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At present, the deep learning super-resolution (SR) method has achieved excellent results, but it also faces problems such as large models, high computational cost, a large amounts of training data, and poor interpretability. However, traditional machine learning-based methods still have room for improvement in feature extraction and model structure. This paper constructs a gradient embedding cascade forest structure on the basis of random forest and proposes a limit gradient embedding cascaded forest SR (LGECFSR) model. In feature construction, we not only adopt the first-order gradient, the second-order gradient, and other features of the image but also fuse the information of the original LR image. In addition, image blocks of different sizes are used for training, which increases the model’s generalization ability. Compared with the state-of-the-art machine learning-based methods, our method achieves the best performance and the second-best computational speed. In addition, compared with some deep learning-based methods, our model has a similar reconstruction effect and the best computational speed. In detail, for some reconstruction tasks, the Multi-Adds of LGECFSR is one-tenth to one-4000th of that of some current models. However, the SR performance of LGECFSR is the same or slightly better than that of some current classical algorithms.

Because the real degradation model is more complex, and the different computing performance of devices leads to different degradation results. The super-resolution based on the real image degradation model has great challenges in practical applications. To solve these problems, we propose a novel SR network based on self-calibration convolution and adaptive dense connection (SCCADC-SR). Firstly, we introduce self-calibration convolution as the basic convolution module and use it as a supplement to the attention mechanism. Secondly, we use efficient channel attention (ECA) to construct an adaptive dense connection structure to deal with the features at the different levels. Then, we use the CutBlur method to enhance the data to improve the generalization ability of the model and use the long skip connection to improve the convergence of the depth model structure. Finally, SCCADC-SR combines self-ensemble and model ensemble to improve the model’s robustness and reduce the noise. Experimental results show that for both real image data and Bicubic data, our SCCADC-SR improves SR reconstruction performance by 5% compared with the state-of-the-art methods.

This article consider a situation of SU(N) system with broken symmetry, and therefore the spin-liquid phase is exist in the phase transition stage. And explore the antiferromagnetic spin interaction with long range order in a two dimension square lattice using the two alkaline earth atoms state short range interaction. The appearance of spin liquid due to noncolline mechanism which causing by destruction of long range order in this model is mentioned. In a N-site fermion-model system, I analyse the effect of fluctuation on order and phase transition. As well as this N-component spin system with disordered spin, the critical phenomenon is showed to reflect the fluctuation effection on process of phase transtion.

Background: Osteosarcomas, the second most prevalent primary malignancy of the bone, are often presented with high-grade subclinical metastatic disease that metastasizes at very early stages. Exosomes, as molecular information carriers, may play a potent role in the occurrence and development of tumors through oncogenic molecular reprogramming of tumor-associated macrophage (TAM). In this study, we will investigate the effect of osteosarcoma-derived exosomes on the polarization of TAM and decipher its underlying molecular mechanism. Material and Methods: Osteosarcoma-derived exosomes from MG63 cells were isolated and characterized by transmission electron microscopy, and nano-particle size analysis. Double fluorescence staining was performed to confirm the macrophages phagocytosis of exosomes. Western blot, qRT-PCR, and transwell assays were conducted to assess the effect of exosomes on migration, invasion, and macrophage differentiation. The mouse model of osteosarcoma was established to evaluate the effects of exosomes on lung metastasis in vivo. Results: MG63 exosomes were successfully isolated and verified to be phagocytized by macrophages through fluorescence confocal microscopy. The results revealed that osteosarcoma cells could induce M2 type differentiation of macrophages largely through Tim-3 mediated by exosomes, which in turn could promote the migration, invasion, EMT, and lung metastasis of osteosarcoma cells through the secretion of cytokines including IL-10, TGFβ, and VEGF. Conclusions: Our results demonstrated that osteosarcoma-derived exosomes induced M2 polarization of macrophages and promoted the invasion and metastasis of tumors through Tim-3; besides, the study also suggests a novel therapeutic target for future studies.

