Underexposed Image Correction via Hybrid Priors Navigated Deep Propagation

Enhancing visual quality for underexposed images is an extensively concerning task that plays an important role in various areas of multimedia and computer vision. Most existing methods often fail to generate high-quality results with appropriate luminance and abundant details. To address these issues, we develop a novel framework, integrating both knowledge from physical principles and implicit distributions from data to address underexposed image correction. More concretely, we propose a new perspective to formulate this task as an energy-inspired model with advanced hybrid priors. A propagation procedure navigated by the hybrid priors is well designed for simultaneously propagating the reflectance and illumination toward desired results. We conduct extensive experiments to verify the necessity of integrating both underlying principles (i.e., with knowledge) and distributions (i.e., from data) as navigated deep propagation. Plenty of experimental results of underexposed image correction demonstrate that our proposed method performs favorably against the state-of-the-art methods on both subjective and objective assessments. In addition, we execute the task of face detection to further verify the naturalness and practical value of underexposed image correction. What is more, we apply our method to solve single-image haze removal whose experimental results further demonstrate our superiorities.

Efficient Representation and Optimization for TPMS-Based Porous Structures

In this approach, we present an efficient topology and geometry optimization of triply periodic minimal surfaces (TPMS) based porous shell structures, which can be represented, analyzed, optimized and stored directly using functions. The proposed framework is directly executed on functions instead of remeshing (tetrahedral/hexahedral), and this framework substantially improves the controllability and efficiency. Specifically, a valid TPMS-based porous shell structure is first constructed by function expressions. The porous shell permits continuous and smooth changes of geometry (shell thickness) and topology (porous period). The porous structures also inherit several of the advantageous properties of TPMS, such as smoothness, full connectivity (no closed hollows), and high controllability. Then, the problem of filling an object's interior region with porous shell can be formulated into a constraint optimization problem with two control parameter functions. Finally, an efficient topology and geometry optimization scheme is presented to obtain optimized scale-varying porous shell structures. In contrast to traditional heuristic methods for TPMS, our work directly optimize both the topology and geometry of TPMS-based structures. Various experiments have shown that our proposed porous structures have obvious advantages in terms of efficiency and effectiveness.

Microstructure and mechanical properties of dissimilar NiTi and 304 stainless steel joints produced by ultrasonic welding

Superelastic NiTi alloy and 304 stainless steel (304 SS) were joined with a Cu interlayer by ultrasonic spot welding (USW) using different welding energy inputs. The surface morphology, interfacial microstructure, mechanical properties, and fracture mechanisms of the dissimilar NiTi/304 SS USWed joints were studied. The results showed that the surface oxidation intensified with increasing ultrasonic welding energy due to mutual rubbing between tools and sheets. The weld interface microstructure exhibited voids or unbonded zones at low energy inputs, while an intimate contact was established at the joining interface when applying a higher energy input of 750 J. With increasing energy input to 750 J, the weld interface shows two interfaces due to the behavior of plastic flow of Cu interlayer. The lap-shear load of the joints first increased, achieving a maximum value of ∼690 N at an energy input of 750 J, and then decreased with further increase in welding energy. Interfacial failure was observed at NiTi/Cu interface at all energy inputs, and no intermetallic compounds were found on the fracture surfaces of both the NiTi/Cu and Cu/304 SS interfaces.

Clinical study of the clinical characteristics and prognosis of 1219 cases of endometrial cancer with lymph node metastasis

To analyze the clinical characteristics and prognosis of endometrial cancer patients with lymph node metastasis to provide a reference for lymphadenectomy in endometrial cancer. The data used in this study were extracted from a tertiary hospital in Guangxi, China based on the hospital information system. 1219 patients with endometrial malignancy who were treated in our hospital. The lymph node metastasis rate was 9.8%. The metastasis rate of the abdominal aorta + pelvic lymph nodes (56.7%) was significantly higher than that of the pelvic (24.2%) or para-aortic (19.2%) lymph nodes alone. The proportion of postmenopausal patients with lymph node metastasis was higher than that of premenopausal patients. The proportion of patients with lymph node metastasis with vaginal and uterus involvement, HPV detection, Thinprep Cytologic Testresults, CRP level <10 ug/mL, G3 tumor grade, postoperative pathology indicating cervical invasion, lymphovascular invasion, and muscular infiltration depth > 1/2 was higher than that of patients without lymph node metastasis. The proportion of endometrial cancer patients with lymph node metastasis with CA125 ≥ 35 U/ml was higher than that of those with CA125 < 35 U/ml. The lymph node-positive rate is related to tissue differentiation, lymphangitic infiltration, cervical invasion, muscle infiltration depth > 1/2, and CA125 level. The metastasis rate of pelvic and para-aortic lymph nodes is higher than that of pelvic lymph nodes or para-aortic lymph nodes alone. There was no statistically significant difference in the overall survival rate among the three groups.

