Fusion Function Research Articles

Comparison of binocular visual quality in six treatment protocols for bilateral cataract surgery with presbyopia correction: a prospective two-center single-blinded cohort study

Objective To compare the postoperative binocular visual quality in six treatment protocols for bilateral age-related cataract surgery with presbyopia correction for clinical decisions. Materials and methods In this prospective two-center single-blinded cohort study, participants from North or South China who underwent bilateral phacoemulsification and intraocular lens implantation were divided into six protocols: monovision, diffractive bifocal, mixed, refractive bifocal, trifocal, and micro-monovision extended range of vision (EROV). Binocular visual quality was evaluated at 3 months postoperatively, including binocular uncorrected full-range visual acuity, binocular defocus curves (depth of focus [DoF] and area under the curve [AUC]), binocular visual function (fusion function and stereopsis), binocular subjective spectacle independence rates, visual analog scale (VAS) of overall satisfaction, 25-item visual function questionnaire (VFQ-25), and binocular dysphotopsia symptoms. Results Of the 300 enrolled patients, 272 (90.7%; 544 eyes) were analyzed. The trifocal protocol showed excellent binocular full-range visual acuity and the best performance for most DoFs and AUCs. The monovision protocol presented the worst binocular visual quality in most perspectives, especially in convergence, distance, and near stereopsis (p < 0.001). The full-range subjective spectacle independence rates were sorted from highest to lowest as follows: trifocal (84.8%), refractive bifocal (80.9%), EROV (80.0%), mixed (73.3%), diffractive bifocal (65.2%), and monovision (32.6%) protocols, with no statistically significant differences between the former five protocols (p > 0.05). The EROV protocol achieved the highest VAS and VFQ-25 scores. The incidence of postoperative binocular dysphotopsia symptoms was comparable in all protocols. Conclusions The trifocal protocol showed the best performance, and the monovision protocol presented the worst performance in most perspectives of binocular visual quality for presbyopia correction. The refractive bifocal, mixed, or EROV protocols can provide an approximate performance as a trifocal protocol. Ophthalmologists can customize therapies using different protocols.

Anemoside B4 attenuates RANKL-induced osteoclastogenesis by upregulating Nrf2 and dampens ovariectomy-induced bone loss

Increased numbers and functional overactivity of osteoclasts are the pathological basis for bone loss diseases such as osteoporosis, which are characterized by cortical bone thinning, decreased trabecular bone quantity, and reduced bone mineral density. Effective inhibition of osteoclast formation and bone resorption are important means of treating such skeletal diseases. Anemoside B4 (AB4), the main active component of Pulsatilla chinensis, possesses a wide range of anti-inflammatory and immunoregulatory effects. However, its effect and mechanism in osteoclast differentiation remain unclear. In this study, we found through tartrate-resistant acidic phosphatase (TRAcP) staining and immunofluorescence staining that AB4 inhibited the differentiation, fusion, and bone-resorption functions of osteoclasts induced by receptor activator of nuclear factor κB ligand (RANKL) in vitro. Additionally, real time PCR (RT-qPCR) and western blot analysis showed AB4 downregulated the expression of osteoclast marker genes, including Nfatc1, Fos, and Ctsk, while upregulating Nrf2 expression. AB4 (5 mg/kg) alleviated bone loss in ovariectomized mice by inhibiting osteoclast formation. Furthermore, the knockout of Nrf2 weakened the inhibitory effects of AB4 on osteoclast formation and related gene expression. In summary, the results suggest AB4 can inhibit osteoclast differentiation and function by activating Nrf2 and indicate AB4 may be a candidate drug for osteoporosis.

