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Abstract Background Migratory birds exhibit heterogeneity in foraging strategies during wintering to cope with environmental and migratory pressures, and gut bacteria respond to changes in host diet. However, less is known about the dynamics of diet and gut fungi during the wintering period in black-necked cranes (Grus nigricollis). Results In this work, we performed amplicon sequencing of the trnL-P6 loop and ITS1 regions to characterize the dietary composition and gut fungal composition of black-necked cranes during wintering. Results indicated that during the wintering period, the plant-based diet of black-necked cranes mainly consisted of families Poaceae, Solanaceae, and Polygonaceae. Among them, the abundance of Solanaceae, Polygonaceae, Fabaceae, and Caryophyllaceae was significantly higher in the late wintering period, which also led to a more even consumption of various food types by black-necked cranes during this period. The diversity of gut fungal communities and the abundance of core fungi were more conserved during the wintering period, primarily dominated by Ascomycota and Basidiomycota. LEfSe analysis (P 2) found that Pyxidiophora, Pseudopeziza, Sporormiella, Geotrichum, and Papiliotrema were significantly enriched in early winter, Ramularia and Dendryphion were significantly enriched in mid-winter, Barnettozyma was significantly abundant in late winter, and Pleuroascus was significantly abundant in late winter. Finally, mantel test revealed a significant correlation between winter diet and gut fungal. Conclusions This study revealed the dynamic changes in the food composition and gut fungal community of black-necked cranes during wintering in Dashanbao. In the late wintering period, their response to environmental and migratory pressures was to broaden their diet, increase the intake of non-preferred foods, and promote a more balanced consumption ratio of various foods. Balanced food composition played an important role in stabilizing the structure of the gut fungal community. While gut fungal effectively enhanced the host’s food utilization rate, they may also faced potential risks of introducing pathogenic fungi. Additionally, we recongnized the limitations of fecal testing in studying the composition of animal gut fungal, as it cannot effectively distinguished between fungal taxa from food or soil inadvertently ingested and intestines. Future research on functions such as cultivation and metagenomics may further elucidate the role of fungi in the gut ecosystem.

Abstract Exploring the changing process of the geographical distribution pattern of Tetracentron sinense Oliv. and its main influencing factors since the last interglacial period can provide a scientific basis for the effective protection and management of the species. The MaxEnt model was used to construct the potential distribution areas of T. sinense in different periods such as the last interglacial (LIG), the last glacial maximum (LGM), the mid‐Holocene (MID), and the current and future (2050s and 2070s). On the premise of discussing the influence of dominant environmental factors on its distribution model, the suitable area changes of T. sinense under different ecological climate situations were quantitatively analyzed. (1) The AUC and TSS values predicted by the optimized model were 0.959 and 0.835, respectively, indicating a good predictive effect by the MaxEnt model; the potential suitable areas for T. sinense in the current period are mainly located in Southwest China, which are wider compared to the actual habitats. (2) Jackknife testing showed that the lowest temperature in the coldest month (Bio6), elevation (Elev), seasonal variation coefficient of temperature (Bio4), and surface calcium carbonate content (T‐CACO3) are the dominant environmental factors affecting the distribution of T. sinense. (3) From the last interglacial period to the current period, the total suitable area of T. sinense showed a decreasing trend; the distribution points of T. sinense populations in mid‐Holocene period may be the origin of the postglacial population, and Southwest China may be its glacial biological refuge. (4) Compared with the current period, the total suitable area ranges of T. sinense in China in the 2050s and 2070s decreased, and the centroid location of its total fitness area all migrated to the northwest, with the largest migration distance in 2070s under the SSPs 7.0 climate scenario. Temperature was the principal factor influencing the geographical distribution of T. sinense. With global warming, the range of T. sinense suitable areas will show a shrinking trend, with a shift toward higher‐latitude regions. Ex situ conservation measures could be taken to preserve its germplasm resources.

Abstract Histopathology plays a great role in diagnosing various diseases, which is considered as a golden standard for tumor identification. The tissue constituents must be stained by visible labels for microscopic analysis by medical experts. However, this process is time‐consuming, labor‐intensive, and expensive, which requires rapid pathological approaches for diagnosis in the operating room. Here, we present an easy‐to‐process and high‐performance perovskite biological probes for rapid and visual pathological diagnosis of glioma. Perovskite quantum dots can be encapsulated by the copolymer into nanocrystals (PNCs) with a diameter of 100 nm, which is modified with chlorotoxin to achieve the specific recognition of glioma. Benefiting from the super photoluminescence quantum yield (above 93%) of EVA@PNCs aqueous solution, the glioma can be clearly imaged and captured via a smartphone under the excitation of a handheld UV lamp. To demonstrate the visualization and efficiency of PNC probes, different malignant grades of brain tumor sections can be distinguished in no more than 5 min. This strategy provides a general auxiliary diagnosis platform for achieving the histopathology analysis near the operating bed, which is currently not feasible with standard histochemical staining methods.

In this present study, three new wood-inhabiting fungal taxa, Hyphoderma niveomarginatum, H. sordidum and H. weishanense, are proposed. Hyphoderma niveomarginatum is characterized by the ceraceous basidiomata having a smooth, cracking hymenial surface and the presence of the moniliform cystidia and ellipsoid basidiospores (7–9 × 3.5–5 µm). Hyphoderma sordidum is characterized by its resupinate basidiomata with a smooth hymenial surface with the fimbriate margin, the presence of the tubular cystidia and ellipsoid basidiospores (3–4.5 × 2–3 µm). Hyphoderma weishanense differs in its membranous basidiomata with a slightly buff to buff hymenial surface and the presence of broadly ellipsoid basidiospores (4.5–8.5 × 4–7 µm). Sequences of ITS+nLSU+mt-SSU+RPB1+RPB2 genes were used for the phylogenetic analyses using three methods. The ITS+nLSU+mt-SSU+RPB1+RPB2 analysis of the genus Hyphoderma indicated that the 3 new species of Hyphoderma were nested into genus Hyphoderma, in which H. niveomarginatum formed a single group and then grouped with H. membranaceum and H. sinense; H. sordidum was a sister to H. nudicephalum; and H. weishanense closely grouped with H. crystallinum.

Osteosarcoma (OS) is the most common primary bone malignancy that exhibits remarkable histologic diversity and genetic heterogeneity. The complex nature of osteosarcoma has confounded precise molecular categorization, prognosis, and prediction for this disease. In this study, we performed a comprehensive multiplatform analysis on 86 osteosarcoma tumors, including somatic copy-number alteration, gene expression and methylation, and identified three molecularly distinct and clinically relevant subtypes of osteosarcoma. The subgrouping criteria was validated on another cohort of osteosarcoma tumors. Previously unappreciated osteosarcoma-type-specific changes in specific genes’ copy number, expression and methylation were revealed based on the subgrouping. The subgrouping and novel gene signatures provide insights into refining osteosarcoma therapy and relationships to other types of cancer.

Rhizoma Gastrodia (Orchidaceae; Gastrodia elata Blume), the rhizome of Gastrodia elata Blume (GE), is traditionally used as both a medicinal and functional food, with proven efficacy in treating mental disorders. In traditional processing, GE is washed, steamed with water, dried, and sliced. In this study, a novel processing technology-alcohol steamed GE (AGE) was proposed as an alternative. Totally, 17 compounds were identified in fresh GE and AGE. Compared with fresh GE, the relative content of parishin A and parishin E decreased after alcohol steaming, whereas gastrodin (GAS), p-hydroxylbenzyl alcohol (HBA), Parishin B, and Parishin C were increased. Additionally, the pentobarbital-induced sleep mice model and Chronic Restraint Stress (CRS) model were applied to evaluate the pharmacological effects of fresh GE and steamed GE, and both fresh and steamed GE showed an intensive hypnotic and anti-anxiety effect. Furthermore, the anti-anxiety mechanism based on serum metabolic was investigated and the tryptophan metabolic pathway was considered the response to the anti-anxiety effect of GE. Although the optimization of the processing technology of AGE still needs to be further explored, the current results have provided new thoughts for the processing technology and clinical application of GE.

