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Electromagnetic imaging achieves enhanced resolution by leveraging the advanced sensing and data analysis capabilities of Internet of Things (IoT) systems. This paper introduces a novel learning approach for generative adversarial networks (GANs) to tackle significant challenges in electromagnetic sensing. The proposed method involves deploying additional transmitters and receivers to irradiate TM (transverse magnetic) and TE (transverse electric) polarization waves around uniaxial objects to capture the scattered field in free space. Subsequently, scattered field generative adversarial networks (SF-GANs) are utilized to simulate and learn the characteristics of Maxwell's equations. Numerical simulations and experimental results demonstrate the superior performance of the SF-GANs compared to backpropagation generative adversarial networks (BP-GANs). Furthermore, it is worth noting that our method is capable of reconstructing high-dielectric-constant objects. [ABSTRACT FROM AUTHOR]

In this paper, artificial intelligence (AI) technology is applied to the electromagnetic imaging of anisotropic objects. Advances in magnetic anomaly sensing systems and electromagnetic imaging use electromagnetic principles to detect and characterize subsurface or hidden objects. We use measured multifrequency scattered fields to calculate the initial dielectric constant distribution of anisotropic objects through the backpropagation scheme (BPS). Later, the estimated multifrequency permittivity distribution is input to a convolutional neural network (CNN) for the adaptive moment estimation (ADAM) method to reconstruct a more accurate image. In the meantime, we also improve the definition of loss function in the CNN. Numerical results show that the improved loss function unifying the structural similarity index measure (SSIM) and root mean square error (RMSE) can effectively enhance image quality. In our simulation environment, noise interference is considered for both TE (transverse electric) and TM (transverse magnetic) waves to reconstruct anisotropic scatterers. Lastly, we conclude that multifrequency reconstructions are more stable and precise than single-frequency reconstructions. [ABSTRACT FROM AUTHOR]

The integration of electromagnetic imaging technology with the Internet of Things plays an important role in fields as diverse as healthcare, geophysics, and industrial diagnostics. This paper presents a novel two-step neural network architecture to solve the electromagnetic imaging for uniaxial objects which can be used in the Internet of Things. We incident TM and TE waves to unknown objects and receive the scattered fields. In order to reduce the training difficulty, we first input the gathered scattered field information into a deep convolutional neural network (DCNN) to obtain the preliminary guess. In the second step, we feed the guessed image into the convolutional neural network (CNN) to reconstruct high-resolution images. Our numerical results demonstrate the real-time imaging capability of our proposed two-step method in reconstructing high-contrast scatterers. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel artificial intelligence technique with an attention mechanism for half-space electromagnetic imaging. A dielectric object in half-space is illuminated by TM (transverse magnetic) waves. Since measurements can only be made in the upper space, the measurement angle will be limited. As a result, we apply a back-propagation scheme (BPS) to generate an initial guessed image from the measured scattered fields for scatterer buried in the lower half-space. This process can effectively reduce the high nonlinearity of the inverse scattering problem. We further input the guessed images into the generative adversarial network (GAN) and the self-attention generative adversarial network (SAGAN), respectively, to compare the reconstruction performance. Numerical results prove that both SAGAN and GAN can reconstruct dielectric objects and the MNIST dataset under same measurement conditions. Our analysis also reveals that SAGAN is able to reconstruct electromagnetic images more accurately and efficiently than GAN. [ABSTRACT FROM AUTHOR]

Transiliac–transsacral screw fixation is widely used to stabilize unstable posterior pelvic ring injuries. Preoperative radiographic assessment of the safe osseous corridor is necessary because the safe space of sacrum is narrower for transiliac–transsacral screw placement than for traditional iliosacral screw placement. However, the radiographic assessment has rarely been studied in the Taiwanese population. We retrospectively analyzed 100 patients with pelvic computed tomography images and divided them into normal and dysmorphic pelvis groups. To determine the safe osseous space, we recorded cross-section area, cross-sectional diameter of the safe zone (CS-szD), and safe zone width on axial view (Ax-szW) in the S1 to S3 segments. The prevalence of dysmorphic pelvis was 48% among all patients. In the S1 segment, no differences were found in the cross-section area and CS-szD been the two groups. However, the Ax-szW was significantly smaller in the dysmorphic pelvis group. In the S2 segment, the cross-section area, CS-szD, and Ax-szW were all significantly larger in the dysmorphic pelvis group. In the S3 segment, the cross-section area and CS-szD of the normal pelvis group were both significantly smaller. No differences were found in the Ax-szW between the two groups. Based on our findings in a Taiwanese population, S1 was the most suitable segment for transiliac–transsacral screw fixation in a normal pelvis, whereas S2, followed by S3, was most suitable in a dysmorphic pelvis. This study offers surgeons information on identifying the optimal sacral segment for transiliac–transsacral screw placement for each pelvic morphology. [Orthopedics. 2024;47(1):e13–e18.] [ABSTRACT FROM AUTHOR]

