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Abstract Background Diabetes is a common chronic metabolic disease. The progression of the disease promotes vascular inflammation and the formation of atherosclerosis, leading to cardiovascular disease. The coronary artery perivascular adipose tissue attenuation index based on CCTA is a new noninvasive imaging biomarker that reflects the spatial changes in perivascular adipose tissue attenuation in CCTA images and the inflammation around the coronary arteries. In this study, a radiomics approach is proposed to extract a large number of image features from CCTA in a high-throughput manner and combined with clinical diagnostic data to explore the predictive ability of vascular perivascular adipose imaging data based on CCTA for coronary heart disease in diabetic patients. Methods R language was used for statistical analysis to screen the variables with significant differences. A presegmentation model was used for CCTA vessel segmentation, and the pericoronary adipose region was screened out. PyRadiomics was used to calculate the radiomics features of pericoronary adipose tissue, and SVM, DT and RF were used to model and analyze the clinical data and radiomics data. Model performance was evaluated using indicators such as PPV, FPR, AAC, and ROC. Results The results indicate that there are significant differences in age, blood pressure, and some biochemical indicators between diabetes patients with and without coronary heart disease. Among 1037 calculated radiomic parameters, 18.3% showed significant differences in imaging omics features. Three modeling methods were used to analyze different combinations of clinical information, internal vascular radiomics information and pericoronary vascular fat radiomics information. The results showed that the dataset of full data had the highest ACC values under different machine learning models. The support vector machine method showed the best specificity, sensitivity, and accuracy for this dataset. Conclusions In this study, the clinical data and pericoronary radiomics data of CCTA were fused to predict the occurrence of coronary heart disease in diabetic patients. This provides information for the early detection of coronary heart disease in patients with diabetes and allows for timely intervention and treatment.

A four-group mechanically compensated continuous-zoom microscope is proposed and designed based on the theory of continuous zoom. The system addresses the limitations of traditional continuous-zoom microscopes, including a small zoom ratio, a short working distance, and the loss of details during 2D–3D switching. The system has a magnification of 0.6×~6.0× under two-dimensional observation, adapts to two-third-inch sensors, has a working distance of 130 mm, and adds a 360-degree rotatable beamsplitter for three-dimensional full-field-of-view observation. The magnification, numerical aperture, and sensor dimensions remain unchanged under both two-dimensional and three-dimensional observation. The design results demonstrate that the system is capable of achieving a high zoom ratio of 10× while maintaining a high level of imaging quality in both two-dimensional and three-dimensional modes. The MTF curves for each magnification are in close proximity to the diffraction limit, the spot diagrams are smaller than the airy disk range, and the zoom cam curves are smooth with no inflection points. Furthermore, the system negates the visual discrepancies and loss of detail that arise when switching observation modes due to alterations in system magnification and numerical aperture, thereby broadening the scope of applications for continuous-zoom microscopes.

BackgroundThere is no direct evidence of gut microbiota disturbance in children with gastroesophageal reflux disease (GERD). This study aimed to provide direct evidence and a comprehensive understanding of gut microbiota disturbance in children with GERD through combined metagenomic and metabolomic analysis.Methods30 children with GERD and 30 healthy controls (HCs) were continuously enrolled, and the demographic and clinical characteristics of the subjects were collected. First, 16S rRNA sequencing was used to evaluate differences in the gut microbiota between children with GERD and HC group, and 10 children with GERD and 10 children in the HC group were selected for metagenomic analysis. Nontargeted metabolomic analysis was performed using liquid chromatography/mass spectrometry (LC/MS), and metagenomic and metabolomic data were analyzed together.ResultsThere were significant differences in the gut microbiota diversity and composition between children with GERD and HCs. The dominant bacteria in children with GERD were Proteobacteria and Bacteroidota. At the species level, the top three core bacterial groups were Bacteroides stercoris, Bacteroides vulgatus and Alistipes putredinis. The main differential pathways were identified to be related to energy, amino acid, vitamin, carbohydrate and lipid metabolism. LC/MS detected 288 different metabolites in the positive and negative ion modes between children with GERD and HCs, which were mainly involved in arachidonic acid (AA), tyrosine, glutathione and caffeine metabolism.ConclusionThis study provides new evidence of the pathogenesis of GERD. There are significant differences in the gut microbiota, metabolites and metabolic pathways between HCs and children with GERD, and the differences in metabolites are related to specific changes in bacterial abundance. In the future, GERD may be treated by targeting specific bacteria related to AA metabolism.

