters are analyzed separately from the global and cross-sectional perspectives. Through on-site collection of tunnel data, this paper verifies the feasibility of the algorithm and compares it with other centerline fitting and point cloud registration algorithms, demonstrating higher accuracy and meeting practical needs.

Automatic block-wise genotype-phenotype association detection based on hidden Markov model.

BACKGROUND: For detecting genotype-phenotype association from case-control single nucleotide polymorphism (SNP) data, one class of methods relies on testing each genomic variant site individually. However, this approach ignores the tendency for associated variant sites to be spatially clustered instead of uniformly distributed along the genome. Therefore, a more recent class of methods looks for blocks of influential variant sites. Unfortunately, existing such methods either assume prior knowledge of the blocks, or rely on ad hoc moving windows. A principled method is needed to automatically detect genomic variant blocks which are associated with the phenotype. RESULTS: In this paper, we introduce an automatic block-wise Genome-Wide Association Study (GWAS) method based on Hidden Markov model. Using case-control SNP data as input, our method detects the number of blocks associated with the phenotype and the locations of the blocks. Correspondingly, the minor allele of each variate site will be classified as having negative influence, no influence or positive influence on the phenotype. We evaluated our method using both datasets simulated from our model and datasets from a block model different from ours, and compared the performance with other methods. These included both simple methods based on the Fisher's exact test, applied site-by-site, as well as more complex methods built into the recent Zoom-Focus Algorithm. Across all simulations, our method consistently outperformed the comparisons. CONCLUSIONS: With its demonstrated better performance, we expect our algorithm for detecting influential variant sites may help find more accurate signals across a wide range of case-control GWAS.

LRBmat: A novel gut microbial interaction and individual heterogeneity inference method for colorectal cancer.

The gut microbial community has been shown to play a significant role in various diseases, including colorectal cancer (CRC), which is a major public health concern worldwide. The accurate diagnosis and etiological analysis of CRC are crucial issues. Numerous methods have utilized gut microbiota to address these challenges; however, few have considered the complex interactions and individual heterogeneity of the gut microbiota, which are important issues in genetics and intestinal microbiology, particularly in high-dimensional cases. This paper presents a novel method called Binary matrix based on Logistic Regression (LRBmat) to address these concerns. The binary matrix in LRBmat can directly mitigate or eliminate the influence of heterogeneity, while also capturing information on gut microbial interactions with any order. LRBmat is highly adaptable and can be combined with any machine learning method to enhance its capabilities. The proposed method was evaluated using real CRC data and demonstrated superior classification performance compared to state-of-the-art methods. Furthermore, the association rules extracted from the binary matrix of the real data align well with biological properties and existing literature, thereby aiding in the etiological analysis of CRC.

Oxygen Vacancy Promoted O2 Activation over Mesoporous Ni-Co Mixed Oxides for Aromatic Hydrocarbon Oxidation.

Whether the oxygen vacancies of heterogeneous catalysts improve their catalytic activity or not has recently been the topic of intense debate in the oxidation of hydrocarbons. We designed an effective strategy to construct mesoporous Ni-Co mixed oxides via a ligand-assisted self-assembly approach. The surface oxygen vacancy concentrations of the mesoporous Ni-Co mixed oxide catalysts were regulated by changing the doping amount of Ni or the reduction method, and the relationship between oxygen vacancies and catalytic activity was studied. Controlled experiments and DFT calculations revealed that oxygen molecules were more favorably adsorbed and activated on oxygen vacancies to form active oxygen species. Increasing the oxygen vacancy concentration within a certain range can effectively enrich the active oxygen species, therefore improving the oxidation rate of ethylbenzene. The optimized mCo3O4-0.1NiO catalyst exhibited a remarkable catalytic activity for the solvent-free oxidation of ethylbenzene to acetophenone, typically including 68.0% conversion and 95.4% selectivity (20 mg mCo3O4-0.1NiO, 10 mL ethylbenzene, and 0.6 MPa O2).

A Novel Anti-Noise Fault Diagnosis Approach for Rolling Bearings Based on Convolutional Neural Network Fusing Frequency Domain Feature Matching Algorithm.

