Identification and characterization of a prokaryotic 6-4 photolyase from Synechococcus elongatus with a deazariboflavin antenna chromophore.

Synechococcus elongatus, formerly known as Anacystis nidulans, is a representative species of cyanobacteria. It is also a model organism for the study of photoreactivation, which can be fully photoreactivated even after receiving high UV doses. However, for a long time, only one photolyase was found in S. elongatus that is only able to photorepair UV induced cyclobutane pyrimidine dimers (CPDs) in DNA. Here, we characterize another photolyase in S. elongatus, which belongs to iron-sulfur bacterial cryptochromes and photolyases (FeS-BCP), a subtype of prokaryotic 6-4 photolyases. This photolyase was named SePhrB that could efficiently photorepair 6-4 photoproducts in DNA. Chemical analyses revealed that SePhrB contains a catalytic FAD cofactor and an iron-sulfur cluster. All of previously reported FeS-BCPs contain 6,7-dimethyl-8-ribityllumazine (DMRL) as their antenna chromophores. Here, we first demonstrated that SePhrB possesses 7,8-didemethyl-8-hydroxy-5-deazariboflavin (8-HDF) as an antenna chromophore. Nevertheless, SePhrB could be photoreduced without external electron donors. After being photoreduced, the reduced FAD cofactor in SePhrB was extremely stable against air oxidation. These results suggest that FeS-BCPs are more diverse than expected which deserve further investigation.

A meta-analysis of the consequences of cognitive training on the cognitive function of aged mild cognitive impairment patients.

Cognitive training has gained popularity as a means to aid older adults with mild cognitive impairment (MCI), a transitional phase between normal ageing and Alzheimer's disease (AD). MCI represents a critical and potentially reversible state that can either improve or progress to full-blown dementia. This study aims to evaluate the impact of cognitive training on cognitive function in aged patients with MCI. PubMed, Embase, Medline, Web of Science, China National Knowledge Infrastructure (CNKI), and Wanfang databases were systematically retrieved from inception until May 2024. We rigorously applied the risk-of-bias methodology recommended by the Cochrane Handbook to assess the quality of the included studies. After two rounds of screening and removing duplicates, a total of 2685 articles were initially identified, from which 28 met the inclusion criteria. The meta-analysis included 28 randomised controlled trials with 1960 participants. In this meta-analysis, Review Manager 5.4 was used for statistical analysis. Findings revealed that cognitive training significantly improved the global cognitive function in aged MCI patients, as evidenced by the results of the Montreal Cognitive Assessment (standard mean difference (SMD) = 3.26; 95% CI, 2.69-3.82; P < 0.00001) and Mini-Mental State Examination (SMD = 2.27; 95% CI, 1.52-3.01; P < 0.00001). The beneficial effects of cognitive training interventions were consistent regardless of duration, including periods of 2 months or less (SMD = 1.94; 95% CI, 1.25-2.63; P < 0.00001), 2 to 6 months (SMD = 2.53; 95% CI, 1.52-3.53; P < 0.00001), and over 6 months (SMD = 4.12; 95% CI, 0.97-7.27; P = 0.01). The analysis indicates that cognitive training significantly benefits overall cognitive function, delayed memory, orientation, attention, and language skills in aged patients with MCI. Furthermore, cognitive training interventions are effective in enhancing cognitive function, irrespective of their duration.

Efficacy and safety of Extrapleural block in patients with coronary artery disease after thoracoscopic surgery.

OBJECTIVE: This study aimed to investigate the efficacy and safety of extrapleural block (EPB) application in patients with coronary artery disease after thoracoscopic surgery. METHODS: Patients with typical symptoms of angina and myocardial ischemia who underwent thoracoscopic surgery at our institution between December 2018 and December 2020 were screened for eligibility and they received paravertebral blocking (PVB), EPB, and patient-controlled intravenous analgesia (PCIA). Visual analog scale (VAS) scores were used to assess the analgesic effect and safety outcomes included heart rate, incidence of postoperative rescue analgesics, cardiac complications, and adverse reactions such as nausea and vomiting. RESULTS: In total, 76 patients (age: 66.5 [61.3, 71] years; male: 63.2%) were eligible, including the PVB group (n = 22), EPB group (n = 25), and PVIA group (n = 29) with comparable baseline characteristics. There was a significantly higher proportion of patients with a VAS score of 1 in the EPB group compared with the other groups at 4 h (88.0% vs. 10.3% for PCIA and 45.5% for PVB; p < .001) and 6 h after the surgery (32.0% vs. 3.4% for PCIA and 13.6% for PVB; p = .012). The preoperative heart rate in the EPB group (81 [71, 94] beats/min) was slightly higher than those in the PVB (76 [70, 85] beats/min) and PCIA groups (76 [69, 84 beats/min]) but without significant difference (p = .193). There was no significant difference in the incidence of rescue analgesia, adverse events, and cardiac complications among the three groups (p = .296, .808, and .669, respectively.) CONCLUSION: Compared with PVB and PCIA, the EPB could more effectively relieve acute pain after thoracoscopic surgery in patients with coronary artery disease and offer comparable safety benefits in the management of postoperative heart rate, adverse events, and cardiac complications.

