[Distribution Prediction of Soil Heavy Metals Based on Remote Sensing Temporal-Spatial-Spectral Features and Random Forest Model].

Obtaining soil heavy metal content characteristics and spatial distribution is crucial for preventing soil pollution and formulating environmental protection policies. We collected 304 surface soil samples (0-20 cm) in the Changqing district. At the same time, the spectral, temporal, and spatial features of soil heavy metals were derived from multi-remote sensing data; the temporal-spatial-spectral features closely related to soil heavy metals were selected via correlation analysis and used as input independent variables. The measured soil arsenic (As) content was used as the dependent variable to establish a spatial prediction model based on the random forest (RF) algorithm. The results showed the following:the As content in the soils exceeded the background value by 43.17% but did not exceed the risk screening values and intervention values, indicating slight heavy metal pollution in the soil. The accuracy ranking of the spatial prediction models with one feature type from high to low was spatial features (ratio of performance to inter-quartile range (RPIQ)=3.87)>temporal features (RPIQ=2.57)>spectral features (RPIQ=2.50). The spatial features were the most informative for predicting soil heavy metals. The models using temporal-spatial, temporal-spectral, and spatial-spectral features were superior to those using only one feature type, and the RPIQ values were 4.81, 4.21, and 4.70, respectively. The RF model with temporal-spatial-spectral features achieved the highest spatial prediction accuracy (R2=0.90; root mean square error (RMSE)=0.77; RPIQ=5.68). The As content decreased from the northwest to the southeast due to Yellow River erosion and industrial activities. The spatial prediction of soil heavy metals incorporating remote sensing temporal-spatial-spectral features and the random forest model provides effective support for soil pollution prevention and environmental risk control.

Correlation between seminal plasma biochemical markers and semen parameters in idiopathic oligoasthenoteratospermia: identification of biomarkers for L-carnitine therapy.

Background: L-carnitine therapy for idiopathic sperm abnormalities exhibits variable effectiveness, and currently, there are no established criteria to predict patient response. This study investigated correlations between seminal plasma markers and semen parameters to identify biomarkers that can guide indications for L-carnitine therapy indications in patients with idiopathic sperm abnormalities. Methods: A retrospective review was conducted on 223 male patients with idiopathic oligoasthenoteratospermia, who sought medical attention at our clinic between January 2020 and October 2022. These patients underwent a pretreatment seminal plasma biochemical analysis, followed by a three-month continuous L-carnitine treatment. The correlation between seminal plasma biochemical parameters and pretreatment semen parameters was analyzed. Semen quality was compared between cases with normal and abnormal seminal plasma biochemical parameters, both pretreatment and posttreatment. The correlation between the changes in semen parameters after treatment and seminal plasma biochemical parameters were investigated. Results: Correlation analyses revealed significant associations between all pretreatment semen parameters and seminal plasma biochemical markers, except for liquefying time and the ratio of normal morphology. Subgroup analysis, stratified by seminal fructose, zinc, citric acid, and neutral glycosidase levels, demonstrated that abnormal groups exhibited significantly different levels of semen parameters compared with the normal groups. The changing difference and changing ratio in the ratio of forward motile sperm showed a negative correlation with seminal fructose levels (r=-0.165 and -0.144). The changing difference in semen volume was negatively correlated with the level of seminal neutral glycosidase (r=-0.158). The changing ratio in semen volume, sperm concentration, total sperm count, and count of forward motile sperm all exhibited negative correlations with the levels of seminal neutral glycosidase (range from -0.178 to -0.224). Conclusion: Seminal plasma biochemical markers, particularly fructose and neutral glycosidase, may serve as valuable indicators for determining the eligibility of patients with idiopathic sperm abnormalities for L-carnitine therapy.

Case report: 177Lu DOTA-TATE: a new scheme for the treatment of prostate neuroendocrine cancer.

