Application of Combined Local and Global Optimization Algorithms in Joint Interpretation of Direct Current Resistivity and Seismic Refraction Data: A Case Study of Dammam Dome, Eastern Saudi Arabia.

The main geological structures in the Dammam Dome are defined by integrating geophysical measurements and applying new methodological approaches. Dammam Dome is characterized by a well-developed fracture/joints system; thus, high complexity of the subsurface is expected. Direct Current Resistivity (DCR) and Seismic Refraction (SR) geophysical survey aimed to map the Dammam Dome's near-surface features. The geophysical data were acquired along two profiles in the northern part of Dammam Dome. To maximize the results from conducting DCR and SR measurements over a complex area, a combined local and global optimization algorithm was used to obtain high-resolution near-surface images in resistivity and velocity models. The local optimization technique involves individual and joint inversion of the DCR and SR data incorporating appropriate regularization parameters, while the global optimization uses single and multi-objective genetic algorithms in model parameter estimation. The combined algorithm uses the output from the local optimization method to define a search space for the global optimization algorithm. The results show that the local optimization produces satisfactory inverted models, and that the global optimization algorithm improves the local optimization results. The joint inversion and processing of the acquired data identified two major faults and a deformed zone with an almost N-S direction that corresponds with an outcrop were mapped in profile one, while profile two shows similar anomalies in both the resistivity and velocity models with the main E-W direction. This study not only demonstrates the capability of using the combined local and global optimization multi-objectives techniques to estimate model parameters of large datasets (i.e., 2D DCR and SR data), but also provides high-resolution subsurface images that can be used to study structural features of the Dammam Dome.

GroningenNet: Deep Learning for Low-Magnitude Earthquake Detection on a Multi-Level Sensor Network.

Automatic detection of low-magnitude earthquakes has become an increasingly important research topic in recent years due to a sharp increase in induced seismicity around the globe. The detection of low-magnitude seismic events is essential for microseismic monitoring of hydraulic fracturing, carbon capture and storage, and geothermal operations for hazard detection and mitigation. Moreover, the detection of micro-earthquakes is crucial to understanding the underlying mechanisms of larger earthquakes. Various algorithms, including deep learning methods, have been proposed over the years to detect such low-magnitude events. However, there is still a need for improving the robustness of these methods in discriminating between local sources of noise and weak seismic events. In this study, we propose a convolutional neural network (CNN) to detect seismic events from shallow borehole stations in Groningen, the Netherlands. We train a CNN model to detect low-magnitude earthquakes, harnessing the multi-level sensor configuration of the G-network in Groningen. Each G-network station consists of four geophones at depths of 50, 100, 150, and 200 m. Unlike prior deep learning approaches that use 3-component seismic records only at a single sensor level, we use records from the entire borehole as one training example. This allows us to train the CNN model using moveout patterns of the energy traveling across the borehole sensors to discriminate between events originating in the subsurface and local noise arriving from the surface. We compare the prediction accuracy of our trained CNN model to that of the STA/LTA and template matching algorithms on a two-month continuous record. We demonstrate that the CNN model shows significantly better performance than STA/LTA and template matching in detecting new events missing from the catalog and minimizing false detections. Moreover, we find that using the moveout feature allows us to effectively train our CNN model using only a fraction of the data that would be needed otherwise, saving plenty of manual labor in preparing training labels. The proposed approach can be easily applied to other microseismic monitoring networks with multi-level sensors.

Deep Learning Driven Noise Reduction for Reduced Flux Computed Tomography.

Deep neural networks have received considerable attention in clinical imaging, particularly with respect to the reduction of radiation risk. Lowering the radiation dose by reducing the photon flux inevitably results in the degradation of the scanned image quality. Thus, researchers have sought to exploit deep convolutional neural networks (DCNNs) to map low-quality, low-dose images to higher-dose, higher-quality images, thereby minimizing the associated radiation hazard. Conversely, computed tomography (CT) measurements of geomaterials are not limited by the radiation dose. In contrast to the human body, however, geomaterials may be comprised of high-density constituents causing increased attenuation of the X-rays. Consequently, higher-dose images are required to obtain an acceptable scan quality. The problem of prolonged acquisition times is particularly severe for micro-CT based scanning technologies. Depending on the sample size and exposure time settings, a single scan may require several hours to complete. This is of particular concern if phenomena with an exponential temperature dependency are to be elucidated. A process may happen too fast to be adequately captured by CT scanning. To address the aforementioned issues, we apply DCNNs to improve the quality of rock CT images and reduce exposure times by more than 60%, simultaneously. We highlight current results based on micro-CT derived datasets and apply transfer learning to improve DCNN results without increasing training time. The approach is applicable to any computed tomography technology. Furthermore, we contrast the performance of the DCNN trained by minimizing different loss functions such as mean squared error and structural similarity index.

Near-surface real-time seismic imaging using parsimonious interferometry.