Distribution and phytotoxicity of soil labile aluminum fractions and aluminum species in soil water extracts and their effects on tall fescue.

Soil acidification can alter the biogeochemistry of ecosystems and adversely affect biota; however, there are still many debates about the toxicity of aluminum (Al) fractions and Al species in soil:water extracts to plants. In this study, five crude soils with different pH values (4.92-8.51) were collected, seeded with tall fescue and grown in rhizosphere boxes for 120 days. Then, soil properties, labile Al fractions and Al species in soil:water extracts were determined, and their toxicities to plants were analyzed. Our study showed that a stable exchangeable Al fraction (ExAl) pool exists and is supplied by other labile Al fractions. Dissolution of Al from adsorbed hydroxyl-Al fraction (HyAl) and organic-Al fraction (OrAl) may play important roles in soil Al toxicity, as HyAl and OrAl account for major parts of soil labile Al. Additionally, Al3+ and mononuclear hydroxyl-Al species in soil:water extracts have few effects to plants. Nevertheless, high negative correlations were found between Al-F- complexes and tall fescue biomass, indicating their toxicity in the natural soil environment. Thus, in many cases, Al3+ toxicity should not be emphasized because of its lower activity in soil water extracts. Moreover, toxicities of AlF3(aq) and AlF4- to plants should be emphasized, because they have been confirmed in soil water extracts in this study.

Connectivity strength-weighted sparse group representation-based brain network construction for MCI classification.

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l1 -norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a "connectivity strength-weighted sparse group constraint." In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. Hum Brain Mapp 38:2370-2383, 2017. © 2017 Wiley Periodicals, Inc.

Hyperspectral imagery super-resolution by compressive sensing inspired dictionary learning and spatial-spectral regularization.

Due to the instrumental and imaging optics limitations, it is difficult to acquire high spatial resolution hyperspectral imagery (HSI). Super-resolution (SR) imagery aims at inferring high quality images of a given scene from degraded versions of the same scene. This paper proposes a novel hyperspectral imagery super-resolution (HSI-SR) method via dictionary learning and spatial-spectral regularization. The main contributions of this paper are twofold. First, inspired by the compressive sensing (CS) framework, for learning the high resolution dictionary, we encourage stronger sparsity on image patches and promote smaller coherence between the learned dictionary and sensing matrix. Thus, a sparsity and incoherence restricted dictionary learning method is proposed to achieve higher efficiency sparse representation. Second, a variational regularization model combing a spatial sparsity regularization term and a new local spectral similarity preserving term is proposed to integrate the spectral and spatial-contextual information of the HSI. Experimental results show that the proposed method can effectively recover spatial information and better preserve spectral information. The high spatial resolution HSI reconstructed by the proposed method outperforms reconstructed results by other well-known methods in terms of both objective measurements and visual evaluation.

Unsupervised Hyperspectral and Multispectral Image Blind Fusion Based on Deep Tucker Decomposition Network With Spatial-Spectral Manifold Learning.