Background: Most instances of small cell carcinoma originate from the lungs, while the gastrointestinal tract serves as a secondary site. Only a minuscule proportion of cases manifest within the urogenital system. Prostate small cell carcinoma (SCCP) represents an exceedingly uncommon pathological subtype within the realm of prostate cancer, displaying significant rarity in clinical settings. This scarcity has resulted in a paucity of adequate foundational and clinical research for SCCP treatment. While investigations have unveiled a certain therapeutic efficacy of radiotherapy and chemotherapy for SCCP, clinical practice has revealed suboptimal treatment outcomes. We hereby present a case report detailing the utilization of 177Lu-DOTA-TATE in the treatment of SCCP, aiming to investigate the therapeutic efficacy of 177Lu-DOTA-TATE for SCCP. Case presentation: A male patient in his 80s presented with elevated prostate-specific antigen (PSA) levels and underwent a biopsy that revealed prostate adenocarcinoma. The patient received CAB (bicalutamide + goserelin) therapy. One year later, disease progression was detected, and a second biopsy confirmed the presence of prostate small cell carcinoma. Following the diagnosis of prostate small cell carcinoma, the patient underwent two cycles of 177Lu-DOTA-TATE treatment. Subsequent to the treatment, the original lesions showed shrinkage, metastatic lesions disappeared, and there was significant improvement, approaching complete remission. Conclusion: SCCP exhibits a high degree of malignancy and aggressive invasiveness, currently lacking effective therapeutic modalities. The treatment course of this patient serves as compelling evidence for the efficacy of 177Lu-DOTA-TATE in managing SCCP, thereby opening new avenues for future SCCP treatments.

Simulation-driven machine learning approach for high-speed correction of slope-dependent error in coherence scanning interferometry.

Slope-dependent error often occurs in the coherence scanning interferometry (CSI) measurement of functional engineering surfaces with complex geometries. Previous studies have shown that these errors can be corrected through the characterization and phase inversion of the instrument's three-dimensional (3D) surface transfer function. However, since CSI instrument is usually not completely shift-invariant, the 3D surface transfer function characterization and correction must be repeated for different regions of the full field of view, resulting in a long computational process and a reduction of measurement efficiency. In this work, we introduce a machine learning approach based on a deep neural network that is trainable for slope-dependent error correction in CSI. Our method leverages a deep neural network to directly learn errors characteristics from simulated surface measurements provided by a previously validated physics-based virtual CSI method. The experimental results demonstrate that the trained network is capable of correcting the surface height map with 1024 × 1024 sampling points within 0.1 seconds, covering a 178 µm field of view. The accuracy is comparable to the previous phase inversion approach while the new method is two orders of magnitude faster under the same computational condition.

Low-dose imaging denoising with one pair of noisy images.

Low-dose imaging techniques have many important applications in diverse fields, from biological engineering to materials science. Samples can be protected from phototoxicity or radiation-induced damage using low-dose illumination. However, imaging under a low-dose condition is dominated by Poisson noise and additive Gaussian noise, which seriously affects the imaging quality, such as signal-to-noise ratio, contrast, and resolution. In this work, we demonstrate a low-dose imaging denoising method that incorporates the noise statistical model into a deep neural network. One pair of noisy images is used instead of clear target labels and the parameters of the network are optimized by the noise statistical model. The proposed method is evaluated using simulation data of the optical microscope, and scanning transmission electron microscope under different low-dose illumination conditions. In order to capture two noisy measurements of the same information in a dynamic process, we built an optical microscope that is capable of capturing a pair of images with independent and identically distributed noises in one shot. A biological dynamic process under low-dose condition imaging is performed and reconstructed with the proposed method. We experimentally demonstrate that the proposed method is effective on an optical microscope, fluorescence microscope, and scanning transmission electron microscope, and show that the reconstructed images are improved in terms of signal-to-noise ratio and spatial resolution. We believe that the proposed method could be applied to a wide range of low-dose imaging systems from biological to material science.

Harm Avoidance is Correlated with the Reward System in Adult Patients with Attention Deficit Hyperactivity Disorder: A Functional Magnetic Resonance Imaging Study.

