Deep Targets Research Articles

Geophysical analysis to delineate a Class-I AVO prospect in the offshore east coast of India: A case study

Characterization of deep-water reservoirs poses enormous challenges to interpreters because of its inherent complexity. Therefore, defining reservoirs' extent and predicting their properties in absence of drilled well or away from drilled wells has always been difficult. In deeper targets, the sensitivity of seismic amplitude to fluid change gets considerably reduced, therefore detection of fluid and their contact remains challenging. In such a case, the application of extended elastic impedance can be helpful in the delineation of prospective reservoirs and detection of fluid contact. This is demonstrated with a case study of a Miocene slope fan prospect in the deepwater Mahanadi basin on the east coast of India. The prospect is typical Class-I in terms of amplitude variation with offset where the presence of fluid is hard to identify in conventional full-stack data. An appropriate extended elastic impedance attribute for fluid has been generated and interpreted to define the reservoir boundary and detect fluid contact. The amplitude of the fluid attribute map is corrected for tuning effect as there are variations in reservoir thickness across the area. Additionally, in order to detect fluid contact, a common contour binning technique has been applied which helps to locate the depth contour where possible fluid contact could be present. The case study has added significant values in de-risking a Class-I prospect without any anomalous amplitude increase with offset.

Connectivity in deep brain stimulation for self-injurious behavior: multiple targets for a common network?

Self-injurious behavior (SIB) is associated with diverse psychiatric conditions. Sometimes (e.g., in patients with autism spectrum disorder or acquired brain injuries), SIB is the most dominant symptom, severely restricting the psychosocial functioning and quality of life of the patients and inhibiting appropriate patient care. In severe cases, it can lead to permanent physical injuries or even death. Primary therapy consists of medical treatment and if implementable, behavioral therapy. For patients with severe SIB refractory to conventional therapy, neuromodulation can be considered as a last recourse. In scientific literature, several successful lesioning and deep brain stimulation targets have been described that can indicate a common underlying neuronal pathway. The objectives of this study were to evaluate the short- and long-term clinical outcome of patients with severe, therapy refractory SIB who underwent DBS with diverse underlying psychiatric disorders and to correlate these outcomes with the activated connectivity networks. We retrospectively analyzed 10 patients with SIB who underwent DBS surgery with diverse psychiatric conditions including autism spectrum disorder, organic personality disorder after hypoxic or traumatic brain injury or Tourette syndrome. DBS targets were chosen according to the underlying disorder, patients were either stimulated in the nucleus accumbens, amygdala, posterior hypothalamus, medial thalamus or ventrolateral thalamus. Clinical outcome was measured 6 months after surgery and at long-term follow-up after 10 or more years using the Early Rehabilitation Barthel index (ERBI) and time of restraint. Connectivity patterns were analyzed using normative connectome. Based on previous literature the orbitofrontal cortex, superior frontal gyrus, the anterior cingulate cortex, the amygdala and the hippocampus were chosen as regions of interest. This analysis showed a significant improvement in the functionality of the patients with DBS in the short- and long-term follow-up. Good clinical outcome correlated with higher connectivity to the amygdala and hippocampus. These findings may suggest a common pathway, which can be relevant when planning a surgical procedure in patients with SIB.

Design of a filtration system to improve the dose distribution of an accelerator-based neutron capture therapy system.