Synthetic lethal (SL) genetic interactions have been regarded as a promising focus for investigating potential targeted therapeutics to tackle cancer. However, the costly investment of time and labor associated with wet-lab experimental screenings to discover potential SL relationships motivates the development of computational methods. Although graph neural network (GNN) models have performed well in the prediction of SL gene pairs, existing GNN-based models are not designed for predicting cancer cell-specific SL interactions that are more relevant to experimental validation in vitro. Besides, neither have existing methods fully utilized diverse graph representations of biological features to improve prediction performance. In this work, we propose MVGCN-iSL, a novel multi-view graph convolutional network (GCN) model to predict cancer cell-specific SL gene pairs, by incorporating five biological graph features and multi-omics data. Max pooling operation is applied to integrate five graph-specific representations obtained from GCN models. Afterwards, a deep neural network (DNN) model serves as the prediction module to predict the SL interactions in individual cancer cells (iSL). Extensive experiments have validated the model’s successful integration of the multiple graph features and state-of-the-art performance in the prediction of potential SL gene pairs as well as generalization ability to novel genes.

Abstract Image captioning means generate descriptive sentences from a query image automatically. It has recently received widespread attention from the computer vision and natural language processing communities as an emerging visual task. Currently, both components have evolved considerably by exploiting object regions, attributes, attention mechanism methods, entity recognition with novelties, and training strategies. However, despite the impressive results, the research has not yet come to a conclusive answer. This survey aims to provide a comprehensive overview of image captioning methods, from technical architectures to benchmark datasets, evaluation metrics, and comparison of state‐of‐the‐art methods. In particular, image captioning methods are divided into different categories based on the technique adopted. Representative methods in each class are summarized, and their advantages and limitations are discussed. Moreover, many related state‐of‐the‐art studies were quantitatively compared to determine the recent trends and future directions in image captioning. The ultimate goal of this work is to serve as a tool for understanding the existing literature and highlighting future directions in the area of image captioning for Computer Vision and Natural Language Processing communities may benefit from.

Herein, two CeO2 samples dominantly decorated with surface and bulk sulfates were constructed and their distinct effects on high-temperature NH3-SCR were investigated by strictly controlling the sulfate content at a comparable level. The obtainment of surface and bulk sulfates was revealed using a designed leaching experiment, and further evidenced by the characterization results from XPS and H2-TPR. In comparison with CeO2 modified with bulk sulfates (B-CeS), sufficient acid sites with strong intensity were generated on CeO2 modified with surface sulfates (S-CeS). In addition, due to electron-withdrawing effect from S=O in sulfate species, NH3 oxidation over S-CeS was greatly suppressed, providing an additional contribution to enhanced performance in high-temperature NH3-SCR.

Background: Lung cancer is one of the major cause of death globally. Crizotinib is a first-line drug used in treating non-small-cell lung cancer (NSCLC). However, the pathophysiological mechanisms underlying its cardiotoxicity are unknown. This study investigated the mechanisms of crizotinib-induced cardiotoxicity and explored whether this toxicity can be prevented by the angiotensin receptor/neprilysin inhibitor sacubitril/valsartan. Methods: Male C57BL/6 mice were randomly divided into three groups: control, crizotinib (40 mg⋅kg-1⋅d-1 for four weeks), and crizotinib + sacubitril/valsartan (40 mg⋅kg-1⋅d-1/60 mg⋅kg-1⋅d-1 for four weeks). Expression of genes in myocardial tissue were detected by transcriptomic sequencing, with verification of the differentially expressed genes (DEGs) using Real time-polymerase chain reaction (RT-PCR). Blood pressure (BP) and cardiac function of animals were measured using non-invasive monitoring and echocardiography approaches. Ventricular refractory period (RP), as well as the induction rate and score of ventricular arrhythmias (VAs) were detected by in vivo electrophysiology. Epicardial conductance was measured by mapping. Expression of Myh7 in myocardium was detected by western blot and RT-PCR. Results: DEGs detected using transcriptomic sequencing included 10 up-regulated and 20 down-regulated genes. The first 5 DEGs identified were Myh7, Ngp, Lcn2, Ciart and Ptgds. Kyoto Encyclopedia of Genes and Genomes (KEGG) result indicated that Myh7 is involved in myocarditis, cardiomyopathy, and cardiac muscle contraction. Crizotinib treatment increased blood pressure, prolonged QTc interval, shortened ventricular RP, increased the incidence and score of right VAs, and increased Myh7 expression. Most of these responses were limited by sacubitril/valsartan. Conclusions: Crizotinib induced a range of cardiotoxic side effects in a mouse model and increased Myh7 expression represents a biomarker for this response. These cardiovascular toxic responses can be largely prevented by sacubitril/valsartan.

Abstract Lots of cell death initiator and effector molecules, signalling pathways and subcellular sites have been identified as key mediators in both cell death processes in cancer. The XDeathDB visualization platform provides a comprehensive cell death and their crosstalk resource for deciphering the signaling network organization of interactions among different cell death modes associated with 1461 cancer types and COVID-19, with an aim to understand the molecular mechanisms of physiological cell death in disease and facilitate systems-oriented novel drug discovery in inducing cell deaths properly. Apoptosis, autosis, efferocytosis, ferroptosis, immunogenic cell death, intrinsic apoptosis, lysosomal cell death, mitotic cell death, mitochondrial permeability transition, necroptosis, parthanatos, and pyroptosis related to 12 cell deaths and their crosstalk can be observed systematically by the platform. Big data for cell death gene-disease associations, gene-cell death pathway associations, pathway-cell death mode associations, and cell death-cell death associations is collected by literature review articles and public database from iRefIndex, STRING, BioGRID, Reactom, Pathway’s commons, DisGeNET, DrugBank, and Therapeutic Target Database (TTD). An interactive webtool, XDeathDB, is built by web applications with R-Shiny, JavaScript (JS) and Shiny Server Iso. With this platform, users can search specific interactions from vast interdependent networks that occur in the realm of cell death. A multilayer spectral graph clustering method that performs convex layer aggregation to identify crosstalk function among cell death modes for a specific cancer. 147 hallmark genes of cell death could be observed in detail in these networks. These potential druggable targets are displayed systematically and tailoring networks to visualize specified relations is available to fulfil user-specific needs. Users can access XDeathDB for free at https://pcm2019.shinyapps.io/XDeathDB/ .

Abstract In the BrighTNess trial, carboplatin added to neoadjuvant chemotherapy (NAC) was associated with increased pathologic complete response (pCR) rates in patients with stage II/III triple-negative breast cancer (TNBC). In this matched cohort study, cases with a germline BRCA1/2 mutation (gBRCA; n = 75) were matched 1:2 with non-gBRCA controls (n = 150) by treatment arm, lymph node status, and age to evaluate pCR rates and association of benefit from platinum/PARP inhibitors with validated RNA expression-based immune, proliferation, and genomic instability scores among gBRCA with the addition of carboplatin ± veliparib to NAC. Among the well-matched cohorts, odds of pCR were not higher in gBRCA cancers who received standard NAC with carboplatin (OR 0.24, 95% CI [0.04-1.24], p = 0.09) or with carboplatin/veliparib (OR 0.44, 95% CI [0.10-1.84], p = 0.26) compared to non-gBRCA cancers. Higher PAM50 proliferation, GeparSixto immune, and CIN70 genomic instability scores were each associated with higher pCR rate in the overall cohort, but not specifically in gBRCA cases. In this study, gBRCA carriers did not have higher odds of pCR than non-gBRCA controls when carboplatin ± veliparib was added to NAC, and showed no significant differences in molecular, immune, chromosomal instability, or proliferation gene expression metrics.

Synthetic lethality (SL) refers to a genetic interaction in which the simultaneous perturbation of two genes leads to cell or organism death, whereas viability is maintained when only one of the pair is altered. The experimental exploration of these pairs and predictive modeling in computational biology contribute to our understanding of cancer biology and the development of cancer therapies. We extensively reviewed experimental technologies, public data sources, and predictive models in the study of synthetic lethal gene pairs and herein detail biological assumptions, experimental data, statistical models, and computational schemes of various predictive models, speculate regarding their influence on individual sample- and population-based synthetic lethal interactions, discuss the pros and cons of existing SL data and models, and highlight potential research directions in SL discovery.

