A Duplex PCR Assay for Rapid Detection of Klebsiella pneumoniae and Chryseobacterium in Large Yellow Croaker Fish.

Both Klebsiella pneumoniae and Chryseobacterium cause an increasing number of diseases in fish, resulting in great economic losses in aquaculture. In addition, the disease infected with Klebsiella pneumoniae or Chryseobacterium exhibited the similar clinical symptoms in aquatic animals. However, there is no effective means for the simultaneous detection of co-infection and discrimination them for these two pathogens. Here, we developed a duplex polymerase chain reaction (PCR) method based on the outer membrane protein A (ompA) gene of Klebsiella pneumoniae and Chryseobacterium. The specificity and validity of the designed primers were confirmed experimentally using simplex PCR. The expected amplicons for Klebsiella pneumoniae and Chryseobacterium had a size of 663 and 1404 bp, respectively. The optimal condition for duplex PCR were determined to encompass a primer concentration of 0.5 µM and annealing temperature of 57°C. This method was analytical specific with no amplification being observed from the genomic DNA of Escherichia coli, Vibrio harveyi, Pseudomonas plecoglossicida, Aeromonas hydrophila and Acinetobacter johnsonii. The limit of detection was estimated to be 20 fg of genomic DNA for Chryseobacterium and 200 fg for Klebsiella pneumoniae, or 100 colony-forming units (CFU) of bacterial cells in both cases. The duplex PCR was capable of simultaneously amplifying target fragments from genomic DNA extracted from the bacteria and fish liver. For practical validation of the method, 20 diseased fish were collected from farms, among which 4 samples were PCR-positive for Klebsiella pneumoniae and Chryseobacterium. The duplex PCR method developed here is time-saving, specific, convenient, and may prove to be an invaluable tool for molecular detection and epidemiological investigation of Klebsiella pneumoniae and Chryseobacterium in the field of aquaculture.

Raman spectroscopy combined with multivariate statistical algorithms for the simultaneous screening of cervical and breast cancers.

Breast and cervical cancers are becoming the leading causes of death among women worldwide, but current diagnostic methods have many drawbacks, such as being time-consuming and high cost. Raman spectroscopy, as a rapid, reliable, and non-destructive spectroscopic detection technique, has achieved many breakthrough results in the screening and prognosis of various cancer tumors. Therefore, in this study, Raman spectroscopy technology was used to diagnose breast cancer and cervical cancer. A total of 225 spectra were recorded from 87 patients with cervical cancer, 60 patients with breast cancer, and 78 healthy individuals. The obvious difference in Raman spectrum between the three groups was mainly shown at 809 cm-1 (tyrosine), 958 cm-1 (carotenoid), 1004 cm-1 (phenylalanine), 1154 cm-1 (ß-carotene), 1267 cm-1 (Amide III), 1445 cm-1 (phospholipids), 1515 cm-1 (ß-carotene), and 1585 cm-1 (C = C olefinic stretch). We used one-way analysis of variance for these peaks and demonstrated that they were significantly different. Then, we combined the detected Raman spectra with multivariate statistical calculations using the principal component analysis-linear discrimination algorithm (PCA-LDA) to discriminate between the three groups of collected serum samples. The diagnostic results showed that the model's accuracy, precision, recall, and F1 score of the model were 92.90%, 92.62%, 92.10%, and 92.36%, respectively. These results suggest that Raman spectroscopy can achieve ultra-sensitive detection of serum, and the developed diagnostic models have great potential for the prognosis and simultaneous screening of cervical and breast cancers.

Development of a Strategy for L-Lactic Acid Production by Rhizopus oryzae Using Zizania latifolia Waste and Cane Molasses as Carbon Sources.