The development of deep learning provides a new research method for fault diagnosis. However, in the industrial field, the labeled samples are insufficient and the noise interference is strong so that raw data obtained by the sensor are occupied with noise signal. It is difficult to recognize time-domain fault signals under the severe noise environment. In order to solve these problems, the convolutional neural network (CNN) fusing frequency domain feature matching algorithm (FDFM), called CNN-FDFM, is proposed in this paper. FDFM extracts key frequency features from signals in the frequency domain, which can maintain high accuracy in the case of strong noise and limited samples. CNN automatically extracts features from time-domain signals, and by using dropout to simulate noise input and increasing the size of the first-layer convolutional kernel, the anti-noise ability of the network is improved. Softmax with temperature parameter T and D-S evidence theory are used to fuse the two models. As FDFM and CNN can provide different diagnostic information in frequency domain, and time domain, respectively, the fused model CNN-FDFM achieves higher accuracy under severe noise environment. In the experiment, when a signal-to-noise ratio (SNR) drops to -10 dB, the diagnosis accuracy of CNN-FDFM still reaches 93.33%, higher than CNN's accuracy of 45.43%. Besides, when SNR is greater than -6 dB, the accuracy of CNN-FDFM is higher than 99%.

General Synthesis of Ordered Mesoporous Carbonaceous Hybrid Nanostructures with Molecularly Dispersed Polyoxometallates.

Hybrid nanomaterials with controlled dimensions, intriguing components and ordered structures have attracted significant attention in nanoscience and technology. Herein, we report a facile and green polyoxometallate (POM)-assisted hydrothermal carbonization strategy for synthesis of carbonaceous hybrid nanomaterials with molecularly dispersed POMs and ordered mesopores. By using various polyoxometallates such as ammonium phosphomolybdate, silicotungstic acid, and phosphotungstic acid, our approach can be generalized to synthesize ordered mesoporous hybrid nanostructures with diverse compositions and morphologies (nanosheet-assembled hierarchical architectures, nanospheres, and nanorods). Moreover, the ordered mesoporous nanosheet-assembled hierarchical hybrids with molecularly dispersed POMs exhibit remarkable catalytic activity toward the dehydration of tert-butanol with the high isobutene selectivity (100 %) and long-term catalytic durability (80 h).

A Bayesian statistical analysis of stochastic phenotypic plasticity model of cancer cells.

The phenotypic plasticity of cancer cells has received special attention in recent years. Even though related models have been widely studied in terms of mathematical properties, a thorough statistical analysis on parameter estimation and model selection is still very lacking. In this study, we present a Bayesian approach which is devised to deal with the data sets containing both mean and variance information of relative frequencies of cancer stem cells (CSCs). Both Gibbs sampling and Metropolis-Hastings (MH) algorithm are used to perform point and interval estimations of cell-state transition rates between CSCs and non-CSCs. Extensive simulations demonstrate the validity of our model and algorithm. By applying this method to a published data on SW620 colon cancer cell line, the model selection favors the phenotypic plasticity model, relative to conventional hierarchical model of cancer cells. Further quantitative analysis shows that, in the presence of phenotypic equilibrium, the variance data greatly influences the time-variant pattern of the parameters. Moreover, it is found that the occurrence of self-renewal of CSCs shows a strong negative correlation with de-differentiation rate from non-CSCs to CSCs, suggesting a balancing mechanism in the heterogenous population of cancer cells.

Entrapping an ionic liquid with nanocarbon: the formation of a tailorable and functional surface.

An interface microenvironment between nanocarbon and ionic liquids (ILs) is presented. By an entrapping effect, a few layers of ILs can be finely deposited on the surface of nanocarbon, endowing amazingly tailorable surface properties. The entrapped IL layer, which was believed to be unable to be charred under pyrolysis conditions alone, can be further carbonized to a functional carbon layer. C, B, and N were confirmed to share the same hexagonal ring in the resultant layer, which provides more designable electronic properties.

Construction of Molecularly Dispersed Polyoxometalate-Alumina Hybrid Hollow Nanoflowers via Water-Induced Kirkendall Effect.

Hybrid nanomaterials with controllable structures and diverting components have attracted significant interest in the functional materials field. Here, we develop a solvent evaporation-induced self-assembly (EISA) strategy to synthesize nanosheet-assembled phosphomolybdic acid (H3PMo)-alumina hybrid hollow spheres. The resulting nanoflowers display a high surface area (up to 697 m2 g-1), adjustable diameter, high chemical/thermal stability, and especially molecularly dispersed H3PMo species. By employing various microscopic and spectroscopic techniques, the formation mechanism is elucidated, revealing the simultaneous control of the morphology by heteropoly acids and water through the water-induced Kirkendall effect. The versatility of the synthesis method is demonstrated by varying surfactants, heteropoly acids, and metal oxide precursors for the facile synthesis of hybrid metal oxides. Spherical hybrid alumina serves as an attractive support material for constructing metal-acid bifunctional catalysts owing to its advantageous surface area, acidity, and mesoporous microenvironment. Pt-loaded hollow flowers exhibit excellent catalytic performance and exceptional stability in the hydrodeoxygenation of vanillin with recyclability for up to 10 cycles. This research presents an innovative strategy for the controllable synthesis of hybrid metal oxide nanospheres and hollow nanoflowers, providing a multifunctional platform for diverse applications.