Results are presented for real-time seismic imaging of subsurface fluid flow by parsimonious refraction and surface-wave interferometry. Each subsurface velocity image inverted from time-lapse seismic data only requires several minutes of recording time, which is less than the time-scale of the fluid-induced changes in the rock properties. In this sense this is real-time imaging. The images are P-velocity tomograms inverted from the first-arrival times and the S-velocity tomograms inverted from dispersion curves. Compared to conventional seismic imaging, parsimonious interferometry reduces the recording time and increases the temporal resolution of time-lapse seismic images by more than an order-of-magnitude. In our seismic experiment, we recorded 90 sparse data sets over 4.5 h while injecting 12-tons of water into a sand dune. Results show that the percolation of water is mostly along layered boundaries down to a depth of a few meters, which is consistent with our 3D computational fluid flow simulations and laboratory experiments. The significance of parsimonious interferometry is that it provides more than an order-of-magnitude increase of temporal resolution in time-lapse seismic imaging. We believe that real-time seismic imaging will have important applications for non-destructive characterization in environmental, biomedical, and subsurface imaging.

Effect of inflammation on molecular targets and drug transporters.

Inflammation, the host's response to infection and injury, is associated with altered expression of genes such as metabolizing enzymes, transporters, receptors and plasma proteins. The purpose of the present work was to characterize the effect of inflammation on selected molecular targets and transporters that affect drugs' action and disposition. We have used rats with adjuvant arthritis (AA), an animal model of chronic inflammation. The AA group received 0.2 ml of 50 mg ml-1 Mycobacterium butyricum suspended in squalene into the tail base. On day 12, the rats were euthanized and their organs (heart, liver, kidneys and intestine) excised. Expression of Cav1.2, ß1-AR, ß2-AR, α1A-AR, Nav1.2, Nav1.6, Kv1.5, Kv2.1, Kv3.1, oatp1a1, oatp1a5, oatp1b2, oatp2b1, oatp4a1, oat2, oat3, oct1, mdr1a, bsep, mrp1, mrp3, mrp6, IL-1α, IFN-γ, iNOS, MCP-1, IL-10, Cox-1 and Cox-2 were determined by real time polymerase chain reaction (RT-PCR). Inflammation resulted in a significant reduction of oct1, oatp4a1 and mrp1 gene expression in the liver and oatp2b1, mrp6 and bsep gene expression in the kidney. Oatp4a1 and mdr1a were found to be significantly upregulated in rat heart. In conclusion, inflammation alters the gene expression of some mediators and drug transporters that can influence the behavior of drugs in the body and contribute to therapeutic failure.

Two applications of time reversal mirrors: seismic radio and seismic radar.

Two seismic applications of time reversal mirrors (TRMs) are introduced and tested with field experiments. The first one is sending, receiving, and decoding coded messages similar to a radio except seismic waves are used. The second one is, similar to radar surveillance, detecting and tracking a moving object(s) in a remote area, including the determination of the objects speed of movement. Both applications require the prior recording of calibration Green's functions in the area of interest. This reference Green's function will be used as a codebook to decrypt the coded message in the first application and as a moving sensor for the second application. Field tests show that seismic radar can detect the moving coordinates (x(t), y(t), z(t)) of a person running through a calibration site. This information also allows for a calculation of his velocity as a function of location. Results with the seismic radio are successful in seismically detecting and decoding coded pulses produced by a hammer. Both seismic radio and radar are highly robust to signals in high noise environments due to the super-stacking property of TRMs.

Intraumbilical injection of three different uterotonics in the management of retained placenta.

AIM: The aim of this work was to compare the effect of intraumbilical injection of three different uterotonic solutions in the management of retained placenta. MATERIALS AND METHODS: This study was conducted in Ain-Shams University Maternity Hospital, Cairo, Egypt. A total of 78 women with retained placenta (>30 min after delivery of the fetus) were included in the study and subdivided into three groups. Each group was injected with a different type of uterotonic into the umbilical vein after clamping it using the Pipingas technique. Uterotonics used were either 20 IU oxytocin dissolved in 30 mL saline (n=26), ergometrine 0.2 mg dissolved in 30 mL saline (n=27) or misoprostol 800 µg dissolved in 30 mL saline (n=25). RESULTS: The overall success rate of spontaneous placental separation within 30 min after intraumbilical injection of uterotonics was 56/78 (71.79%). The success rate was higher with misoprostol when compared to oxytocin and ergometrine but the difference was not significant (20/25 [80%], 19/26 [73.08%], 17/27 [62.96%], respectively, P>0.05). The injection-to-separation interval was significantly shorter in the misoprostol group than in the oxytocin and ergometrine groups (7.0±2.2 min, 13.14±3.76 min, 22.5±4.37 min, respectively, P<0.001). CONCLUSION: Intraumbilical injection of uterotonics, namely oxytocin, ergometrine and dissolved misoprostol in saline, are closely effective in the management of retained placenta, with misoprostol being slightly more effective. This method may have a role in minimizing the need for manual removal of the placenta and its adverse sequelae.

Adjuvant arthritis influences expression of housekeeping genes.