Objective: Hypoactivity in the reward system among patients with attention deficit hyperactivity disorder (ADHD) is a well-known phenomenon. Whether the activity in the reward pathway is related to harm avoidance, such as in sensitivity to punishment, is unclear. Evidence regarding the potential difference between ADHD patients and controls in terms of this association is scarce. Methods: Event-related functional magnetic resonance imaging was conducted on subjects performing the Iowa gambling test. Fourteen adults with ADHD and 14 controls were enrolled in the study. Results: Harm avoidance was found to be positively correlated with the activities of the bilateral orbitofrontal cortex and right insula in individuals with ADHD. A group difference was also confirmed. Conclusion: Understanding the roles of harm avoidance and brain activation during risk tasks is important.

Three-dimensional phase and intensity reconstruction from coherent modulation imaging measurements.

Coherent modulation imaging is a lensless imaging technique, where a complex-valued image can be recovered from a single diffraction pattern using the iterative algorithm. Although mostly applied in two dimensions, it can be tomographically combined to produce three-dimensional (3D) images. Here we present a 3D reconstruction procedure for the sample's phase and intensity from coherent modulation imaging measurements. Pre-processing methods to remove illumination probe, inherent ambiguities in phase reconstruction results, and intensity fluctuation are given. With the projections extracted by our method, standard tomographic reconstruction frameworks can be used to recover accurate quantitative 3D phase and intensity images. Numerical simulations and optical experiments validate our method.

Fringe projection profilometry method with high efficiency, precision, and convenience: theoretical analysis and development.

Fringe projector profilometry (FPP) is an important three-dimensional (3D) measurement technique, especially when high precision and speed are required. Thus, theoretical interrogation is critical to provide deep understanding and possible improvement of FPP. By dividing an FPP measurement process into four steps (system calibration, phase measurement, pixel correspondence, and 3D reconstruction), we give theoretical analysis on the entire process except for the extensively studied calibration step. Our study indeed reveals a series of important system properties, to the best of our knowledge, for the first time: (i) in phase measurement, the optimal and worst fringe angles are proven perpendicular and parallel to epipolar line, respectively, and can be considered as system parameters and can be directly made available during traditional calibration, highlighting the significance of the epipolar line; (ii) in correspondence, when two sets of fringes with different fringe orientations are projected, the highest correspondence precision can be achieved with arbitrary orientations as long as these two orientations are perpendicular to each other; (iii) in reconstruction, a higher reconstruction precision is given by the 4-equation methods, while we notice that the 3-equation methods are almost dominatingly used in literature. Based on these theoretical results, we propose a novel FPP measurement method which (i) only projects one set of fringes with optimal fringe angle to explicitly work together with the epipolar line for precise pixel correspondence; (ii) for the first time, the optimal fringe angle is determined directly from the calibration parameters, instead of being measured; (iii) uses 4 equations for precise 3D reconstruction but we can remove one equation which is equivalent to an epipolar line, making it the first algorithm that can use 3-equation solution to achieve 4-equation precision. Our method is efficient (only one set of fringe patterns is required in projection and the speed is doubled in reconstruction), precise (in both pixel correspondence and 3D reconstruction), and convenient (the computable optimal fringe angle and a closed-form 3-equation solution). We also believe that our work is insightful in revealing fundamental FPP properties, provides a more reasonable measurement for practice, and thus is beneficial to further FPP studies.

Coherent modulation imaging using a physics-driven neural network.

Coherent modulation imaging (CMI) is a lessness diffraction imaging technique, which uses an iterative algorithm to reconstruct a complex field from a single intensity diffraction pattern. Deep learning as a powerful optimization method can be used to solve highly ill-conditioned problems, including complex field phase retrieval. In this study, a physics-driven neural network for CMI is developed, termed CMINet, to reconstruct the complex-valued object from a single diffraction pattern. The developed approach optimizes the network's weights by a customized physical-model-based loss function, instead of using any ground truth of the reconstructed object for training beforehand. Simulation experiment results show that the developed CMINet has a high reconstruction quality with less noise and robustness to physical parameters. Besides, a trained CMINet can be used to reconstruct a dynamic process with a fast speed instead of iterations frame-by-frame. The biological experiment results show that CMINet can reconstruct high-quality amplitude and phase images with more sharp details, which is practical for biological imaging applications.

