Targeting histone deacetylases for cancer therapy: Trends and challenges.

Dysregulation of histone deacetylases (HDACs) is closely related to tumor development and progression. As promising anticancer targets, HDACs have gained a great deal of research interests and two decades of effort has led to the approval of five HDAC inhibitors (HDACis). However, currently traditional HDACis, although effective in approved indications, exhibit severe off-target toxicities and low sensitivities against solid tumors, which have urged the development of next-generation of HDACi. This review investigates the biological functions of HDACs, the roles of HDACs in oncogenesis, the structural features of different HDAC isoforms, isoform-selective inhibitors, combination therapies, multitarget agents and HDAC PROTACs. We hope these data could inspire readers with new ideas to develop novel HDACi with good isoform selectivity, efficient anticancer effect, attenuated adverse effect and reduced drug resistance.

A robust Cubature Kalman filter for nonlinear systems subject to randomly occurring measurement anomalies without a priori statistic.

In this work, we investigate the problem of state estimation for a class of nonlinear systems subjected to randomly occurring measurement anomalies (ROMAs) without a priori statistic. To address the problem, first, a novel measurement model is constructed, in which the anomalous measurements and anomaly probability are modeled as Gaussian mixture distribution (GMD) and Beta distribution, respectively. Different from the existing researches assuming that the statistical information of anomalous measurements is known in advance, the model does not require a priori statistical knowledge of anomalous measurements. Moreover, by adaptive learning of the anomaly probability, the measurement model is identical with the classical cubature Kalman filter (CKF) in the absence of measurement anomalies. Then, the variational Bayesian inference (VBI) is employed to approximately calculate the joint posterior distribution of the system state and unknown parameters, and a robust filter is derived. Finally, the effectiveness of our filter is demonstrated by the numerical simulation.

Early predictors of brain injury in patients with acute carbon monoxide poisoning and the neuroprotection of mild hypothermia.

INTRODUCTION: Carbon monoxide (CO) poisoning can cause serious neurological sequelae. However, there is neither effective treatment strategy nor reliable indicators to determine the prognosis of patients with CO poisoning. The present study aimed to observe the changes of neurological function score, disease severity score, cerebral oxygen utilization (O2UCc), bispectral (BIS) index and neuron-specific enolase (NSE) concentration, and to elucidate the clinical significance of these potential indicators and the neuroprotective effect of mild hypothermia on brain injury in patients with severe acute CO poisoning. MATERIALS AND METHODS: A total of 277 patients with acute severe CO poisoning from 2013 to 2018 were enrolled in our hospital. Patients were divided into three groups according to their body temperature on the day of admission and their willingness to treat: a fever group (n = 78), a normal temperature group (NT group, n = 113), and a mild hypothermia group (MH group, n = 86). All patients were given hyperbaric oxygen therapy, while those in the MH group received additional mild hypothermia treatment. The severity of the disease, the neurobehavioral status, the incidence of delayed encephalopathy after acute carbon monoxide poisoning (DEACMP), and other indicators including BIS, O2UCc, NSE were further evaluated in all patients at given time-points. RESULTS: Mild hypothermia therapy improved the prognosis of patients with CO poisoning, significantly decreased the value of O2UCc and NSE, and up-regulated BIS. The incidence of DEACMP at 6 months was 27% in the fever group, 23% in the NT group, and 8% in the MH group. The values of Glasgow-Pittsburgh coma scale (G-P score), BIS index and NSE were closely related to the occurrence of DEACMP, the cutoff values were 12.41, 52.17 and 35.20 ng/mL, and the sensitivity and specificity were 79.3%, 77.6%, 79.3% and 67.6%, 89.5%, 88.6% in the receiver operating characteristic curve (ROC), respectively. CONCLUSIONS: Early mild hypothermia treatment could significantly reduce the severity of brain injury after CO poisoning, and might be further popularized in clinic. G-P scores, NSE and BIS index can be regarded as the prediction indicators in the occurrence and development of DEACMP. CLINICAL TRIAL REGISTRATION: The study protocol was granted from Qingdao University Research Ethics Committee (Clinical trial registry and ethical approval number: QD81571283).

Multi-stage classification method oriented to aerial image based on low-rank recovery and multi-feature fusion sparse representation.