In fault diagnosis of rotating machinery, Hil- bert-Huang transform （HHT） is often used to extract the fault characteristic signal and analyze decomposition results in time-frequency domain. However, end effect occurs in HHT, which leads to a series of problems such as modal aliasing and false IMF （Intrinsic Mode Func- tion）. To counter such problems in HHT, a new method is put forward to process signal by combining the general- ized regression neural network （GRNN） with the bound- ary local characteristic-scale continuation （BLCC）. Firstly, the improved EMD （Empirical Mode Decompo- sition） method is used to inhibit the end effect problem that appeared in conventional EMD. Secondly, the gen- erated IMF components are used in HHT. Simulation and measurement experiment for the cases of time domain, frequency domain and related parameters of Hilbert- Huang spectrum show that the method described here can restrain the end effect compared with the results obtained through mirror continuation, as the absolute percentage of the maximum mean of the beginning end point offset and the terminal point offset are reduced from 30.113% and 27.603% to 0.510% and 6.039% respectively, thus reducing the modal aliasing, and eliminating the false IMF components of HHT. The proposed method caneffectively inhibit end effect, reduce modal aliasing and false IMF components, and show the real structure of signal components accuratelX.

This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This challenge involves three tracks to super-resolve an input image for \\(\\times\\)2, \\(\\times\\)3 and \\(\\times\\)4 scaling factors, respectively. The goal is to attract more attention to realistic image degradation for the SR task, which is much more complicated and challenging, and contributes to real-world image super-resolution applications. 452 participants were registered for three tracks in total, and 24 teams submitted their results. They gauge the state-of-the-art approaches for real image SR in terms of PSNR and SSIM.

Nitrogen-based heterocycles have aroused widespread interest due to their reoccurrence in many pharmaceuticals. Amongst these motifs, the enantioenriched lactams are the ubiquitous scaffolds found in myriad biologically active natural products and drugs. Recently, the transition metal-catalyzed asymmetric carbamoylation has been widely employed as a straightforward arsenal for chiral lactam architecture synthesis, including β-lactam and γ-lactam. However, despite the extensive efforts, there still remains no protocol to accomplish the related δ-lactam synthesis. In this manuscript, the Ni-catalyzed enantioselective carbamoylation of unactivated alkenes by the leverage of reductive dicarbofunctionalization strategy allows for the expedient access to two types of mostly common six-membered lactams: 3,4-dihydroquinolinones and 2-piperidinone in high yield and enantioselectivity. This protocol features with good functional group tolerance, as well as broad substrate scope. The newly developed chiral 8-Quinox skeleton ligand is the key parameter for this transformation, which significantly enhances the reactivity and enantioselectivity. Six-membered chiral lactams are common structural motifs of pharmaceuticals. Here, the authors describe a nickel-catalyzed reductive carbamoylation of alkenes to form enantioenriched six-membered lactams.