Summary: Combinatorial CRISPR screening is useful for investigating synthetic lethality (SL) gene pairs. Here, we detail the steps for dual-gRNA library construction, with the introduction of two backbones, LentiGuide_DKO and LentiCRISPR_DKO. We describe steps for in vitro screening with 22Rv1-Cas9 and SaOS2-Cas9 cells followed by sequencing and data analysis. By introducing two backbones, we optimized the library construction process, facilitated standard pair-end sequencing, and provided options of screening on cells with or without modification of Cas9 expression. : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

PurposeThe mechanism of sodium–glucose cotransporter-2 inhibitor (SGLT-2i) reducing the incidence of atrial fibrillation remains unclear. We hypothesize that sodium–glucose cotransporter-2 inhibitor alleviated atrial remodeling in STZ-induced diabetic rats by targeting TLR4 pathway.MethodsA total of 42 rats were randomly assigned into three groups: control group (CON group); diabetes group (DM group): diabetes mellitus rats were established by 65 mg/kg streptozotocin (STZ) intraperitoneal injection; and diabetes + dapagliflozin group (DM + DAPA group): diabetic rats were given DAPA gavage administration (DAPA 2mg/kg/d for 4 weeks by gavage administration), 14 rats in each group. Epicardial multiple-lead recording and intracardiac electrophysiology studies were performed to investigate the electrical remodeling in the heart and the atrial fibrillation inducibility in each group. Western blot analysis and real-time PCR were used to determine the protein and mRNA expression of toll-like receptor 4 (TLR4), interleukin receptor-associated kinase 1 (IRAK1), tumor necrosis factor receptor-associated factor 6 (TRAF6), nuclear factor-kappa B (NF-κB), and type I collagen (collagen I).ResultsCompared with rats in CON group, rats in DM group showed marked myocardial fibrosis, ectopic pacing excitement, reduced conduction velocity, decreased cardiac function. TLR4/IRAK1/TRAF6/NF-κB, collagen I proteins expressions and incidence of atrial fibrillation (27.3%) were increased in DM group. Parts of these changes were reversed by treatment of DAPA. Incidence of atrial fibrillation was decreased in DM + DAPA group (2.8%).ConclusionsSGLT-2i dapagliflozin may prevent diabetic rats' atrial remodeling and reduce the inducibility of atrial fibrillation partly by targeting TLR4/IRAK1/TRAF6/NF-κB inflammatory pathway.

Cancer is a complex disease with usually multiple disease mechanisms. Target combination is a better strategy than a single target in developing cancer therapies. However, target combinations are generally more difficult to be predicted. Current CRISPR-cas9 technology enables genome-wide screening for potential targets, but only a handful of genes have been screend as target combinations. Thus, an effective computational approach for selecting candidate target combinations is highly desirable. Selected target combinations also need to be translational between cell lines and cancer patients. We have therefore developed DSCN (double-target selection guided by CRISPR screening and network), a method that matches expression levels in patients and gene essentialities in cell lines through spectral-clustered protein-protein interaction (PPI) network. In DSCN, a sub-sampling approach is developed to model first-target knockdown and its impact on the PPI network, and it also facilitates the selection of a second target. Our analysis first demonstrated a high correlation of the DSCN sub-sampling-based gene knockdown model and its predicted differential gene expressions using observed gene expression in 22 pancreatic cell lines before and after MAP2K1 and MAP2K2 inhibition (R2 = 0.75). In DSCN algorithm, various scoring schemes were evaluated. The 'diffusion-path' method showed the most significant statistical power of differentialting known synthetic lethal (SL) versus non-SL gene pairs (P = 0.001) in pancreatic cancer. The superior performance of DSCN over existing network-based algorithms, such as OptiCon and VIPER, in the selection of target combinations is attributable to its ability to calculate combinations for any gene pairs, whereas other approaches focus on the combinations among optimized regulators in the network. DSCN's computational speed is also at least ten times fast than that of other methods. Finally, in applying DSCN to predict target combinations and drug combinations for individual samples (DSCNi), DSCNi showed high correlation between target combinations predicted and real synergistic combinations (P = 1e-5) in pancreatic cell lines. In summary, DSCN is a highly effective computational method for the selection of target combinations.

Single-cell mass cytometry, also known as cytometry by time of flight (CyTOF) is a powerful high-throughput technology that allows analysis of up to 50 protein markers per cell for the quantification and classification of single cells. Traditional manual gating utilized to identify new cell populations has been inadequate, inefficient, unreliable, and difficult to use, and no algorithms to identify both calibration and new cell populations has been well established. A deep learning with graphic cluster (DGCyTOF) visualization is developed as a new integrated embedding visualization approach in identifying canonical and new cell types. The DGCyTOF combines deep-learning classification and hierarchical stable-clustering methods to sequentially build a tri-layer construct for known cell types and the identification of new cell types. First, deep classification learning is constructed to distinguish calibration cell populations from all cells by softmax classification assignment under a probability threshold, and graph embedding clustering is then used to identify new cell populations sequentially. In the middle of two-layer, cell labels are automatically adjusted between new and unknown cell populations via a feedback loop using an iteration calibration system to reduce the rate of error in the identification of cell types, and a 3-dimensional (3D) visualization platform is finally developed to display the cell clusters with all cell-population types annotated. Utilizing two benchmark CyTOF databases comprising up to 43 million cells, we compared accuracy and speed in the identification of cell types among DGCyTOF, DeepCyTOF, and other technologies including dimension reduction with clustering, including Principal Component Analysis (PCA), Factor Analysis (FA), Independent Component Analysis (ICA), Isometric Feature Mapping (Isomap), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) with k-means clustering and Gaussian mixture clustering. We observed the DGCyTOF represents a robust complete learning system with high accuracy, speed and visualization by eight measurement criteria. The DGCyTOF displayed F-scores of 0.9921 for CyTOF1 and 0.9992 for CyTOF2 datasets, whereas those scores were only 0.507 and 0.529 for the t-SNE+k-means; 0.565 and 0.59, for UMAP+ k-means. Comparison of DGCyTOF with t-SNE and UMAP visualization in accuracy demonstrated its approximately 35% superiority in predicting cell types. In addition, observation of cell-population distribution was more intuitive in the 3D visualization in DGCyTOF than t-SNE and UMAP visualization. The DGCyTOF model can automatically assign known labels to single cells with high accuracy using deep-learning classification assembling with traditional graph-clustering and dimension-reduction strategies. Guided by a calibration system, the model seeks optimal accuracy balance among calibration cell populations and unknown cell types, yielding a complete and robust learning system that is highly accurate in the identification of cell populations compared to results using other methods in the analysis of single-cell CyTOF data. Application of the DGCyTOF method to identify cell populations could be extended to the analysis of single-cell RNASeq data and other omics data.

Background: The superior cervical ganglion (SCG) plays a key role in cardiovascular diseases. The aim of this study was to determine the changes in the ion channel characteristics of the SCG following myocardial infarction (MI) and the role of pretreatment with the P2Y12 receptor antagonist ticagrelor (TIC). Methods: A total of 18 male rabbits were randomly divided into a control group, MI group, and P2Y12 receptor antagonist (TIC) group (abbreviated as the TIC group). Rabbit MI was performed via two abdominal subcutaneous injections of 150 mg·kg−1·d−1 of isoproterenol (ISO) with an interval of 24 h. TIC pretreatment at 20 mg·kg−1·d−1 was administered via gavage for two consecutive days. The cardiac function of each group was evaluated with echocardiography. ADP receptor P2Y12 expressions in SCGs were determined using RT-PCR and immunofluorescence staining. Ion channel characteristics of SCG neurons were measured using a whole-cell patch clamp. Intracellular calcium concentrations for SCG neurons were measured using confocal microscopy. Results: Cardiac function was reduced in the rabbits of the MI group, the sympathetic nerve activity of SCGs was increased, and the current amplitude of the neuron ion channel was increased. MI led to alterations in the activation and inactivation characteristics of INa channels accompanied by increased expression of P2Y12 in SCGs. Most of these abnormalities were prevented by TIC pretreatment in the TIC group. Conclusions: TIC pretreatment could attenuate the increase in P2Y12 expression in SCGs and the changes to the ion channel characteristics of SCG neurons after MI. This may be the mechanism underlying the cardiac protective effects of TIC.