Hyperspectral image (HSI) and multispectral image (MSI) fusion aims to generate high spectral and spatial resolution hyperspectral image (HR-HSI) by fusing high-resolution multispectral image (HR-MSI) and low-resolution hyperspectral image (LR-HSI). However, existing fusion methods encounter challenges such as unknown degradation parameters, and incomplete exploitation of the correlation between high-dimensional structures and deep image features. To overcome these issues, in this article, an unsupervised blind fusion method for LR-HSI and HR-MSI based on Tucker decomposition and spatial-spectral manifold learning (DTDNML) is proposed. We design a novel deep Tucker decomposition network that maps LR-HSI and HR-MSI into a consistent feature space, achieving reconstruction through decoders with shared parameters. To better exploit and fuse spatial-spectral features in the data, we design a core tensor fusion network (CTFN) that incorporates a spatial-spectral attention mechanism for aligning and fusing features at different scales. Furthermore, to enhance the capacity to capture global information, a Laplacian-based spatial-spectral manifold constraint is introduced in shared-decoders. Sufficient experiments have validated that this method enhances the accuracy and efficiency of hyperspectral and multispectral fusion on different remote sensing datasets. The source code is available at https://github.com/Shawn-H-Wang/DTDNML.

Bayesian Nonlocal Patch Tensor Factorization for Hyperspectral Image Super-Resolution.

The synthesis of high-resolution (HR) hyperspectral image (HSI) by fusing a low-resolution HSI with a corresponding HR multispectral image has emerged as a prevalent HSI super-resolution (HSR) scheme. Recent researches have revealed that tensor analysis is an emerging tool for HSR. However, most off-the-shelf tensor-based HSR algorithms tend to encounter challenges in rank determination and modeling capacity. To address these issues, we construct nonlocal patch tensors (NPTs) and characterize low-rank structures with coupled Bayesian tensor factorization. It is worth emphasizing that the intrinsic global spectral correlation and nonlocal spatial similarity can be simultaneously explored under the proposed model. Moreover, benefiting from the technique of automatic relevance determination, we propose a hierarchical probabilistic framework based on Canonical Polyadic (CP) factorization, which incorporates a sparsity-inducing prior over the underlying factor matrices. We further develop an effective expectation-maximization-type optimization scheme for framework estimation. In contrast to existing works, the proposed model can infer the latent CP rank of NPT adaptively without tuning parameters. Extensive experiments on synthesized and real datasets illustrate the intrinsic capability of our model in rank determination as well as its superiority in fusion performance.

A regularization perspective based theoretical analysis for adversarial robustness of deep spiking neural networks.

Spiking Neural Network (SNN) has been recognized as the third generation of neural networks. Conventionally, a SNN can be converted from a pre-trained Artificial Neural Network (ANN) with less computation and memory than training from scratch. But, these converted SNNs are vulnerable to adversarial attacks. Numerical experiments demonstrate that the SNN trained by optimizing the loss function will be more adversarial robust, but the theoretical analysis for the mechanism of robustness is lacking. In this paper, we provide a theoretical explanation by analyzing the expected risk function. Starting by modeling the stochastic process introduced by the Poisson encoder, we prove that there is a positive semidefinite regularizer. Perhaps surprisingly, this regularizer can make the gradients of the output with respect to input closer to zero, thus resulting in inherent robustness against adversarial attacks. Extensive experiments on the CIFAR10 and CIFAR100 datasets support our point of view. For example, we find that the sum of squares of the gradients of the converted SNNs is 13∼160 times that of the trained SNNs. And, the smaller the sum of the squares of the gradients, the smaller the degradation of accuracy under adversarial attack.

Network-Pharmacology-Based Study on Active Phytochemicals and Molecular Mechanism of Cnidii Fructus in Treating Colorectal Cancer.

AIMS: Cnidii Fructus (CF) is known for its antibacterial, anti-inflammatory, and antitumor properties, as well as its activities against kidney deficiency and impotence. In this study, we aimed to explore the anti-CRC cancer effect and molecular mechanism of CF via network pharmacology and in vitro antitumor experiments. METHODS: Network pharmacology was used to investigate the anti-CRC mechanism of CF. First, a series of databases was used to screen the active phytochemical targets and anti-CRC core targets. Then, the GO and KEGG pathways were analyzed to predict possible mechanisms. Molecular docking analysis explore core targets-phytochemicals interactions. In vitro antitumor experiments were carried on verifying anti-CRC mechanism of CF. RESULTS: In this study, 20 active ingredient targets and 50 intersecting targets were analyzed by Cytoscape software 3.9.1 to obtain the core genes and phytochemicals. Then, the GO and KEGG pathways of 50 intersecting targets were analyzed to predict possible mechanisms. The results from GO and KEGG indicated that CF has significant antitumor efficacy, which involves many signaling pathways, such as PI3K/AKT and p53. The five core targets and five core phytochemicals were screened for molecular docking to show protein-ligand interactions. According to the results of molecular docking, the compound O-acetylcolumbianetin was selected for the anti- CRC functional verification in vitro. MTT assay showed that O-acetylcolumbianetin significantly inhibited the proliferation of colorectal HCT116 cells in a time- and quantity-dependent manner. O-acetylcolumbianetin can promote the expression of CASP3 protein, induce HCT116 cells apoptosis, thus exert anti-CRC effect. CONCLUSION: This study preliminarily verified the anti-CRC effect and molecular mechanism of CF and provided a reference for Traditional Chinese Medicine anti-tumor subsequent research.

