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The clinical efficacy of current cancer therapies falls short, and there is a pressing demand to integrate new targets with conventional therapies. Autophagy, a highly conserved self-degradation process, has received considerable attention as an emerging therapeutic target for cancer. With the rapid development of nanomedicine, nanomaterials have been widely utilized in cancer therapy due to their unrivaled delivery performance. Hence, considering the potential benefits of integrating autophagy and nanotechnology in cancer therapy, we outline the latest advances in autophagy-based nanotherapeutics. Based on a brief background related to autophagy and nanotherapeutics and their impact on tumor progression, the feasibility of autophagy-based nanotherapeutics for cancer treatment is demonstrated. Further, emerging nanotherapeutics developed to modulate autophagy are reviewed from the perspective of cell signaling pathways, including modulation of the mammalian target of rapamycin (mTOR) pathway, autophagy-related (ATG) and its complex expression, reactive oxygen species (ROS) and mitophagy, interference with autophagosome-lysosome fusion, and inhibition of hypoxia-mediated autophagy. In addition, combination therapies in which nano-autophagy modulation is combined with chemotherapy, phototherapy, and immunotherapy are also described. Finally, the prospects and challenges of autophagy-based nanotherapeutics for efficient cancer treatment are envisioned.

Reliable pose estimation for non-cooperative spacecraft is a key technology for in-orbit service and active debris removal missions. Utilizing deep learning techniques for processing monocular camera images is effective and is a hotspot of current research. To reduce errors and improve model generalization, researchers often design multi-head loss functions or use generative models to achieve complex data augmentation, which makes the task complex and time-consuming. We propose a pyramid vision transformer spatial-aware keypoints regression network and a stereo-aware augmentation strategy to achieve robust prediction. Specifically, we primarily use the eight vertices of a cuboid satellite body as landmarks and the observable surfaces can be transformed by, respectively, using the pose labels. The experimental results on the SPEED+ dataset show that by using the existing EPNP algorithm and pseudo-label self-training method, we can achieve high-precision pose estimation for target cross-domains. Compared to other existing methods, our model and strategy are more straightforward. The entire process does not require the generation of new images, which significantly reduces the storage requirements and time costs. Combined with a Kalman filter, the robust and continuous output of the target position and attitude is verified by the SHIRT dataset. This work realizes deployment on mobile devices and provides strong technical support for the application of an automatic visual navigation system in orbit.

Accurate on-orbit wavelength calibration of the spaceborne hyperspectral payload is the key to the quantitative analysis and application of observational data. Due to the high spectral resolution of general spaceborne hyperspectral greenhouse gas (GHG) detection instruments, the common Fraunhofer lines in the solar atmosphere can be used as a reference for on-orbit wavelength calibration. Based on the performances of a GHG detection instrument under development, this study simulated the instrument’s solar-viewing measurement spectra and analyzed the main sources of errors in the on-orbit wavelength calibration method of the instrument using the solar Fraunhofer lines, including the Doppler shift correction error, the instrumental measurement error, and the peak-seek algorithm error. The calibration accuracy was independently calculated for 65 Fraunhofer lines within the spectral range of the instrument. The results show that the wavelength calibration accuracy is mainly affected by the asymmetry of the Fraunhofer lines and the random error associated with instrument measurement, and it can cause calibration errors of more than 1/10 of the spectral resolution at maximum. A total of 49 Fraunhofer lines that meet the requirements for calibration accuracy were screened based on the design parameters of the instrument. Due to the uncertainty of simulation, the results in this study have inherent limitations, but provide valuable insights for quantitatively analyzing the errors of the on-orbit wavelength calibration method using the Fraunhofer lines, evaluating the influence of instrumental parameters on the calibration accuracy, and enhancing the accuracy of on-orbit wavelength calibration for similar GHG detection payloads.