Shale gas plays a significant role in meeting the increasing demand of energy resources, which enables the exploration and exploitation of gas from shale reservoir to be emphasized gradually. Aiming to serve a more responsible and efficient shale gas development, for example, to guide the hydraulic/nonaqueous fracturing operation, this work organized a comprehensive assessment on the petrophysical characterization and gas accumulation of the Upper Ordovician Wufeng-Lower Silurian Longmaxi shale reservoir (abbreviated as WL shale). Results indicate that the WL shale in Well-YC4 contains three third-order sequences (SS1-lower WL, SS2-middle WL, and SS3-upper WL), and each sequence contains a transgressive system tract (TST) and a high-stand system tract (HST), according to the logging information. Meanwhile, the SS1, SS2, and SS3 experienced variable sedimentary environment—with different relatively oxygenic and hydrodynamics. And the bottom section (SS1 and SS2) has a soft great brittle index (BI) value than the upper section (SS3), suggesting the bottom WL shale is better suited for hydraulic fracturing than the upper section. Besides, the organic matter indicates the WL shale has a decent hydrocarbon generation ability, and the thermal evolution degree is also conducive to the full generation of shale gas. The gas content of WL shale ranges in the scope of 0.76 m3/t~2.38 m3/t, in which CH4 is the primary composition and occupies 95.51%~99.36%, and the content of heavy hydrocarbon gases is limited. Besides, the drying coefficient indicates the dry gas is the dominate molecular composition in the gas content of WL shale. Hopefully, this work is instructive to a certain extent for the researchers and engineers who are working on WL shale gas in Sichuan basin.

Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is the most common pancreatic malignancy. Due to its wide heterogeneity, PDAC acts aggressively and responds poorly to most chemotherapies, causing an urgent need for the development of new therapeutic strategies. Cell lines have been used as the foundation for drug development and disease modeling. CRISPR-Cas9 plays a key role in every step-in drug discovery: from target identification and validation to preclinical cancer cell testing. Using cell-line models and CRISPR-Cas9 technology together make drug target prediction feasible. However, there is still a large gap between predicted results and actionable targets in real tumors. Biological network models provide great modus to mimic genetic interactions in real biological systems, which can benefit gene perturbation studies and potential target identification for treating PDAC. Nevertheless, building a network model that takes cell-line data and CRISPR-Cas9 data as input to accurately predict potential targets that will respond well on real tissue remains unsolved. Methods We developed a novel algorithm ‘Spectral Clustering for Network-based target Ranking’ (SCNrank) that systematically integrates three types of data: expression profiles from tumor tissue, normal tissue and cell-line PDAC; protein-protein interaction network (PPI); and CRISPR-Cas9 data to prioritize potential drug targets for PDAC. The whole algorithm can be classified into three steps: 1. using STRING PPI network skeleton, SCNrank constructs tissue-specific networks with PDAC tumor and normal pancreas tissues from expression profiles; 2. With the same network skeleton, SCNrank constructs cell-line-specific networks using the cell-line PDAC expression profiles and CRISPR-Cas 9 data from pancreatic cancer cell-lines; 3. SCNrank applies a novel spectral clustering approach to reduce data dimension and generate gene clusters that carry common features from both networks. Finally, SCNrank applies a scoring scheme called ‘Target Influence score’ (TI), which estimates a given target’s influence towards the cluster it belongs to, for scoring and ranking each drug target. Results We applied SCNrank to analyze 263 expression profiles, CRPSPR-Cas9 data from 22 different pancreatic cancer cell-lines and the STRING protein-protein interaction (PPI) network. With SCNrank, we successfully constructed an integrated tissue PDAC network and an integrated cell-line PDAC network, both of which contain 4414 selected genes that are overexpressed in tumor tissue samples. After clustering, 4414 genes are distributed into 198 clusters, which include 367 targets of FDA approved drugs. These drug targets are all scored and ranked by their TI scores, which we defined to measure their influence towards the network. We validated top-ranked targets in three aspects: Firstly, mapping them onto the existing clinical drug targets of PDAC to measure the concordance. Secondly, we performed enrichment analysis to these drug targets and the clusters there are within, to reveal functional associations between clusters and PDAC; Thirdly, we performed survival analysis for the top-ranked targets to connect targets with clinical outcomes. Survival analysis reveals that overexpression of three top-ranked genes, PGK1, HMMR and POLE2, significantly increases the risk of death in PDAC patients. Conclusion SCNrank is an unbiased algorithm that systematically integrates multiple types of omics data to do potential drug target selection and ranking. SCNrank shows great capability in predicting drug targets for PDAC. Pancreatic cancer-associated gene candidates predicted by our SCNrank approach have the potential to guide genetics-based anti-pancreatic drug discovery.

The stellate ganglion (SG) of the autonomic nervous system plays important role in cardiovascular diseases (CDs). Myocardial infarction (MI) is associated with sustained increasing cardiac sympathetic nerve activity. Expressions and functions of proteins in SG tissue after MI are remaining unclear. This study is to explore the expression characteristics of proteins in SGs associated with MI. Japanese big-ear white rabbits (n = 22) were randomly assigned to the control group and MI group. The MI model was established by left anterior descending coronary artery ligation and confirmed by serum myocardial enzymes increasing 2,3,5-triphenyltetrazolium (TTC) staining and echocardiography. The expressions of proteins in rabbit SGs after MI were detected using tandem mass tags (TMT) quantitative proteomic sequencing. There were 3,043 credible proteins were predicted in rabbit SG tissues and 383 differentially expressed proteins (DEPs) including 143 upregulated and 240 downregulated proteins. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEPs involved in adrenergic signaling in cardiomyocytes, positive regulation of ERK1 and ERK2 cascade, and other biological processes. Three kinds of proteins directly correlated to CDs were selected to be validated by the subsequent western blot experiment. This study first identified the characterization of proteins in rabbit SG after MI, which laid a solid foundation for revealing the mechanism of roles of SG on the MI process.

Background: Foreign body aspiration (FBA) in children is a common emergency that can easily be missed, leading to delays in treatment. Few large cohort studies have focused on errors in diagnostic assessment. The main purpose of this study was to analyze factors contributing to the initial misdiagnosis of FBA in children.Methods: We retrospectively reviewed the charts of 226 children diagnosed with FBA at the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University from January 2018 to November 2020. Cases were divided into two groups according to whether or not patients were initially misdiagnosed. The clinical characteristics of the two groups were then compared. The Diagnosis Error Evaluation and Research (DEER) taxonomy tool was applied to cases with initial misdiagnosis.Results: Of the 226 included children with a final diagnosis of FBA, 153 (67.7%) were boys. Ninety percent of patients were under 3 years old. More than half (61.9%) of the children were referred from primary institutions, and 38.1% visited tertiary hospitals directly. A total of 80 (35.4%) patients were initially misdiagnosed. More than half of misdiagnosed children received an alternative diagnosis of bronchiolitis (51.3%), the most common alternative diagnosis. Test failures (i.e., errors in test ordering, test performance, and clinician processing) were primarily responsible for the majority of initial diagnostic errors (76.3%), followed by failure or delay in eliciting critical case history information (20.0%). Characteristics significantly associated with initial misdiagnosis were: presentation over 24 h (OR 9.2, 95% CI 4.8–17.5), being referred from primary institutions (OR 8.8, 4.1–19.0), no witnessed aspiration crisis (OR 7.8, 3.0–20.3), (4) atypical signs or symptoms (OR 3.2, 1.8–5.7), foreign body not visible on CT (OR 36.2, 2.1–636.8), foreign body located in secondary bronchi (OR 4.8, 1.3–17.2), organic foreign body (OR 6.2, 1.4–27.2), and history of recurrent respiratory infections (OR 2.7, 1.4–5.3). Children with misdiagnosis tended to have a longer time from symptom onset to the definitive diagnosis of FBA (P < 0.001).Conclusions: More than one-third of children with FBA were missed at first presentation. Errors in diagnostic testing and history taking were the main reasons leading to initial misdiagnosis.

