Predictive value of PD-L1 and TMB for short-term efficacy prognosis in non-small cell lung cancer and construction of prediction models.

Objective: To investigate the correlation between programmed death ligand 1(PD-L1), tumor mutation burden (TMB) and the short-term efficacy and clinical characteristics of anti-PD-1 immune checkpoint inhibitor combination chemotherapy in NSCLC patients. The efficacy of the prediction model was evaluated. Methods: A total of 220 NSCLC patients receiving first-line treatment with anti-PD-1 immune checkpoint inhibitor combined with chemotherapy were retrospectively collected. The primary endpoint was short-term efficacy ORR. The correlation between short-term efficacy, PD-L1, TMB, and clinical characteristics using χ2 test or t-test was evaluated. Screen the independent prognostic factors using univariate and multivariate logistic regression analyses, and construct a nomogram prediction model using the "rms" package in R software. Using receiver operating characteristic (ROC) curve analysis to evaluate the independent Prognostic factors and the prediction model. Using decision curve analysis (DCA) to verify the superiority of the prediction model. Results: The mean values of PD-L1, TMB, neutrophils, lymphocytes, neutrophil-to-lymphocyte ratio, and albumin were the highest in the ORR group, PD-L1 expression and TMB correlated with epidermal growth factor receptor expression. Multivariate analyses showed that PD-L1, TMB, and neutrophil were independent prognostic factors for ORR. The area under the ROC curve (AUC) values of the ROC constructed based on these three indicators were 0.7104, 0.7139, and 0.7131, respectively. The AUC value under the ROC of the nomogram model was 0.813. The DCA of the model showed that all three indicators used together to build the prediction model of the net return were higher than those of the single indicator prediction model. Conclusion: PD-L1, TMB, and neutrophils are independent prognostic factors for short-term efficacy. The nomogram prediction model constructed using these three indicators can further improve predictive efficacy of ICIs in patients with NSCLC.

Disposal and resource utilization of waste masks: a review.

Waste masks pose a serious threat to the environment, including marine plastic pollution and soil pollution risks caused by landfills since the outbreak of COVID-19. Currently, numerous effective methods regarding disposal and resource utilization of waste masks have been reported, containing physical, thermochemical, and solvent-based technologies. As for physical technologies, the mechanical properties of the mask-based materials could be enhanced and the conductivity or antibacterial activity was endowed by adding natural fibers or inorganic nanoparticles. Regarding thermochemical technologies, catalytic pyrolysis could yield considerable hydrogen, which is an eco-friendly resource, and would mitigate the energy crisis. Noticeably, the solvent-based technology, as a more convenient and efficient method, was also considered in this paper. In this way, soaking the mask directly in a specific chemical reagent changes the original structure of polypropylene and obtains multi-functional materials. The solvent-based technology is promising in the future with the researches of sustainable and universally applicable reagents. This review could provide guidance for utilizing resources of waste masks and address the issues of plastic pollution.

Signal Source Localization with Long-Term Observations in Distributed Angle-Only Sensors.

Angle-only sensors cannot provide range information of targets and in order to determine accurate position of a signal source, one can connect distributed passive sensors with communication links and implement a fusion algorithm to estimate target position. To measure moving targets with sensors on moving platforms, most of existing algorithms resort to the filtering method. In this paper, we present two fusion algorithms to estimate both the position and velocity of moving target with distributed angle-only sensors in motion. The first algorithm is termed as the gross least square (LS) algorithm, which takes all observations from distributed sensors together to form an estimate of the position and velocity and thus needs a huge communication cost and a huge computation cost. The second algorithm is termed as the linear LS algorithm, which approximates locations of sensors, locations of targets, and angle-only measures for each sensor by linear models and thus does not need each local sensors to transmit raw data of angle-only observations, resulting in a lower communication cost between sensors and then a lower computation cost at the fusion center. Based on the second algorithm, a truncated LS algorithm, which estimates the target velocity through an average operation, is also presented. Numerical results indicate that the gross LS algorithm, without linear approximation operation, often benefits from more observations, whereas the linear LS algorithm and the truncated LS algorithm, both bear lower communication and computation costs, may endure performance loss if the observations are collected in a long period such that the linear approximation model becomes mismatch.

