Search

Most of the surface wave-based geophysical methods require an accurate estimate of the shear wave velocity (Vs) for geotechnical site investigation. The Vs gives a measure of shear modulus which is an important engineering parameter and also owns implications for delineating weak zones and cavities. Geotechnical investigation of such zones is imperative to avoid hazards related to high building construction. Among different surface wave-based geophysical methods, the multi-channel analysis of surface waves (MASW) is most widely used technique for geotechnical investigation by measuring in situ Vs. The present work focuses on MASW data acquisition and processing for accurate estimation of Vs by using dispersion analysis and different iteration-based inversion techniques (i.e., Monte Carlo and least square approaches). The proposed methodology is investigated at two sites in Abu Dhabi, UAE, using 24-channel seismograph network. The obtained results suggest that Monte Carlo approach is more efficient and reliable for Vs profile estimation and is recommended for geotechnical investigation. Monte Carlo approach-based results of the MASW velocity-profiles at both sites confirmed cavities and their geometries within the subsurface. Furthermore, the identified cavities are well in agreement with the trail borehole data, thus improving the confidence level of the obtained results. The characterization of the soil of both the sites and its implications are also presented with the help of NEHRP and IBC codes.

Motivation Characterization of protein subcellular localization has become an important and long-standing task in bioinformatics and computational biology, which provides valuable information for elucidating various cellular functions of proteins and guiding drug design. Results Here, we develop a novel bioimage-based computational approach, termed PScL-DDCFPred, to accurately predict protein subcellular localizations in human tissues. PScL-DDCFPred first extracts multiview image features, including global and local features, as base or pure features; next, it applies a new integrative feature selection method based on stepwise discriminant analysis and generalized discriminant analysis to identify the optimal feature sets from the extracted pure features; Finally, a classifier based on deep neural network (DNN) and deep-cascade forest (DCF) is established. Stringent 10-fold cross-validation tests on the new protein subcellular localization training dataset, constructed from the human protein atlas databank, illustrates that PScL-DDCFPred achieves a better performance than several existing state-of-the-art methods. Moreover, the independent test set further illustrates the generalization capability and superiority of PScL-DDCFPred over existing predictors. In-depth analysis shows that the excellent performance of PScL-DDCFPred can be attributed to three critical factors, namely the effective combination of the DNN and DCF models, complementarity of global and local features, and use of the optimal feature sets selected by the integrative feature selection algorithm. Availability and implementation https://github.com/csbio-njust-edu/PScL-DDCFPred. Supplementary information Supplementary data are available at Bioinformatics online.

Protein subcellular localization plays a crucial role in characterizing the function of proteins and understanding various cellular processes. Therefore, accurate identification of protein subcellular location is an important yet challenging task. Numerous computational methods have been proposed to predict the subcellular location of proteins. However, most existing methods have limited capability in terms of the overall accuracy, time consumption and generalization power. To address these problems, in this study, we developed a novel computational approach based on human protein atlas (HPA) data, referred to as PScL-HDeep, for accurate and efficient image-based prediction of protein subcellular location in human tissues. We extracted different handcrafted and deep learned (by employing pretrained deep learning model) features from different viewpoints of the image. The step-wise discriminant analysis (SDA) algorithm was applied to generate the optimal feature set from each original raw feature set. To further obtain a more informative feature subset, support vector machine–based recursive feature elimination with correlation bias reduction (SVM-RFE + CBR) feature selection algorithm was applied to the integrated feature set. Finally, the classification models, namely support vector machine with radial basis function (SVM-RBF) and support vector machine with linear kernel (SVM-LNR), were learned on the final selected feature set. To evaluate the performance of the proposed method, a new gold standard benchmark training dataset was constructed from the HPA databank. PScL-HDeep achieved the maximum performance on 10-fold cross validation test on this dataset and showed a better efficacy over existing predictors. Furthermore, we also illustrated the generalization ability of the proposed method by conducting a stringent independent validation test.