The proximity ligation-based Hi-C and derivative methods are the mainstream tools to study genome-wide chromatin interactions. These methods often fragment the genome using enzymes functionally irrelevant to the interactions per se, restraining the efficiency in identifying structural features and the underlying regulatory elements. Here we present Footprint-C, which yields high-resolution chromatin contact maps built upon intact and genuine footprints protected by transcription factor (TF) binding. When analyzed at one-dimensional level, the billions of chromatin contacts from Footprint-C enable genome-wide analysis at single footprint resolution, and reveal preferential modes of local TF co-occupancy. At pairwise contact level, Footprint-C exhibits higher efficiency in identifying chromatin structural features when compared with other Hi-C methods, segregates chromatin interactions emanating from adjacent TF footprints, and uncovers multiway interactions involving different TFs. Altogether, Footprint-C results suggest that rich regulatory modes of TF may underlie both local residence and distal chromatin interactions, in terms of TF identity, valency, and conformational configuration. Efficiency of Hi-C methods is restrained by usage of enzymes irrelevant to TF mediated chromatin interactions. Here, the authors present Footprint-C for constructing chromatin contact maps upon genuine TF footprints and reveal a regulatory lexicon of TF local residence and long-range interactions.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that can cause difficulty with communication and social interactions as well as complicated family dynamics. Digital health interventions can reduce treatment costs and promote healthy lifestyle changes. These therapies can be adjunctive or replace traditional treatments. However, issues with cooperation and compliance prevent preschool patients with ASD from applying these tools. In this open-label, randomized controlled trial, we developed a nonwearable digital therapy called virtual reality–incorporated cognitive behavioral therapy (VR-CBT). The aim of this study was to assess the adjunctive function of VR-CBT by comparing the effects of VR-CBT plus learning style profile (LSP) intervention with those of LSP-only intervention in preschool children with ASD. This trial was performed in China on 78 preschool children (age 3-6 years, IQ>70) diagnosed with ASD who were randomized to receive a 20-week VR-CBT plus LSP intervention (intervention group, 39/78, 50%) or LSP intervention only (control group, 39/78, 50%). The primary outcome was the change of scores from baseline to week 20, assessed by using the parent-rated Autism Behavior Checklist (ABC). Secondary outcomes included the Childhood Autism Rating Scale (CARS), Attention-Deficit/Hyperactivity Disorder Rating Scale-IV (ADHD-RS-IV), and behavioral performance data (accuracy and reaction time) in go/no-go tasks. All primary and secondary outcomes were analyzed in the intention-to-treat population. After the intervention, there was an intervention effect on total ABC (β=–5.528; P<.001) and CARS scores (β=–1.365; P=.02). A similar trend was observed in the ABC subscales: sensory (β=–1.133; P=.047), relating (β=–1.512; P=.03), body and object use (β=–1.211; P=.03), and social and self-help (β=–1.593; P=.03). The intervention also showed statistically significant effects in improving behavioral performance (go/no-go task, accuracy, β=2.923; P=.04). Moreover, a significant improvement of ADHD hyperactivity-impulsivity symptoms was observed in 53 children with comorbid ADHD based on ADHD-RS-IV (β=–1.269; P=.02). No statistically significant intervention effect was detected in the language subscale of ABC (β=–.080; P=.83). Intervention group girls had larger improvements in ABC subscales, that is, sensory and body and object use and in the CARS score and accuracy of go/no-go task (all P<.05) than the control group girls. Statistically significant intervention effects could be observed in hyperactivity-impulsivity symptoms in the intervention group boys with comorbid ADHD compared with those in the control group boys (β=–1.333; P=.03). We found potentially positive effects of nonwearable digital therapy plus LSP on core symptoms associated with ASD, leading to a modest improvement in the function of sensory, motor, and response inhibition, while reducing impulsivity and hyperactivity in preschoolers with both ASD and ADHD. VR-CBT was found to be an effective and feasible adjunctive digital tool.

As one of the key components of wind turbines, gearboxes are under complex alternating loads for a long time, and the safety and reliability of the whole machine are often affected by the failure of internal gears and bearings. Aiming at the difficulty of optimizing the parameters of wind turbine gearbox fault detection models based on extreme random forest, a fault detection model with extreme random forest optimized by the improved butterfly optimization algorithm (IBOA-ERF) is proposed. The algebraic sum of the false alarm rate and the missing alarm rate of the fault detection model is constructed as the fitness function, and the initial position and position update strategy of the individual are improved. A chaotic mapping strategy is introduced to replace the original population initialization method to enhance the randomness of the initial population distribution. An adaptive inertia weight factor is proposed, combined with the landmark operator of the pigeon swarm optimization algorithm to update the population position iteration equation to speed up the convergence speed and improve the diversity and robustness of the butterfly optimization algorithm. The dynamic switching method of local and global search stages is adopted to achieve dynamic balance between global exploration and local search, and to avoid falling into local optima. The ERF fault detection model is trained, and the improved butterfly optimization algorithm is used to obtain optimal parameters to achieve fast response of the proposed model with good robustness and generalization under high-dimensional data. The experimental results show that, compared with other optimization algorithms, the proposed fault detection method of wind turbine gearboxes has a lower false alarm rate and missing alarm rate.