Convolutional neural network-based image super-resolution methods have achieved great success in recent years. However, the huge memory and computational costs make most of the existing methods difficult to be applied to resource-constrained scenarios such as edge devices. To tackle this problem, we propose a generic, lightweight and efficient feature fusion block to replace the commonly used 1*1 convolution. In addition, we propose the enhanced shallow residual blocks to improve the super-resolution performance. By combining these two novel blocks, we design an efficient similar feature fusion network for single image super-resolution, based on the observation that cross-layer features of the same channel usually have high similarities. The similar feature fusion block utilizes similarity as a guide for feature clustering, enabling efficient and high-performance cross-layer feature fusion. On the other hand, the enhanced shallow residual blocks are used as the base feature extraction model for the network to improve super-resolution performance in conjunction with the feature fusion module. In the enhanced shallow residual blocks, we combine convolution with identity connection to maintain the similarity of cross-layer features that are fed into the similar feature fusion block. The spatial attention mechanism is also introduced to reinforce the useful spatial features. Experimental results on the benchmark datasets show that the proposed method can achieve comparable results to state-of-the-art methods with a small number of parameters.

Hypertension is the primary cause of cardiovascular disease, which is a leading killer worldwide. Despite the prevalence of this non-communicable disease, still between 90% and 95% of cases are of unknown or multivariate cause (“essential hypertension”). Current therapeutic options focus primarily on lowering blood pressure through decreasing peripheral resistance or reducing fluid volume, but fewer than half of hypertensive patients can reach blood pressure control. Hence, identifying unknown mechanisms causing essential hypertension and designing new treatment accordingly are critically needed for improving public health. In recent years, the immune system has been increasingly implicated in contributing to a plethora of cardiovascular diseases. Many studies have demonstrated the critical role of the immune system in the pathogenesis of hypertension, particularly through pro-inflammatory mechanisms within the kidney and heart, which, eventually, drive a myriad of renal and cardiovascular diseases. However, the precise mechanisms and potential therapeutic targets remain largely unknown. Therefore, identifying which immune players are contributing to local inflammation and characterizing pro-inflammatory molecules and mechanisms involved will provide promising new therapeutic targets that could lower blood pressure and prevent progression from hypertension into renal or cardiac dysfunction.

The Atmospheric Radiative Transfer Simulator (ARTS) has been widely used in the radiation transfer simulation from microwave to terahertz. Due to the same physical principles, ARTS can also be used for simulations of thermal infrared (TIR). However, thorough evaluations of ARTS in the TIR region are still lacking. Here, we evaluated the performance of ARTS in 600–1650 cm−1 taking the Line-By-Line Radiative Transfer Model (LBLRTM) as a reference model. Additionally, the moderate resolution atmospheric transmission (MODTRAN) band model (BM) and correlated-k (CK) methods were also used for comparison. The comparison results on the 0.001 cm−1 spectral grid showed a high agreement (sub-0.1 K) between ARTS and LBLRTM, while the mean bias difference (MBD) and root mean square difference (RMSD) were less than 0.05 K and 0.3 K, respectively. After convolving with the spectral response functions of the Atmospheric Infra-Red Sounder (AIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), the brightness temperature (BT) differences between ARTS and LBLRTM became smaller with RMSDs of 3 (excellent Jacobian fit). For the water vapor Jacobian, the Jacobian difference increased with an increasing water vapor content. However, at extremely low water vapor values (0.016 ppmv in this study), LBLRTM exhibited non-physical mutations, while ARTS was smooth. This study aims to help users understand the simulation accuracy of ARTS in the TIR region and the improvement of ARTS via the community.

Compared to wide-field telescopes, small-field detection systems have higher spatial resolution, resulting in stronger detection capabilities and higher positioning accuracy. When detecting by small fields in synchronous orbit, both space debris and fixed stars are imaged as point targets, making it difficult to distinguish them. In addition, with the improvement in detection capabilities, the number of stars in the background rapidly increases, which puts higher requirements on recognition algorithms. Therefore, star detection is indispensable for identifying and locating space debris in complex backgrounds. To address these difficulties, this paper proposes a real-time star extraction method based on adaptive filtering and multi-frame projection. We use bad point repair and background suppression algorithms to preprocess star images. Afterwards, we analyze and enhance the target signal-to-noise ratio (SNR). Then, we use multi-frame projection to fuse information. Subsequently, adaptive filtering, adaptive morphology, and adaptive median filtering algorithms are proposed to detect trajectories. Finally, the projection is released to locate the target. Our recognition algorithm has been verified by real star images, and the images were captured using small-field telescopes. The experimental results demonstrate the effectiveness of the algorithm proposed in this paper. We successfully extracted hip-27066 star, which has a magnitude of about 12 and an SNR of about 1.5. Compared with existing methods, our algorithm has advantages in both recognition rate and false-alarm rate, and can be used as a real-time target recognition algorithm for space-based synchronous orbit detection payloads.