As a biodegradable and renewable material, polylactic acid is considered a major environmentally friendly alternative to petrochemical plastics. Microbial fermentation is the traditional method for lactic acid production, but it is still too expensive to compete with the petrochemical industry. Agro-industrial wastes are generated from the food and agricultural industries and agricultural practices. The utilization of agro-industrial wastes is an important way to reduce costs, save energy and achieve sustainable development. The present study aimed to develop a method for the valorization of Zizania latifolia waste and cane molasses as carbon sources for L-lactic acid fermentation using Rhizopus oryzae LA-UN-1. The results showed that xylose derived from the acid hydrolysis of Z. latifolia waste was beneficial for cell growth, while glucose from the acid hydrolysis of Z. latifolia waste and mixed sugars (glucose and fructose) from the acid hydrolysis of cane molasses were suitable for the accumulation of lactic acid. Thus, a three-stage carbon source utilization strategy was developed, which markedly improved lactic acid production and productivity, respectively reaching 129.47 g/L and 1.51 g/L·h after 86 h of fermentation. This work demonstrates that inexpensive Z. latifolia waste and cane molasses can be suitable carbon sources for lactic acid production, offering an efficient utilization strategy for agro-industrial wastes.

Antimicrobial activity of phenyllactic acid against Klebsiella pneumoniae and its effect on cell wall membrane and genomic DNA.

As Klebsiella pneumoniae (KP) has acquired high levels of resistance to multiple antibiotics, it is considered a worldwide pathogen of concern, and substitutes for traditional antibiotics are urgently needed. 3-Phenyllactic acid (PLA) has been reported to have antimicrobial activity against food-borne bacteria. However, there was no experiment evidence for the exact antibacterial effect and mechanism of PLA kills pathogenic KP. In this study, the Oxford cup method indicated that PLA is effective to KP with a minimum inhibitory concentration of 2.5 mg/mL. Furthermore, PLA inhibited the growth and biofilm formation of in a time- and concentration-dependent manner. In vivo, PLA could significantly increase the survival rate of infected mice and reduce the pathological tissue damage. The antibacterial mode of PLA against KP was further explored. Firstly, scanning electron microscopy illustrated the disruption of cellular ultrastructure caused by PLA. Secondly, measurement of leaked alkaline phosphatase demonstrated that PLA disrupted the cell wall integrity of KP and flow cytometry analysis with propidium iodide staining suggested that PLA damaged the cell membrane integrity. Finally, the results of fluorescence spectroscopy and agarose gel electrophoresis demonstrated that PLA bound to genomic DNA and initiated its degradation. The anti-KP mode of action of PLA was attributed to the destruction of the cell wall, membrane, and genomic DNA binding. These findings suggest that PLA has great potential applications as antibiotic substitutes in feed additives against KP infection in animals.

High-definition image transmission through dynamically perturbed multimode fiber by a self-attention based neural network.

We implement faithful multimode fiber (MMF) image transmission by a self-attention-based neural network. Compared with a real-valued artificial neural network (ANN) based on a convolutional neural network (CNN), our method utilizes a self-attention mechanism to achieve a higher image quality. The enhancement measure (EME) and structural similarity (SSIM) of the dataset collected in the experiment improved by 0.79 and 0.04; the total number of parameters can be reduced by up to 25%. To enhance the robustness of the neural network to MMF bending in image transmission, we use a simulation dataset to prove that the hybrid training method is helpful in MMF transmission of a high-definition image. Our findings may pave the way for simpler and more robust single-MMF image transmission schemes with hybrid training; SSIM on datasets under different disturbances improve by 0.18. This system has the potential to be applied to various high-demand image transmission tasks, such as endoscopy.

Isolation and screening of a chitin deacetylase producing Bacillus cereus and its potential for chitosan preparation.