Seismic reservoir characterization is a renowned technique to obtain a better understandingand quantification of hydrocarbon bearing reservoirs by integration of seismic attributes, post stack inversion and wireline log analysis. In current case study, a methodology based on these techniques is proposed, and its applicability is tested for delineating the secondary reservoir potential of the Eocene carbonates (Habib Rahi and Sui Upper Limestone) in Qadirpur area, Lower Indus Basin, Pakistan. The application of post stack inversion provides a reasonable estimate of the acoustic impedance and enables the spatial distribution of important petrophysical properties (effective porosity and water saturation) available at sparse well locations. This distribution is facilitated by developing geostatistical relations between seismic and well derived reservoir properties in the range of seismic resolution. The results from all integrated techniques depict fluid saturated zones at Habib Rahi (gas at 907–922 m; water at 953–973 m) and Sui Upper Limestone (gas at 1192–1208 m) levels. The spatial distribution of the reservoir properties (effective porosity and water saturation) obtained via seismic data shows a very good calibration at the location of wells and reflects significant secondary reservoir potential for Eocene carbonates in the study area (effective porosity in the range of 7–26% and water saturation in the range of 35–60%). Furthermore, the methodology tested over here will be advantageous for the characterization of oil/gas reservoirs in various basins of Asia and other parts of world with similar geological settings.

DNA-binding proteins play a crucial role in various biological processes such as regulation of DNA modification, repair, replication, and transcription. These proteins widely participate in the production of drugs, antibiotics, and steroids. Many computational approaches have been developed to identify DNA-binding proteins, but some methods are time-consuming and expensive while some are laborious. Still, it is a challenging task for the researchers to develop highly promising computational methods to identify DNA-binding proteins with high precision. In our work, we developed a new computational approach named as DBPPred-PDSD which has more promising prediction power for DNA-binding proteins. We employed two datasets, extracted features via Split Amino Acid Composition (SAAC) and Position Specific Scoring Matrix (PSSM). Further, we applied the Discrete Wavelet Transform (DWT) on PSSM to extract dominant features. From these features space, optimal features are generated by Maximum Relevance and Minimum Redundancy (mRMR) and fused. To obtain highly informative features, we used Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and provided to well-known classifiers namely Support Vector Machine (SVM) and Random Forest (RF). Our model with the SVM classifier on three tests i.e. Jackknife cross-validation, 10-fold cross-validation and Independent tests achieved the highest success rate than other existing methods in the literature.

The exploration and production of unconventional resources has increased significantly over the past few years around the globe to fulfill growing energy demands. Hydrocarbon potential of these unconventional petroleum systems depends on the presence of significant organic matter; their thermal maturity and the quality of present hydrocarbons i.e. gas or oil shale. In this work, we present a workflow for estimating Total Organic Content (TOC) from seismic reflection data. To achieve the objective of this study, we have chosen a classic potential candidate for exploration of unconventional reserves, the shale of the Sembar Formation, Lower Indus Basin, Pakistan. Our method includes the estimation of TOC from the well data using the Passey’s ΔlogR and Schwarzkofp’s methods. From seismic data, maps of Relative Acoustic Impedance (RAI) are extracted at maximum and minimum TOC zones within the Sembar Formation. A geostatistical trend with good correlation coefficient (R2) for cross-plots between TOC and RAI at well locations is used for estimation of seismic based TOC at the reservoir scale. Our results suggest a good calibration of TOC values from seismic at well locations. The estimated TOC values range from 1 to 4% showing that the shale of the Sembar Formation lies in the range of good to excellent unconventional oil/gas play within the context of TOC. This methodology of source rock evaluation provides a spatial distribution of TOC at the reservoir scale as compared to the conventional distribution generated from samples collected over sparse wells. The approach presented in this work has wider applications for source rock evaluation in other similar petroliferous basins worldwide.

Gas hydrates are part of the unconventional energy resources called the gas clathrates. The present work focused on the assessment of gas hydrates potential of Makran area, Offshore, Pakistan. The presence of a clear bottom simulating reflector (BSR) is primary indicator for the occurrence of gas hydrates and generally referred to as base of gas hydrates stability zone. The gas hydrates zone in the study area was identified by the application of seismic attributes (sweetness, relative acoustic impedance and normalized apparent polarity) using major characteristics of BSR i.e. amplitude blanking, adopting the shape of seafloor, cross cutting the sedimentary structures and opposite polarity to seafloor. A low velocity layer was also interpreted beneath the hydrates layer due to the existence of free gas. Rock physics modelling for the two different geometrical distribution of hydrates was also applied to obtain effective elastic properties. The results of rock physics modelling showed that the hydrates as part of sediments have higher elastic properties compared to hydrates in pore spaces, and these properties were controlled by saturation of gas hydrates. Using the effective elastic properties, amplitude versus offset (AVO) forward modelling (Exact Zoeppritz + ray tracing) for different scenarios was performed to characterize the presence of BSR more efficiently.