Bone flare after initiation of novel hormonal therapy in patients with metastatic hormone-sensitive prostate cancer: A case report.

BACKGROUND: The 2020 European Association of Urology prostate cancer guidelines recommend androgen deprivation therapy (ADT) in combination with apalutamide and enzalutamide, a new generation of androgen receptor antagonists, as first-line therapy. A decrease in prostate-specific antigen (PSA) levels may occur in the early stages of novel hormonal therapy; however, radionuclide bone imaging may suggest disease progression. During follow-up, PSA, radionuclide bone imaging, and prostate-specific membrane antigen (PSMA) positron emission tomography - computed tomography (PET-CT) are needed for systematic evaluation. CASE SUMMARY: We admitted a 56-year-old male patient with metastatic hormone-sensitive prostate cancer. Initial radionuclide bone imaging, magnetic resonance imaging (MRI), and PSMA PET-CT showed prostate cancer with multiple bone metastases. Ultrasound-guided needle biopsy of the prostate revealed a poorly differentiated adenocarcinoma of the prostate with a Gleason score: 5+4 = 9. The final diagnosis was a prostate adenocarcinoma (T4N1M1). ADT with novel hormonal therapy (goseraline sustained-release implant 3.6 mg monthly and apalutamide 240 mg daily) was commenced. Three months later, radionuclide bone imaging and MRI revealed advanced bone metastasis. However, PSMA PET-CT examination showed a significant reduction in PSMA aggregation on the bone, indicating improved bone metastases. Considering that progressive decrease in the presenting lumbar pain, treatment strategies were considered to be effective. CONCLUSION: ADT using novel hormonal therapy is effective for treating patients with prostate adenocarcinoma. Careful evaluation must precede treatment plan changes.

Case Report: 18F-FDG PET/CT and Laparoscopic Nephron Sparing Surgery in the Management of Bleeding From Renal Metastases of Choriocarcinoma.

Choriocarcinoma is a cancer that usually occurs in the uterus during pregnancy. Although choriocarcinoma with renal metastasis and spontaneous renal hemorrhage is very rare, it can occur. We describe a rare case of metastatic choriocarcinoma, wherein the patient presented with acute abdominal pain due to a subcapsular hematoma secondary to a bleeding renal metastasis. We performed a laparoscopic nephron sparing surgery to remove the tumor and control the bleeding. A retrospective analysis revealed that metastasis was detected on 18F-fluorodeoxyglucose PET/CT, but not on CT alone. To our knowledge, a case of choriocarcinoma with such symptoms and treatment has not been described in recent literature. Our case illustrates that acute bleeding from a renal metastasis can be effectively managed by laparoscopic nephron sparing surgery. It also demonstrates the advantage 18F-FDG PET/CT may have in the evaluation of metastatic choriocarcinoma.

Resolution-enhanced ptychography framework with an equivalent upsampling and precise position.

As a lensless imaging technique, ptychography provides a new way to resolve the conflict between the spatial resolution and the field of view. However, due to the pixel size limit of the sensor, a compromise has to be reached between the spatial resolution and the signal-to-noise ratio. Here, we propose a resolution-enhanced ptychography framework with equivalent upsampling and subpixel accuracy in position to further improve the resolution of ptychography. According to the theory of pixel superresolved techniques, the inherent shift illumination scheme in ptychography can additionally enhance the resolution with the redundant data. An additional layer of pooling is used to simulate the downsampling of a digital record, and the pixel superresolved problem is transformed into an automatic optimization problem. The proposed framework is verified by optical experiments, both in biological samples and the resolution targets. Compared to the traditional algorithm, the spatial lateral resolution is twice as large using the same data set.

Digital twin data-driven proactive job-shop scheduling strategy towards asymmetric manufacturing execution decision.