OBJECTIVE AND METHOD: We determined whether adjuvant arthritis (AA) affects expression of three housekeeping (HK) genes, glyceraldehyde-3-phosphate-dehydrogenase (GAPDH), ß-actin and 18s ribosomal RNA (18s rRNA) in organs of Sprague-Dawley rats using the real-time reverse-transcription polymerase chain reaction (RT-PCR). RESULTS: Expression of 18s rRNA was unaffected by AA whereas that of ß-actin was reduced by 52, 62 and 81% in the liver, kidney and intestine, respectively, but not in the heart. AA significantly reduced GAPDH expression in the intestine. CONCLUSION: For reliable results, the HK gene must be carefully selected.

Inflammation alters angiotensin converting enzymes (ACE and ACE-2) balance in rat heart.

Angiotensin converting enzymes (ACE) and more recently discovered ACE-2 are important proteins involved in the renin-angiotensin system. The balance between ACE and ACE-2 is important for the regulation of blood pressure and electrolyte homeostasis. Inflammatory diseases like rheumatoid arthritis are associated with increased risk for cardiovascular complications. We studied the effect of inflammation on the expression levels of ACE and ACE-2 in two groups (n = 4/group) of adjuvant arthritis (AA) and healthy (control) rats. The AA group received 0.2 ml of 50 mg ml(-1) of Mycobacterium butyricum suspended in squalene into the tail base. On day 12, rats were euthanized and their organs (hearts, liver, kidney, and intestine) were excised. The mRNA of ACE and ACE-2 were determined by real-time polymerase chain reaction. ACE and ACE-2 protein expression in rat heart was determined by Western blot. Inflammation resulted in 80% reduction of ACE-2 gene expression in rat heart. ACE-2/ACE expression ratio was significantly reduced from 0.7 ± 0.4 in control rats to 0.07 ± 0.09 in AA. Similarly, ACE-2/ACE protein expression ratio was also disrupted with a significant reduction in AA animals (6.7 ± 4.8 vs. 0.9 ± 05 in control and AA, respectively). ACE-2 has been found to provide negative feedback of renin-angiotensin system and protection of the heart and kidneys. Disruption of the balance between ACE and ACE-2 observed in inflammation may be, at least in part, involved in the cardiovascular complications seen in patients with inflammatory diseases.

Drug-disease interaction: reduced verapamil response in isoproterenol-induced myocardial injury in rats.

Inflammation is involved in the pathogenesis of cardiovascular diseases. We investigated whether the response to verapamil is altered in experimental acute myocardial injury (AMI). Two groups of male Sprague-Dawley rats (230-280 g) were divided into control (n = 8) and post-AMI (n = 13). Myocardial injury was induced by 2 daily doses of 150 mg·kg(-1) isoproterenol (ISP). Subcutaneous ECG leads were implanted, and 2 days following the second injection, each rat was dosed with 25 mg·kg(-1) verapamil per os, and an ECG was recorded over 4 h after dosing. The animals were euthanized and blood samples collected for analysis of inflammatory mediators and cardiac troponin I (cTnI). Cardiac L-type calcium channel (Ca(v)1.2) protein levels and mRNA were determined by Western blot and real-time PCR, respectively. ISP treatment caused a 170% increase in serum cTnI, J point elevation, R wave amplitude reduction and Q wave development. Cardiac injury caused a 75% reduction in verapamil potency by prolonging the PR interval and reducing the heart rate. Cardiac tissue injury also caused a significant reduction in the Ca(v)1.2 protein level. Verapamil response was significantly correlated with cTnI. The reduced potency of verapamil in myocardial injury appears to result from a reduction in the drug target protein Ca(v)1.2. If extrapolated to humans, our observations may suggest that downregulation of calcium channel proteins is a contributory factor in the poor outcome in myocardial infarction.

Effect of obesity on response to cardiovascular drugs in pediatric patients with renal disease.

Obesity is associated with an increased concentration of inflammatory mediators, which in turn, in adults, reduces the response to calcium channel blockers (CCBs). We reviewed the medical charts of 263 pediatric nephrology patients with renal conditions, with the aim of studying the effect of obesity on the response to L-type CCBs, angiotensin interrupting agents (ANGIs), or a combination of the two. Forty-eight patients were ultimately enrolled in the study: 25 obese and 23 non-obese patients. The effect of the treatments on lowering the blood pressure was compared in obese versus non-obese patients. The systolic response to CCBs, measured as at least a 10% reduction from the baseline, was significantly lower in the obese (12.5%) patients than in the non-obese (52.9%) ones. The differences in diastolic response (58.8 and 25% for non-obese and obese patients, respectively) did not reach significance. The percentage response to CCBs, however, was significantly less in the obese patients than in the non-obese patients for both systolic and diastolic blood pressure. Corticosteroids also significantly influenced the response to CCBs in terms of diastolic pressure (62.9 and 25% for non-obese and obese patients, respectively). None of the tested covariates, including obesity, was found to significantly influence the response to ANGIs alone or in various combinations with CCBs. In conclusion, obesity and corticosteroid therapy should be considered when initiating antihypertensive drug treatment in children with kidney disease as both may contribute to a reduced efficacy of the antihypertensive therapy.