Rationale for immune checkpoint inhibitors plus targeted therapy for advanced renal cell carcinoma.

Renal cell carcinoma (RCC) is a frequent urological malignancy characterized by a high rate of metastasis and lethality. The treatment strategy for advanced RCC has moved through multiple iterations over the past three decades. Initially, cytokine treatment was the only systemic treatment option for patients with RCC. With the development of medicine, antiangiogenic agents targeting vascular endothelial growth factor and mammalian target of rapamycin and immunotherapy, immune checkpoint inhibitors (ICIs) have emerged and received several achievements in the therapeutics of advanced RCC. However, ICIs have still not brought completely satisfactory results due to drug resistance and undesirable side effects. For the past years, the interests form researchers have been attracted by the combination of ICIs and targeted therapy for advanced RCC and the angiogenesis and immunogenic tumor microenvironmental variations in RCC. Therefore, we emphasize the potential principle and the clinical progress of ICIs combined with targeted treatment of advanced RCC, and summarize the future direction.

Enhancing the spatial resolution of time-of-flight based non-line-of-sight imaging via instrument response function deconvolution.

Non-line-of-sight (NLOS) imaging retrieves the hidden scenes by utilizing the signals indirectly reflected by the relay wall. Benefiting from the picosecond-level timing accuracy, time-correlated single photon counting (TCSPC) based NLOS imaging can achieve theoretical spatial resolutions up to millimeter level. However, in practical applications, the total temporal resolution (also known as total time jitter, TTJ) of most current TCSPC systems exceeds hundreds of picoseconds due to the combined effects of multiple electronic devices, which restricts the underlying spatial resolution of NLOS imaging. In this paper, an instrument response function deconvolution (IRF-DC) method is proposed to overcome the constraints of a TCSPC system's TTJ on the spatial resolution of NLOS imaging. Specifically, we model the transient measurements as Poisson convolution process with the normalized IRF as convolution kernel, and solve the inverse problem with iterative deconvolution algorithm, which significantly improves the spatial resolution of NLOS imaging after reconstruction. Numerical simulations show that the IRF-DC facilitates light-cone transform and frequency-wavenumber migration solver to achieve successful reconstruction even when the system's TTJ reaches 1200 ps, which is equivalent to what was previously possible when TTJ was about 200 ps. In addition, the IRF-DC produces satisfactory reconstruction outcomes when the signal-to-noise ratio (SNR) is low. Furthermore, the effectiveness of the proposed method has also been experimentally verified. The proposed IRF-DC method is highly applicable and efficient, which may promote the development of high-resolution NLOS imaging.

High accuracy ranging for space debris with spaceborne single photon Lidar.

The increasing risk posed by space debris highlights the need for accurate localization techniques. Spaceborne single photon Lidar (SSPL) offers a promising solution, overcoming the limitations of traditional ground-based systems by providing expansive coverage and superior maneuverability without being hindered by weather, time, or geographic constraints. This study introduces a novel approach leveraging non-parametric Bayesian inference and the Dirichlet process mixture model (DPMM) to accurately determine the distance of space debris in low Earth orbit (LEO), where debris exhibits nonlinear, high dynamic motion characteristics. By integrating extended Kalman filtering (EKF) for range gating, our method captures the temporal distribution of reflected photons, employing Markov chain Monte Carlo (MCMC) for iterative solutions. Experimental outcomes demonstrate our method's superior accuracy over conventional statistical techniques, establishing a clear correlation between radial absolute velocity and ranging error, thus significantly enhancing monostatic space debris localization.

Ranging analysis of a moving target based on the dynamic instrument response function.

A ranging high-speed moving target with a high accuracy is challenging for a single-photon ranging system (SPRS). In this Letter, the dynamic instrument response function (IRF) is proposed to establish a dynamic discrete model (DDM) by introducing a velocity and a system timing resolution, which leads to better accuracy of cross-correlation results. And with the data of a dynamic Monte Carlo (DMC), the ranging accuracy can be improved with DDM.

Protective effect of ligustrazine on oxidative stress and apoptosis following testicular torsion in rats.