Long-term synergic removal performance of N, P, and CuO nanoparticles in constructed wetlands along with temporal record of Cu pollution in substrate-biofilm.

With continued exposure to CuO nanoparticles (NPs) which were toxic to organisms, the performance of wastewater treatment facility might be affected. In present study, the feasibility of constructed wetlands (CWs) for wastewater treatment containing CuO NPs and common pollutants was comprehensively explored. It was found that CWs removed 98.80-99.84% CuO NPs and 90.91-91.83% COD within 300 days. However, N and P removals were affected to varying degrees by CuO NPs. N removal was inhibited only by 0.5 mg/L CuO NPs with 19.75% decreases on the mean from day 200-300. P removal was reduced by 3.80-50.75% and 1.92-7.19% under exposure of 0.5 and 5 mg/L CuO NPs throughout the experiment. Moreover, CuO NPs changed the adsorption potential of P and ammonium-N on sand-biofilm. Cu concentrations in spatial distribution decreased, while they in temporal distribution increased from 36.94 to 97.78 µg/g and from 70.92 to 282.66 µg/g at middle sand layer exposed to 0.5 and 5 mg/L CuO NPs. Mass balance model showed that substrate-biofilm was main pollutant sink for CuO NPs, N, and P. The minor Cu was absorbed by plants exposed to 0.5 and 5 mg/L CuO NPs, which decreased N by 53.40% and 18.51%，and P by 52.35% and 21.62%. Sequencing analysis indicated that CuO NPs also altered spatial microbial community. N-degrading bacteria (Rhodanobacter, Thauera, Nitrospira) changed differently, while phosphate accumulation organisms (Acinetobacter, Pseudomonas, Microlunatus) reduced. Overall, the negative effects of CuO NPs on N and P removal should be noted when CWs as ecological technologies are used to treat CuO NPs-containing wastewater.

Exploring the mechanism of atherosclerosis and the intervention of traditional Chinese medicine combined with mesenchymal stem cells based on inflammatory targets.

Atherosclerosis (AS) is a chronic inflammatory vascular disease, which is the common pathological basis of cardiovascular and cerebrovascular diseases. The immune inflammatory response throughout the course of AS has been evidenced by studies, in which a large number of immune cells and inflammatory factors play a crucial role in the pathogenesis of AS. The inflammation related to AS is mainly mediated by inflammatory cytokines (IL-1ß, IL-6, IL-18, TNF-α, hs-CRP, SAA), inflammatory enzymes (Lp-PLA2, sPLA2-IIA, MMPs), and inflammatory signaling pathways (P38 MAPK signaling pathway, NF-κB signaling pathway, TLR2/4 signaling pathway). It is involved in the pathophysiological process of AS, and the degree of inflammation measured by it can be used to evaluate the risk of progression of AS plaque instability. In recent years, traditional Chinese medicine (TCM) has shown the advantage of minimal side effects in immune regulation and has made some progress in the prevention and treatment of AS. Mesenchymal stem cells (MSCs), as self-renewal, highly differentiated, and pluripotent stem cells with anti-inflammatory properties and immune regulation, have been widely used for AS treatment. They also play an important inflammation-immune regulatory function in AS. Notably, in terms of regulating immune cells and inflammatory factors, compared with TCM and its compound, the combination therapy has obvious anti-inflammatory advantages over the use of MSCs alone. It is an important means to further improve the efficacy of AS and provides a new way for the prevention and treatment of AS.

Comparison of eco-improvement on constructed wetlands with nano zero valent iron introduction under different levels of PFOA stress: Perspectives on plant, microbe, and PFOA removal.