Chitosan is a biopolymer material extracted from marine biomass waste such as shrimp and crab shells, which has good biocompatibility and degradability with great potential for application in the field of wastewater treatment and soil remediation. The higher the degree of deacetylation (DD), the better the adsorption performance of chitosan. Chitin deacetylase (CDA) can specifically catalyze the deacetylate of chitin in a green reaction that is environmentally friendly. However, the scarcity of high yielding chitin deacetylase strains has been regarded as the technical bottleneck of chitosan green production. Here, we screened a natural chitin degrading bacterium from coastal mud and identified it as Bacillus cereus ZWT-08 by re-screening the chitin deacetylase activity and degree of deacetylation values. By optimizing the medium conditions and enzyme production process, ZWT-08 was cultured in fermentation medium with 1% (m/V) glucose and yeast extract at pH 6.0, 37°C, and a stirring speed of 180 r/min. After fermenting in 5 L fermenter for 48 h, the deacetylation activity of the supernatant reached 613.25 U/mL. Electron microscopic examination of the chitin substrate in the fermentation medium revealed a marshmallow-like fluffy texture on its structural surface. Meanwhile, 89.29% of the acetyl groups in this chitin substrate were removed by enzymatic digestion of chitin deacetylase produced by ZWT-08, resulting in the preparation of chitosan a degree of deacetylation higher than 90%. As an effective strain for chitosan production, Bacillus cereus ZWT-08 plays a positive role in the bioconversion of chitin and the upgrading of the chitosan industry.

Diagnoses in multiple types of cancer based on serum Raman spectroscopy combined with a convolutional neural network: Gastric cancer, colon cancer, rectal cancer, lung cancer.

Cancer is one of the major diseases that seriously threaten human health. Timely screening is beneficial to the cure of cancer. There are some shortcomings in current diagnosis methods, so it is very important to find a low-cost, fast, and nondestructive cancer screening technology. In this study, we demonstrated that serum Raman spectroscopy combined with a convolutional neural network model can be used for the diagnosis of four types of cancer including gastric cancer, colon cancer, rectal cancer, and lung cancer. Raman spectra database containing four types of cancer and healthy controls was established and a one-dimensional convolutional neural network (1D-CNN) was constructed. The classification accuracy of the Raman spectra combined with the 1D-CNN model was 94.5%. A convolutional neural network (CNN) is regarded as a black box, and the learning mechanism of the model is not clear. Therefore, we tried to visualize the CNN features of each convolutional layer in the diagnosis of rectal cancer. Overall, Raman spectroscopy combined with the CNN model is an effective tool that can be used to distinguish different cancer from healthy controls.

Compared between support vector machine (SVM) and deep belief network (DBN) for multi-classification of Raman spectroscopy for cervical diseases.

In this study, a minimally invasive test method for cervical cancer in vitro was proposed by comparing Raman spectroscopy with support vector machine (SVM) model and deep belief network (DBN) model. The serum Raman spectra of cervical cancer, hysteromyoma, and healthy people were collected. After data processing, SVM classification model and DBN classification model were built respectively. The experimental results show that when the DBN network algorithm is used, the sample test set can be divided accurately and the result of cross-validation is ideal. Compared with the traditional SVM algorithm, this method firstly screened the effective feature matrix from the data, and then classified the data. With high efficiency and accuracy, based on 445 samples collected, this method improved the accuracy by 13.93%±2.47% compared with the SVM method, and provided a new direction and idea for the in vitro diagnosis of cervical diseases.

A classification model for detection of ductal carcinoma in situ by Fourier transform infrared spectroscopy based on deep structured semantic model.