The aim of this study is to design and evaluate a neutron filtration system to improve the dose distribution of an accelerator-based neutron capture therapy system. An LiF-sintered plate composed of 99%-enriched 6 Li was utilized to filter out low-energy neutrons to increase the average neutron energy at the beam exit. A 5-mm thick filter to fit inside a 12-cm diameter circular collimator was manufactured, and experimental measurements were performed to measure the thermal neutron flux and gamma-ray dose rate inside a water phantom. The experimental measurements were compared with the Monte Carlo simulation, particle, and heavy ion transport code system. Following the experimental verification, three filter designs were modeled, and the thermal neutron flux and the biologically weighted dose distribution inside a phantom were simulated. Following the phantom simulation, a dummy patient CT dataset was used to simulate a boron neutron capture therapy (BNCT) irradiation of the brain. A mock tumor located at 4, 6, 8cm along the central axis and 4-cm off-axis was set, and the dose distribution was simulated for a maximum total biologically weighted brain dose of 12.5Gy with a beam entering from the vertex. All three filters improved the beam penetration of the accelerator-based neutron source. Filter design C was found to be the most suitable filter, increasing the advantage depth from 9.1 to 9.9cm. Compared with the unfiltered beam, the mean weighted dose in the tumor located at a depth of 8cm along the beam axis was increased by ∼25%, and 34% for the tumor located at a depth of 8cm and off-axis by 4cm. A neutron filtration system for an accelerator-based BNCT system was investigated using Monte Carlo simulation. The proposed filter design significantly improved the dose distribution for the treatment of deep targets in the brain.

Pseudo-Isolated α-Helix Platform for the Recognition of Deep and Narrow Targets.

Although interest in stabilized α-helical peptides as next-generation therapeutics for modulating biomolecular interfaces is increasing, peptides have limited functionality and stability due to their small size. In comparison, α-helical ligands based on proteins can make steric clash with targets due to their large size. Here, we report the design of a monomeric pseudo-isolated α-helix (mPIH) system in which proteins behave as if they are peptides. The designed proteins contain α-helix ligands that do not require any covalent chemical modification, do not have frayed ends, and importantly can make sterically favorable interactions similar to isolated peptides. An optimal mPIH showed a more than 100-fold increase in target selectivity, which might be related to the advantages in conformational selection due to the absence of frayed ends. The α-helical ligand in the mPIH displayed high thermal stability well above human body temperature and showed reversible and rapid folding/unfolding transitions. Thus, mPIH can become a promising protein-based platform for developing stabilized α-helix pharmaceuticals.

Ultra Deep Sequencing of Circulating Cell-Free DNA as a Potential Tool for Hepatocellular Carcinoma Management.

Simple SummaryIn this unicentric prospective study, we analyzed the most prevalent mutations in HCC (TERT promoter, TP53, CTNNB1, AXIN1 and ARID1A) in plasma cfDNA by next-generation sequencing, aiming to elucidate their value as prognostic noninvasive biomarkers. Total cfDNA (cut-off value 2 ng/µL), number of mutated genes and number of detectable mutations on cfDNA were significantly related to mortality. Number of mutated genes and number of detected mutations in cfDNA and the ratio between number of mutations and total amount of cfDNA were also significantly associated with recurrence. Detection of more than four mutations in cfDNA correlated with a higher risk of death. Dynamic changes in cfDNA mutations were detected prior to radiological detection of HCC recurrence. We believe that these results support the proof of principle and launching of validation studies to confirm that total cfDNA and detection of prevalent HCC mutations could have prognostic implications in early-stage HCC patients.Background: Cell-free DNA (cfDNA) concentrations have been described to be inversely correlated with prognosis in cancer. Mutations in HCC-associated driver genes in cfDNA have been reported, but their relation with patient’s outcome has not been described. Our aim was to elucidate whether mutations found in cfDNA could be representative from those present in HCC tissue, providing the rationale to use the cfDNA to monitor HCC. Methods: Tumoral tissue, paired nontumor adjacent tissue and blood samples were collected from 30 HCC patients undergoing curative therapies. Deep sequencing targeting HCC driver genes was performed. Results: Patients with more than 2 ng/µL of cfDNA at diagnosis had higher mortality (mean OS 24.6 vs. 31.87 months, p = 0.01) (AUC = 0.782). Subjects who died during follow-up, had a significantly higher number of mutated genes (p = 0.015) and number of mutations (p = 0.015) on cfDNA. Number of mutated genes (p = 0.001), detected mutations (p = 0.001) in cfDNA and ratio (number of mutations/cfDNA) (p = 0.003) were significantly associated with recurrence. However, patients with a ratio (number of mutations/cfDNA) above 6 (long-rank p = 0.0003) presented a higher risk of recurrence than those with a ratio under 6. Detection of more than four mutations in cfDNA correlated with higher risk of death (long-rank p = 0.042). Conclusions: In summary, cfDNA and detection of prevalent HCC mutations could have prognostic implications in early-stage HCC patients