Optical computing and optical neural network have gained increasing attention in recent years because of their potential advantages of parallel processing at the speed of light and low power consumption by comparison with electronic computing. The optical implementation of the fundamental building blocks of a digital computer, i.e. logic gates, has been investigated extensively in the past few decades. Optical logic gate computing is an alternative approach to various analogue optical computing architectures. In this paper, the latest development of optical logic gate computing with different kinds of implementations is reviewed. Firstly, the basic concepts of analogue and digital computing with logic gates in the electronic and optical domains are introduced. And then a comprehensive summary of various optical logic gate schemes including spatial encoding of light field, semiconductor optical amplifiers (SOA), highly nonlinear fiber (HNLF), microscale and nanoscale waveguides, and photonic crystal structures is presented. To conclude, the formidable challenges in developing practical all-optical logic gates are analyzed and the prospects of the future are discussed.

Phase-sensitive optical time-domain reflectometry (Φ-OTDR) has attracted numerous attention due to its superior performance in detecting the weak perturbations along the fiber. Relying on the ultra-sensitivity of light phase to the tiny deformation of optical fiber, Φ-OTDR has been treated as a powerful technique with a wide range of applications. It is fundamental to extract the phase of scattering light wave accurately and the methods include coherent detection, I/Q demodulation, 3 by 3 coupler, dual probe pulses, and so on. Meanwhile, researchers have also made great efforts to improve the performance of Φ-OTDR. The frequency response range, the measurement accuracy, the sensing distance, the spatial resolution, and the accuracy of event discrimination, all have been enhanced by various techniques. Furthermore, lots of researches on the applications in various kinds of fields have been carried out, where certain modifications and techniques have been developed. Therefore, Φ-OTDR remains as a booming technique in both researches and applications.

The Vernier effect created using an incorporated Lyot–Sagnac loop is used to create an ultra-high sensitivity temperature sensor based on a ring laser cavity. Unlike standard double Sagnac loop systems, the proposed sensor is fused into a single Sagnac loop by adjusting the welding angle between two polarization-maintaining fibers (PMFs) to achieve effective temperature sensitivity amplification. The PMFs are separated into two arms of 0.8 m and 1 m in length, with a 45° angle difference between the fast axes. The sensor’s performance is examined both theoretically and experimentally. The experimental results reveal that the Vernier amplification effect can be achieved via PMF rotating shaft welding. The temperature sensitivity in the laser cavity can reach 2.391 nm/°C, which is increased by a factor of more than eight times compared with a single Sagnac loop structure (0.298 nm/°C) with a length of 0.8 m without the Vernier effect at temperatures ranging from 20 °C to 30 °C. Furthermore, unlike traditional optical fiber sensing that uses a broadband light source (BBS) for detection, which causes issues such as low signal-to-noise ratio and broad bandwidth, the Sagnac loop can be employed as a filter by inserting itself into the fiber ring laser (FRL) cavity. When the external parameters change, the laser is offset by the interference general modulation, allowing the external temperature to be monitored. The superior performance of signal-to-noise ratios of up to 50 dB and bandwidths of less than 0.2 nm is achieved. The proposed sensor has a simple structure and high sensitivity and is expected to play a role in biological cell activity monitoring.

The density map is an effective manner to infer how many cells a cell image contains, and it carries valuable information. However, a fine-grained density map requires rich spatial information to recover the distribution details. In this paper, we propose a cell counting network with two paths, i.e. detail path and context path, which respectively extract spatial details and semantics. The detail path encodes the spatial information with small convolutional kernels. The context path rapidly enlarges the receptive field and extracts multi-scale features with an atrous spatial pyramid pooling. At the end of the two paths, we design a feature fusion module to merge the high-level feature maps from the two paths. To decrease the parameters and computation, we directly upsample the fused feature maps to the input size and decode them to obtain the density map. The proposed model is evaluated on three cell datasets and a popular crowd dataset Shanghaitech Part-B. The experiments illustrated that the proposed model not only achieves superior performance on cell datasets but also generalizes well on the crowd dataset.

In this paper, we propose a simple but effective sharpness prior—inner channel prior—to detect the focused area of multi-focus images. The inner channel prior is a kind of location feature of natural image sharpness. It is based on a key observation—the high-frequency information of the saturation channel is located inside the object. The inner channel prior indicates the focus degree inside objects, which can obtain better color fidelity and sharper object. Using the proposed prior combined with the multi-scale image fusion, we can directly obtain a sharp image with an extended depth of field focusing on all the objects. Results on a variety of defocused image sequences demonstrate the availability of the proposed prior. In addition, the proposed prior is insensitive to overexposure and can better maintain the color of multi-focus images.