At present, deep learning is widely used in spectral data processing. Deep learning requires a large amount of data for training, while the collection of biological serum spectra is limited by sample numbers and labor costs, so it is impractical to obtain a large amount of serum spectral data for disease detection. In this study, we propose a spectral classification model based on the deep structured semantic model (DSSM) and successfully apply it to Fourier Transform Infrared (FT-IR) spectroscopy for ductal carcinoma in situ (DCIS) detection. Compared with the traditional deep learning model, we match the spectral data into positive and negative pairs according to whether the spectra are from the same category. The DSSM structure is constructed by extracting features according to the spectral similarity of spectra pairs. This new construction model increases the data amount used for model training and reduces the dimension of spectral data. Firstly, the FT-IR spectra are paired. The spectra pairs are labeled as positive pairs if they come from the same category, and the spectra pairs are labeled as negative pairs if they come from different categories. Secondly, two spectra in each spectra pair are put into two deep neural networks of the DSSM structure separately. Then the spectral similarity between the output feature maps of two deep neural networks is calculated. The DSSM structure is trained by maximizing the conditional likelihood of the spectra pairs from the same category. Thirdly, after the training of DSSM is done, the training set and testing set are input into two deep neural networks separately. The output feature maps of the training set are put into the reference library. Lastly, the k-nearest neighbor (KNN) model is used for classification according to Euclidean distances between the output feature map of each unknown sample to the reference library. The category of the unknown sample is judged according to the categories of k nearest samples. We also use principal component analysis (PCA) to reduce dimension for comparison. The accuracies of the KNN model, principal component analysis-k nearest neighbor (PCA-KNN) model, and deep structured semantic model-k nearest neighbor (DSSM-KNN) model are 78.8%, 72.7%, and 97.0%, which proves that our proposed model has higher accuracy.

Efficient Co-production of Docosahexaenoic Acid Oil and Carotenoids in Aurantiochytrium sp. Using a Light Intensity Gradient Strategy.

Aurantiochytrium is a promising source of docosahexaenoic acid (DHA) and carotenoids, but their synthesis is influenced by environmental stress factors. In this study, the effect of different light intensities on the fermentation of DHA oil and carotenoids using Aurantiochytrium sp. TZ209 was investigated. The results showed that dark culture and low light intensity conditions did not affect the normal growth of cells, but were not conducive to the accumulation of carotenoids. High light intensity promoted the synthesis of DHA and carotenoids, but caused cell damage, resulting in a decrease of oil yield. To solve this issue, a light intensity gradient strategy was developed, which markedly improved the DHA and carotenoid content without reducing the oil yield. This strategy produced 30.16 g/L of microalgal oil with 15.11 g/L DHA, 221 µg/g astaxanthin, and 386 µg/g ß-carotene. This work demonstrates that strain TZ209 is a promising DHA producer and provides an efficient strategy for the co-production of DHA oil together with carotenoids.

Simultaneous improvement of the thermostability and activity of lactic dehydrogenase from Lactobacillus rossiae through rational design.

d-Phenyllactic acid, is a versatile organic acid with wide application prospects in the food, pharmaceutical and material industries. Wild-type lactate dehydrogenase LrLDH from Lactobacillus rossiae exhibits a high catalytic performance in the production of d-phenyllactic acid from phenylpyruvic acid or sodium phenylpyruvate, but its industrial application is hampered by poor thermostability. Here, computer aided rational design was applied to improve the thermostability of LrLDH. By using HotSpot Wizard 3.0, five hotspot residues (N218, L237, T247, D249 and S301) were identified, after which site-saturation mutagenesis and combined mutagenesis were performed. The double mutant D249A/T247I was screen out as the best variant, with optimum temperature, t 1/2, and T 10 50 that were 12 °C, 17.96 min and 19 °C higher than that of wild-type LrLDH, respectively. At the same time, the k cat/K m of D249A/T247I was 1.47 s-1 mM-1, which was 3.4 times higher than that of the wild-type enzyme. Thus rational design was successfully applied to simultaneously improve the thermostability and catalytic activity of LrLDH to a significant extent. The results of molecular dynamics simulations and molecular structure analysis could explain the mechanisms for the improved performance of the double mutant. This study shows that computer-aided rational design can greatly improve the thermostability of d-lactate dehydrogenase, offering a reference for the modification of other enzymes.

Edge detection in single multimode fiber imaging based on deep learning.

We propose a new edge detection scheme based on deep learning in single multimode fiber imaging. In this scheme, we creatively design a novel neural network, whose input is a one-dimensional light intensity sequence, and the output is the edge detection result of the target. Different from the traditional scheme, we can directly obtain the edge information of unknown objects by using this neural network without rebuilding the image. Simulation and experimental results show that, compared with the traditional method, this method can get better edge details, especially in the case of low sampling rates. It can increase the structural similarity index of edge detection imaging from 0.38 to 0.62 at the sampling rate of 0.6%. At the same time, the robustness of the method to fiber bending is also proved. This scheme improves the edge detection performance of endoscopic images and provides a promising way for the practical application of multimode fiber endoscopy.