The ecological hazards of perfluoro octanoic acid (PFOA, a typical perfluoroalkyl substances) have been continually reported in constructed wetlands (CWs) for wastewater treatment. In present study, nano zero valent iron (nZVI) was adopted to alleviate PFOA stress at different levels (1 and 10 mg/L) in CWs. It was revealed that the effects of nZVI on specific ecological parameters varied at different PFOA dosages. PFOA influenced plant photosynthetic and antioxidant parameters with significant concentration-dependence. NZVI addition caused more obvious promotion of chlorophyll (25.30-31.84 %) and reduction of catalase (172.64 %) and malondialdehyde (83.01 %) with 10 mg/L PFOA exposure. For microbe, nZVI was prone to stimulate enzyme activities under 1 mg/L PFOA, in which the relative activity of dehydrogenase, urease, phosphatase, and four nitrogen cycling enzymes increased by 86.25-375.56 %, 43.10-71.16 %, 1.52-29.38 %, and 4.49-315.18 %. However, nZVI caused more abundant of functional bacteria (like nitrifying bacteria and phosphorus-accumulating organisms) and function genes (like amoA, hao, and ppx) with PFOA at 10 mg/L. On the whole, changes in bacterial community confirmed the enhancement potential of nZVI on ammonium and phosphorous removal. PFOA removal at 10 mg/L was higher compared to 1 mg/L, resulting from higher abundance of class Gammaproteobacteria, and nZVI addition further contributed to the highest removal efficiency (73.54 %). This study provided evidence on nZVI as a possible manner for optimizing eco-function in CWs with PFOA stress at different levels.

All-Dielectric Gratings with High-Quality Structural Colors.

We present a dual-layer hafnium dioxide (HfO2) grating capable of full-color modulation in the visible spectrum by leveraging the magnetic dipole resonance induced by the lower-layer HfO2 grating, while the upper-layer HfO2 grating serves as a refractive index matching layer to effectively suppress high-order Mie resonances at shorter wavelengths. The HfO2/HfO2 grating exhibits a significantly larger distribution area in the CIE 1931 chromaticity diagram compared to the HfO2 grating. Furthermore, the structural color saturation closely approximates that of monochromatic light. Under varying background refractive index environments, this structure consistently exhibits high-quality structural color. However, the hue of the structural color undergoes alterations. When the polarization angle is below 20°, the saturation of the acquired structural color remains remarkably consistent. However, exceeding 20° results in a significant degradation in the quality of the structural color. This study demonstrates the promising potential for diverse applications, encompassing fields such as imaging and displays.

On Adaptive Learning Framework for Deep Weighted Sparse Autoencoder: A Multiobjective Evolutionary Algorithm.

In this article, an adaptive learning framework is established for a deep weighted sparse autoencoder (AE) by resorting to the multiobjective evolutionary algorithm (MOEA). The weighted sparsity is introduced to facilitate the design of the varying degrees of the sparsity constraints imposed on the hidden units of the AE. The MOEA is exploited to adaptively seek appropriate hyperparameters, where the divide-and-conquer strategy is implemented to enhance the MOEA's performance in the context of deep neural networks. Moreover, a sharing scheme is proposed to further reduce the time complexity of the learning process at the slight expense of the learning precision. It is shown via extensive experiments that the established adaptive learning framework is effective, where different sparse models are utilized to demonstrate the generality of the proposed results. Then, the generality of the proposed framework is examined on the convolutional AE and VGG-16 network. Finally, the developed framework is applied to the blind image quantity assessment that illustrates the applicability of the established algorithms.

Reducing Complications and Enhancing the Functional Outcome of Total Hip Arthroplasty without Increasing Operation Time by Repairing Posterolateral Soft Tissues.

Total hip arthroplasty (THA) surgeries are increasing annually. Despite continuous improvements in the surgical technique and prosthetic design, there is still no consensus on whether it is beneficial to reconstruct the posterolateral soft tissue. This paper systematically reviews randomized controlled trials (RCTs) addressing the efficacy and safety of posterolateral soft tissue during total hip replacement to provide evidence-based guidance for clinical practice. We searched PubMed, EMBASE, Cochrane Library, CNKI, and Wanfang databases for RCTs. Experimental results show that repair of the posterolateral soft tissue can reduce complications and improve the function of total hip arthroplasty without increasing operative time.

Evaluating the immediate and delayed effects of psychological need thwarting of online teaching on Chinese primary and middle school teachers' psychological well-being.