Automatic classification of terrain surfaces from an aerial image is essential for an autonomous unmanned aerial vehicle (UAV) landing at an unprepared site by using vision. Diverse terrain surfaces may show similar spectral properties due to the illumination and noise that easily cause poor classification performance. To address this issue, a multi-stage classification algorithm based on low-rank recovery and multi-feature fusion sparse representation is proposed. First, color moments and Gabor texture feature are extracted from training data and stacked as column vectors of a dictionary. Then we perform low-rank matrix recovery for the dictionary by using augmented Lagrange multipliers and construct a multi-stage terrain classifier. Experimental results on an aerial map database that we prepared verify the classification accuracy and robustness of the proposed method.

Effects of N-butylphthalide on the activation of Keap1/Nrf-2 signal pathway in rats after carbon monoxide poisoning.

Carbon monoxide (CO) is the leading cause of death by poisoning all over the world and may result in neuropathologic changes and cognitive and neurologic sequelae, yet little is known regarding its outcomes. The present study aimed to evaluate the neuroprotective effects of N-butylphthalide (NBP) against brain damage after acute CO poisoning. The animal model of CO poisoning was established by exposed to 1000 ppm CO in air for 40 min and then to 3000 ppm for another 20 min. RT-PCR was used to assess the expressions of apoptosis-associated genes Bcl-2 mRNA and Bax mRNA. Mitochondrial membrane potential (MMP) was detected by fluorescent probe JC-1. Immunohistochemistry stain and Western blot assay were used to evaluate the expression levels of Kelch-like ECH-associated protein 1 (Keapl), nuclear factor erythroid 2-related factor 2 (Nrf-2) and NAD(P)H: quinone oxidoreductase 1(NQO-1). CO poisoning could increase the levels of Bcl-2 mRNA and Bax mRNA expressions, and obviously decrease the MMP of cells. NBP treatment could maintain the high MMP, significantly up-regulate Bcl-2 mRNA and down-regulate Bax mRNA expression, and the ratio of Bcl-2 mRNA/Bax mRNA expressions was higher than that in the CO poisoning group (P<0.05). CO poisoning could start oxidative stress response. The expressions of Keap1, Nrf-2 and NQO-1 proteins significantly increased at 1, 3 and 7 day after NBP administration as compared with the CO poisoning group (P<0.01). These findings suggest that N-butylphthalide may protect mitochondrial function, balance the expressions of anti-apoptosis genes and pro-apoptosis genes, be in part associated with activation of Keap1-Nrf-2/antioxidant response element (ARE) signaling pathway, and play a neuroprotective role in brain damage after acute CO poisoning.

Effects of N-Butylphthalide on the expressions of Nogo/NgR in rat brain tissue after carbon monoxide poisoning.

Carbon monoxide (CO) intoxication is one of the most common types of poisoning worldwide, and may result in neuropathologic sequelae, yet its pathogenesis is not clear and there is no optimal management strategy for patients with CO poisoning. In this study, the rat model of CO poisoning was established in a hyperbaric chamber by CO exposure. Rats were administered orally N-Butylphthalide (NBP) at a dose of 1 ml/100g. Neuronal apoptosis was assessed by TUNEL stain and flow cytometry. The expressions of neurite outgrowth inhibitor (Nogo), myelin-associated glycoprotein (MAG) and Nogo receptor-1 (NgR1) were observed in rat brain tissue by immunohistochemistry and double immunofluorescence staining. As we expected, CO poisoning could start the mechanism of apoptosis. The number of apoptotic cells and the early neuronal apoptosis percentage (EAR) were significantly increased at 1 day, 3 day after CO exposure. NBP treatment obviously reduce neuronal apoptosis and the EAR (P<0.05). CO poisoning could induce Nogo, MAG and NgR1 expressions. The increased Nogo, MAG and NgR1 proteins were still observed at 4 week after CO poisoning. NBP could significantly reduce the levels of Nogo and NgR1 proteins. Then we suspected that the expressions of Nogo, MAG and NGR1 proteins might be associated with brain injury and demyelination induced by CO poisoning. NBP might inhibit neuronal apoptosis and the EAR, down-regulate the expressions of Nogo and NgR1 proteins (but not MAG), and play a neuro-protective role in brain damage after acute CO poisoning.

Physiochemical and biological properties of phosphorylated polysaccharides from Dictyophora indusiata.