Quantitative analysis of blended corn-olive oil based on Raman spectroscopy and one-dimensional convolutional neural network.

Blended vegetable oil is a vital product in the vegetable oil market, and quantifying high-value vegetable oil is of great significance to protect the rights and interests of consumers. In this study, we established a one-dimensional convolutional neural network (1D CNN) quantitative identification model based on Raman spectra to identify the amount of olive oil in a corn-olive oil blend. The results show that the 1D CNN model based on 315 extended average Raman spectra can quantitatively identify the content of olive oil, with R2p and RMSEP values of 0.9908 and 0.7183 respectively. Compared with partial least squares regression (PLSR) and support vector regression (SVR), although the index is not optimal, it provides a new analytical method for the quantitative identification of vegetable oil.

Identification and quantification of adulterated honey by Raman spectroscopy combined with convolutional neural network and chemometrics.

In this study, Raman spectroscopy combined with convolutional neural network (CNN) and chemometrics was used to achieve the identification and quantification of honey samples adulterated with high fructose corn syrup, rice syrup, maltose syrup and blended syrup, respectively. The shallow CNNs utilized to analyze honey mixed with single-variety syrup classified samples into four categories by the adulteration concentration with more than 97% accuracy, and the general CNN model for simultaneously detecting honey adulterated with any type of syrup obtained an accuracy of 94.79%. The established CNNs had the best performance compared with several chemometric classification algorithms. In addition, partial least square regression (PLS) successfully predicted the purity of honey mixed with single syrup, while coefficients of determination and root mean square errors of prediction were greater than 0.98 and less than 3.50, respectively. Therefore, the proposed methods based on Raman spectra have important practical significance for food safety and quality control of honey products.

Rice black-streaked dwarf virus P10 promotes phosphorylation of GAPDH (glyceraldehyde-3-phosphate dehydrogenase) to induce autophagy in Laodelphax striatellus.

Macroautophagy/autophagy is an important innate and adaptive immune response that can clear microbial pathogens through guiding their degradation. Virus infection in animals and plants is also known to induce autophagy. However, how virus infection induces autophagy is largely unknown. Here, we provide evidence that the early phase of rice black-streaked dwarf virus (RBSDV) infection in Laodelphax striatellus can also induce autophagy, leading to suppression of RBSDV invasion and accumulation. We have determined that the main capsid protein of RBSDV (P10) is the inducer of autophagy. RBSDV P10 can specifically interact with GAPDH (glyceraldehyde-3-phosphate dehydrogenase), both in vitro and in vivo. Silencing of GAPDH in L. striatellus could significantly reduce the activity of autophagy induced by RBSDV infection. Furthermore, our results also showed that both RBSDV infection and RBSDV P10 alone can promote phosphorylation of AMP-activated protein kinase (AMPK), resulting in GAPDH phosphorylation and relocation of GAPDH from the cytoplasm into the nucleus in midgut cells of L. striatellus or Sf9 insect cells. Once inside the nucleus, phosphorylated GAPDH can activate autophagy to suppress virus infection. Together, these data illuminate the mechanism by which RBSDV induces autophagy in L. striatellus, and indicate that the autophagy pathway in an insect vector participates in the anti-RBSDV innate immune response.Abbreviations3-MA: 3-methyladenine; AMPK: AMP-activated protein kinase; ATG: autophagy-related; co-IP: co-immunoprecipitation; DAPI: 4',6-diamidino-2-phenylindole; dpf: days post-feeding; dsRNA: double-stranded RNA; GAPDH: glyceraldehyde-3-phosphate dehydrogenase; GST: glutathione-S-transferase; RBSDV: Rice black-streaked dwarf virus; TEM: transmission electron microscope.

Detection of Tomato Spotted Wilt Virus (TSWV) Infection in Plants Using DAS-ELISA and Dot-ELISA.