A series of thiobarbiturates 4a–4e and bis-thiobarbiturates analogues 5a–5o has been synthesized by condensing 1,3-diethylthiobarbituric acid 3 with a variety of aromatic aldehydes with varied structural features and substitution at active methylene position of thiobarbituric acid. Afterward, chemical structures of newly synthesized analogues of thiobarbituric acid were characterized through FT-IR, NMR spectroscopy and mass spectrometry. Subsequently, the inhibitory potential of thiobarbiturates 4a–4e and bis-thiobarbiturates analogues 5a–5o against urease enzyme was evaluated. The inhibitory potential of all synthesized analogues in terms of IC50 value was observed in the range of 8.42 ± 0.32 to 79.34 ± 0.52 μM by comparing with thiourea (IC50 21.25 ± 0.15 μM) as a standard urease inhibitor. Out of these twenty analogues, most of the analogues exhibited potent inhibitory activity against urease. After interesting findings, structure activity relationship (SAR) has been established for all analogues. Docking studies revealed that synthesized analogues interacted with active site residues of bimetallic nickel center of the urease enzyme through, thiolate, π-π stacking and hydrogen bonding interactions.

In the literature of computer vision, many techniques have demonstrated their potentials for interactive image segmentation. However, most of these state-of-the-art algorithms are unable to produce accurate boundaries without more user interaction, as they are highly sensitive to the seed's quantity and quality. These techniques frequently depend on more user interaction to refine the boundaries. In order to solve the problem and get accurate boundaries via less user interaction, in this work, a robust interactive image segmentation method is proposed based on generic multiscale oriented contours via single forward pass Convolutional Neural Networks (CNNs) and graph cut framework. We first utilize CNN to construct the boundary-level information and then combine this boundary-level information with the boundary energy term of graph cut framework. The proposed method exhibits a significant leap in robustness to user interaction, smooth boundaries, accurate segmentation and the ability to handle the changes. We show that the sensitivity to the seeds can be controlled and accuracy can be improved via boundary-level information. We further conduct both qualitative and quantitative experiments on benchmark datasets, showing that our proposed method outperforms the state-of-the-art interactive image segmentation techniques.

Seismic post-stack inversion facilitates the interpretation, mapping and quantification of hydrocarbon-bearing zones. This study estimates reservoir properties (i.e. acoustic impedance and porosity) by applying post-stack seismic inversion techniques to a gas prone reservoir in the Sawan area, Southern Indus Basin, Pakistan. In this particular study, model-based and sparse-spike inversion algorithms are successfully applied on 3D seismic and wireline log data to predict reservoir character in the Lower Goru Formation (C-sand interval). Our results suggest that model-based post-stack seismic inversion provides more reasonable estimates (i.e. returning detailed spatial variations) for acoustic impedance and porosity when compared to sparse-spike inversion algorithms. The calibration of these estimates with petrophysical data from wireline log data indicates an appropriate agreement amongst them. Importantly, the results obtained in our case study can be applied to similar basins in Asia with 'tight' oil and 'tight' gas filling sand-shale intercalations with different thickness and areal distributions.

Classification of hyperspectral images (HSI) can benefit from deep learning models with deep architecture in remote sensing. In this letter, a novel method based on Convolutional Neural Network (CNN) is proposed for the classification of hyperspectral images. Due to using more spatio-spectral features for the classification of hyperspectral images, the proposed method outperforms the existing state-of-the-art classification techniques. Our proposed method first reduces the dimension of hyperspectral images using Principle component analysis (PCA). The spatial and spectral features are then exploited by a fixed size convolutional filter to generate the combine spatio-spectral feature maps. Finally, these feature maps are fed into a Multi-Layer Perceptron (MLP) classifier that predicts the class of the pixel vector. To validate the effectiveness of our proposed method, computer simulations are conducted using three datasets namely Indian Pines, Salinas and Pavia University and comparisons with existing techniques are made.