Using FADOF to eliminate the background light influence in ghost imaging.

The high solar background during the day adversely affects the long distance daytime operations of ghost imaging. It is extremely hard to distinguish the signal light from the background noise light after they are both converted to voltage or current signals by the bucket detector, so spectral filtering before the detector is quite important. In this work, a Faraday anomalous dispersion optical filter (FADOF) is used in eliminating the background light influence in ghost imaging. Results of lab experiment show that the background light noise tolerance of the ghost imaging with FADOF is at least 18 times bigger than that with a 10 nm optical filter. The method has simple structure, great performance and great algorithms compatibility.

Breast cancer early detection by using Fourier-transform infrared spectroscopy combined with different classification algorithms.

Early detection of breast cancer is of great value in improving the prognosis. The current detection methods of breast cancer have their own limitations. In this study, we investigated the feasibility of Fourier Transform Infrared (FT-IR) spectroscopy combined with different classification algorithms for the early detection of breast cancer in a large sample of 526 patients, including 308 invasive breast cancer, 101 ductal carcinoma in situ, and 117 healthy controls. The serum was measured with FT-IR spectroscopy. Kennard-Stone (KS) algorithm was used to divide the data into the training set and testing set. Support vector machine (SVM) model and back propagation neural network (BPNN) model were used to distinguish ductal carcinoma in situ, invasive breast cancer from healthy controls. The accuracies of the SVM model and BPNN model were 92.9% and 94.2%. To determine the effect of different material absorption bands on early detection, the band was divided into four parts including 900-1425 cm-1, 1475-1710 cm-1, 2800-3000 cm-1, and 3090-3700 cm-1, to be modeled and detected respectively. The final results showed that the ranges 900-1425 cm-1 and 1475-1710 cm-1 had superior classification accuracies. The region 900-1425 cm-1 corresponded to the lipids, proteins, sugar, and nucleic acids, and the region 1475-1710 cm-1 corresponded to the proteins. The biochemical substances in other bands also contributed some unique potential to the classification, so the classification accuracy was the best in the full band. The study indicates that serum FT-IR spectroscopy combined with SVM and BPNN models is an effective tool for the early detection of breast cancer.

Rapid discrimination of hepatic echinococcosis patients' serum using vibrational spectroscopy combined with support vector machines.

Echinococcosis is a severe zoonotic parasitic disease, and it is continuing to be a significant public health issue. The course of the disease is usually slow, and patients often remain asymptomatic for years. There is no standardized and widely accepted treatment, so early and accurate diagnosis is essential. Herein, this study utilized vibrational spectroscopic techniques, namely Raman and Fourier Transform Infrared (FTIR) spectroscopy, to quickly and accurately distinguish hepatic echinococcosis (HE) patients' serum from the healthy group. Serum samples were collected from HE patients as well as healthy control subjects, and then the Raman and FTIR spectra of the two groups were recorded. After a series of pre-processing, support vector machines (SVMs) were then used to establish the classification models for the two spectral data sets. The performance of each diagnostic model was evaluated using leave-one-out cross-validation (LOOCV) and hold-out validation methods, respectively. For the distinction between HE and healthy groups, these two spectroscopic techniques had achieved satisfactory classification results, and the diagnostic capabilities of the Raman technique were comparable to that of the FTIR method. The results demonstrate that vibrational spectroscopy has great potential in the rapid and accurate detection of HE and is expected to make up for the shortcomings of the existing clinical diagnosis methods.

Rapid detection of hysteromyoma and cervical cancer based on serum surface-enhanced Raman spectroscopy and a support vector machine.