Plant viruses cause severe damages to crop productions each year worldwide. To prevent the losses caused by plant viruses, it is necessary to develop specific and efficient diagnostic tools to detect viruses. Among the current virus detection techniques, serological detection methods are considered to be rapid, simple, sensitive, and high throughput. Therefore, serological detection methods such as double antibody sandwich enzyme-linked immunosorbent assay (DAS-ELISA), triple antibody sandwich ELISA (TAS-ELISA), antigen coated plate-ELISA (ACP-ELISA), Dot-ELISA and tissue print-ELISA as well as colloidal gold immunochromatographic strip are now wildly used to detect viruses in plants. In this chapter, we describe the DAS-ELISA and Dot-ELISA methods, and their applications in the detection of Tomato spotted wilt virus (TSWV) infection in plants. These two methods can be easily adapted for diagnosis of other plant viruses.

A Novel Rice Curl Dwarf-Associated Picornavirus Encodes a 3C Serine Protease Recognizing Uncommon EPT/S Cleavage Sites.

Picornaviruses cause diseases in a wide range of vertebrates, invertebrates and plants. Here, a novel picornavirus was identified by RNA-seq technology from rice plants showing dwarfing and curling symptoms, and the name rice curl dwarf-associated virus (RCDaV) is tentatively proposed. The RCDaV genome consists of an 8,987 nt positive-stranded RNA molecule, excluding a poly(A) tail, that encodes two large polyproteins. Using in vitro cleavage assays, we have identified that the RCDaV 3C protease (3Cpro) as a serine protease recognizes the conserved EPT/S cleavage site which differs from the classic Q(E)/G(S) sites cleaved by most picornaviral 3C chymotrypsin-like cysteine proteases. Therefore, we comprehensively deciphered the RCDaV genome organization and showed that the two polyproteins of RCDaV can be cleaved into 12 mature proteins. We found that seven unclassified picornaviruses also encode a 3Cpro similar to RCDaV, and use the highly conserved EPT/S as the cleavage site. The precise genome organizations of these viruses were illustrated. Moreover, RCDaV and the seven unclassified picornaviruses share high sequence identities and similar genome organizations, and cluster into a distinct clade in the order Picornavirales. Our study provides valuable information for the understanding of picornaviral 3Cpros, deciphers the genome organization of a few relatively obscure picornaviruses, and lays the foundation for further pathogenesis research on these viruses.

Prediction of Soil Clay Content and Cation Exchange Capacity Using Visible Near-Infrared Spectroscopy, Portable X-ray Fluorescence, and X-ray Diffraction Techniques.

This article investigates a novel data fusion method to predict clay content and cation exchange capacity using visible near-infrared (visNIR) spectroscopy, portable X-ray fluorescence (pXRF), and X-ray diffraction (XRD) techniques. A total of 367 soil samples from two study areas in regional Australia were analyzed and intra- and interarea calibration options were explored. Cubist models were constructed using information from each device independently and in combination. pXRF produced the most accurate predictions of any individual device. Models based on fused data significantly improved the accuracy of predictions compared with those based on individual devices. The combination of pXRF and visNIR had the greatest performance. Overall, the relative increase in Lin's concordance correlation coefficient ranged from 1% to 12% and the corresponding decrease in root-mean-square error (RMSE) ranged from 10% to 46%. Provision of XRD data resulted in a decrease in observed RMSE values, although differences were not significant. Validation metrics were less promising when models were calibrated in one study area and then transferred to the other. Observed RMSE values were ∼2 to 3 times larger under this model transfer scenario and independent use of XRD was found to have the best overall performance.

Exploration of total synchronous fluorescence spectroscopy combined with pre-trained convolutional neural network in the identification and quantification of vegetable oil.

In order to distinguish different vegetable oils, adulterated vegetable oils, and to identify and quantify counterfeit vegetable oils, a method based on a small sample size of total synchronous fluorescence (TSyF) spectra combined with convolutional neural network (CNN) was proposed. Four typical vegetable oils were classified by three ways of fine-tuning the pre-trained CNN, the pre-trained CNN as a feature extractor, and traditional chemometrics. The pre-trained CNN was combined with support vector machines to distinguish adulterated sesame oil and counterfeit sesame oil separately with 100% correct classification rates. The pre-trained CNN combined with partial least square regression was used to predict the level of counterfeit sesame oil. The coefficient of determination for calibration (Rc2) values were all greater than 0.99, and the root mean square errors of validation were 0.81% and 1.72%, respectively. These results show that it is feasible to combine TSyF spectra with CNN for vegetable oil identification.

