Optical fiber sensor for wearable and accurate human respiratory monitoring.

Accurate respiratory monitoring is of great significance in assessing and analyzing physical health, and preventing respiratory diseases. The recently emerged wearable respiratory sensors are confronted with the challenges such as complex fabrication processes, limited accuracy, and stringent wearing requirements. An optical fiber sensor for accurate human respiratory monitoring is proposed and experimentally verified. The sensor head is composed of a piece of seven core fiber sandwiched between two single-mode fibers by two fiber bitapers, which is embedded in a textile sheet and freely worn on the upper body. An efficient signal demodulation system is set up to acquire the respiratory signal, while Fourier transform (FFT) and short-time Fourier transform (STFT) methods are used to analyze the measured signal. Six volunteers are invited to perform the respiratory experiment, and the experimental results demonstrate that the sensor can accurately detect and distinguish respiratory signals under different humans, different states (normal, slow, fast), different body parts (abdomen, chest, back), different postures (standing, sitting, lying), and irregular respiration. The Pearson correlation coefficient of the sensor is higher than 0.9, which is consistent with commercial respiratory sensor. Meanwhile, the instability of the sensor is 0.003 Hz for the same volunteer in 6 months. The sensor has the advantages of high sensitivity, good stability and wearing comfort, showing good potential in healthcare applications.

Evolutionary origin and gradual accumulation with plant evolution of the LACS family.

BACKGROUND: LACS (long-chain acyl-CoA synthetase) genes are widespread in organisms and have multiple functions in plants, especially in lipid metabolism. However, the origin and evolutionary dynamics of the LACS gene family remain largely unknown. RESULTS: Here, we identified 1785 LACS genes in the genomes of 166 diverse plant species and identified the clades (I, II, III, IV, V, VI) of six clades for the LACS gene family of green plants through phylogenetic analysis. Based on the evolutionary history of plant lineages, we found differences in the origins of different clades, with Clade IV originating from chlorophytes and representing the origin of LACS genes in green plants. The structural characteristics of different clades indicate that clade IV is relatively independent, while the relationships between clades (I, II, III) and clades (V, VI) are closer. Dispersed duplication (DSD) and transposed duplication (TRD) are the main forces driving the evolution of plant LACS genes. Network clustering analysis further grouped all LACS genes into six main clusters, with genes within each cluster showing significant co-linearity. Ka/Ks results suggest that LACS family genes underwent purifying selection during evolution. We analyzed the phylogenetic relationships and characteristics of six clades of the LACS gene family to explain the origin, evolutionary history, and phylogenetic relationships of different clades and proposed a hypothetical evolutionary model for the LACS family of genes in plants. CONCLUSIONS: Our research provides genome-wide insights into the evolutionary history of the LACS gene family in green plants. These insights lay an important foundation for comprehensive functional characterization in future research.

Multi-scale Lesion Feature Fusion and Location-Aware for Chest Multi-disease Detection.

Accurately identifying and locating lesions in chest X-rays has the potential to significantly enhance diagnostic efficiency, quality, and interpretability. However, current methods primarily focus on detecting of specific diseases in chest X-rays, disregarding the presence of multiple diseases in a single chest X-ray scan. Moreover, the diversity in lesion locations and attributes introduces complexity in accurately discerning specific traits for each lesion, leading to diminished accuracy when detecting multiple diseases. To address these issues, we propose a novel detection framework that enhances multi-scale lesion feature extraction and fusion, improving lesion position perception and subsequently boosting chest multi-disease detection performance. Initially, we construct a multi-scale lesion feature extraction network to tackle the uniqueness of various lesion features and locations, strengthening the global semantic correlation between lesion features and their positions. Following this, we introduce an instance-aware semantic enhancement network that dynamically amalgamates instance-specific features with high-level semantic representations across various scales. This adaptive integration effectively mitigates the loss of detailed information within lesion regions. Additionally, we perform lesion region feature mapping using candidate boxes to preserve crucial positional information, enhancing the accuracy of chest disease detection across multiple scales. Experimental results on the VinDr-CXR dataset reveal a 6% increment in mean average precision (mAP) and an 8.4% improvement in mean recall (mR) when compared to state-of-the-art baselines. This demonstrates the effectiveness of the model in accurately detecting multiple chest diseases by capturing specific features and location information.