Template Evolution Induced Relay Self-Assembly for Mesoporous Carbonaceous Materials via Hydrothermal Carbonization.

Constructing carbonaceous materials with versatile surface structures still remains a great challenge due to limited self-assembly methods, especially at high temperatures. This study presents an innovative template evolution induced relay self-assembly (TEIRSA) for the fabrication of large polyoxometalate (POM)-mixed carbonaceous nanosheets featuring surface mesoporous structures through hydrothermal carbonization (HTC). The method employs POM and acetone as additives, cleverly modulating the Ostwald ripening-like process of P123-based micelles, effectively addressing the instability challenges inherent in traditional soft-template methods, especially within the demanding carbohydrate HTC process. Additionally, this method allows for the independent regulation of surface architectures through the selection of organic additives. The resulting nanosheets exhibit diverse surface morphologies, including surface spherical mesopores, 1D open channels, and smooth surfaces. Their unexpectedly versatile properties have swiftly garnered recognition, showing potential in the application of lithium-sulfur batteries.

Mediating trade-off between activity and selectivity in alkynes semi-hydrogenation via a hydrophilic polar layer.

As a crucial industrial process for the production of bulk and fine chemicals, semi-hydrogenation of alkynes faces the trade-off between activity and selectivity due to undesirable over-hydrogenation. By breaking the energy linear scaling relationships, we report an efficient additive-free WO3-based single-atom Pd catalytic system with a vertical size effect of hydrogen spillover. Hydrogen spillover induced hydrophilic polar layer (HPL) with limited thickness on WO3-based support exhibits unconventional size effect to Pd site, in which over-hydrogenation is greatly suppressed on Pd1 site due to the polar repulsive interaction between HPL and nonpolar C=C bonds, whereas this is invalid for Pd nanoparticles with higher altitudes. By further enhancing the HPL through Mo doping, activated Pd1/MoWO3 achieves recorded performance of 98.4% selectivity and 10200 h-1 activity for semi-hydrogenation of 2-methyl-3-butyn-2-ol, 26-fold increase in activity of Lindlar catalyst. This observed vertical size effect of hydrogen spillover offers broad potential in catalytic performance regulation.

LMFD: lightweight multi-feature descriptors for image stitching.

Image stitching is a fundamental pillar of computer vision, and its effectiveness hinges significantly on the quality of the feature descriptors. However, the existing feature descriptors face several challenges, including inadequate robustness to noise or rotational transformations and limited adaptability during hardware deployment. To address these limitations, this paper proposes a set of feature descriptors for image stitching named Lightweight Multi-Feature Descriptors (LMFD). Based on the extensive extraction of gradients, means, and global information surrounding the feature points, feature descriptors are generated through various combinations to enhance the image stitching process. This endows the algorithm with formidable rotational invariance and noise resistance, thereby improving its accuracy and reliability. Furthermore, the feature descriptors take the form of binary matrices consisting of 0s and 1s, not only facilitating more efficient hardware deployment but also enhancing computational efficiency. The utilization of binary matrices significantly reduces the computational complexity of the algorithm while preserving its efficacy. To validate the effectiveness of LMFD, rigorous experimentation was conducted on the Hpatches and 2D-HeLa datasets. The results demonstrate that LMFD outperforms state-of-the-art image matching algorithms in terms of accuracy. This empirical evidence solidifies the superiority of LMFD and substantiates its potential for practical applications in various domains.

Tailoring a local acid-like microenvironment for efficient neutral hydrogen evolution.

Electrochemical hydrogen evolution reaction in neutral media is listed as the most difficult challenges of energy catalysis due to the sluggish kinetics. Herein, the Ir-HxWO3 catalyst is readily synthesized and exhibits enhanced performance for neutral hydrogen evolution reaction. HxWO3 support is functioned as proton sponge to create a local acid-like microenvironment around Ir metal sites by spontaneous injection of protons to WO3, as evidenced by spectroscopy and electrochemical analysis. Rationalize revitalized lattice-hydrogen species located in the interface are coupled with Had atoms on metallic Ir surfaces via thermodynamically favorable Volmer-Tafel steps, and thereby a fast kinetics. Elaborated Ir-HxWO3 demonstrates acid-like activity with a low overpotential of 20 mV at 10 mA cm-2 and low Tafel slope of 28 mV dec-1, which are even comparable to those in acidic environment. The concept exemplified in this work offer the possibilities for tailoring local reaction microenvironment to regulate catalytic activity and pathway.

Decoupling the electronic and geometric effects of Pt catalysts in selective hydrogenation reaction.