Total synchronous fluorescence spectroscopy coupled with deep learning to rapidly identify the authenticity of sesame oil.

The quality of sesame oil (SO) has been paid more and more attention. In this study, total synchronous fluorescence (TSyF) spectroscopy and deep neural networks were utilized to identify counterfeit and adulterated sesame oils. Firstly, typical samples including pure SO, counterfeit sesame oil (CSO) and adulterated sesame oil (ASO) were characterized by TSyF spectra. Secondly, three data augmentation methods were selected to increase the number of spectral data and enhance the robustness of the identification model. Then, five deep network architectures, including Simple Recurrent Neural Network (Simple RNN), Long Short-Term Memory (LSTM) network, Gated Recurrent Unit (GRU) network, Bidirectional LSTM (BLSTM) network and LSTM fortified with Convolutional Neural Network (LSTMC), were designed to identify the CSO and trace the source with 100% accuracy. Finally, ASO samples were also 100% correctly identified by training these network architectures. These results supported the feasibility of the novel method.

An Advanced Lipid Metabolism System Revealed by Transcriptomic and Lipidomic Analyses Plays a Central Role in Peanut Cold Tolerance.

Cold stress restricts peanut (Arachis hypogaea L.) growth, development, and yield. However, the specific mechanism of cold tolerance in peanut remains unknown. Here, the comparative physiological, transcriptomic, and lipidomic analyses of cold tolerant variety NH5 and cold sensitive variety FH18 at different time points of cold stress were conducted to fill this gap. Transcriptomic analysis revealed lipid metabolism including membrane lipid and fatty acid metabolism may be a significant contributor in peanut cold tolerance, and 59 cold-tolerant genes involved in lipid metabolism were identified. Lipidomic data corroborated the importance of membrane lipid remodeling and fatty acid unsaturation. It indicated that photosynthetic damage, resulted from the alteration in fluidity and integrity of photosynthetic membranes under cold stress, were mainly caused by markedly decreased monogalactosyldiacylglycerol (MGDG) levels and could be relieved by increased digalactosyldiacylglycerol (DGDG) and sulfoquinovosyldiacylglycerol (SQDG) levels. The upregulation of phosphatidate phosphatase (PAP1) and phosphatidate cytidylyltransferase (CDS1) inhibited the excessive accumulation of PA, thus may prevent the peroxidation of membrane lipids. In addition, fatty acid elongation and fatty acid ß-oxidation were also worth further studied in peanut cold tolerance. Finally, we constructed a metabolic model for the regulatory mechanism of peanut cold tolerance, in which the advanced lipid metabolism system plays a central role. This study lays the foundation for deeply analyzing the molecular mechanism and realizing the genetic improvement of peanut cold tolerance.

Comparative Transcriptome-Based Mining and Expression Profiling of Transcription Factors Related to Cold Tolerance in Peanut.

Plants tolerate cold stress by regulating gene networks controlling cellular and physiological traits to modify growth and development. Transcription factor (TF)-directed regulation of transcription within these gene networks is key to eliciting appropriate responses. Identifying TFs related to cold tolerance contributes to cold-tolerant crop breeding. In this study, a comparative transcriptome analysis was carried out to investigate global gene expression of entire TFs in two peanut varieties with different cold-tolerant abilities. A total of 87 TF families including 2328 TF genes were identified. Among them, 445 TF genes were significantly differentially expressed in two peanut varieties under cold stress. The TF families represented by the largest numbers of differentially expressed members were bHLH (basic helix-loop-helix protein), C2H2 (Cys2/His2 zinc finger protein), ERF (ethylene-responsive factor), MYB (v-myb avian myeloblastosis viral oncogene homolog), NAC (NAM, ATAF1/2, CUC2) and WRKY TFs. Phylogenetic evolutionary analysis, temporal expression profiling, protein-protein interaction (PPI) network, and functional enrichment of differentially expressed TFs revealed the importance of plant hormone signal transduction and plant-pathogen interaction pathways and their possible mechanism in peanut cold tolerance. This study contributes to a better understanding of the complex mechanism of TFs in response to cold stress in peanut and provides valuable resources for the investigation of evolutionary history and biological functions of peanut TFs genes involved in cold tolerance.