Multidisciplinary Geophysical Investigations over Deep Coal-Bearing Strata: A Case Study in Yangjiazhangzi, Northeast China

With the majority of coal mines in uncovered and semi-covered coal strata now explored and developed, most of the undiscovered coal-bearing strata are concealed. Compared with expensive drilling, deep targets such as concealed coal-bearing strata can be more efficiently and cost-effectively discovered through geophysical methods. We designed an integrated geophysical exploration approach to detect coal-bearing strata in the Yangjiazhangzi (YJZZ) area. Large-scale magnetotellurics (MT) is used to describe the geological structure along with its profile through the YJZZ area. An aeromagnetic survey was used to delineate the spatial distribution characteristics of the YJZZ syncline, a coal-bearing prospect tectonic unit. Localized exploration with controlled-source audio magnetotellurics (CSAMT) and MT reveals coal-bearing targets for drilling. Drilling results verified the targets identified through the integrated geophysical approach. Coal-bearing strata in the Benxi formation, the Taiyuan formation, and the Shanxi formation of the Permo-Carboniferous age are found between 630 and 770 m. This case study demonstrates that the multidisciplinary geophysical strategy can provide reliable results and credible data interpretation for deep coal seam resources exploration. The findings of this study can provide reference for explorers to carry out their specific exploration cases.

3D reflection seismic imaging of the Zinkgruvan mineral‐bearing structures in the south‐eastern Bergslagen mineral district (Sweden)

AbstractMineral exploration is facing greater challenges nowadays because of the increasing demand for raw materials and the lesser chance of finding large deposits at shallow depths. To be efficient and address new exploration challenges, high‐resolution and sensitive methods that are cost‐effective and environmentally friendly are required. In this work, we present the results of a sparse 3D seismic survey that was conducted in the Zinkgruvan mining area, in the Bergslagen mineral district of central Sweden. The survey covers an area of 10.5 km2 for deep targeting of massive sulphides in a polyphasic tectonic setting. A total of 1311 receivers and 950 shot points in a fixed 3D geometry setup were employed for the survey. Nine 2D profiles and a smaller 3D mesh were used. Shots were generated at every 10 m, and receivers were placed at every 10–20 m, along the 2D profiles, and 40–80 m in the mesh area. An analysis of the seismic fold coverage at depth was used to determine the potential resolving power of this sparse 3D setup. The data processing had to account for cultural noise from the operating mine and strong source‐generated surface waves, which were attenuated during both pre‐ and post‐stack processing steps. The processing workflow employed a combination of 2D and 3D refraction static corrections, and post‐stack FK filters along inlines and crosslines. The resulting 3D seismic volume is correlated with downhole data (density and P‐wave, acoustic impedance, reflection coefficient), synthetic seismograms, surface geology and a 3D model of mineral‐bearing horizons in order to suggest new exploration targets at depth. The overall geological architecture at Zinkgruvan is interpreted as two EW overturn folds, an antiform and a synform, affected by later NS‐trending folding. Two strong sets of shallow reflections, associated with the Zn–Pb mineralization, are located at the hinge of an EW‐trending antiform, while a strong set of reflections, associated with the main mineralization, is located at the overturned apex of the EW synform. The NS Knalla fault that crosses the study area terminates the continuation of the mineral‐bearing deposits at depth towards the west, a conclusion solely based on the reflectivity character of the seismic volume. This study illustrates that sparse 3D data acquisition, while it has its own challenges, can be a suitable replacement for 2D profiles while line cutting, and environmental footprints can totally be avoided.