The information asymmetry phenomenon widely exists in production management decisions due to the latency of manufacturing data transmissions. Also, stochastic events on the physical production site will result in information asymmetry, which may lead to inconsistency between current execution and previous resource allocation plans. It is meaningful and important for developing an information model based on the Internet of Manufacturing Things to timely and actively adjust the scheduling strategy to meet the symmetry requirements of the production execution process. Based on the digital twin data collected from the workshop, a proactive job-shop scheduling strategy was discussed in this paper. Firstly, the mechanism for the influence of delayed local operations on makespan was deduced. Then, a framework for implementing the proactive job-shop scheduling strategy was proposed. Coordination point was used to determine the adjustment interval of local operations; right-shift rule with delay time constraints was used to adjust the unprocessed operation sequences on machines. Finally, the examples including 6*6 (6 jobs, 6 machines) and 20*40 (20 jobs, 40 machines) were presented to verify the effectiveness and scalability of the proposed method. It can be predicted that the proactive scheduling strategy provides the online decisions for the efficient and smooth execution of the digital twin-driven workshop production.

Spatiotemporal coherent modulation imaging for dynamic quantitative phase and amplitude microscopy.

The single-shot capability of coherent modulation imaging (CMI) makes it have great potential in the investigation of dynamic processes. Its main disadvantage is the relatively low signal-to-noise ratio (SNR) which affects the spatial resolution and reconstruction accuracy. Here, we propose the improvement of a general spatiotemporal CMI method for imaging of dynamic processes. By making use of the redundant information in time-series reconstructions, the spatiotemporal CMI can achieve robust and fast reconstruction with higher SNR and spatial resolution. The method is validated by numerical simulations and optical experiments. We combine the CMI module with an optical microscope to achieve quantitative phase and amplitude reconstruction of dynamic biological processes. With the reconstructed complex field, we also demonstrate the 3D digital refocusing ability of the CMI microscope. With further development, we expect the spatiotemporal CMI method can be applied to study a range of dynamic phenomena.

Dynamic coherent diffractive imaging with a physics-driven untrained learning method.

Reconstruction of a complex field from one single diffraction measurement remains a challenging task among the community of coherent diffraction imaging (CDI). Conventional iterative algorithms are time-consuming and struggle to converge to a feasible solution because of the inherent ambiguities. Recently, deep-learning-based methods have shown considerable success in computational imaging, but they require large amounts of training data that in many cases are difficult to obtain. Here, we introduce a physics-driven untrained learning method, termed Deep CDI, which addresses the above problem and can image a dynamic process with high confidence and fast reconstruction. Without any labeled data for pretraining, the Deep CDI can reconstruct a complex-valued object from a single diffraction pattern by combining a conventional artificial neural network with a real-world physical imaging model. To our knowledge, we are the first to demonstrate that the support region constraint, which is widely used in the iteration-algorithm-based method, can be utilized for loss calculation. The loss calculated from support constraint and free propagation constraint are summed up to optimize the network's weights. As a proof of principle, numerical simulations and optical experiments on a static sample are carried out to demonstrate the feasibility of our method. We then continuously collect 3600 diffraction patterns and demonstrate that our method can predict the dynamic process with an average reconstruction speed of 228 frames per second (FPS) using only a fraction of the diffraction data to train the weights.

Optical image hiding based on spectrum encoding with structured illumination.

Structured illumination microscopy (SIM) is combined with optical image hiding for the first time, to the best of our knowledge. In a linear phase encoding system, secret information might be divulged with the input related to the correct image. In this paper, we propose an optical hiding method in which the concept of SIM is used to create reconstructed host images with an extended spectrum. This method not only improves the security of the image hiding system, but also creates a new perspective for optical image hiding and makes solutions for the defect of the linear phase encoding system.

A pilot study on the association between the blood oxygen level-dependent signal in the reward system and dopamine transporter availability in adults with attention deficit hyperactivity disorder.