In this study, we aim to investigate the physiochemical and biological properties of water-soluble phosphorylated polysaccharides (P-DIP) obtained from a water-insoluble polysaccharide (DIP) extracted from Dictyophora indusiata. A series of physiochemical properties were determined, including morphology, water-solubility, molecular weight, and degree of substitution (DS). To investigate the antioxidant activity of P-DIP, we determined the scavenging activity of hydroxyl radicals and DPPH, as well as the reducing power. MTT assay was performed to determine the cytotoxic effects of DIP and P-DIP on the cellular proliferation of MCF-7 and B16 cells. Compared with DIP, P-DIP showed a satisfactory water-solubility and significant increase in the antioxidant properties. Moreover, P-DIP also showed more significant inhibitory effects on the growth of MCF-7 and B16 tumor cells than the water-insoluble DIP. These results indicated that phosphorylation might contribute to the improvement of water solubility, as well as antioxidant and anti-tumor activities of natural DIP.

[Culture conditions optimization and high cell density fermentation of recombinant bacteria producing heparinase II from Flavobacterium heparinum].

Heparinase II (Hep II) from Flavobacterium heparinum is an enzyme that could specifically cleave certain sequence of heparin and heparan sulfate. In this work, fermentation conditions of recombinant heparinase II (His-Hep II) producing bacteria were optimized, including initial induction time, inducer (IPTG) concentration, induction temperature and induction time. The optimum conditions were as follows: cultivating recombinant bacteria to exponential prophase under 37 degrees C, then adding IPTG to a final concentration of 0.3 g/L, finally cultivating recombinant bacteria under 20 degrees C for 10 h. The total crude enzyme activity reached 570 U/L. Based on these results, high cell density fermentation of recombinant bacteria was studied. The final OD600 could reach 98 and the total crude enzyme activity of His-Hep II increased to 9 436 U/L.

Supervised method for periodontitis phenotypes prediction based on microbial composition using 16S rRNA sequences.

Microbes play an important role on human health, however, little is known on microbes in the past decades for the limitation of culture-based techniques. Recently, with the development of next-generation sequencing (NGS) technologies, it is now possible to sequence millions of sequences directly from environments samples, and thus it supplies us a sight to probe the hidden world of microbial communities and detect the associations between microbes and diseases. In the present work, we proposed a supervised learning-based method to mine the relationship between microbes and periodontitis with 16S rRNA sequences. The jackknife accuracy is 94.83% and it indicated the method can effectively predict disease status. These findings not only expand our understanding of the association between microbes and diseases but also provide a potential approach for disease diagnosis and forensics.

The preparation, characterization and evaluation of regenerated cellulose/collagen composite hydrogel films.

Porous structured regenerated cellulose films were oxidized by periodate oxidation to obtain 2,3-dialdehyde cellulose (DARC) films, which were then reacted with collagen to obtain DARC/Col composite films. The subsequent FT-IR spectra indicated that collagen was immobilized on the DARC matrix via the Schiff base reaction between NH2 in collagen and CHO in DARC backbone. Scanning electron microscopy revealed that DARC/Col exhibited a refined 3D network structure and its porosity and pore size decreased with increasing of collagen concentration. The composite films demonstrated a good equilibrium-swelling ratio, air permeability and water retention properties. The composite films also showed excellent mechanical properties, which was vital for practical application. Finally, the cytotoxicity of the composite film was evaluated using NIH3T3 mice fibroblast cells, the results revealed that DARC/Col composite films have good biocompatibility for use as scaffold material in tissue engineering.

Effect of PEG-mediated pore forming on Ca-alginate immobilization of nitrilase-producing bacteria Pseudomonas putida XY4.

Effect of PEG-mediated pore forming on Ca-alginate immobilization of nitrilase-producing bacteria Pseudomonas putida XY4 was studied. Through using PEG as porogen, the environmental tolerance as well as the biocatalytic reaction efficiency of immobilized cells was greatly improved, i.e., Ca-alginate-PEG immobilized cells got better temperature and substrate concentration tolerance than Ca-alginate immobilized cells and showed similar efficiency with free cells, suggesting that the intrinsic mass transfer resistance of immobilization obviously decreased. It was also observed that the pore diameter and porosity of immobilization beads were related with the molecular weight of PEG. PEG400 was found to be a relatively suitable porogen for Ca-alginate-PEG immobilized cells catalyzed hydrolysis of glycinonitrile. It was noteworthy that the Ca-alginate-PEG immobilized cells could be reused more than 18 times with little loss of enzyme activity which had shown good operation ability and great application potential.

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.

MSClust: A Multi-Seeds based Clustering algorithm for microbiome profiling using 16S rRNA sequence.