Reflection-based traveltime and waveform inversion with second-order optimization

Reflection-based inversion that aims to reconstruct the low-to-intermediate wavenumbers of the subsurface model, can be a complementary to refraction-data-driven full-waveform inversion (FWI), especially for the deep target area where diving waves cannot be acquired at the surface. Nevertheless, as a typical nonlinear inverse problem, reflection waveform inversion may easily suffer from the cycle-skipping issue and have a slow convergence rate, if gradient-based first-order optimization methods are used. To improve the accuracy and convergence rate, we introduce the Hessian operator into reflection traveltime inversion (RTI) and reflection waveform inversion (RWI) in the framework of second-order optimization. A practical two-stage workflow is proposed to build the velocity model, in which Gauss-Newton RTI is first applied to mitigate the cycle-skipping problem and then Gauss-Newton RWI is employed to enhance the model resolution. To make the Gauss-Newton iterations more efficiently and robustly for large-scale applications, we introduce proper preconditioning for the Hessian matrix and design appropriate strategies to reduce the computational costs. The example of a real dataset from East China Sea demonstrates that the cascaded Hessian-based RTI and RWI have good potential to improve velocity model building and seismic imaging, especially for the deep targets.

Deep representation-based transfer learning for deep neural networks

In recent years, deep neural networks (DNNs) have become the de facto models for practically all visual tasks and most temporal analysis tasks due to the abundance of available labeled data and advances in computational resources. Deep transfer learning, a learning paradigm that investigates how deep neural networks can leverage knowledge from other domains, has been successful in many practical applications. Most deep transfer learning methods rely on directly available source domain data, especially instances-based and mapping-based methods. However, the source domain data required for transfer cannot be provided in some cases because of personal privacy or copyright implications. In this regard, we propose a deep representation-based transfer learning method for deep neural networks to deal with transfer scenarios without source domain data. The proposed method induces the target deep neural network to learn the similarity structure between source deep representations by minimizing the representational similarity discrepancy between the networks, thereby transferring the domain knowledge from the source network to the target network. In addition, the proposed method combines the boosting-based deep representation transfer method to more fully utilize the source domain knowledge, so that the target network learns in both the reweighted source deep representation and target deep representation to further improve the generalization ability. The proposed method is applied to image classification and time series prediction tasks and achieves better transfer performance than other similar methods, which shows that the method has good practicability and generality in practical applications.

Research and Implementation of Robot Vision Scanning Tracking Algorithm Based on Deep Learning.

In order to solve the difficult problem of deep learning-based robot vision tracking algorithm research and implementation, a deep learning-based target tracking algorithm and a classical tracking algorithm were proposed. It mainly uses the combination of traditional TLD algorithm and GOTURN algorithm to benefit from a large number of offline training data and updates the learner online, so that the whole system has better performance in real-time and accuracy. The results show that the performance of the TLD algorithm is poor regardless of the accuracy curve or the accuracy curve, and the performance of GOTURN-LD is significantly improved when the illumination changes. In the face of occlusion problem, the TLD algorithm shows strong robustness. Although GOTURN-LD is not very stable, its performance is better than GOTURN on the whole.

A 3D Predictive Method for Deep-Seated Gold Deposits in the Northwest Jiaodong Peninsula and Predicted Results of Main Metallogenic Belts

With the rapid depletion of mineral resources, deep prospecting is becoming a frontier field in international geological exploration. The prediction of deep mineral resources is the premise and foundation of deep prospecting. However, conventional metallogenic predictive methods, which are mainly based on surface geophysical, geochemical, and remote sensing data and geological information, are no longer suitable for deep metallogenic prediction due to the large burial depth of deep-seated deposits. Consequently, 3D metallogenic prediction becomes a critical method for delineating deep prospecting target areas. As a world-class giant gold metallogenic province, the Jiaodong Peninsula is at the forefront in China in terms of deep prospecting achievements and exploration depth. Therefore, it has unique conditions for 3D metallogenic prediction and plays an important exemplary role in promoting the development of global deep prospecting. This study briefly introduced the method, bases, and results of the 3D metallogenic prediction in the northwest Jiaodong Peninsula and then established 3D geological models of gold concentration areas in the northwest Jiaodong Peninsula using drilling combined with geophysics. Since gold deposits in the northwest Jiaodong Peninsula are often controlled by faulting in the 3D space, this study proposed a method for predicting deep prospecting target areas based on a stepped metallogenic model and a method for predicting the deep resource potential of gold deposits based on the shallow resources of ore-controlling faults. Multiple characteristic variables were extracted from the 3D geological models of the gold concentration areas, including the buffer zone and dip angle of faults, the changing rate of fault dip angle, and the equidistant distribution of orebodies. Using these characteristic variables, five deep prospecting target areas in the Jiaojia and Sanshandao faults were predicted. Moreover, based on the proven gold resources at an elevation of −2000 m and above, the total gold resources of the Sanshandao, Jiaojia, and Zhaoping ore-controlling faults at an elevation of −5000–−2000 m were predicted to be approximately 3377–6490 t of Au. Therefore, it is believed that the total gold resources in the Jiaodong Peninsula are expected to exceed 10,000 t. These new predicted results suggest that the northwest Jiaodong Peninsula has huge potential for the resources of deep gold deposits, laying the foundation for further deep prospecting.