Featured Application: Electromagnetic imaging. Featured applications include military measurements, medical imaging, industrial applications, underground gas pipe and electrical high-voltage cable detection, etc. In the past, many conventional algorithms, such as self-adaptive dynamic differential evolution and asynchronous particle swarm optimization, were used to reconstruct buried objects in the frequency domain; these were unfortunately time-consuming during the iterative, repeated computing process of the scattered field. Consequently, we propose an innovative deep convolutional neural network approach to solve the electromagnetic inverse scattering problem for buried conductors in this paper. Different shapes of conductors are buried in one half-space and the electromagnetic wave from the other half-space is incident. The shape of the conductor can be reconstructed promptly by inputting the received scattered fields measured from the upper half-space into the deep convolutional neural network module, which avoids the computational complexity of Green's function for training. Numerical results show that the root mean square error for differently shaped—circular, elliptical, arrow, peanut, four-petal, and three-petal—reconstructed images are, respectively, 2.95%, 3.11%, 17.81%, 15.10%, 14.14%, and 15.24%. Briefly speaking, not only can circular and elliptical buried conductors be reconstructed; some irregular shapes can be reconstructed well. On the contrary, the reconstruction result by U-Net for buried objects is worse since it is not able to obtain a good preliminary image by processing only the upper scattered field—that is, rather than the full space. In other words, our proposed deep convolutional neural network can efficiently solve the electromagnetic inverse scattering problem of buried conductors and provide a novel method for the microwave imaging of the buried conductors. This is the first successful attempt at using deep convolutional neural networks for buried conductors in the frequency domain, which may be useful for practical applications in various fields such as the medical, military, or industrial fields, including magnetic resonance imaging, mine detection and clearance, non-destructive testing, gas or wire pipeline detection, etc. [ABSTRACT FROM AUTHOR]

The nucleolus is best known for its cellular role in regulating ribosome production and growth. More recently, an unanticipated role for the nucleolus in innate immunity has recently emerged whereby downregulation of fibrillarin and nucleolar contraction confers pathogen resistance across taxa. The mechanism of this downregulation, however, remains obscure. Here we report that rather than fibrillarin itself being the proximal factor in this pathway, the key player is a fibrillarin‐stabilizing deubiquitinylase USP‐33. This was discovered by a candidate‐gene search of Caenorhabditis elegans in which CED‐3 caspase was revealed to execute targeted cleavage of USP‐33, thus destabilizing fibrillarin. We also showed that cep‐1 and ced‐3 mutant worms altered nucleolar size and decreased antimicrobial peptide gene, spp‐1, expression rendering susceptibility to bacterial infection. These phenotypes were reversed by usp‐33 knockdown, thus linking the CEP‐1‐CED‐3‐USP‐33 pathway with nucleolar control and resistance to bacterial infection in worms. Parallel experiments with the human analogs of caspases and USP36 revealed similar roles in coordinating these two processes. In summary, our work outlined a conserved cascade that connects cell death signaling to nucleolar control and innate immune response. [ABSTRACT FROM AUTHOR]

Dear Editor, Injuries of the collateral ligaments (CLs) of the distal interphalangeal (DIP) joints are rare compared with other finger joints, and surgical repair of such injuries has seldom been reported. Therefore, the CLs of the DIP joint are less frequently injured than the CLs of the PIP and MCP joints. [Extracted from the article]

Background: This retrospective study investigates the complications, particularly subacromial osteolysis (SAO), associated with hook plate (HP) fixation, in the treatment of unstable distal clavicle fractures characterized by complete coracoclavicular (CC) ligament rupture. The decision-making process for employing HP in fractures of this nature, such as Neer types IIB and V and Cho classification IIC, involves considerations of distal fragment size and displacement. While HP offers advantages in clinical practice, it is not without complications, with SAO being a notable concern. Factors such as non-anatomic hook tip placement and fracture classification may influence the risk of SAO., Methods: The study comprises a retrospective analysis of unstable distal clavicle fractures treated with HP at our institution from 2019 to 2022. Exclusions include non-displaced fractures, those treated with other locking plates, and pathologic fractures. A total of 91 patients with displaced distal clavicle fractures underwent open reduction and internal fixation with HP. Cho classification was employed to differentiate cases with CC ligament rupture. Patient demographics, classifications, postoperative radiographs, distal fragment size, plate position, timing of implant removal, and complications, including SAO, were recorded., Results: Among the 91 patients, 32 were classified as Cho IIB, 43 as Cho IIC, and 16 as Cho IID. Ninety-one percent exhibited solid union before implant removal. The prevalence of SAO was 43.8%, 76.7%, and 62.5% in Cho IIB, IIC, and IID, respectively. Univariate analysis revealed a significant difference only in Cho classification ( p = 0.014). Binary logistic regression identified Cho classification type IIC as the sole risk factor for SAO ( p = 0.021; odds ratio, 4.48; 95% confidence interval, 1.56-12.87)., Conclusions: Cho type IIC fractures, characterized by CC ligament deficiency causing horizontal instability, demonstrated the highest SAO rate. In contrast, Neer type IIB fractures retained the trapezoid ligament, and Neer type V fractures had intact CC ligaments, resulting in lower SAO rates. Biomechanically, combining HPs with CC ligament reconstruction provided better structural stability than using HPs alone in treating Cho type IIC fractures., Competing Interests: CONFLICT OF INTEREST: No potential conflict of interest relevant to this article was reported., (Copyright © 2024 by The Korean Orthopaedic Association.)