The genomic evolution of H1 influenza A viruses from swine detected in the United States between 2009 and 2016.

Transmission of influenza A virus (IAV) from humans to swine occurs with relative frequency and is a critical contributor to swine IAV diversity. Subsequent to the introduction of these human seasonal lineages, there is often reassortment with endemic viruses and antigenic drift. To address whether particular genome constellations contributed to viral persistence following the introduction of the 2009 H1N1 human pandemic virus to swine in the USA, we collated and analysed 616 whole genomes of swine H1 isolates. For each gene, sequences were aligned, the best-known maximum likelihood phylogeny was inferred, and each virus was assigned a clade based upon its evolutionary history. A time-scaled Bayesian approach was implemented for the haemagglutinin (HA) gene to determine the patterns of genetic diversity over time. From these analyses, we observed an increase in genome diversity across all H1 lineages and clades, with the H1-γ and H1-δ1 genetic clades containing the greatest number of unique genome patterns. We documented 74 genome patterns from 2009 to 2016, of which 3 genome patterns were consistently detected at a significantly higher level than others across the entire time period. Eight genome patterns increased significantly, while five genome patterns were shown to decline in detection over time. Viruses with genome patterns identified as persisting in the US swine population may possess a greater capacity to infect and transmit in swine. This study highlights the emerging genetic diversity of US swine IAV from 2009 to 2016, with implications for swine and public health and vaccine control efforts.

Dead-End Elimination with a Polarizable Force Field Repacks PCNA Structures.

A balance of van der Waals, electrostatic, and hydrophobic forces drive the folding and packing of protein side chains. Although such interactions between residues are often approximated as being pairwise additive, in reality, higher-order many-body contributions that depend on environment drive hydrophobic collapse and cooperative electrostatics. Beginning from dead-end elimination, we derive the first algorithm, to our knowledge, capable of deterministic global repacking of side chains compatible with many-body energy functions. The approach is applied to seven PCNA x-ray crystallographic data sets with resolutions 2.5-3.8 Å (mean 3.0 Å) using an open-source software. While PDB_REDO models average an Rfree value of 29.5% and MOLPROBITY score of 2.71 Å (77th percentile), dead-end elimination with the polarizable AMOEBA force field lowered Rfree by 2.8-26.7% and improved mean MOLPROBITY score to atomic resolution at 1.25 Å (100th percentile). For structural biology applications that depend on side-chain repacking, including x-ray refinement, homology modeling, and protein design, the accuracy limitations of pairwise additivity can now be eliminated via polarizable or quantum mechanical potentials.

Unsupervised change detection of satellite images using low rank matrix completion.

Traditional unsupervised change detection methods need to generate a difference image (DI) for subsequent processing to produce a binary change map. In addition, few methods explore global structures. This Letter presents a novel unsupervised change detection approach based on low rank matrix completion. Other than generating a DI, the changed pixels are modeled as the estimated missing values for matrix completion, where the changed pixels are represented by a sparse term. A common low rank matrix is recovered by two temporal images. The changed pixels are separated out from the low rank matrix, in which the local information is introduced via graph cuts. The global and local structures are utilized in our model. Experimental results validate the effectiveness of the proposed approach. The proposed method is a new view for change detection.

Method of visual and infrared fusion for moving object detection.

A method based on low rank and sparse decomposition is proposed for moving object detection by the fusion of visual and infrared video. The visual and infrared image sequences are decomposed into the joint low rank background term, the uncorrelated sparse moving nonobject term, and the common sparse moving object term via a joint minimization cost of nuclear norm, F norm, and l(1) norm. This method provides a flexible framework that can easily fuse information from visual and infrared video. The prior fusion strategies are not required. The complementary information on visual and infrared images can be naturally fused in the procedure of object detection. The experimental results show that the proposed algorithm is effective.