[An optimized protocol of meniscus cell extraction for single-cell RNA sequencing]

OBJECTIVE

To optimize the protocol of meniscus cell extraction to enhance the efficiency of cell suspension preparation and maintain a high cell viability for single-cell RNA sequencing.

METHODS

We compared the efficiency of the routine cell extraction methods (short-time digestion and long-time digestion) and the optimized protocol for obtaining meniscus cell suspensions by evaluating the cell number obtained and the cell viability. Single-cell RNA sequencing datasets were analyzed to evaluate the stability of the cell suspension prepared using the optimized protocol. The reliability of the optimized protocol was assessed by comparing the single-cell RNA sequencing dataset obtained by the optimized protocol with published single-cell RNA sequencing datasets of the meniscus.

RESULTS

The optimized protocol harvested a greater number of cells (over 1×10⁵) than the routine protocols. The cell suspension prepared with the optimized protocol showed a cell viability higher than 80%, the highest among the 3 methods. Analysis of single-cell RNA sequencing datasets showed that the ratio of the mitochondrial genes was below 20% in over 80% of the cells. CD34⁺ cells, MCAM⁺ cells and COL1A1⁺ cells were identified in the datasets. Comparison with the publish datasets showed that the optimized protocol was capable of harvesting COL3A1⁺, COL1A1⁺, MYLK⁺, BMP2⁺, CD93⁺ and CDK1⁺ cells.

CONCLUSION

Single-cell suspension prepared from the meniscus can be stably obtained using the optimized protocol for single-cell RNA sequencing using the 10× Genomics platform.

[Surface modification of titanium implant with hBMP-2/hIGF-1 for promoting biocompatibility and osteogenesis]

OBJECTIVE

To prepare the human bone morphogenetic protein-2(hBMP-2)/human insulin-like growth factor-1(hIGF-1)coating titanium(Ti)and assess its performance as a dental implant material.

METHODS

hBMP-2 and hIGF-1 were coated to the smooth surface of a Ti plate, and its efficacy for promoting bone formation and bone integration was compared with a pristine Ti plate.The surface characteristics of the metal samples were evaluated using scanning electron microscope (SEM) and by contact angle measurement.MG63 cells were seeded on the surface of the Ti plates, and MTT assay and alizarin red staining was used to examine the cell proliferation and formation of calcified nodules, respectively.Alkaline phosphatase (ALP)secretion of the cells was examined with ELISA, and cellular expressions of osteocalcin and osteopontin were detected with Western blotting for assessing osteogenesis.

RESULTS

SEM examination showed that the surface of Ti with hBMP-2 and hIGF-1 coating presented with a radial pattern resembling snowflakes.The contact angles of non-coated Ti, hBMP-2-coated Ti, hIGF-1-coated, and hBMP-2/-hIGF-1-coated Ti samples were 83.2°, 54°, 56° and 54°, respectively.Compared with the non-coated Ti plate, the surface-modified Ti samples showed a significantly smaller contact angle (P=0.032, 0.029, and 0.028), indicating a good hydrophilicity of the samples.MTT assay showed that MG63 cells grew well on the surface of the coated Ti plates.The hBMP-2/IGF-1 coating significantly induced cellular secretion of ALP(P=0.021, 0.014)and obviously promoted osteogenesis of MG63 cells (P < 0.05).Western blotting results showed that hBMP-2/IGF-1 coating significantly enhanced the expressions of osteocalcin and osteopontin in the seeded cells (P < 0.05).

CONCLUSION

hBMP-2 and hIGF-1 coating of Ti material can promote osteogenesis of the cells seeded on its surface to improve the performance of such Ti material as dental implants.