Testicular torsion is a common urologic emergency and one of the causes of infertility in males. It has been reported that ligustrazine may decrease oxidative stress and reduce ischemia-reperfusion injury. This study aims to investigate the protective effect of ligustrazine in ischemia-reperfusion injury after testicular torsion-detorsion. First, 40 rats were randomly and equally divided into TMP (Ligustrazine) group, the Testicular torsion (T/D) group, the Sham (Sham operation) group, and Control group. The left testis of rats in the TMP and T/D group was rotated for 2 h. The TMP group was intraperitoneally injected with ligustrazine solution and the T/D and the Sham groups were injected with normal saline. The left testes of four groups were obtained for assay on the 4th day after the operation. Average level of superoxide dismutase (SOD), glutathione peroxidase (GPX), and catalase (CAT) were higher in Sham and Control groups than T/D group and TMP group. Conversely, average level of malondialdehyde (MDA) and reactive oxygen species (ROS) was lower in Sham and Control groups than T/D group and TMP group. In contrast with the T/D group, SOD, GPX, and CAT enzymatic activities increased, whereas MDA and ROS content decreased in the TMP group (P < 0.05). Microscopic observation showed that the testicular tissue of the Sham and Control groups were basically normal. The TMP and T/D groups had significant testicular tissue damage, whereas the TMP group had less damage and apoptosis than the T/D group. The apoptotic index of germ cells in the TMP group (13.05 ± 4.41) was lower than the T/D group (30.23 ± 11.31) (P < 0.05) and higher (P < 0.05) than the Sham group (0.56 ± 0.29). So we found that Ligustrazine lowered ischemia-reperfusion injury after testicular torsion-detorsion by decreasing the reactive oxygen species and suppressing apoptosis.

Underwater single photon 3D imaging with millimeter depth accuracy and reduced blind range.

Mono-static system benefits from its more flexible field of view and simplified structure, however, the backreflection photons from mono-static system lead to count loss for target detection. Counting loss engender range-blind, impeding the accurate acquisition of target depth. In this paper, count loss is reduced by introducing a polarization-based underwater mono-static single-photon imaging method, and hence reduced blind range. The proposed method exploits the polarization characteristic of light to effectively reduce the count loss of the target, thus improving the target detection efficiency. Experiments demonstrate that the target profile can be visually identified under our method, while the unpolarization system can not. Moreover, the ranging precision of system reaches millimeter-level. Finally, the target profile is reconstructed using non-local pixel correlations algorithm.

P300/SP1 complex mediating elevated METTL1 regulates CDK14 mRNA stability via internal m7G modification in CRPC.

BACKGROUND: N7-methylguanosine (m7G) modification is, a more common epigenetic modification in addition to m6A modification, mainly found in mRNA capsids, mRNA interiors, transfer RNA (tRNA), pri-miRNA, and ribosomal RNA (rRNA). It has been found that m7G modifications play an important role in mRNA transcription, tRNA stability, rRNA processing maturation, and miRNA biosynthesis. However, the role of m7G modifications within mRNA and its "writer" methyltransferase 1(METTL1) in tumors, particularly prostate cancer (PCa), has not been revealed. METHODS: The differential expression level of METTL1 between hormone-sensitive prostate cancer (HSPC) and castrate-resistant prostate cancer (CRPC) was evaluated via RNA-seq and in vitro experiments. The effects of METTL1 on CRPC progression were investigated through in vitro and in vivo assays. The upstream molecular mechanism of METTL1 expression upregulation and the downstream mechanism of its action were explored via Chromatin Immunoprecipitation quantitative reverse transcription polymerase chain reaction (CHIP-qPCR), Co-immunoprecipitation (Co-IP), luciferase reporter assay, transcriptome-sequencing, m7G AlkAniline-Seq, and mRNA degradation experiments, etc. RESULTS AND CONCLUSION: Here, we found that METTL1 was elevated in CRPC and that patients with METTL1 elevation tended to have a poor prognosis. Functionally, the knockdown of METTL1 in CRPC cells significantly limited cell proliferation and invasive capacity. Mechanistically, we unveiled that P300 can form a complex with SP1 and bind to the promoter region of the METTL1 gene via SP1, thereby mediating METTL1 transcriptional upregulation in CRPC. Subsequently, our findings indicated that METTL1 leads to enhanced mRNA stability of CDK14 by adding m7G modifications inside its mRNA, ultimately promoting CRPC progression.

TEAD3 inhibits the proliferation and metastasis of prostate cancer via suppressing ADRBK2.