Although several biclustering algorithms have been studied, few are used for cross-pattern identification across species using multi-omics data mining. A fast empirical Bayesian biclustering (Bi-EB) algorithm is developed to detect the patterns shared from both integrated omics data and between species. The Bi-EB algorithm addresses the clinical critical translational question using the bioinformatics strategy, which addresses how modules of genotype variation associated with phenotype from cancer cell screening data can be identified and how these findings can be directly translated to a cancer patient subpopulation. Empirical Bayesian probabilistic interpretation and ratio strategy are proposed in Bi-EB for the first time to detect the pairwise regulation patterns among species and variations in multiple omics on a gene level, such as proteins and mRNA. An expectation–maximization (EM) optimal algorithm is used to extract the foreground co-current variations out of its background noise data by adjusting parameters with bicluster membership probability threshold Ac; and the bicluster average probability p. Three simulation experiments and two real biology mRNA and protein data analyses conducted on the well-known Cancer Genomics Atlas (TCGA) and The Cancer Cell Line Encyclopedia (CCLE) verify that the proposed Bi-EB algorithm can significantly improve the clustering recovery and relevance accuracy, outperforming the other seven biclustering methods—Cheng and Church (CC), xMOTIFs, BiMax, Plaid, Spectral, FABIA, and QUBIC—with a recovery score of 0.98 and a relevance score of 0.99. At the same time, the Bi-EB algorithm is used to determine shared the causality patterns of mRNA to the protein between patients and cancer cells in TCGA and CCLE breast cancer. The clinically well-known treatment target protein module estrogen receptor (ER), ER (p118), AR, BCL2, cyclin E1, and IGFBP2 are identified in accordance with their mRNA expression variations in the luminal-like subtype. Ten genes, including CCNB1, CDH1, KDR, RAB25, PRKCA, etc., found which can maintain the high accordance of mRNA–protein for both breast cancer patients and cell lines in basal-like subtypes for the first time. Bi-EB provides a useful biclustering analysis tool to discover the cross patterns hidden both in multiple data matrixes (omics) and species. The implementation of the Bi-EB method in the clinical setting will have a direct impact on administrating translational research based on the cancer cell screening guidance.

Chemoresistance has been a major challenge in the treatment of patients with breast cancer. The diverse omics platforms and small sample sizes reported in the current studies of chemoresistance in breast cancer limit the consensus regarding the underlying molecular mechanisms of chemoresistance and the applicability of these study findings. Therefore, we built two transcriptome datasets for patients with chemotherapy-resistant breast cancers—one comprising paired transcriptome samples from 40 patients before and after chemotherapy and the second including unpaired samples from 690 patients before and 45 patients after chemotherapy. Subsequent conventional pathway analysis and new subpathway analysis using these cohorts uncovered 56 overlapping upregulated genes (false discovery rate [FDR], 0.018) and 36 downregulated genes (FDR, 0.016). Pathway analysis revealed the activation of several pathways in the chemotherapy-resistant tumors, including those of drug metabolism, MAPK, ErbB, calcium, cGMP-PKG, sphingolipid, and PI3K-Akt, as well as those activated by Cushing’s syndrome, human papillomavirus (HPV) infection, and proteoglycans in cancers, and subpathway analysis identified the activation of several more, including fluid shear stress, Wnt, FoxO, ECM-receptor interaction, RAS signaling, Rap1, mTOR focal adhesion, and cellular senescence (FDR < 0.20). Among these pathways, those associated with Cushing’s syndrome, HPV infection, proteoglycans in cancer, fluid shear stress, and focal adhesion have not yet been reported in breast cancer chemoresistance. Pathway and subpathway analysis of a subset of triple-negative breast cancers from the two cohorts revealed activation of the identical chemoresistance pathways.

Gastrodia elata Blume (GE) is an orchid herbal material widely used in China for thousands of years. Recently, the Chinese government has noted that GE can be legally used as functional food, which brings a huge food market for GE. The processing of herbal materials is considered to be the most critical technology to ensure quality and efficacy. Proper processing can transform raw materials into food or drugs with appropriate ingredients and functions. However, due to the unprocessed or the fresh GE can be easily bought in the market, the ingredient and efficiency difference between fresh and processed GE need to further investigate. Based on the understanding of the mechanism of processing in the GE and its possible application in the dietary treatment for anxious people, this paper studied the ingredient changes of the main active constituents of the GE before and after processing. Meanwhile, blood concentrations of three active ingredient including gastrodin (GAS), p-hydroxylbenzyl alcohol (HBA) and Parishin were investigated. Moreover, the different efficacy of the GE in various animal models and the potential mechanisms were studied. Finally, water steamed GE showed distinct better bioactivity than the fresh GE. Further mechanism based on the metabolomics was investigated. The difference of tryptophan related metabolites was observed and discussed to be the possible cause of the increasing bioactive efficiency in GE with steaming process.

Abstract Because there is no effective treatment for late‐stage prostate cancer (PCa) at this moment, identifying novel targets for therapy of advanced PCa is urgently needed. A new network‐based systems biology approach, XDeath, is developed to detect crosstalk of signaling pathways associated with PCa progression. This unique integrated network merges gene causal regulation networks and protein‐protein interactions to identify novel co‐targets for PCa treatment. The results show that polo‐like kinase 1 (Plk1) and DNA methyltransferase 3A (DNMT3a)‐related signaling pathways are robustly enhanced during PCa progression and together they regulate autophagy as a common death mode. Mechanistically, it is shown that Plk1 phosphorylation of DNMT3a leads to its degradation in mitosis and that DNMT3a represses Plk1 transcription to inhibit autophagy in interphase, suggesting a negative feedback loop between these two proteins. Finally, a combination of the DNMT inhibitor 5‐Aza‐2’‐deoxycytidine (5‐Aza) with inhibition of Plk1 suppresses PCa synergistically.

The application of iron oxide to NH3-SCR is attractive but largely hindered by its poor acid properties, and surface sulfation is proven to be a prominent way of enhancing the acidity. As such, the method of enriching the sulfate species on iron oxide is crucial for improving the NH3-SCR performance. In the present study, by employing ammonium bisulfate (ABS) as the source of gaseous SO2 for the purpose of trapping, we reported an effective strategy for enhancing the SO42− immobilization on α-Fe2O3 catalyst via spatial confinement in a mesoporous SBA-15 framework. Interestingly, although the presence of the mesopore channel had an adverse effect on the ABS decomposition, which was expected to produce less available SO2, the measured SO42− immobilized on α-Fe2O3 in the mesoporous SBA-15 system was significantly greater than that of the regular SiO2, demonstrating the promoting effect of the spatial confinement on the SO42− enrichment. Further characterizations of the NH3-TPD, NO oxidation, and NH3-SCR performance tests proved that, as a result of the enhanced acidity, the enrichment of SO42− on α-Fe2O3 displayed a clear correlation with the SCR activity. The results of the present study provide an effective strategy for boosting the catalytic performance of iron oxide in NH3-SCR via SO42− enrichment.

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, and it has gradually become the main disease burden in the world. However, the pathogenesis of NAFLD is complex, involving such things as dyslipidemia, oxidative stress, inflammation, etc. This brings to the table a significant challenge for drug development, and there is still no drug approved by the FDA on the market to treat the disease. GAS and HBA are active ingredients of the orchidaceae plant gastrodia elata and have exhibit effects in ameliorating nervous system diseases caused by oxidative stress. HBA is a metabolite of GAS that could perform lipid regulation and improve oxidative stress on HCD-induced NAFLD larval zebrafish, as reported by previous studies; we therefore explored the role of HBA in lipid regulation and oxidative stress on HCD-induced NAFLD larval zebrafish in vivo and FFA-induced BRL-3A hepatocyte in vitro. The gene expression of lipogenesis, inflammation, and oxidative stress were measured to investigate the underlying mechanism of HBA, and the potential protein target of HBA was explored by immunofluorescence. Altogether, our data highlight the role of HBA in improving NAFLD by use of its lipid-lowering and anti-oxidative properties via the Nrf2/HO-1 signaling pathway, providing a potential therapeutic compound for NAFLD treatment.

This study aims to create a database for quantifying the fraction of metabolism of cytochrome P450 isozymes for cancer drugs approved by the US Food and Drug Administration. A reproducible data collection protocol was developed to extract essential information, including both substrate‐depletion and metabolite‐formation data from publicly available in vitro selective cytochrome P450 enzyme inhibition studies. We estimated the fraction of metabolism from the curated data. To demonstrate the utility of this database, we conducted an in vitro drug interaction prediction for the 42 cancer drugs. In the drug–drug interaction prediction, we identified 31 drug pairs with at least one cancer drug in each pair that had predicted area under concentration ratios > 2. We further found clinical drug interaction pieces of evidence in the literature to support 20 of these 31 drug–drug interaction pairs.