A health condition assessment and prediction method of Francis turbine units using heterogeneous signal fusion and graph-driven health benchmark model

To ensure the safety and efficiency of hydroelectric power generation, the health condition assessment and prediction (HCAP) of Francis turbine units (FTUs) have been widely concerned. To this end, some data-driven methods based on health benchmark model (HBM) and performance deterioration index (PDI) have been proposed, but there are still some shortcomings: 1) Only one type of signal is used for FTU health monitoring and assessment, which cannot fully represent the deterioration of the system. 2) The establishment of HBM only focuses on the time sequence dependences of signals, while ignoring the inter-correlations between signals. 3) PDI based on linear difference measurement cannot integrate heterogeneous signal representations to fully assess FTU status. In this paper, a HCAP method of FTUs using heterogeneous signal fusion and graph-driven HBM is proposed. First, the multivariate data of health status are transformed into spatial-temporal graphs by establishing connections between similar signals. Furthermore, a hybrid neural network-based HBM is designed to excavate the spatial-temporal dependence relationships existing in these graphs, and learn the mapping relationship between working condition parameters and monitoring signals. Finally, Mahalanobis distance between the heterogeneous signals predicted by HBM and the measured signals in the degraded status is calculated, and the comprehensive PDI for HCAP tasks is obtained by the designed heterogeneous signal fusion function. Verification experiments show that the proposed HCAP method effectively assesses FTU deterioration degree earlier with a higher sensitivity.

New Classifier Ensemble and Fuzzy Community Detection Methods Using POP Choquet-like Integrals

Among various data analysis methods, classifier ensemble (data classification) and community network detection (data clustering) have aroused the interest of many scholars. The maximum operator, as the fusion function, was always used to fuse the results of the base algorithms in the classifier ensemble and the membership degree of nodes to classes in the fuzzy community. It is vital to use generalized fusion functions in ensemble and community applications. Since the Pseudo overlap function and the Choquet-like integrals are two new fusion functions, they can be combined as a more generalized fusion function. Along this line, this paper presents new classifier ensemble and fuzzy community detection methods using a pseudo overlap pair (POP) Choquet-like integral (expressed as a fraction). First, the pseudo overlap function pair is proposed to replace the product operator of the Choquet integral. Then, the POP Choquet-like integrals are defined to perform the combinatorial step of ensembles of classifiers and to generalize the GN modularity for the fuzzy community network. Finally, two new algorithms are designed for experiments, and some computational experiments with other algorithms show the importance of POP Choquet-like integrals. All of the experimental results show that our algorithms are practical.

SegForest: A Segmentation Model for Remote Sensing Images

The accurate estimation of forest area is of paramount importance for carbon sequestration projects, ecotourism and ecological safety. Forest segmentation using remote sensing images is a crucial technique for estimating forest area. However, due to the complex features, such as the size, shape and color of forest plots, traditional segmentation algorithms struggle to achieve accurate segmentation. Therefore, this study proposes a remote sensing image forest segmentation model named SegForest. To enhance the model, we introduce three new modules: multi-feature fusion (MFF), multi-scale multi-decoder (MSMD) and weight-based cross entropy loss function (WBCE) in the decoder. In addition, we propose two new forest remote sensing image segmentation binary datasets: DeepGlobe-Forest and Loveda-Forest. SegForest is compared with multiple advanced segmentation algorithms on these two datasets. On the DeepGlobe-Forest dataset, SegForest achieves a mean intersection over union (mIoU) of 83.39% and a mean accuracy (mAcc) of 91.00%. On the Loveda-Forest dataset, SegForest achieves a mIoU of 73.71% and a mAcc of 85.06%. These metrics outperform other algorithms in the comparative experiments. The experimental results of this paper demonstrate that by incorporating the three proposed modules, the SegForest model has strong performance and generalization ability in forest remote sensing image segmentation tasks.

Characteristics and risk factors for spontaneous and postoperative consecutive exotropia in children with esotropia.