Novelty Detection and Online Learning for Chunk Data Streams

Datastream analysis aims at extracting discriminative information for classification from continuously incoming samples. It is extremely challenging to detect novel data while incrementally updating the model efficiently and stably, especially for high-dimensional and/or large-scale data streams. This paper proposes an efficient framework for novelty detection and incremental learning for unlabeled chunk data streams. First, an accurate factorization-free kernel discriminative analysis (FKDA-X) is put forward through solving a linear system in the kernel space. FKDA-X produces a Reproducing Kernel Hilbert Space (RKHS), in which unlabeled chunk data can be detected and classified by multiple known-classes in a single decision model with a deterministic classification boundary. Moreover, based on FKDA-X, two optimal methods FKDA-CX and FKDA-C are proposed. FKDA-CX uses the micro-cluster centers of original data as the input to achieve excellent performance in novelty detection. FKDA-C and incremental FKDA-C (IFKDA-C) using the class centers of original data as their input have extremely fast speed in online learning. Theoretical analysis and experimental validation on under-sampled and large-scale real-world datasets demonstrate that the proposed algorithms make it possible to learn unlabeled chunk data streams with significantly lower computational costs and comparable accuracies than the state-of-the-art approaches.

Location-Aware and Regularization-Adaptive Correlation Filters for Robust Visual Tracking

Correlation filter (CF) has recently been widely used for visual tracking. The estimation of the search window and the filter-learning strategies is the key component of the CF trackers. Nevertheless, prevalent CF models separately address these issues in heuristic manners. The commonly used CF models directly set the estimated location in the previous frame as the search center for the current one. Moreover, these models usually rely on simple and fixed regularization for filter learning, and thus, their performance is compromised by the search window size and optimization heuristics. To break these limits, this article proposes a location-aware and regularization-adaptive CF (LRCF) for robust visual tracking. LRCF establishes a novel bilevel optimization model to address simultaneously the location-estimation and filter-training problems. We prove that our bilevel formulation can successfully obtain a globally converged CF and the corresponding object location in a collaborative manner. Moreover, based on the LRCF framework, we design two trackers named LRCF-S and LRCF-SA and a series of comparisons to prove the flexibility and effectiveness of the LRCF framework. Extensive experiments on different challenging benchmark data sets demonstrate that our LRCF trackers perform favorably against the state-of-the-art methods in practice.

A Bilevel Integrated Model With Data-Driven Layer Ensemble for Multi-Modality Image Fusion

Image fusion plays a critical role in a variety of vision and learning applications. Current fusion approaches are designed to characterize source images, focusing on a certain type of fusion task while limited in a wide scenario. Moreover, other fusion strategies (i.e., weighted averaging, choose-max) cannot undertake the challenging fusion tasks, which furthermore leads to undesirable artifacts facilely emerged in their fused results. In this paper, we propose a generic image fusion method with a bilevel optimization paradigm, targeting on multi-modality image fusion tasks. Corresponding alternation optimization is conducted on certain components decoupled from source images. Via adaptive integration weight maps, we are able to get the flexible fusion strategy across multi-modality images. We successfully applied it to three types of image fusion tasks, including infrared and visible, computed tomography and magnetic resonance imaging, and magnetic resonance imaging and single-photon emission computed tomography image fusion. Results highlight the performance and versatility of our approach from both quantitative and qualitative aspects.

A Deep Framework Assembling Principled Modules for CS-MRI: Unrolling Perspective, Convergence Behaviors, and Practical Modeling

Compressed Sensing Magnetic Resonance Imaging (CS-MRI) significantly accelerates MR acquisition at a sampling rate much lower than the Nyquist criterion. A major challenge for CS-MRI lies in solving the severely ill-posed inverse problem to reconstruct aliasing-free MR images from the sparse k -space data. Conventional methods typically optimize an energy function, producing restoration of high quality, but their iterative numerical solvers unavoidably bring extremely large time consumption. Recent deep techniques provide fast restoration by either learning direct prediction to final reconstruction or plugging learned modules into the energy optimizer. Nevertheless, these data-driven predictors cannot guarantee the reconstruction following principled constraints underlying the domain knowledge so that the reliability of their reconstruction process is questionable. In this paper, we propose a deep framework assembling principled modules for CS-MRI that fuses learning strategy with the iterative solver of a conventional reconstruction energy. This framework embeds an optimal condition checking mechanism, fostering efficient and reliable reconstruction. We also apply the framework to three practical tasks, i.e., complex-valued data reconstruction, parallel imaging and reconstruction with Rician noise. Extensive experiments on both benchmark and manufacturer-testing images demonstrate that the proposed method reliably converges to the optimal solution more efficiently and accurately than the state-of-the-art in various scenarios.