Optimized Deep Brain Stimulation Surgery to Avoid Vascular Damage: A Single-Center Retrospective Analysis of Path Planning for Various Deep Targets by MRI Image Fusion.

Co-registration of stereotactic and preoperative magnetic resonance imaging (MRI) images can serve as an alternative for trajectory planning. However, the role of this strategy has not yet been proven by any control studies, and the trajectories of commonly used targets have not been systematically studied. The purpose of this study was to analyze the trajectories for various targets, and to assess the role of trajectories realized on fused images in preventing intracranial hemorrhage (ICH). Data from 1019 patients who underwent electrode placement for deep brain stimulation were acquired. Electrode trajectories were not planned for 396 patients, whereas trajectories were planned for 623 patients. Preoperative various MRI sequences and frame-placed MRI images were fused for trajectory planning. The patients’ clinical characteristics, the stereotactic systems, intracranial hemorrhage cases, and trajectory angles were recorded and analyzed. No statistically significant differences in the proportions of male patients, patients receiving local anesthesia, and diseases or target distributions (p > 0.05) were found between the trajectory planning group and the non-trajectory planning group, but statistically significant differences were observed in the numbers of both patients and leads associated with symptomatic ICH (p < 0.05). Regarding the ring and arc angle values, statistically significant differences were found among various target groups (p < 0.05). The anatomic structures through which leads passed were found to be diverse. Trajectory planning based on MRI fusion is a safe technique for lead placement. The electrode for each given target has its own relatively constant trajectory.

Seismic reservoir characterization and pressure depletion of the Abu Madi Formation in the NW Khilala field, onshore Nile Delta, Egypt

The Late Messinian Abu Madi Formation represents the most prospective deep reservoir target in the Nile Delta. Hydrocarbon exploration in the Nile Delta over the last few decades has been targeted to understand the Abu Madi reservoir quality and distribution. The integration of rock physics, amplitude versus angle (AVA), seismic interpretation, seismic attributes, and seismic inversion could successfully determine the characteristics of the late Messinian Abu Madi Formation in the North West Khilala (NWKh) field, which is located in the central onshore part of the Nile Delta, Egypt. The NWKh field is located on the eastern margin of the Disouq concession close to producing the West Khilala (WKh) field, with highly heterogeneous reservoir parameters. The reservoir is classified as lacustrine turbidites deposited in semi-isolated Late Messinian Basins. The Abu Madi Formation can be subdivided into upper and lower sand members. It is very common that they are separated by an intraformational shale layer. AVA analysis performed on the top of the Abu Madi reservoir suggested a class 2p. Seismic data were inverted to delineate the overall lateral extent of the reservoir. The prestack inversion process was integrated with rock physics modelling of the wells to generate high-resolution multiple rock property models to characterize the reservoir and observe or investigate different pressure depletion trends. The pressure depletion of the lower Abu Madi in the North West Khilala field was investigated laterally and vertically by the relative/full spectrum inversion and formation modular dynamic tester (MDT) pressure data, respectively. This depletion is due to communication with the West Khilala field, which was produced from the Lower Abu Madi (LAM) sand interval in 2007.