In this study, we investigated the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with a support vector machine (SVM) algorithm to discriminate hysteromyoma and cervical cancer from healthy volunteers rapidly. SERS spectra of serum samples were recorded from 30 hysteromyoma patients, 36 cervical cancer patients as well as 30 healthy subjects. SVM was used to establish the classification models, and three types of kernel functions, namely linear, polynomial, and Gaussian radial basis function (RBF), were utilized for comparison. When the polynomial kernel function was employed, the overall diagnostic accuracy for classifying the three groups could achieve 86.5%. In addition, when the optimal kernel function was selected, the diagnostic accuracy for identifying healthy versus hysteromyoma, healthy versus cervical cancer, and hysteromyoma versus cervical cancer reached 98.3%, 93.9%, and 90.9%, respectively. The current results indicate that serum SERS technology, together with the SVM algorithm, is expected to become a clinical tool for rapid screening of hysteromyoma and cervical cancer.

Enhanced Antimicrobial Cellulose/Chitosan/ZnO Biodegradable Composite Membrane.

In this study, chitosan and sugarcane cellulose were used as film-forming materials, while the inorganic agent zinc oxide (ZnO) and natural compound phenyllactic acid (PA) were used as the main bacteriostatic components to fabricate biodegradable antimicrobial composite membranes. The water absorption and antimicrobial properties were investigated by adjusting the concentration of PA. The scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) results demonstrated that the components of the composite membrane were successfully integrated. The addition of ZnO improved the mechanical and antimicrobial properties of the composite membrane, while the addition of PA with high crystallinity significantly reduced the water absorption and swelling. Moreover, the addition of 0.5% PA greatly improved the water absorption of the composite membrane. The results of antimicrobial experiments showed that PA improved the antimicrobial activity of the composite membrane against Staphylococcus aureus, Escherichia coli, Aspergillus niger and Penicillium rubens. Among them, 0.3% PA had the best antimicrobial effect against S. aureus, E. coli and A. niger, while 0.7% PA had the best antimicrobial effect against P. rubens.

Clustering-based CLEAN algorithm in ghost imaging with sparse spatial frequencies.

When insufficient samples in the spatial frequency domain could be effectively compensated by the modified CLEAN algorithm, a novel aperture-synthetic scheme of ghost imaging takes advantage of a superior speed of modulation and an enhancement on the spatial resolution. However, there still exist some imperfections in the modified CLEAN reconstructions, such as the fact that some omitted scatter noise still remains or the object contour may be incomplete. Therefore, we optimize the modified CLEAN algorithm by proposing a density clustering algorithm to overcome these drawbacks and improve the visual quality.

Thermal and temporal characteristics of Faraday anomalous dispersion optical filters based on a hollow cathode lamp.

As a new, to the best of our knowledge, alternative to the saturated vapor-cell-based Faraday anomalous dispersion optical filter (FADOF), the FADOF based on a hollow cathode lamp (HCL-FADOF) not only enables the FADOF to work normally at room temperature without heating, but also has some new features due to the inherent characteristics of the HCL. In this Letter, we implement an HCL-FADOF operating on the rubidium D2 line and experimentally investigate the effect of ambient temperature on its performance and cold-start characteristics. Results show that the HCL-FADOF can provide excellent stability within a large temperature range, even at temperatures below 0°C. A comparison of the start performance between the HCL-FADOF and FADOF using saturated vapor cells is also provided. This work shows unique features of the HCL-FADOF in a low-temperature environment and its quick-start advantage, which provides a solid foundation for extensive applications.

Compressive endoscopic imaging with complementary light modulation.

We propose an effective endoscopic imaging method utilizing compressive sensing (CS) theory on the basis of complementary light modulation of a spatial light modulator. Both the simulated and the experimental results show that complementary compressive sensing (CCS) always needs less time to obtain better work than conventional CS with normal modulation at the same sampling rate. First, the speed of CCS is at least twice as fast as CS. Second, in comparison with CS, CCS can improve the signal-to-noise ratio of the reconstructed image by 49.7%, which indicates that this method is of great significance to endoscopic applications in terms of image fidelity and denoising performance.