Raman spectroscopy is an important and powerful technique for analyzing the chemical composition of biological or nonbiological samples in many fields. A serious challenge frequently encountered in Raman measurements arises from the existence of the concurrent fluorescence background. The fluorescence intensity is normally several orders of magnitude larger than the Raman scattering signal, especially in biological samples. Such fluorescence background must be suppressed in order to obtain accurate Raman spectra. Several different techniques have been explored for this purpose. These techniques could be generally grouped into time-domain, frequency-domain, wavelength-domain, and computational methods in addition to various Raman enhancement techniques and other unconventional methods. This review briefly describes the fundamental principles of each group of methods, reports the most recent advances, and makes comparison across those major categories of techniques in terms of cost and performance in a hope to guide interested readers to select proper methods for specific applications.

Raman spectroscopy is a powerful non-destructive technique for qualitatively and quantitatively characterizing materials. However, noise often obscures interesting Raman peaks due to the inherently weak Raman signal, especially in biological samples. In this study, we develop a method based on spectral reconstruction to recover Raman spectra with low signal-to-noise ratio (SNR). The synthesis of narrow-band measurements from low-SNR Raman spectra eliminates the effect of noise by integrating the Raman signal along the wavenumber dimension, which is followed by spectral reconstruction based on Wiener estimation to recover the Raman spectrum with high spectral resolution. Non-negative principal components based filters are used in the synthesis to ensure that most variance contained in the original Raman measurements are retained. A total of 25 agar phantoms and 20 bacteria samples were measured and data were used to validate our method. Four commonly used de-noising methods in Raman spectroscopy, i.e. Savitzky-Golay (SG) algorithm, finite impulse response (FIR) filtration, wavelet transform and factor analysis, were also evaluated on the same set of data in addition to the proposed method for comparison. The proposed method showed the superior accuracy in the recovery of Raman spectra from measurements with extremely low SNR, compared with the four commonly used de-noising methods.

Microorganisms play a great role in ecosystem, wastewater treatment, monitoring of environmental changes, and decomposition of waste materials. However, some of them are harmful to humans and animals such as tuberculosis bacteria and plasmodium. In such course, it is important to identify, track, analyze, consider the beneficial side and get rid of the negative effects of microorganisms using fast, accurate, and reliable methods. In recent decades, image analysis techniques have been used to address the drawbacks of manual traditional approaches in the identification and analysis of microorganisms. As image segmentation being an important step (technique) in the detection, tracking, monitoring, feature extraction, modeling, and analysis of microorganisms, different methods have been deployed, from classical approaches to current deep neural networks upon different challenges on microorganism images. This survey comprehensively analyses the various studies focused on developing microorganism image segmentation methods in the last 30 years (since 1989). In this survey, segmentation methods are categorized into classical and machine learning methods. Furthermore, these methods are subcategorized into threshold-based, region-based, and edge-based which belong to classical methods, supervised and unsupervised machine leaning-based methods which belong to machine learning category. A growth trend of different methods and most frequently used methods in each category are meticulously analyzed. A clear explanation of the suitability of these methods for different segmentation challenges encountered on microscopic microorganism images is also enlightened.

The accurate and noninvasive preoperative prediction of the state of the axillary lymph nodes is significant for breast cancer staging, therapy and the prognosis of patients. In this study, we analyzed the possibility of axillary lymph node metastasis directly based on Magnetic Resonance Imaging (MRI) of the breast in cancer patients. After mass segmentation and feature analysis, the SVM, KNN, and LDA three classifiers were used to distinguish the axillary lymph node state in 5-fold cross-validation. The results showed that the effect of the SVM classifier in predicting breast axillary lymph node metastasis was significantly higher than that of the KNN classifier and LDA classifier. The SVM classifier performed best, with the highest accuracy of 89.54%, and obtained an AUC of 0.8615 for identifying the lymph node status. Each feature was analyzed separately and the results showed that the effect of feature combination was obviously better than that of any individual feature on its own.

Flexible electronics is expected to be one of the most active research areas in the next decade. In this study, a mechanically strong and flexible epoxy/GnP composite film was fabricated having a percolation threshold of electrical conductivity at 1.08 vol% GnPs and high thermal conductivity as 1.07 W m ⁻¹ K ⁻¹ at 10 vol% GnPs. The composite film shows high mechanical performance: Young’s modulus and tensile strength were improved by 1344% and 66.7%, respectively, at 10 vol%. The film demonstrated high sensitivity to various mechanical loads: (i) it has gauge factors of 2 at strain range 0%–7% and 6 at range 7%–10%; (ii) it gives good electrical response with bending and twisting angles up to 180°; and (iii) it displays a good compressive load response up to 2 N where the absolute value of electrical resistance change increased by 71%. Furthermore, the film showed an excellent reliability up to 5.5 × 10 ³ cycles with minor zero-point error. Above 20 °C, the film solely acts as a temperature sensor; upon cyclic temperature testing, the film demonstrated a stable resistive response in the range of 30–75 °C with a temperature sensitivity coefficient of 0.0063 °C ⁻¹ . This flexible composite film has remarkable properties that enable it to be used as a full-fledged sensor for universal applications in aerospace, automotive and civil engineering.

The assessment of the muscle invasion of bladder cancer typically plays a crucial role in therapeutic decision-making and has significant impacts on the recurrence rate and survival rate. Although histopathology is sufficiently accurate and usually served as the gold standard for bladder cancer diagnosis, it is invasive, time-consuming, and requires intensive sample preparation by a well-trained pathologist to achieve an optimal diagnosis. Therefore, a fast and noninvasive method to accurately identify non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) is in demand. In this study, the SERS technique combined with the PLS-LDA method based on a small amount of blood serum samples is employed to distinguish healthy volunteers, NMIBC, and MIBC patients. According to the results, the overall diagnostic accuracy is 93.3%. The diagnostic accuracies are 97.8% and 93.2% for healthy versus bladder cancer groups and NMIBC versus MIBC groups, respectively. Therefore, the proposed method has demonstrated excellent performance on accurately identifying muscle invasion of bladder cancer, which can assist timely diagnosis and proper treatment for bladder cancer patients.