AFFPN: Attention Fusion Feature Pyramid Network for Small Infrared Target Detection

The detection of small infrared targets lacking texture and shape information in the presence of complex background clutter is a challenge that has attracted considerable research attention in recent years. Typical deep learning-based target detection methods are designed with deeper network structures, which may lose targets in the deeper layers and cannot directly be used for small infrared target detection. Therefore, we designed the attention fusion feature pyramid network (AFFPN) specifically for small infrared target detection. Specifically, it consists of feature extraction and feature fusion modules. In the feature extraction stage, the global contextual prior information of small targets is first considered in the deep layer of the network using the atrous spatial pyramid pooling module. Subsequently, the spatial location and semantic information features of small infrared targets in the shallow and deep layers are adaptively enhanced by the designed attention fusion module to improve the feature representation capability of the network for targets. Finally, high-performance detection is achieved through the multilayer feature fusion mechanism. Moreover, we performed a comprehensive ablation study to evaluate the effectiveness of each component. The results demonstrate that the proposed method performs better than state-of-the-art methods on a publicly available dataset. Furthermore, AFFPN was deployed on an NVIDIA Jetson AGX Xavier development board and achieved real-time target detection, further advancing practical research and applications in the field of unmanned aerial vehicle infrared search and tracking.

Target-oriented high-resolution elastic full-waveform inversion with an elastic redatuming method

Characterizing the elastic properties in deep-buried reservoirs beneath complex overburden structures remains challenging for seismic inversion. Elastic full-waveform inversion (FWI) is capable of quantitatively estimating the subsurface elastic properties with a reasonably high resolution. However, elastic FWI using high frequencies is computationally expensive because fine discretization is required to stabilize the wavefield simulation. In addition, it is challenging for elastic FWI to obtain the high-resolution inversion results of the deep targets due to complex overburden structures and limited energy illumination of the target of interest. To overcome these limitations, we propose a target-oriented high-resolution elastic FWI scheme by using the estimated elastic data for a virtual survey deployed only above a zone of interest. Specifically, we have developed an elastic redatuming approach to retrieve virtual elastic data by solving an iterative least-squares data-fitting problem. In addition to the elastic multicomponent data recorded at the acquisition surface, an estimate of the overburden model is required. A synthetic data example indicates that an overburden model estimated by elastic FWI with a low-frequency band enables our elastic redatuming approach to reconstruct the virtual elastic data representing the seismic reflection response from the target zone. We then perform regularized elastic FWI by using the redatumed elastic data to estimate the elastic properties in the target zone with reduced computational cost. In the numerical examples, the Marmousi2 synthetic data and 2D Volve field data are used to demonstrate the feasibility and practicality of the proposed method.

Relative Comparison of Non-Invasive Brain Stimulation Methods for Modulating Deep Brain Targets.

This study models and investigates whether temporally interfering electric fields (TI EFs) could function as an effective non-invasive brain stimulation (NIBS) method for deep brain structure targeting in humans, relevant for psychiatric applications. Here, electric fields off- and on-target are modelled and compared with other common NIBS modalities (tACS, TMS). Additionally, local effects of the field strength are modelled on single-compartment neuronal models. While TI EFs are able to effectively reach deep brain targets, the ratio of off- to on-target stimulation remains high and comparable to other NIBS and may result in off-target neural blocks. Clinical Relevance- This study builds on earlier work and demonstrates some of the challenges -such as off-target conduction blocks- of applying TI EFs for targeting deep brain structures important in understanding the potential of treating neuropsychiatric conditions in the future.

Pig Ulnar Nerve Recording with Sinusoidal and Temporal Interference Stimulation.

Temporal interference stimulation has been suggested as a method to reach deep targets during transcutaneous electrical stimulation. Despite its growing use in transcutaneous stimulation therapies, the mechanism of its operation is not fully understood. Recent efforts to fill that gap have focused on computational modelling, in vitro and in vivo experiments relying on physical observations - e.g., sensation or movement. This paper expands the current range of experimental methods by demonstrating in vivo extraneural recordings from the ulnar nerve of a pig while applying temporal interference stimulation at a location targeting a distal part of the nerve. The main aim of the experiment was to compare neural activation using sinusoidal stimulation (100 Hz, 2 kHz, 4 kHz) and temporal interference stimulation (2 kHz and 4 kHz). The recordings showed a significant increase in the magnitude of stimulation artefacts at higher frequencies. While those artefacts could be removed and provided an indication of the depth of modulation, they resulted in the saturation of the amplifiers, limiting the stimulation currents and amplifier gains used. The results of the 100 Hz sine wave stimulation showed clear neural activity correlated to the stimulation waveform. However, this was not observed with temporal interference stimulation. The results suggest that, despite its greater penetration, higher currents might be required to observe a neural response with temporal interference stimulation, and more complex artefact rejection techniques may be required to validate the method.