Decoupling the electronic and geometric effects has been a long cherished goal for heterogeneous catalysis due to their tangled relationship. Here, a novel orthogonal decomposition method is firstly proposed to settle this issue in p-chloronitrobenzene hydrogenation reaction on size- and shape-controlled Pt nanoparticles (NPs) carried on various supports. Results suggest Fermi levels of catalysts can be modulated by supports with varied work function (Wf). And the selectivity on Pt NPs of similar size and shape is linearly related with the Wf of support. Optimized Fermi levels of the catalysts with large Wf weaken the ability of Pt NPs to fill valence electrons into the antibonding orbital of C-Cl bond, finally suppressing the hydrodehalogenation side reaction. Foremost, the geometric effect is firstly spun off through orthogonal relation based on series of linear relationships over various sizes of Pt NPs reflecting the electronic effect. Moreover, separable nested double coordinate system is established to quantitatively evaluate the two effects.

Fe2 Dimers for Non-Polar Diatomic O2 Electroreduction.

Non-polar diatomic molecule activation is of great significance for catalysis. Despite the high atomic efficiency, the catalytic performance of single-atom catalysts is limited by insufficient receiving sites for diatomic molecule adsorption. Here, Fe2 dimers were successfully synthesized through precisely regulating the metal loading on metal-organic frameworks. The unique role of metal dimers in activating diatomic O2 molecules was explored. In alkaline electrolytes, the specific oxygen reduction reaction activity of Fe2 dimers was 7 times higher than that of Fe1 counterparts. The hydrogen atom transfer probes indicated a different activation mode for O2 on Fe1 and Fe2 dimers, respectively. Theoretical calculation results revealed that Fe2 dimers opened up a new reaction pathway by promoting the direct breaking of O=O bonds, thus avoiding the usual formation of *OOH intermediates, which helped explain the lower H2 O2 yield and higher specific activity.

Strong Oxide-Support Interaction over IrO2 /V2 O5 for Efficient pH-Universal Water Splitting.

Constructing strong oxide-support interaction (SOSI) is compelling for modulating the atomic configurations and electronic structures of supported catalysts. Herein, ultrafine iridium oxide nanoclusters (≈1 nm) are anchored on vanadium oxide support (IrO2 /V2 O5 ) via SOSI. The as made catalyst, with a unique distorted IrO2 structure, is discovered to significantly boost the performance for pH-universal oxygen evolution reaction (OER). Based on experimental results and theoretical calculations, the distorted IrO2 active sites with flexible redox states in IrO2 /V2 O5 server as electrophilic centers balance the adsorption of oxo-intermediates and effectively facilitate the process of OO coupling, eventually propelling the fast turnover of water oxidation. As a result, IrO2 /V2 O5 demonstrates not only ultralow overpotentials at 10 mA cm-2 (266 mV, pH = 0; 329 mV, pH = 7; 283 mV, pH = 14) for OER, but also high-performance overall water electrolysis over a broad pH range, with a potential of mere 1.50 V (pH = 0), 1.65 V (pH = 7) or 1.49 V (pH = 14) at 10 mA cm-2 . In addition, SOSI can simultaneously secure the distorted active sites and thus remarkably improving the catalytic stability, making it a promising strategy to develop high-performance catalytic systems.

Application of the improved simple bedside method for emergency temporary pacemaker implantation suitable for primary hospitals.

The purpose of the research was to evaluate the safety and effectiveness of the X-ray-free improved simple bedside method for emergency temporary pacemaker implantation as well as the practicability of the method in primary hospitals. Patients [including those suffering from sick sinus syndrome and third-degree and advanced atrioventricular blockage (AVB)] who needed emergency temporary pacemaker implantation from July 2017 to August 2020 in Hunan Provincial People's Hospital were selected. They were stochastically divided into a research group (95 cases) treated with the improved simple bedside method and a control group (95 cases) with X-ray guidance. The ordinary bipolar electrodes were used in both groups. On this condition, the operation duration, the first-attempt success rate of electrodes, pacing threshold, success rate of the operation, the rate of electrode displacement, and complications in the two groups were separately calculated. The comparison results of the research group with the control group are shown as follows: operation time [(18 ± 5.91) min vs. (43 ± 2.99) min, P < 0.05], the first-attempt success rate of the electrode (97% vs. 98%, P > 0.05), pacing threshold [(0.97 ± 0.35) vs. (0.97 ± 0.32) V, P > 0.05], success rate of the operation (98.9% vs. 100%, P > 0.05), the rate of electrode displacement (8.4% vs. 7.3%, P > 0.05) and complications (3.2% vs. 2.1%, P > 0.05). The emergency temporary pacemaker implantation based on the improved simple bedside method is as safe and effective as the surgical method under X-ray guidance, and the operation is simpler and easier to learn and requires a shorter operating time, therefore, it is more suitable for use in emergency and primary hospitals.