Reinforced nanohydroxyapatite/polyamide66 scaffolds by chitosan coating for bone tissue engineering.

High porosity of scaffold is always accompanied by poor mechanical property; the aim of this study was to enhance the strength and modulus of the highly porous scaffold of nanohydroxyapatite/polyamide66 (n-HA/PA66) by coating chitosan (CS) and to investigate the effect of CS content on the scaffold physical properties and cytological properties. The results show that CS coating can reinforce the scaffold effectively. The compress modulus and strength of the CS coated n-HA/PA66 scaffolds are improved to 32.71 and 2.38 MPa, respectively, being about six times and five times of those of the uncoated scaffolds. Meanwhile, the scaffolds still exhibit a highly interconnected porous structure and the porosity is approximate about 78%, slightly lower than the value (84%) of uncoated scaffold. The cytological properties of scaffolds were also studied in vitro by cocultured with osteoblast-like MG63 cells. The cytological experiments demonstrate that the reinforced scaffolds display favorable cytocompatibility and have no significant difference with the uncoated n-HA/PA66 scaffolds. The CS reinforced n-HA/PA66 scaffolds can meet the basic mechanical requirement of bone tissue engineering scaffold, presenting a potential for biomedical application in bone reconstruction and repair.

Characterization and cytocompatibility of biphasic calcium phosphate/polyamide 6 scaffolds for bone regeneration.

Porous scaffolds of biphasic calcium phosphate (BCP)/polyamide 6 (PA6) with weight ratios of 30/70, 45/55, and 55/45 have been fabricated through a modified thermally induced phase separation technique. The chemical structure properties, macrostructure, and mechanical strength of the scaffolds were characterized by Fourier transform infrared spectroscopy, X-ray diffraction, thermogravimetric analysis, scanning electron microscopy, and mechanical testing. The results indicated that the BCP/PA6 scaffolds had an interconnected porous structure with a pore size mainly ranging from 100 to 900 µm and many micropores on the rough pore walls. The mechanical property of the scaffold was significantly enhanced by the addition of BCP inorganic fillers. The 55/45 BCP/PA6 composite scaffold with 76.5% ± 2.1% porosity attained a compressive strength of 1.86 ± 0.14 MPa. Moreover, the BCP/PA6 porous scaffold was cultured with rat calvarial osteoblasts to investigate the cell proliferation, viability, and differentiation function (alkaline phosphatase). The type I collagen expression was also used to characterize the differentiation of rat calvarial osteoblasts on BCP/PA6 composite scaffold by immunocytochemistry. The in vitro cytocompatibility evaluation demonstrated that the BCP/PA6 scaffold acted as a good template for the cells adhesion, spreading, growth, and differentiation. These results suggest that the BCP/PA6 porous composite could be a candidate as an excellent substitute for damaged or defect bone.

Classification of 5-HT(1A) receptor ligands on the basis of their binding affinities by using PSO-Adaboost-SVM.

In the present work, the support vector machine (SVM) and Adaboost-SVM have been used to develop a classification model as a potential screening mechanism for a novel series of 5-HT(1A) selective ligands. Each compound is represented by calculated structural descriptors that encode topological features. The particle swarm optimization (PSO) and the stepwise multiple linear regression (Stepwise-MLR) methods have been used to search descriptor space and select the descriptors which are responsible for the inhibitory activity of these compounds. The model containing seven descriptors found by Adaboost-SVM, has showed better predictive capability than the other models. The total accuracy in prediction for the training and test set is 100.0% and 95.0% for PSO-Adaboost-SVM, 99.1% and 92.5% for PSO-SVM, 99.1% and 82.5% for Stepwise-MLR-Adaboost-SVM, 99.1% and 77.5% for Stepwise-MLR-SVM, respectively. The results indicate that Adaboost-SVM can be used as a useful modeling tool for QSAR studies.