Recent developments of next generation sequencing technologies have led to rapid accumulation of 16S rRNA sequences for microbiome profiling. One key step in data processing is to cluster short sequences into operational taxonomic units (OTUs). Although many methods have been proposed for OTU inferences, a major challenge is the balance between inference accuracy and computational efficiency, where inference accuracy is often sacrificed to accommodate the need to analyze large numbers of sequences. Inspired by the hierarchical clustering method and a modified greedy network clustering algorithm, we propose a novel multi-seeds based heuristic clustering method, named MSClust, for OTU inference. MSClust first adaptively selects multi-seeds instead of one seed for each candidate cluster, and the reads are then processed using a greedy clustering strategy. Through many numerical examples, we demonstrate that MSClust enjoys less memory usage, and better biological accuracy compared to existing heuristic clustering methods while preserving efficiency and scalability.

A comparison of methods for clustering 16S rRNA sequences into OTUs.

Recent studies of 16S rRNA sequences through next-generation sequencing have revolutionized our understanding of the microbial community composition and structure. One common approach in using these data to explore the genetic diversity in a microbial community is to cluster the 16S rRNA sequences into Operational Taxonomic Units (OTUs) based on sequence similarities. The inferred OTUs can then be used to estimate species, diversity, composition, and richness. Although a number of methods have been developed and commonly used to cluster the sequences into OTUs, relatively little guidance is available on their relative performance and the choice of key parameters for each method. In this study, we conducted a comprehensive evaluation of ten existing OTU inference methods. We found that the appropriate dissimilarity value for defining distinct OTUs is not only related with a specific method but also related with the sample complexity. For data sets with low complexity, all the algorithms need a higher dissimilarity threshold to define OTUs. Some methods, such as, CROP and SLP, are more robust to the specific choice of the threshold than other methods, especially for shorter reads. For high-complexity data sets, hierarchical cluster methods need a more strict dissimilarity threshold to define OTUs because the commonly used dissimilarity threshold of 3% often leads to an under-estimation of the number of OTUs. In general, hierarchical clustering methods perform better at lower dissimilarity thresholds. Our results show that sequence abundance plays an important role in OTU inference. We conclude that care is needed to choose both a threshold for dissimilarity and abundance for OTU inference.

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.

[Quantitative evaluation of the degrees of traditional Chinese medicine qualitative syndromes of osteoporosis].

OBJECTIVE: To establish a quantitative estimate model for diagnosing traditional Chinese medicine (TCM) syndromes of patients with osteoporosis. METHODS: Symptoms and signs of osteoporosis and methodology related to syndrome research were collected by reviewing medical literature. The symptoms and sighs were quantitatively classified into three, two or one category according to a 100-mm visual analog scale. Fuzzy comprehensive evaluation model of TCM qualitative syndromes was performed based on analytic hierarchy process. Then "Hall for Workshop of Metasynthetic Engineering" expert symposium was held on subjects of syndrome quantification method and weight of evaluation indices in different levels for developing the analysis model of common syndromes. For clinical verification, the created models were applied to patients with osteoporosis for discriminating syndromes. Syndrome of each patient was also identified by 8 experts major in integrative medicine treating osteoporosis for comparing the coincidence rate using a self-made clinical questionnaire. RESULTS: Through literature reviewing, symptoms and signs quantification and expert discussing, the authors formed estimate models of essence deficit, qi deficiency, yin deficiency, yang deficiency, and blood stasis. A total of 220 patients with osteoporosis were enrolled and filled the clinical questionnaire. All 8 experts completed and returned the questionnaire (1 760 cases), and 1 545 of them were filled in completely. Experts' opinion on syndrome differentiation was exactly coincidence to estimate model in 611 cases and almost coincidence in 639 cases. The total coincidence rate reached to 94.05%. CONCLUSION: The estimate model for syndrome differentiation of osteoporosis has a high-coincidence rate with the fuzzy evaluation from experts, with good rationality and feasibility, and is worthy of promotion in the clinical study.

[A study of diagnostic criteria for traditional Chinese medicine syndromes in osteoporosis].

OBJECTIVE: To establish diagnostic criteria for common traditional Chinese medicine (TCM) syndromes in osteoporosis. METHODS: Based on the collection and analysis of related medical literature, clinical investigation, and expert discussion, a draft of preliminary diagnostic criteria for the basic syndromes of TCM in patients with osteoporosis was formulated. Then it was used in clinic for verification and revised repeatedly until a formal version of diagnostic criteria was satisfactorily achieved. RESULTS: The basic syndromes listed in the diagnostic criteria for patients with osteoporosis consisted of two parts: qualitative diagnosis and localization diagnosis. Results of qualitative diagnosis showed that the qualitative syndromes included damage of essence, deficiency of vital energy, deficiency of yin, deficiency of yang and blood stasis. The localization diagnosis showed that location of osteoporosis is bone and corresponds to the kidney, and also involves liver, lung, spleen (stomach) and heart. The diagnostic content has established the specific symptoms and the non-specific symptoms during various stages. Each of the above syndromes could be diagnosed according to a specific combination of its corresponding symptoms or signs. The clinical verification results showed that the total matching ratio of qualitative diagnosis was 80.56% between the diagnoses made according to the criteria and the diagnoses acquired from the experts' experience, and the total matching ratio of localization diagnosis was 85.56%. CONCLUSION: The TCM syndrome diagnostic criteria for osteoporosis is generally consistent with TCM clinical practice, worthy of further popularization and application in clinical practice.