On the Convergence of Learning-Based Iterative Methods for Nonconvex Inverse Problems

Numerous tasks at the core of statistics, learning and vision areas are specific cases of ill-posed inverse problems. Recently, learning-based (e.g., deep) iterative methods have been empirically shown to be useful for these problems. Nevertheless, integrating learnable structures into iterations is still a laborious process, which can only be guided by intuitions or empirical insights. Moreover, there is a lack of rigorous analysis about the convergence behaviors of these reimplemented iterations, and thus the significance of such methods is a little bit vague. This paper moves beyond these limits and proposes Flexible Iterative Modularization Algorithm (FIMA), a generic and provable paradigm for nonconvex inverse problems. Our theoretical analysis reveals that FIMA allows us to generate globally convergent trajectories for learning-based iterative methods. Meanwhile, the devised scheduling policies on flexible modules should also be beneficial for classical numerical methods in the nonconvex scenario. Extensive experiments on real applications verify the superiority of FIMA.

LncRNA INHBA-AS1 promotes cell growth, migration, and invasion of oral squamous cell carcinoma by sponging miR-143-3p

Since this article has been suspected of research misconduct and the corresponding authors did not respond to our request to prove originality of data and figures, "LncRNA INHBA-AS1 promotes cell growth, migration, and invasion of oral squamous cell carcinoma by sponging miR-143-3p, by W.-Q. Ma, J. Chen, W. Fang, X.-Q. Yang, A. Zhu, D. Zhang, H.-L. Zhong, B. Yang, Z. Luo, published in Eur Rev Med Pharmacol Sci 2020; 24 (4): 1821-1828-DOI: 10.26355/eurrev_202002_20360-PMID: 32141551" has been withdrawn. The Publisher apologizes for any inconvenience this may cause. https://www.europeanreview.org/article/20360.

Gender and age differences in the association between sleep characteristics and fasting glucose levels in Chinese adults

AIM

The present study examined the associations between night-time sleep duration, midday napping duration and bedtime, and fasting glucose levels, and whether or not such associations are dependent on gender and age.

METHODS

This study was a cross-sectional analysis of 172,901 adults aged≥40 years living in mainland China. Sleep duration was obtained by self-reports of bedtime at night, waking-up time the next morning and average napping duration at midday. Fasting plasma glucose (FPG)≥7.0mmol/L was defined as hyperglycaemia. Independent associations between night-time sleep duration, midday naptime duration and bedtime with hyperglycaemia were evaluated using regression models.

RESULTS

Compared with night-time sleep durations of 6-7.9h, both short (<6h) and long (≥8h) night-time sleep durations were significantly associated with an increased risk of hyperglycaemia in women [odds ratio (OR): 1.12, 95% confidence interval (CI): 1.01-1.29 and OR: 1.14, 95% CI: 1.08-1.21, respectively], and revealed a U-shaped distribution of risk in women and no significant association in men. Long midday nap durations (≥1h) were significantly but weakly associated with hyperglycaemia (OR: 1.04, 95% CI: 1.01-1.09) compared with no napping without interactions from gender or age, whereas the association between bedtime and fasting glucose levels did vary according to gender and age.

CONCLUSION

Night-time sleep duration, midday napping duration and bedtime were all independently associated with the risk of hyperglycaemia, and some of the associations between these sleep characteristics and hyperglycaemia were gender- and age-dependent.