TEAD3 acts as a transcription factor in many tumors to promote tumor occurrence and development. But in prostate cancer (PCa), it appears as a tumor suppressor gene. Recent studies have shown that this may be related to subcellular localization and posttranslational modification. We found that TEAD3 was down-expressed in PCa. Immunohistochemistry of clinical PCa specimens confirmed that TEAD3 expression was the highest in benign prostatic hyperplasia (BPH) tissues, followed by primary PCa tissues, and the lowest in metastatic PCa tissues, and its expression level was positively correlated with overall survival. MTT assay, clone formation assay, and scratch assay confirmed that overexpression of TEAD3 could significantly inhibit the proliferation and migration of PCa cells. Next-generation sequencing results indicated that Hedgehog (Hh) signaling pathway was significantly inhibited after overexpression of TEAD3. Rescue assays suggested that ADRBK2 could reverse the proliferation and migration ability caused by overexpression of TEAD3. TEAD3 is downregulated in PCa and associated with poor patient prognosis. Overexpression of TEAD3 inhibits the proliferation and migration ability of PCa cells via restraining the mRNA level of ADRBK2. These results indicate that TEAD3 was down-expressed in PCa patients and was positively correlated with a high Gleason score and poor prognosis. Mechanistically, we found that the upregulation of TEAD3 inhibits the proliferation and metastasis of prostate cancer by inhibiting the expression of ADRBK2.

Fast non-line-of-sight imaging based on product-convolution expansions.

Non-line-of-sight (NLoS) imaging reveals a hidden scene using indirect diffuse reflections. A common choice for analyzing the time-of-flight (ToF) data from a non-confocal system is an ellipsoid model whose operator is high-dimensional, leading to a computationally arduous task. In this Letter, the product-convolution expansions method is utilized to formulate the operator and its adjoint based on the observation of a shift-variant point spread function (PSF) in the ToF data. The operator and its adjoint are locally approximated as a convolution, which allows the forward and backward procedure to be computed efficiently through fast Fourier transform (FFT). Moreover, the low-rank approximation of the operator is obtained by matrix decompositions, further improving the computational efficiency. The proposed method is validated using publicly accessible datasets.

Efficient learning-based blur removal method based on sparse optimization for image restoration.

In imaging systems, image blurs are a major source of degradation. This paper proposes a parameter estimation technique for linear motion blur, defocus blur, and atmospheric turbulence blur, and a nonlinear deconvolution algorithm based on sparse representation. Most blur removal techniques use image priors to estimate the point spread function (PSF); however, many common forms of image priors are unable to exploit local image information fully. In this paper, the proposed method does not require models of image priors. Further, it is capable of estimating the PSF accurately from a single input image. First, a blur feature in the image gradient domain is introduced, which has a positive correlation with the degree of blur. Next, the parameters for each blur type are estimated by a learning-based method using a general regression neural network. Finally, image restoration is performed using a half-quadratic optimization algorithm. Evaluation tests confirmed that the proposed method outperforms other similar methods and is suitable for dealing with motion blur in real-life applications.

Learning Non-Local Spatial Correlations To Restore Sparse 3D Single-Photon Data.

This paper presents a new algorithm for the learning of spatial correlation and non-local restoration of single-photon 3-Dimensional Lidar images acquired in the photon starved regime (fewer or less than one photon per pixel) or with a reduced number of scanned spatial points (pixels). The algorithm alternates between three steps: (i) extract multi-scale information, (ii) build a robust graph of non-local spatial correlations between pixels, and (iii) the restoration of depth and reflectivity images. A non-uniform sampling approach, which assigns larger patches to homogeneous regions and smaller ones to heterogeneous regions, is adopted to reduce the computational cost associated with the graph. The restoration of the 3D images is achieved by minimizing a cost function accounting for the multi-scale information and the non-local spatial correlation between patches. This minimization problem is efficiently solved using the alternating direction method of multipliers (ADMM) that presents fast convergence properties. Various results based on simulated and real Lidar data show the benefits of the proposed algorithm that improves the quality of the estimated depth and reflectivity images, especially in the photon-starved regime or when containing a reduced number of spatial points.

Improvement in Luminous Efficacy and Thermal Performance Using Quantum Dots Spherical Shell for White Light Emitting Diodes.

White light-emitting diodes (WLEDs) based on quantum dots (QDs) are gaining increasing attention due to their excellent color quality. QDs films with planar structure are universally applied in WLEDs for color conversion, while they still face great challenges in high light extraction and thermal stability. In this study, a QDs film with a spherical shell structure was proposed to improve the optical and thermal performance for WLEDs. Compared with the conventional planar structure, the luminous efficacy of the QDs spherical shell structure is improved by 12.9% due to the reduced total reflection effect, and the angular-dependent correlated color temperature deviation is decreased from 2642 to 283 K. Moreover, the highest temperature of the WLED using a QDs spherical shell is 4.8 °C lower than that of the conventional WLED with a planar structure, which is mainly attributed to larger heat dissipation area and separated heat source. Consequently, this QDs spherical shell structure demonstrates superior performance of QDs films for WLEDs applications.