Identification of protein-RNA interaction sites using the information of spatial adjacent residues.

BACKGROUND: Protein-RNA interactions play an important role in numbers of fundamental cellular processes such as RNA splicing, transport and translation, protein synthesis and certain RNA-mediated enzymatic processes. The more knowledge of Protein-RNA recognition can not only help to understand the regulatory mechanism, the site-directed mutagenesis and regulation of RNA-protein complexes in biological systems, but also have a vitally effecting for rational drug design. RESULTS: Based on the information of spatial adjacent residues, novel feature extraction methods were proposed to predict protein-RNA interaction sites with SVM-KNN classifier. The total accuracies of spatial adjacent residue profile feature and spatial adjacent residues weighted accessibility solvent area feature are 78%, 67.07% respectively in 5-fold cross-validation test, which are 1.4%, 3.79% higher than that of sequence neighbour residue profile feature and sequence neighbour residue accessibility solvent area feature. CONCLUSIONS: The results indicate that the performance of feature extraction method using the spatial adjacent information is superior to the sequence neighbour information approach. The performance of SVM-KNN classifier is little better than that of SVM. The feature extraction method of spatial adjacent information with SVM-KNN is very effective for identifying protein-RNA interaction sites and may at least play a complimentary role to the existing methods.

[Model and analysis of spectropolarimetric BRDF of painted target based on GA-LM method].

Models based on microfacet were used to describe spectropolarimetric BRDF (short for bidirectional reflectance distribution function) with experimental data. And the spectropolarimetric BRDF values of targets were measured with the comparison to the standard whiteboard, which was considered as Lambert and had a uniform reflectance rate up to 98% at arbitrary angle of view. And then the relationships between measured spectropolarimetric BRDF values and the angles of view, as well as wavelengths which were in a range of 400-720 nm were analyzed in details. The initial value needed to be input to the LM optimization method was difficult to get and greatly impacted the results. Therefore, optimization approach which combines genetic algorithm and Levenberg-Marquardt (LM) was utilized aiming to retrieve parameters of nonlinear models, and the initial values were obtained using GA approach. Simulated experiments were used to test the efficiency of the adopted optimization method. And the simulated experiment ensures the optimization method to have a good performance and be able to retrieve the parameters of nonlinear model efficiently. The correctness of the models was validated by real outdoor sampled data. The parameters of DoP model retrieved are the refraction index of measured targets. The refraction index of the same color painted target but with different materials was also obtained. Conclusion has been drawn that the refraction index from these two targets are very near and this slight difference could be understood by the difference in the conditions of paint targets' surface, not the material of the targets.

[Classification of multi-class homo-oligomer based on a novel method of feature extraction from protein primary structure].

A novel method of feature extraction from protein primary structure has been proposed and applied to classify the protein homodimer, homotrimer, homotetramer and homohexamer, i. e. one protein sequence can be represented by a feature vector composed of amino acid compositions and a set of weighted auto-correlation function factors of amino acid residue index. As a result, high classification accuracies are obtained. For example, with the same support vector machine (SVM), the total accuracies of QIANA, AIANB, MEEJ, ROBB and SNEP sets based on this novel feature extraction method are 77.63, 77.16, 76.46, 76.70 and 75.06% respectively in Jackknife test, which are 6.39, 5.92, 5.22, 5.46 and 3.82 percent points respectively higher than that of COMP set based on the conventional method composed of amino acid compositions. With the same QIANA set, the total accuracy of SVM is 77.63%, which is 16.29 percent points higher than that of covariant discriminant algorithm. These results show: (1) The novel feature extraction method is effective and feasible, and the feature vectors based on this method may contain more protein quaternary structure information and appear to capture essential information about the composition and hydrophobicity of residues in the surface patches buried in the interfaces of associated subunits; (2) SVM can be referred as a powerful computational tool for classifying the homo-oligomers of proteins.