BACKGROUND: It is well-known that attention deficit hyperactivity disorder (ADHD) is associated with changes in the dopaminergic system. However, the relationship between central dopaminergic tone and the blood oxygen level-dependent (BOLD) signal during receipt of rewards and penalties in the corticostriatal pathway in adults with ADHD is unclear. METHODS: Single-photon emission computed tomography with [99mTC]TRODAT-1 was used to assess striatal dopamine transporter (DAT) availability. Event-related functional magnetic resonance imaging was conducted on subjects performing the Iowa Gambling Test. RESULT: DAT availability was found to be associated with the BOLD response, which was a covariate of monetary loss, in the medial prefrontal cortex (r = 0.55, P = .03), right ventral striatum (r = 0.69, P = .003), and right orbital frontal cortex (r = 0.53, P = .03) in adults with ADHD. However, a similar correlation was not found in the controls. CONCLUSIONS: The results confirmed that dopaminergic tone may play a different role in the penalty-elicited response of adults with ADHD. It is plausible that a lower neuro-threshold accompanied by insensitivity to punishment could be exacerbated by the hypodopaminergic tone in ADHD.

Effects of Acupuncture Treatment in Reducing Sleep Disorder and Gut Microbiota Alterations in PCPA-Induced Insomnia Mice.

Chronic insomnia without intervention will do harm to people's physical and psychological health as well as the quality of life. While ensuring efficacy, traditional Chinese medicine therapy, such as acupuncture, overcomes the side effects of drugs. However, the molecular mechanism of traditional medicine is unclear and it encounters many obstacles in repetitiveness and popularization. On the other side, the placebo effects also need to be eliminated during the intervention. In this study, a number of indicators such as duration of sleep latency, serum markers, pineal gland immunohistochemistry, and gut microbes were detected in the PCPA-induced insomnia mice to compare the effects between acupuncture and hypnotic drug treatments. Although the food intake and weight were not changed, the results show that serum maker and gut microbiota alterations were mediated by concurrent changes in sleep disorder induced by PCPA in mice. Compared with the PCPA-induced insomnia group, dopamine, 5-hydroxytryptamine, and norepinephrine were reduced in serum, and the melatonin was increased in the pineal gland of the acupuncture group as well as zopiclone drug group. Moreover, the analysis results from 16S tag sequencing of the gut microbiome bacterial rRNA hypervariable region show the same improvement effects between the two medical intervention groups. A co-occurrence network analysis showed that blank and acupuncture networks exhibited higher similarity than sham and zopiclone networks and the sham network possessed the highest complexity of microbial communities. Taken together, the gut microbiome will likely be a new target for improving sleep disorders, and taking into account the side effects of hypnotic drugs, nonpharmacological interventions such as acupuncture may be an effective means and have greater clinical benefits.

Natural speckle-based watermarking with random-like illuminated decoding.

We propose an optical watermarking method based on a natural speckle pattern. In the watermarking process, the watermark information is embedded into the natural speckle pattern. Then the random-like watermarked image is generated with the proposed grayscale reordering algorithm. During the extraction procedure, the watermarked image is projected to the natural speckle pattern as illumination. Subsequently, they are incoherently superimposed to extract the watermark information directly by human vision. Optical experiments and a hypothesis test are conducted to demonstrate the proposed method with high reliability, imperceptibility and robustness. The proposed method is the first watermarking method utilizing the natural diffuser as the core element in encoding and decoding.

Capacity of turbulent ocean links with carrier Bessel-Gaussian localized vortex waves.

Based on the power spectrum of the index fluctuation with the outer scale of seawater turbulence, we develop the channel capacity of oceanic turbulence links with carrier Bessel-Gaussian vortex localized waves. By this capacity model, we investigate the influences of seawater turbulence and carrier parameters on the channel capacity. The results show that a higher rate of dissipation of kinetic energy per unit mass of fluid, larger inner scale, or lower dissipation rate of the mean-squared temperature causes the higher channel capacity; the Bessel-Gaussian localized vortex wave with a larger source transverse size, smaller Bessel cone angle, lower orbital angular momentum quantum number, or broader initial half-pulse width has stronger resistance to oceanic turbulent perturbation. This work provides a theoretical basis for realizing high-capacity oceanic optical communication with carrier Bessel-Gaussian vortex localized waves.