Intrinsic negative Poisson's ratio of the monolayer semiconductorß-TeO2.

Monolayer semiconductors with unique mechanical responses are promising candidates for novel electromechanical applications. Here, through first-principles calculations, we discover that the monolayerß-TeO2, a high-mobilityp-type and environmentally stable 2D semiconductor, exhibits an unusual out-of-plane negative Poisson's ratio (NPR) when a uniaxial strain is applied along the zigzag direction. The NPR originates from the unique six-sublayer puckered structure and hinge-like Te-O bonds in the 2Dß-TeO2. We further propose that the sign of the Raman frequency change under uniaxial tensile strain could assist in determining the lattice orientation of monolayerß-TeO2, which facilitates the experimental study of the NPR. Our results is expected to motivate further experimental and theoretical studies of the rich physical and mechanical properties of monolayerß-TeO2.

PusALDH1 gene confers high levels of volatile aroma accumulation in both pear and tomato fruits.

Aroma is an important commercial trait that determines fruit quality and has an important influence on the overall flavor of fruits. Plant ALDH genes have been implicated in diverse pathways and play crucial roles in physiological activities. In this study, via genome resequencing we identified one gene PusALDH1 (Pbr034873.1) related to aroma biosynthesis that can respond to the induction of methyl jasmonate. Transient transformation of pear fruits and heterologous stable transformation of tomato further confirmed the function of PusALDH1 in aroma accumulation. The content of ALDH precursor substance, benzaldehyde, was reduced in the overexpressing pear and tomato fruits, and the content of ALDH product, benzoic acid and benzoic acid derivatives, was increased in the pear fruits. Meanwhile, transgenic tomato fruits with PusALDH1 overexpression exhibited a greater area of yellow placenta, indicating that the gene may be related to the growth and development of the fruit. Taken together, PusALDH1 could act as a strong candidate gene in aroma synthesis.

Deciphering the selectivity descriptors of heterogeneous metal phthalocyanine electrocatalysts for hydrogen peroxide production.

The electrocatalytic 2e- oxygen reduction reaction (2e- ORR) provides an appealing pathway to produce hydrogen peroxide (H2O2) in a decentralized and clean manner, which drives the demand for developing high selectivity electrocatalysts. However, current understanding on selectivity descriptors of 2e- ORR electrocatalysts is still insufficient, limiting the optimization of catalyst design. Here we study the catalytic performances of a series of metal phthalocyanines (MPcs, M = Co, Ni, Zn, Cu, Mn) for 2e- ORR by combining density functional theory calculations with electrochemical measurements. Two descriptors (ΔG *O - ΔG *OOH and ΔG *H2O2 ) are uncovered for manipulating the selectivity of H2O2 production. ΔG *O - ΔG *OOH reflects the preference of O-O bond breaking of *OOH, affecting the intrinsic selectivities. Due to the high value of ΔG *O - ΔG *OOH, the molecularly dispersed electrocatalyst (MDE) of ZnPc on carbon nanotubes exhibits high selectivity, even superior to the previously reported NiPc MDE. ΔG *H2O2 determines the possibility of further H2O2 reduction to affect the measured selectivities. Enhancing the hydrophobicity of the catalytic layer can increase ΔG *H2O2 , leading to selectivity improvement, especially under high H2O2 production rates. In the gas diffusion electrode measurements, both ZnPc and CoPc MDEs with polytetrafluoroethylene (PTFE) exhibit low overpotentials, high selectivities, and good stability. This study provides guidelines for rational design of 2e- ORR electrocatalysts.

Automatic foreground extraction based on difference of Gaussian.

A novel algorithm for automatic foreground extraction based on difference of Gaussian (DoG) is presented. In our algorithm, DoG is employed to find the candidate keypoints of an input image in different color layers. Then, a keypoints filter algorithm is proposed to get the keypoints by removing the pseudo-keypoints and rebuilding the important keypoints. Finally, Normalized cut (Ncut) is used to segment an image into several regions and locate the foreground with the number of keypoints in each region. Experiments on the given image data set demonstrate the effectiveness of our algorithm.

A primal analysis system of brain neurons data.