Abstract Background While most pediatric sarcomas respond to front-line therapy, some bone sarcomas do not show radiographic response like soft-tissue sarcomas (rhabdomyosarccomas) but do show 90% necrosis. Though, new therapies are urgently needed to improve survival and quality of life in pediatric patients with sarcomas. Complex chromosomal aberrations such as amplifications and deletions of DNA sequences are frequently observed in pediatric sarcomas. Evaluation of copy number variations (CNVs) associated with pediatric sarcoma patients at the time of diagnosis or following therapy offers an opportunity to assess dysregulated molecular targets and signaling pathways that may drive sarcoma development, progression, or relapse. The objective of this study was to utilize publicly available data sets to identify potential predictive biomarkers of chemotherapeutic response in pediatric Osteosarcoma (OS), Rhabdomyosarcoma (RMS) and Ewing’s Sarcoma Family of Tumors (ESFTs) based on CNVs following chemotherapy (OS n = 117, RMS n = 64, ESFTs n = 25 tumor biopsies). Methods There were 206 CNV profiles derived from pediatric sarcoma biopsies collected from the public databases TARGET and NCBI-Gene Expression Omnibus (GEO). Through our comparative genomic analyses of OS, RMS, and ESFTs and 22,255 healthy individuals called from the Database of Genomic Variants (DGV), we identified CNVs (amplifications and deletions) pattern of genomic instability in these pediatric sarcomas. By integrating CNVs of Cancer Cell Line Encyclopedia (CCLE) identified in the pool of genes with drug-response data from sarcoma cell lines (n = 27) from Cancer Therapeutics Response Portal (CTRP) Version 2, potential predictive biomarkers of therapeutic response were identified. Results Genes associated with survival and/recurrence of these sarcomas with statistical significance were found on long arm of chromosome 8 and smaller aberrations were also identified at chromosomes 1q, 12q and x in OS, RMS, and ESFTs. A pool of 63 genes that harbored amplifications and/or deletions were frequently associated with recurrence across OS, RMS, and ESFTs. Correlation analysis of CNVs from CCLE with drug-response data of CTRP in 27 sarcoma cell lines, 33 CNVs out of 63 genes correlated with either sensitivity or resistance to 17 chemotherapies from which actionable CNV signatures such as IGF1R, MYC, MAPK1, ATF1, and MDM2 were identified. These CNV signatures could potentially be used to delineate patient populations that will respond versus those that will not respond to a particular chemotherapy. Conclusions The large-scale analyses of CNV-drug screening provides a platform to evaluate genetic alterations across aggressive pediatric sarcomas. Additionally, this study provides novel insights into the potential utilization of CNVs as not only prognostic but also as predictive biomarkers of therapeutic response. Information obtained in this study may help guide and prioritize patient-specific therapeutic options in pediatric bone and soft-tissue sarcomas.

Acyl-protein thioesterase 1 (APT1) can affect H-Ras localization and function by promoting its depalmitoylation. However, relatively little attention has been paid to the effects of APT1 on H-Ras in the cardiovascular system. In this study, we revealed its roles in atherosclerosis development using oxidative low-density lipoprotein (ox-LDL)-induced endothelial dysfunction models and a Western diet-induced ApoE−/− mouse model. The results showed that APT1 expression was up-regulated, while that of miR-138-5p (miR-138) was down-regulated (p < 0.05) in this model. In the meantime, APT1 and H-Ras were translocated from the cytoplasm to the plasma membrane. Bioinformatic analysis and double fluorescence identified miR-138 as the upstream regulator of APT1. APT1 knockdown regulated H-Ras localization and expression, which subsequently affected the MAPK signaling pathway and the expression of its downstream factors. Further research indicated that human umbilical vein endothelial cells (HUVECs)-derived biogenic nanoparticles (BiNPs), hBPs secretion, and RNA expression of hBP-loaded APT1 were increased (p < 0.05) in the ox-LDL induced endothelial dysfunction model. Meanwhile, the HUVECs-derived APT1 could further affect macrophage function through hBP transportation. Altogether, this study demonstrated that the miR-138-APT1 axis may be partially responsible for atherosclerosis development by regulating the H-Ras-MAPK signaling pathway and hBP transportation. The results also shed novel insight on the underlying mechanisms of, and identify potential diagnostic and therapeutic targets for, atherosclerotic cardiovascular diseases in the future.

In this paper, a large-scale experimental system was established to identify the migration and distribution laws of complex fracture proppant in shale reservoir volume fracturing. With this system, the effects of secondary fracture angle, fluid displacement, proppant concentration and size, fracturing fluid viscosity and other factors on the migration and distribution of proppant were tested, and the migration and distribution of proppant in primary/secondary fractures were analyzed. The following results were obtained. First, the fluid flow pattern in fractures transforms gradually from laminar flow into turbulent flow with the increase of fracture supporting height. Second, the migration modes of proppant in fractures mainly include suspended migration and gliding migration. Third, the distribution form of proppant in primary fractures before branching is related to secondary fracture angle, fluid displacement and proppant concentration and size, among which the fluid displacement is the most important factor. Fourth, the mass ratio of proppant in primary fractures after branching is proportional to the secondary fracture angle, fluid displacement, fracturing fluid viscosity and proppant concentration and size, and is inversely proportional to the flow ratio between secondary fractures and primary fractures. Fifth, the mass ratio of proppant in secondary fractures after branching is proportional to fluid displacement, fracturing fluid viscosity and flow ratio between secondary fractures and primary fractures, and is inversely proportional to secondary fracture angle and proppant concentration and size. Sixth, the angle at the leading edge of proppant bank in the primary fractures after branching is proportional to the proppant concentration and size and the flow ratio between secondary fractures and primary fractures, but is inversely proportional to secondary fracture angle, fluid displacement and fracturing fluid viscosity. Seventh, the angle at the leading edge of proppant bank in the secondary fractures after branching is proportional to the secondary fracture angle and the proppant concentration and size, but is inversely proportional to the fluid displacement, fracturing fluid viscosity and flow ratio between secondary fractures and primary fractures. In conclusion, the research results can provide a theoretical support for proppant optimization and program design of shale reservoir volume fracturing. Keywords: Shale, Reservoir, Volume fracturing, Complex fracture, Proppant, Migration and distribution, Experimental system

In order to obtain high peak power RF intensity-modulated pulses for long-distance applications, a Q-switched Nd:YAG laser is injection seeded by a dual-frequency laser signal. The beat note frequency of the dual-frequency laser signal is adjusted to the frequency spacing of two adjacent longitudinal modes of the Q-switched laser. When the dual-frequency signal is injected into the Q-switched slave resonator, simultaneously two cavity axial modes are tuned to allow the injected wave frequencies within the axial modes selection range, the two selected slave cavity modes will oscillate due to phase-pulling effect. The Interference of the two modes results in intensity modulated pulses. The modulation frequency equals to a free spectral range of the Q-switched cavity, which is 225 MHz in our experiment. Maximum intensity-modulated pulse energy of 7 mJ and pulsewidth of 16 ns are measured. The dependence of locking bandwidth to the injected power is also studied experimentally, it is confirmed that the locking bandwidth increases with respect to the injected power of the seed signal. The high peak power RF intensity-modulated pulsed light source is a promising candidate for long-distance lidar-radar applications.