Crystallographic and calorimetric analysis on Pleurotus ostreatus lectin and its sugar complexes - promiscuous binding driven by geometry

Carbohydrate recognition is established as a property of lectins and implicated in many functions including immunity and defense against pathogens. Many lectins are characterized and proposed for various applications owing to the above said recognition. The crystal structure of a lectin from Pleurotus ostreatus has been determined and shown to be calcium dependent. The overall structure is a tandem repeat of two β-jelly roll domains, a new fold for lectins. The calcium dependence of sugar binding is analyzed in-detail through isothermal titration calorimetry. The serendipitous observation of malonate and glycerol, the intentional N-Acetyl-D-galactosamine, D-Galactose and L-Rhamnose binding to Pleurotus ostreatus lectin by Ca²⁺ coordination revealed that the binding site is promiscuous. Among these sugars, Rhamnose binding found to be thermodynamically most favourable. In all these structures, a vicinal diol motif, one at axial and the other at equatorial positions could be established as a specific requirement for binding. Interestingly, when compared with other calcium mediated lectin structures; this geometric requirement is found conserved. This observation could lead to the conclusion that lectins are not 'molecule specific' but 'geometry specific' so that any molecule not necessarily a sugar may be recognized by this lectin if the geometry exists.

Knowledge-Driven Deep Unrolling for Robust Image Layer Separation

Single-image layer separation targets to decompose the observed image into two independent components in terms of different application demands. It is known that many vision and multimedia applications can be (re)formulated as a separation problem. Due to the fundamentally ill-posed natural of these separations, existing methods are inclined to investigate model priors on the separated components elaborately. Nevertheless, it is knotty to optimize the cost function with complicated model regularizations. Effectiveness is greatly conceded by the settled iteration mechanism, and the adaption cannot be guaranteed due to the poor data fitting. What is more, for a universal framework, the most taxing point is that one type of visual cue cannot be shared with different tasks. To partly overcome the weaknesses mentioned earlier, we delve into a generic optimization unrolling technique to incorporate deep architectures into iterations for adaptive image layer separation. First, we propose a general energy model with implicit priors, which is based on maximum a posterior, and employ the extensively accepted alternating direction method of multiplier to determine our elementary iteration mechanism. By unrolling with one general residual architecture prior and one task-specific prior, we attain a straightforward, flexible, and data-dependent image separation framework successfully. We apply our method to four different tasks, including single-image-rain streak removal, high-dynamic-range tone mapping, low-light image enhancement, and single-image reflection removal. Extensive experiments demonstrate that the proposed method is applicable to multiple tasks and outperforms the state of the arts by a large margin qualitatively and quantitatively.

Involvement of nitrogen in storage root growth and related gene expression in sweet potato (Ipomoea batatas)

Nitrogen (N) could affect storage root growth and development of sweet potato. To manage external N concentration fluctuations, plants have developed a wide range of strategies, such as growth changes and gene expression. Five sweet potato cultivars were used to analyse the functions of N in regulating storage root growth. Growth responses and physiological indicators were measured to determine the physiological changes regulated by different N concentrations. Expression profiles of related genes were analysed via microarray hybridization data and qRT-PCR analysis to reveal the molecular mechanisms of storage root growth regulated by different N concentrations. The growth responses and physiological indicators of the five cultivars were changed by N concentration. The root fresh weight of two of the sweet potato cultivars, SS19 and GS87, was higher under low N concentrations compared with the other cultivars. SS19 and GS87 were found to be having greater tolerance to low N concentration. The expression of N metabolism and storage root growth related genes was regulated by N concentration in sweet potato. These results reveal that N significantly regulated storage root growth. SS19 and GS87 were more tolerant to low N concentration and produced greater storage root yield (at 30 days). Furthermore, several N response genes were involved in both N metabolism and storage root growth.

Quantum Rotation Sensing with Dual Sagnac Interferometers in an Atom-Optical Waveguide

We describe a Sagnac interferometer suitable for rotation sensing, implemented using an atomic Bose-Einstein condensate confined in a harmonic magnetic trap. The atom wave packets are split and recombined by standing-wave Bragg lasers, and the trapping potential steers the packets along circular trajectories with a radius of 0.2 mm. Two conjugate interferometers are implemented simultaneously to provide common-mode rejection of noise and to isolate the rotation signal. With interference visibilities of about 50%, we achieve a rotation sensitivity comparable to Earth's rate in about 10 min of operation. Gyroscope operation was demonstrated by rotating the optical table on which the experiment was performed.