It is a very challenging work to classify the 86 billions of neurons in the human brain. The most important step is to get the features of these neurons. In this paper, we present a primal system to analyze and extract features from brain neurons. First, we make analysis on the original data of neurons in which one neuron contains six parameters: room type, X, Y, Z coordinate range, total number of leaf nodes, and fuzzy volume of neurons. Then, we extract three important geometry features including rooms type, number of leaf nodes, and fuzzy volume. As application, we employ the feature database to fit the basic procedure of neuron growth. The result shows that the proposed system is effective.

Convergence results on iteration algorithms to linear systems.

In order to solve the large scale linear systems, backward and Jacobi iteration algorithms are employed. The convergence is the most important issue. In this paper, a unified backward iterative matrix is proposed. It shows that some well-known iterative algorithms can be deduced with it. The most important result is that the convergence results have been proved. Firstly, the spectral radius of the Jacobi iterative matrix is positive and the one of backward iterative matrix is strongly positive (lager than a positive constant). Secondly, the mentioned two iterations have the same convergence results (convergence or divergence simultaneously). Finally, some numerical experiments show that the proposed algorithms are correct and have the merit of backward methods.

Multilabel image annotation based on double-layer PLSA model.

Due to the semantic gap between visual features and semantic concepts, automatic image annotation has become a difficult issue in computer vision recently. We propose a new image multilabel annotation method based on double-layer probabilistic latent semantic analysis (PLSA) in this paper. The new double-layer PLSA model is constructed to bridge the low-level visual features and high-level semantic concepts of images for effective image understanding. The low-level features of images are represented as visual words by Bag-of-Words model; latent semantic topics are obtained by the first layer PLSA from two aspects of visual and texture, respectively. Furthermore, we adopt the second layer PLSA to fuse the visual and texture latent semantic topics and achieve a top-layer latent semantic topic. By the double-layer PLSA, the relationships between visual features and semantic concepts of images are established, and we can predict the labels of new images by their low-level features. Experimental results demonstrate that our automatic image annotation model based on double-layer PLSA can achieve promising performance for labeling and outperform previous methods on standard Corel dataset.

First- and second-order full-differential in edge analysis of images.

Two concepts of first- and second-order differential of images are presented to deal with the changes of pixels. These are the basic ideas in mathematics. We propose and reformulate them with a uniform definition framework. Based on our observation and analysis with the difference, we propose an algorithm to detect the edge from image. Experiments on Corel5K and PASCAL VOC 2007 are done to show the difference between the first order and the second order. After comparison with Canny operator and the proposed first-order differential, the main result is that the second-order differential has the better performance in analysis of changes of the context of images with good selection of control parameter.

Real-time hand gesture recognition using finger segmentation.

Hand gesture recognition is very significant for human-computer interaction. In this work, we present a novel real-time method for hand gesture recognition. In our framework, the hand region is extracted from the background with the background subtraction method. Then, the palm and fingers are segmented so as to detect and recognize the fingers. Finally, a rule classifier is applied to predict the labels of hand gestures. The experiments on the data set of 1300 images show that our method performs well and is highly efficient. Moreover, our method shows better performance than a state-of-art method on another data set of hand gestures.

A novel support vector machine with globality-locality preserving.

Support vector machine (SVM) is regarded as a powerful method for pattern classification. However, the solution of the primal optimal model of SVM is susceptible for class distribution and may result in a nonrobust solution. In order to overcome this shortcoming, an improved model, support vector machine with globality-locality preserving (GLPSVM), is proposed. It introduces globality-locality preserving into the standard SVM, which can preserve the manifold structure of the data space. We complete rich experiments on the UCI machine learning data sets. The results validate the effectiveness of the proposed model, especially on the Wine and Iris databases; the recognition rate is above 97% and outperforms all the algorithms that were developed from SVM.

Gender classification based on geometry features of palm image.

This paper presents a novel gender classification method based on geometry features of palm image which is simple, fast, and easy to handle. This gender classification method based on geometry features comprises two main attributes. The first one is feature extraction by image processing. The other one is classification system with polynomial smooth support vector machine (PSSVM). A total of 180 palm images were collected from 30 persons to verify the validity of the proposed gender classification approach and the results are satisfactory with classification rate over 85%. Experimental results demonstrate that our proposed approach is feasible and effective in gender recognition.