Background: Cancer cell lines are frequently used in research as in-vitro tumor models. Genomic data and large-scale drug screening have accelerated the right drug selection for cancer patients. Accuracy in drug response prediction is crucial for success. Due to data-type diversity and big data volume, few methods can integrative and efficiently find the principal low-dimensional manifold of the high-dimensional cancer multi-omics data to predict drug response in precision medicine. Method: A novelty k-means Ensemble Support Vector Regression (kESVR) is developed to predict each drug response values for single patient based on cell-line gene expression data. The kESVR is a blend of supervised and unsupervised learning methods and is entirely data driven. It utilizes embedded clustering (Principal Component Analysis and k-means clustering) and local regression (Support Vector Regression) to predict drug response and obtain the global pattern while overcoming missing data and outliers’ noise. Results: We compared the efficiency and accuracy of kESVR to 4 standard machine learning regression models: (1) simple linear regression, (2) support vector regression (3) random forest (quantile regression forest) and (4) back propagation neural network. Our results, which based on drug response across 610 cancer cells from Cancer Cell Line Encyclopedia (CCLE) and Cancer Therapeutics Response Portal (CTRP v2), proved to have the highest accuracy (smallest mean squared error (MSE) measure). We next compared kESVR with existing 17 drug response prediction models based a varied range of methods such as regression, Bayesian inference, matrix factorization and deep learning. After ranking the 18 models based on their accuracy of prediction, kESVR ranks first (best performing) in majority (74%) of the time. As for the remaining (26%) cases, kESVR still ranked in the top five performing models. Conclusion: In this paper we introduce a novel model (kESVR) for drug response prediction using high dimensional cell-line gene expression data. This model outperforms current existing prediction models in terms of prediction accuracy and speed and overcomes overfitting. This can be used in future to develop a robust drug response prediction system for cancer patients using the cancer cell-lines guidance and multi-omics data.

Abstract Background Several of the thousands of human long noncoding RNAs (lncRNAs) have been functionally characterized, yet their potential involvement in hepatocellular carcinoma (HCC) remains poorly understood. Methods LncRNA-HOXD-AS1 was identified by microarray and validated by real-time PCR. The clinicopathological significance of HOXD-AS1 was analyzed by Kaplan-Meier method. Chromatin immunoprecipitation was conducted to examine the mechanism of HOXD-AS1 upregulation. The role of HOXD-AS1 in HCC cells was assessed both in vitro and in vivo. ceRNA function of HOXD-AS1 was evaluated by RNA immunoprecipitation and biotin-coupled miRNA pull down assays. Results In this study, we found that HOXD-AS1 was significantly upregulated in HCC tissues. Clinical investigation demonstrated high expression level of HOXD-AS1 was associated with poor prognosis and high tumor node metastasis stage of HCC patients, and was an independent risk factor for survival. Moreover, our results revealed that STAT3 could specifically interact with the promoter of HOXD-AS1 and activate HOXD-AS1 transcription. Knockdown of HOXD-AS1 significantly inhibited migration and invasion of HCC cells in vitro and distant lung metastasis in vivo. Additionally, HOXD-AS1 was enriched in the cytoplasm, and shared miRNA response elements with SOX4. Overexpression of HOXD-AS1 competitively bound to miR-130a-3p that prevented SOX4 from miRNA-mediated degradation, thus activated the expression of EZH2 and MMP2 and facilitated HCC metastasis. Conclusions In summary, HOXD-AS1 is a prognostic marker for HCC patients and it may play a pro-metastatic role in hepatocarcinogenesis.

Patients with pancreatic ductal adenocarcinoma (PDAC) suffer debilitating and deadly weight loss, known as cachexia. Development of therapies requires biomarkers to diagnose, and monitor cachexia; however, no such markers are in use. Via Somascan, we measured ~1300 plasma proteins in 30 patients with PDAC vs. 11 controls. We found 60 proteins specific to local PDAC, 46 to metastatic, and 67 to presence of >5% cancer weight loss (FC ≥ |1.5|, p ≤ 0.05). Six were common for cancer stage (Up: GDF15, TIMP1, IL1RL1; Down: CCL22, APP, CLEC1B). Four were common for local/cachexia (C1R, PRKCG, ELANE, SOST: all oppositely regulated) and four for metastatic/cachexia (SERPINA6, PDGFRA, PRSS2, PRSS1: all consistently changed), suggesting that stage and cachexia status might be molecularly separable. We found 71 proteins that correlated with cachexia severity via weight loss grade, weight loss, skeletal muscle index and radiodensity (r ≥ |0.50|, p ≤ 0.05), including some known cachexia mediators/markers (LEP, MSTN, ALB) as well as novel proteins (e.g., LYVE1, C7, F2). Pathway, correlation, and upstream regulator analyses identified known (e.g., IL6, proteosome, mitochondrial dysfunction) and novel (e.g., Wnt signaling, NK cells) mechanisms. Overall, this study affords a basis for validation and provides insights into the processes underpinning cancer cachexia.

Pore size distribution is vital in the preservation and exploitation of shale gas; the pores in shales are mainly of nanometer scale. This study focuses on the pore structures of the Upper Ordovician-Lower Silurian black shales that were sampled from the Well YC7 in southeastern Chongqing area. Nitrogen and carbon dioxide gas adsorption were conducted at 77.4 K and 273.15K, respectively. Pore structures were characterized by the modified BET, BJH, DFT, and Stoeckli methods. The results show that: (1) micropores, mesopores, and macropores are all well developed in both the Lower Silurian and Upper Ordovician shales; (2) the mean diameter of micropores for our samples is approximately 1.26 nm; and (3) the micropore size distribution curves derived from the Stoeckli method together with the CO2 adsorption data at 273.15K can be well connected with the calculated DFT model values through nitrogen adsorption data at 77.4 K, indicating that a full pore size distribution curve could be achieved for micropores, mesopores, and a portion of macropores in the shales by combing the N2 and CO2 adsorption data.

In the prediction of the synergy of drug combinations, systems pharmacology models expand the scope of experiment screening and overcome the limitations of current computational models posed by their lack of mechanical interpretation and integration of gene essentiality. We therefore investigated the synergy of drug combinations for cancer therapies utilizing records in NCI ALMANAC, and we employed logistic regression to test the statistical significance of gene and pathway features in that interaction. We trained our predictive models using 43 NCI-60 cell lines, 165 KEGG pathways, and 114 drug pairs. Scores of drug-combination synergies showed a stronger correlation with pathway than gene features in overall trend analysis and a significant association with both genes and pathways in genome-wide association analyses. However, we observed little overlap of significant gene expressions and essentialities and no significant evidence that associated target and non-target genes and their pathways. We were able to validate four drug-combination pathways between two drug combinations, Nelarabine-Exemestane and Docetaxel-Vermurafenib, and two signaling pathways, PI3K-AKT and AMPK, in 16 cell lines. In conclusion, pathways significantly outperformed genes in predicting drug-combination synergy, and because they have very different mechanisms, gene expression and essentiality should be considered in combination rather than individually to improve this prediction.

Osteosarcoma (OS) patients exhibit poor overall survival, partly due to copy number variations (CNVs) resulting in dysregulated gene expression and therapeutic resistance. To identify actionable prognostic signatures of poor overall survival, we employed a systems biology approach using public databases to integrate CNVs, gene expression, and survival outcomes in pediatric, adolescent, and young adult OS patients. Chromosome 8 was a hotspot for poor prognostic signatures. The MYC-RAD21 copy number gain (8q24) correlated with increased gene expression and poor overall survival in 90% of the patients (n = 85). MYC and RAD21 play a role in replication-stress, which is a therapeutically actionable network. We prioritized replication-stress regulators, bromodomain and extra-terminal proteins (BETs), and CHK1, in order to test the hypothesis that the inhibition of BET + CHK1 in MYC-RAD21+ pediatric OS models would be efficacious and safe. We demonstrate that MYC-RAD21+ pediatric OS cell lines were sensitive to the inhibition of BET (BETi) and CHK1 (CHK1i) at clinically achievable concentrations. While the potentiation of CHK1i-mediated effects by BETi was BET-BRD4-dependent, MYC expression was BET-BRD4-independent. In MYC-RAD21+ pediatric OS xenografts, BETi + CHK1i significantly decreased tumor growth, increased survival, and was well tolerated. Therefore, targeting replication stress is a promising strategy to pursue as a therapeutic option for this devastating disease.