Sarcasm is a linguistic phenomenon indicating a difference between literal meanings and implied intentions. It is commonly used on blogs, e-commerce platforms, and social media. Numerous NLP tasks, such as opinion mining and sentiment analysis systems, are hampered by its linguistic nature in detection. Traditional techniques concentrated mostly on textual incongruity. Recent research demonstrated that the addition of commonsense knowledge into sarcasm detection is an effective new method. However, existing techniques cannot effectively capture sentence “incongruity” information or take good advantage of external knowledge, resulting in imperfect detection performance. In this work, new modules are proposed for maximizing the utilization of the text, the commonsense knowledge, and their interplay. At first, we propose an adaptive incongruity extraction module to compute the distance between each word in the text and commonsense knowledge. Two adaptive incongruity extraction modules are applied to text and commonsense knowledge, respectively, which can obtain two adaptive incongruity attention matrixes. Therefore, each of the words in the sequence receives a new representation with enhanced incongruity semantics. Secondly, we propose the incongruity cross-attention module to extract the incongruity between the text and the corresponding commonsense knowledge, thereby allowing us to pick useful commonsense knowledge in sarcasm detection. In addition, we propose an improved gate module as a feature fusion module of text and commonsense knowledge, which determines how much information should be considered. Experimental results on publicly available datasets demonstrate the superiority of our method in achieving state-of-the-art performance on three datasets as well as enjoying improved interpretability.

This paper presented a new kind of salinity and temperature dual-parameter sensor based on a fiber ring laser (FRL) with tapered side-hole fiber (SHF) embedded in a Sagnac interferometer. The sensing structure is majorly composed of tapered SHF located in the middle of SHF inside the Sagnac interferometer loop structure. The influences of the SHF’s diameters of different tapered in the Sagnac interferometer loop on the FRL sensing system are studied. The presence of air holes in the SHF makes the cladding mode easier to excite, and the interaction between the cladding mode with its surroundings is enhanced, thus having higher salinity sensitivity. Besides, the unique advantages of high resolution, narrower linewidth, and high signal-to-noise ratio (SNR) of fiber laser make the measurement results more accurate. In this experiment, the SHF with different taper diameters was made, and it was found that reducing the diameter of the taper waist diameter could further improve the salinity sensitivity. When the waist diameter was 9.70 μm, the maximum salinity sensitivity of 0.2867 nm/‰ was achieved. Temperature sensing experiments were also carried out. The maximum temperature sensitivity of the FRL sensing system was −0.3041 nm/°C at the temperature range from 20 to 30 °C. The sensor has the characteristics of easy manufacture, good selectivity, and high sensitivity, proving the feasibility of simultaneous measurement of seawater salinity and temperature.

We demonstrated a new method for temperature measurement inside a fiber ring laser (FRL) cavity. Different from traditional FRL temperature sensing system which need additional filter working as a sensor, a micro-fiber coupler (MFC) was designed as a beam splitter, filter, and temperature sensor. In addition, isopropanol, a liquid with very high photothermal coefficient, is selectively filled in the MFC in order to improve the sensitivity of the system on temperature. In the dynamic range of 20–40 °C, we obtained a good temperature sensitivity of −1.29 nm/°C, with linear fitting up to 0.998. Benefiting from the advantages of laser sensing, the acquired laser has a 3 – dB bandwidth of less than 0.2 nm and a signal-to-noise ratio (SNR) of up to 40 dB. The proposed sensor has a low cost and high sensitivity, which is expected to be used in biomedical health detection, real-time monitoring of ocean temperature, and other application scenarios.

Data storage is a problem that cannot be ignored in the long-term monitoring of a phase-sensitive optical time-domain reflectometry (Φ-OTDR) system. In this paper, we proposed a data-reduction approach for heterodyne Φ-OTDR using an ultra-low sampling resolution and undersampling techniques. The operation principles were demonstrated and experiments with different sensing configurations were carried out to verify the proposed method. The results showed that the vibration signal could be accurately reconstructed from the undersampled 1-bit data. A space saving ratio of 98.75% was achieved by converting 128 MB of data (corresponding to 268.44 ms of sensing time) to 1.6 MB. The proposed method led to a potentially new data-reduction approach for heterodyne Φ-OTDR, which also provided economical guidance for the selection of the data-acquisition device.