Recent studies on the effects of mandatory online teaching, resulting from the COVID-19 pandemic, have widely reported low levels of satisfaction, unwillingness to continue online teaching, and negative impacts on the psychological well-being of teachers. Emerging research has highlighted the potential role of psychological need thwarting (PNT), in terms of autonomy, competence, and relatedness thwarting, resulting from online teaching. The aim of this study was to evaluate the immediate and delayed (longitudinal) effects of PNT of online teaching on teachers' well-being (including distress and burnout), intention to continue online teaching, and job satisfaction. Moreover, data collected from both cross-sectional and longitudinal surveys allowed for a systematic validation of an important instrument in the field of teacher psychology, the Psychological Need Thwarting Scale of Online Teaching (PNTSOT), in terms of longitudinal reliability and validity. The data reveal the usefulness of the construct of PNT in terms predicting and explaining teachers' willingness to continue using online teaching as well as the degree of burnout after a period of 2 months, such that PNT is positively associated with burnout and negatively associated with willingness to continue online teaching. As such, the PNTSOT is recommended for future research evaluating the long-term psychological, affective, and intentional outcomes stemming from teachers' PNT. Moreover, based on our findings that the impact from PNT of online teaching is persistent and long-term, we suggest that school leaders provide flexible and sustained professional development, model respectful and adaptive leadership, and create opportunities for mastery for the development of community of practice that can mitigate the thwarting of teachers' autonomy, competence, and relatedness during times of uncertainty. Additionally, in terms of the psychometric properties of the PNTSOT instrument, our empirical findings demonstrate internal reliability, test-retest reliability, measurement invariance, and criterion validity (concurrent and predictive) based on cross-sectional and longitudinal data.

Human induced-T-to-natural killer cells have potent anti-tumour activities.

BACKGROUND: Adoptive cell therapy (ACT) is a particularly promising area of cancer immunotherapy, engineered T and NK cells that express chimeric antigen receptors (CAR) are being explored for treating hematopoietic malignancies but exhibit limited clinical benefits for solid tumour patients, successful cellular immunotherapy of solid tumors demands new strategies. METHODS: Inactivation of BCL11B were performed by CRISPR/Cas9 in human T cells. Immunophenotypic and transcriptional profiles of sgBCL11B T cells were characterized by cytometer and transcriptomics, respectively. sgBCL11B T cells are further engineered with chimeric antigen receptor. Anti-tumor activity of ITNK or CAR-ITNK cells were evaluated in preclinical and clinical studies. RESULTS: We report that inactivation of BCL11B in human CD8+ and CD4+ T cells induced their reprogramming into induced T-to-natural killer cells (ITNKs). ITNKs contained a diverse TCR repertoire; downregulated T cell-associated genes such as TCF7 and LEF1; and expressed high levels of NK cell lineage-associated genes. ITNKs and chimeric antigen receptor (CAR)-transduced ITNKs selectively lysed a variety of cancer cells in culture and suppressed the growth of solid tumors in xenograft models. In a preliminary clinical study, autologous administration of ITNKs in patients with advanced solid tumors was well tolerated, and tumor stabilization was seen in six out nine patients, with one partial remission. CONCLUSIONS: The novel ITNKs thus may be a promising novel cell source for cancer immunotherapy. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03882840 . Registered 20 March 2019-Retrospectively registered.

Simultaneous Video Stabilization and Rolling Shutter Removal.

Due to the delay in the row-wise exposure and the lack of stable support when a photographer holds a CMOS camera, video jitter and rolling shutter distortion are closely coupled degradations in the captured videos. However, previous methods have rarely considered both phenomena and usually treat them separately, with stabilization approaches that are unable to handle the rolling shutter effect and rolling shutter removal algorithms that are incapable of addressing motion shake. To tackle this problem, we propose a novel method that simultaneously stabilizes and rectifies a rolling shutter shaky video. The key issue is to estimate both inter-frame motion and intra-frame motion. Specifically, for each pair of adjacent frames, we first estimate a set of spatially variant inter-frame motions using a neighbor-motion-aware local motion model, where the classical mesh-based model is improved by introducing a new constraint to enhance the neighbor motion consistency. Then, different from other 2D rolling shutter removal methods that assume the pixels in the same row have a single intra-frame motion, we build a novel mesh-based intra-frame motion calculation model to cope with the depth variation in a mesh row and obtain more faithful estimation results. Finally, temporal and spatial motion constraints and an adaptive weight assignment strategy are considered together to generate the optimal warping transformations for different motion situations. Experimental results demonstrate the effectiveness and superiority of the proposed method when compared with other state-of-the-art methods.