Background: Large-scale screening of drug sensitivity on cancer cell models can mimic in vivo cellular behavior providing wider scope for biological research on cancer. Since the therapeutic effect of a single drug or drug combination depends on the individual patient’s genome characteristics and cancer cells integration reaction, the identification of an effective agent in an in vitro model by using large number of cancer cell models is a promising approach for the development of targeted treatments. Precision cancer medicine is to select the most appropriate treatment or treatments for an individual patient. However, it still lacks the tools to bridge the gap between conventional in vitro cancer cell models and clinical patient response to inhibitors. Methods: An optimal two-layer decision system model is developed to identify the cancer cells that most closely resemble an individual tumor for optimum therapeutic interventions in precision cancer medicine. Accordingly, an optimal grid parameters selection is designed to seek the highest accordance for treatment selection to the patient’s preference for drug response and in vitro cancer cell drug screening. The optimal two-layer decision system model overcomes the challenge of heterology data comparison between the tumor and the cancer cells, as well as between the continual variation of drug responses in vitro and the discrete ones in clinical practice. We simulated the model accuracy using 681 cancer cells’ mRNA and associated 481 drug screenings and validated our results on 315 breast cancer patients drug selection across seven drugs (docetaxel, doxorubicin, fluorouracil, paclitaxel, tamoxifen, cyclophosphamide, lapitinib). Results: Comparing with the real response of a drug in clinical patients, the novel model obtained an overall average accordance over 90.8% across the seven drugs. At the same time, the optimal cancer cells and the associated optimal therapeutic efficacy of cancer drugs are recommended. The novel optimal two-layer decision system model was used on 1097 patients with breast cancer in guiding precision medicine for a recommendation of their optimal cancer cells (30 cancer cells) and associated efficacy of certain cancer drugs. Our model can detect the most similar cancer cells for each individual patient. Conclusion: A successful clinical translation model (optimal two-layer decision system model) was developed to bridge in-vitro basic science to clinical practice in a therapeutic intervention application for the first time. The novel tool kills two birds with one stone. It can help basic science to seek optimal cancer cell models for an individual tumor, while prioritizing clinical drugs’ recommendations in practice. Tool associated platform website: We extended the breast cancer research to 32 more types of cancers across 45 therapy predictions. The website is set up and can be accessed by the link: https://pcm2019.shinyapps.io/drug_response_prediction/.

Background: The superior cervical ganglion (SCG) of the autonomic nervous system plays an important role in different cardiovascular diseases. In this study, we investigated the effects of ischemia and fluvastatin treatment on the ion channel characteristics of SCG neurons in a rabbit myocardial ischemia (MI) model.Methods: MI was induced by abdominal subcutaneous injections of isoproterenol (ISO). The properties of the delayed rectifier potassium channel current (IK), sodium channel current (INa), and action potential (APs) on isolated SCG neurons in the control, MI-7d, MI-14d, fluvastatin-7d (fluvastatin pretreated 14 days and treated 7 days after ISO-induced MI), and fluvastatin-14d (fluvastatin pretreated 14 days and treated 14 days after ISO-induced MI) groups were studied. In addition, the RNA expressions of KCNQ3 and SCN9A in the SCG tissue were determined by performing real-time PCR. Intracellular calcium concentration was monitored using laser scanning confocal microscopy.Results: Compared with the control group, the current amplitude of IK and INa were increased in the MI-7d and MI-14d groups. KCNQ3 RNA (corresponding to channel proteins of IK) expression and SCN9A RNA (corresponding to channel proteins of INa) expression were also increased in MI groups. Activation and inactivation curves for INa in the two MI groups shifted negatively compared with the control group. These changes were reversed by fluvastatin treatment. Intracellular calcium concentration in SCG neurons was not altered significantly by MI or fluvastatin treatment. By contrast, increased AP amplitude and shortened APD90 were observed in the MI-7d and MI-14d groups. These changes were reversed in the fluvastatin-treated MI group.Conclusion: Fluvastatin treatment partly reversed the characteristics of SCG neurons in MI. The ion channel of SCG neurons could be one of the potential targets of fluvastatin in treating coronary heart diseases.

Oropharyngeal head and neck squamous cell carcinoma is a common malignant tumor in the oral cavity. High-risk human papillomavirus 16 infection is a major cause of oropharyngeal head and neck squamous cell carcinoma development. Strong antitumor immune responses, especially CD8 + T cell responses, are thought to be essential to effective cancer treatment and are associated with better prognosis in oropharyngeal head and neck squamous cell carcinoma. In this study, we examined the role of the Tim-3/Gal-9 pathway in oropharyngeal head and neck squamous cell carcinoma patients. We found that Gal-9 expression by CD4 + T cells was increased in human papillomavirus-positive oropharyngeal head and neck squamous cell carcinoma patients, but not in human papillomavirus-negative oropharyngeal head and neck squamous cell carcinoma patients. Increased Gal-9 secretion by CD4 + T cells presented multiple immunosuppressive effects. Coculturing monocytes with high Gal-9-expressing CD4 + T cells resulted in the expansion of Tim-3 + monocytes, which suppressed interferon gamma production by activated CD8 + T cells. Subsequently, total monocytes incubated with exogenous Gal-9, or high Gal-9-expressing CD4 + T cells, suppressed the expression of interferon gamma by CD8 + T cells. Exogenous Gal-9 and high Gal-9-expressing CD4 + T cells also suppressed the secretion of both interleukin 10 and interleukin 12 by monocytes. These effects are Tim-3/Gal-9-dependent because blocking Tim-3 and/or Gal-9 could enhance the support of CD8 + T cell interferon gamma production and the interleukin 10 and interleukin 12 secretion by monocytes. Together, these data suggest that the high Tim-3 expression in monocytes could be utilized by tumor-promoting Gal-9 expression on CD4 + T cells. Immunotherapy in human papillomavirus-positive oropharyngeal head and neck squamous cell carcinoma patients therefore faces an additional challenge posed by Tim-3 and Gal-9 and likely requires the blockade of these molecules.

Alternatively-activated pathways have been observed in biological experiments in cancer studies, but the concept had not been fully explored in computational cancer system biology. Therefore, an alternatively-activated pathway identification method was proposed and applied to primary breast cancer and breast cancer liver metastasis research using microarray data. Interestingly, the results show that cytokine-cytokine receptor interaction and calcium signaling were significantly enriched under both conditions. TGF beta signaling was found to be the hub in network topology analysis. In total, three types of alternatively-activated pathways were recognized. In the cytokine-cytokine receptor interaction pathway, four active alteration patterns in gene pairs were noticed. Thirteen cytokine-cytokine receptor pairs with inverse activity changes of both genes were verified by the literature. The second type was that some sub-pathways were active under only one condition. For the third type, nodes were significantly active in both conditions, but with different active genes. In the calcium signaling and TGF beta signaling pathways, node E2F5 and E2F4 were significantly active in primary breast cancer and metastasis, respectively. Overall, our study demonstrated the first time using microarray data to identify alternatively-activated pathways in breast cancer liver metastasis. The results showed that the proposed method was valid and effective, which could be helpful for future research for understanding the mechanism of breast cancer metastasis.

Non-alcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) is the most common chronic liver disease in the world. However, there are still no drugs for NAFLD/NASH in the market. Gastrodin (GAS) is a bioactive compound that is extracted from Gastrodia elata, which is used as an active compound on nervous system diseases. Recent reports showed that GAS and Gastrodia elata possess anti-oxidant activity and lipid regulating effects, which makes us curious to reveal the anti-NAFLD effect of GAS. A high cholesterol diet (HCD) was used to induce a NAFLD larval zebrafish model, and the lipid regulation and anti-oxidant effects were tested on the model. Furthermore, qRT-PCR studied the underlying mechanism of GAS. To conclude, this study revealed a lipid regulation and anti-oxidant insights of GAS on NAFLD larval zebrafish model and provided a potential therapeutic compound for NAFLD treatment.

In this paper, an operational drought risk management framework based on the stream-flow intelligent internet control is proposed. In the proposed framework drought can be predicted, evaluated and mitigated by using a dynamic stream-flow control under the sensors detection. The framework mainly includes four sequential steps: (i) the stream-flow prediction, (ii) the stream-flow deficit index (SDI) analysis, (iii) the drought multiple regions response, and (iv) the stream-flow balance control. In order to instantiate a specific framework management, intelligence methods are utilized in these processes, namely the generalized regression neural network (GRNN) algorithm for the stream-flow prediction and the collaborative particle swarm optimization (CPSO) for the reservoirs water collaborative operation. Finally, a specific case study corresponding to the Fu basin in China is investigated to test the operability and reliability of the proposed drought risk management.