Islanding detection is one of the conditions necessary for the safe operation of the microgrid. The detection technology should provide the ability to differentiate islanded operations from power grid disturbances effectively. Given that it is difficult to set the fault threshold using the passive detection method, and because the traditional active detection method affects the output power quality, a microgrid islanding detection method based on the Sliding Window Discrete Fourier Transform (SDFT)-Empirical Mode Decomposition (EMD) and Long Short-Term Memory (LSTM) network optimized by an attention mechanism is proposed. In this paper, the inverter output current and voltage at the point of common coupling (PCC) are transformed by the SDFT. The positive sequence, zero sequence, and negative sequence components of voltage and current harmonics are calculated and reconstructed by adopting the symmetrical component method (SCM). Meanwhile, the current and voltage are decomposed into a mono intrinsic mode function (IMF). The symmetric components of voltage, current, and IMFs are used as inputs to the deep learning algorithm. An LSTM with the features extracted to classify islanding and grid disturbance is proposed. By using the attention mechanism to set the weight values of the features of hidden states obtained by the LSTM network, the proportion of important features increases, which improves the classification effect. MATLAB/Simulink simulation results indicate that the proposed method can effectively classify the islanding state under different working conditions with an accuracy level of 98.4% and a loss value of 0.0725 with a maximal detection time of 66.94 ms. It can also reduce the non-detection zone (NDZ) and detection time and has a certain level of noise resistance. Meanwhile, the problem whereby the active method affects the microgrid power quality is avoided without disturbing the current or power of the microgrid.

This paper proposes a real-time multi-class disturbance detection algorithm based on YOLO for distributed fiber vibration sensing. The algorithm achieves real-time detection of event location and classification on external intrusions sensed by distributed optical fiber sensing system (DOFS) based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). We conducted data collection under perimeter security scenarios and acquired five types of events with a total of 5787 samples. The data is used as a spatial–temporal sensing image in the training of our proposed YOLO-based model (You Only Look Once-based method). Our scheme uses the Darknet53 network to simplify the traditional two-step object detection into a one-step process, using one network structure for both event localization and classification, thus improving the detection speed to achieve real-time operation. Compared with the traditional Fast-RCNN (Fast Region-CNN) and Faster-RCNN (Faster Region-CNN) algorithms, our scheme can achieve 22.83 frames per second (FPS) while maintaining high accuracy (96.14%), which is 44.90 times faster than Fast-RCNN and 3.79 times faster than Faster-RCNN. It achieves real-time operation for locating and classifying intrusion events with continuously recorded sensing data. Experimental results have demonstrated that this scheme provides a solution to real-time, multi-class external intrusion events detection and classification for the Φ-OTDR-based DOFS in practical applications.

We demonstrate a new concept for an all-fiber inclinometer based on a tapered fiber Bragg grating (tFBG) in a fiber ring laser (FRL) with the capability of measuring the tilt angle and temperature simultaneously. The sensor performance is analyzed theoretically and investigated experimentally. The dependence of tilt angle on the spectral response in variable temperature conditions was measured. Two inclinometers with different lengths have been fabricated and characterized in FRL. The sensitivity is 0.583 dB/° and 0.849 dB/°, respectively, in the range of 0° to 90°. Thanks to the FRL system, narrow 3-dB bandwidth (

Cell counting is a fundamental part of biomedical and pathological research. Predicting a density map is the mainstream method to count cells. As an easy-trained and well-generalized model, the random forest is often used to learn the cell images and predict the density maps. However, it cannot predict the data that are beyond the training data, which may result in underestimation. To overcome this problem, we propose a cell counting framework to predict the density map by detecting cells. The cell counting framework contains two parts: the training data preparation and the detection framework. The former makes sure that the cells can be detected even when overlapping, and the latter makes sure the count result accurate and robust. The proposed method uses multiple random forests to predict various probability maps where the cells can be detected by Hessian matrix. Take all the detection results into consideration to get the density map and achieve better performance. We conducted experiments on three public cell datasets. Experimental results showed that the proposed model performs better than the traditional random forest (RF) in terms of accuracy and robustness, and even superior to some state-of-the-art deep learning models. Especially when the training data are small, which is the usual case in cell counting, the count errors on VGG cells, and MBM cells were decreased from 3.4 to 2.9, from 11.3 to 9.3, respectively. The proposed model can obtain the lowest count error and achieves state-of-the-art.