Noninvasive Stimulation of Peripheral Nerves using Temporally-Interfering Electrical Fields.

Electrical stimulation of peripheral nerves is a cornerstone of bioelectronic medicine. Effective ways to accomplish peripheral nerve stimulation (PNS) noninvasively without surgically implanted devices are enabling for fundamental research and clinical translation. Here, it is demonstrated how relatively high-frequency sine-wave carriers (3kHz) emitted by two pairs of cutaneous electrodes can temporally interfere at deep peripheral nerve targets. The effective stimulation frequency is equal to the offset frequency (0.5 - 4Hz) between the two carriers. This principle of temporal interference nerve stimulation (TINS) in vivo using the murine sciatic nerve model is validated. Effective actuation is delivered at significantly lower current amplitudes than standard transcutaneous electrical stimulation. Further, how flexible and conformable on-skin multielectrode arrays can facilitate precise alignment of TINS onto a nerve is demonstrated. This method is simple, relying on the repurposing of existing clinically-approved hardware. TINS opens the possibility of precise noninvasive stimulation with depth and efficiency previously impossible with transcutaneous techniques.

Deep Learning Target Detection System for Sewage Treatment.

Object detection is to identify objects and then find some objects of interest. With the development of computers, target detection has evolved from traditional detection methods to artificial intelligence methods, and the latter are mainly based on some algorithms of deep learning. This paper mainly tests the treated sewage. First, the neural network and convolutional neural network algorithms in deep learning are studied, and then a target detection system is built based on these two algorithms. Finally, the treated sewage is detected and then compared with that of the traditional target detection system. The experimental results show that the target detection system of the convolutional neural network algorithm has a very stable recognition rate for the treated sewage, swinging around 70%, and the amplitude is not large. However, the target detection system of the neural network algorithm is not very stable in the recognition rate of the treated sewage, and the recognition rate is about 60%.

EMvelop stimulation: minimally invasive deep brain stimulation using temporally interfering electromagnetic waves

Objective. Recently, the temporal interference stimulation (TIS) technique for focal noninvasive deep brain stimulation (DBS) was reported. However, subsequent computational modeling studies on the human brain have shown that while TIS achieves higher focality of electric fields than state-of-the-art methods, further work is needed to improve the stimulation strength. Here, we investigate the idea of EMvelop stimulation, a minimally invasive DBS setup using temporally interfering gigahertz (GHz) electromagnetic (EM) waves. At GHz frequencies, we can create antenna arrays at the scale of a few centimeters or less that can be endocranially implanted to enable longitudinal stimulation and circumvent signal attenuation due to the scalp and skull. Furthermore, owing to the small wavelength of GHz EM waves, we can optimize both amplitudes and phases of the EM waves to achieve high intensity and focal stimulation at targeted regions within the safety limit for exposure to EM waves. Approach. We develop a simulation framework investigating the propagation of GHz EM waves generated by line current antenna elements and the corresponding heat generated in the brain tissue. We propose two optimization flows to identify antenna current amplitudes and phases for either maximal intensity or maximal focality transmission of the interfering electric fields with EM waves safety constraint. Main results. A representative result of our study is that with two endocranially implanted arrays of size × each, we can achieve an intensity of 12 V m−1 with a focality of at a target deep in the brain tissue. Significance. In this proof-of-principle study, we show that the idea of EMvelop stimulation merits further investigation as it can be a minimally invasive way of stimulating deep brain targets and offers benefits not shared by prior methodologies of electrical or magnetic stimulation.