Severe renal fibrosis often occurs in obstructive kidney disease, not only in the obstructed kidney but also in the contralateral kidney, causing renal dysfunction. Although the mechanisms of injury in obstructed kidney have been studied for years, the pathogenesis of fibrosis in the contralateral kidney remains largely unknown. Here, we examined long-term unilateral ureteral obstruction (UUO) model in male Sprague–Dawley rats and found that macrophage-to-myofibroblast transition (MMT) is contributing to renal fibrosis in the contralateral kidney of UUO rats. Interestingly, this process was attenuated by treatment of eplerenone, a specific blocker of the mineralocorticoid receptor (MR). In-vitro, stimulating MR in primary cultured or cell line macrophages enhances MMT, which were also inhibited by MR blockade. Collectively, these findings provide a plausible mechanism for UUO-induced injury in the contralateral kidney, suggesting the benefit of using MR blockage as a part of treatment to UUO to protect the contralateral kidney thereby preserve renal function.

The influence of motivation on selective visual attention in states of high vs. low vigilance is poorly understood. To explore the possible differences in the influence of motivation on behavioral performance and neural activity in high and low vigilance levels, we conducted a prolonged 2 h 20 min flanker task and provided monetary rewards during the 20- to 40- and 100- to 120-min intervals of task performance. Both the behavioral and electrophysiological measures were modulated by prolonged task engagement. Moreover, the effect of reward was different in high vs. low vigilance states. The monetary reward increased accuracy and decreased the reaction time (RT) and number of omitted responses in the low but not in the high vigilance state. The fatigue-related decrease in P300 amplitude recovered to its level in the high vigilance state by manipulating motivation, whereas the fatigue-related increase in P300 latency was not modulated by reward. Additionally, the fatigue-related increase in event-related spectral power at 1–4 Hz was sensitive to vigilance decrement and reward. However, the spectral power at 4–8 Hz was only affected by the decrease in vigilance. These electrophysiological measures were not influenced by motivation in the state of high vigilance. Our results suggest that neural processing capacity, but not the timing of processing, is sensitive to motivation. These findings also imply that the fatigue-related impairments in behavioral performance and neural activity underlying selective visual attention only partly recover after manipulating motivation. Furthermore, our results provide evidence for the dissociable neural mechanisms underlying the fatigue-related decrease vs. reward-related increase in attentional resources.

T cells contribute to development of high blood pressure but their role in salt-sensitive hypertension is less clear. Liuet al. show that CD8+ T cells upregulate and activate Na-Cl co-transporter NCC in distal convoluted tubules via direct cell-cell contact and ROS-Src activation, leading to Na+retention and salt-sensitive hypertension.

The trinucleotide repeats AAT•ATT are simple DNA sequences that potentially form different types of non-B DNA secondary structures and cause genomic instabilities in vivo.The molecular mechanism underlying the maintenance of a 24-triplet AAT•ATT repeat was examined in E. coli by cloning the repeats into the EcoRI site in plasmid pUC18 and into the attB site on the E. coli genome. Either the AAT or the ATT strand acted as lagging strand template in a replication fork. Propagations of the repeats in either orientation on plasmids did not affect colony morphology when triplet repeat transcription using the lacZ promoter was repressed either by supplementing LacI(Q)in trans or by adding glucose into the medium. In contrast, transparent colonies were formed by inducing transcription of the repeats, suggesting that transcription of AAT•ATT repeats was toxic to cell growth. Meanwhile, significant IS1E transposition events were observed both into the triplet repeats region proximal to the promoter side, the promoter region of the lacZ gene, and into the AAT•ATT region itself. Transposition reversed the transparent colony phenotype back into healthy, convex colonies. In contrast, transcription of an 8-triplet AAT•ATT repeat in either orientation on plasmids did not produce significant changes in cell morphology and did not promote IS1E transposition events. We further found that a role of IS1E transposition into plasmids was to inhibit transcription through the repeats, which was influenced by the presence of the H-NS protein, but not of its paralogue StpA.Our findings thus suggest that the longer AAT•ATT triplet repeats in E. coli become vulnerable after transcription. H-NS and its facilitated IS1E transposition can silence long triplet repeats transcription and preserve cell growth and survival.