To investigate the risk factors for the development of consecutive exotropia (CXT) by comparing patients with spontaneous or postoperative CXT during follow-up with another group of patients who had no deviation or less than 10 prism dioptre (PD) esotropia. In this retrospective cohort study, 6 patients with spontaneous CXT (group A), 13 patients with postoperative CXT (group B), and 39 patients with no exotropia (group C) were enrolled. Probable risk factors for CXT were evaluated among the groups. Kruskal-Wallis H test was used to determine if any significant differences were present among the groups. Fisher's exact test or Mann-Whitney U test was used for univariate analyses to compare differences between case groups or between case and control groups. The Bonferroni method was used to conduct multiple comparisons. The follow-up period of spontaneous CXT patients was significantly longer than that of postoperative CXT and nonconsecutive exotropia patients (p = 0.035 and p < 0.001, respectively). The interval between alignment and CXT onset in spontaneous CXT patients was slightly longer than that in postoperative CXT patients, but not significantly difference (6.50 vs. 5.00 years, p = 0.072). Vertical deviation was associated with a high risk of postoperative CXT (p = 0.001). Most [38 (97.44%)] nonconsecutive exotropia patients had fusion; conversely, the absence of fusion function (p < 0.001) as well as stereoacuity (p = 0.029) were associated with a high risk of CXT. Vertical deviation and poor binocular function are strongly associated with a high risk of CXT. Children with spontaneous CXT are highly recommended to be followed-up long-term, while they maintain long-term ocular alignment before developing consecutive exotropia from comitant esotropia (CE).

Piroctone olamine disrupts mitochondrial dynamics in glioma cells through the PI3K/AKT pathway

To investigate the growth-inhibitory and pro-apoptotic effects of piroctone olamine (PO) on glioma cells and explore the underlying mechanism. Human glioma cell lines U251 and U373 were treated with PO and the changes in cell proliferation were detected using CCK-8 assay and EdU assay. Clone formation assay and flow cytometry were used to examine the changes in clone formation ability and apoptosis of the treated cells. Mitochondrial membrane potential of the cells and morphological changes of the mitochondria were detected using JC-1 staining and a fluorescence probe, respectively. The expressions of mitochondrial fission protein DRP1 and the fusion protein OPA1 were determined with Western blotting. Transcriptome sequencing and differential gene enrichment analysis was performed, and the expression levels of PI3K, AKT and p-AKT in the treated cells were verified using Western blotting. CCK-8 assay showed that PO significantly inhibited the proliferation of U251 and U373 cells in a time- and dose-dependent manner (P < 0.001). EdU test showed that the proliferative activity of PO-treated cells was significantly decreased, and the number of cell colonies also decreased significantly (P < 0.01). PO treatment significantly increased apoptotic rates (P < 0.01), decreased mitochondrial membrane potential and caused obvious changes in mitochondrial morphology of the cells. Pathway enrichment analysis showed that the down-regulated genes were significantly enriched in the PI3K/AKT pathway, which was verified by Western blotting showing significantly down-regulated expression levels of PI3K, AKT and p-AKT in PO-treated cells (P < 0.05). PO interferes with mitochondrial fusion and fission function through the PI3K/AKT pathway, thereby inhibiting the proliferation and increasing apoptosis of glioma cells.

Enhanced stability of the SARS CoV-2 spike glycoprotein following modification of an alanine cavity in the protein core.

The spike (S) glycoprotein of SARS CoV-2 is the target of neutralizing antibodies (NAbs) that are crucial for vaccine effectiveness. The S1 subunit binds ACE2 while the S2 subunit mediates virus-cell membrane fusion. S2 is a class I fusion glycoprotein subunit and contains a central coiled coil that acts as a scaffold for the conformational changes associated with fusion function. The coiled coil of S2 is unusual in that the 3-4 repeat of inward-facing positions are mostly occupied by polar residues that mediate few inter-helical contacts in the prefusion trimer. We examined how insertion of bulkier hydrophobic residues (Val, Leu, Ile, Phe) to fill a cavity next to Ala1016 and Ala1020 in the 3-4 repeat affects the stability and antigenicity of S trimers. Substitution of Ala1016 with bulkier hydrophobic residues in the context of a prefusion-stabilized S trimer, S2P-FHA, was associated with increased thermal stability. S glycoprotein membrane fusion function was retained with Ala1016/Ala1020 cavity-filling mutations associated with improved recombinant S2P-FHA thermostability, however 2 mutants, A1016L and A1016V/A1020I, lacked ability to mediate entry of S-HIV-1 pseudoparticles into 293-ACE2 cells. When assessed as immunogens, two thermostable S2P-FHA mutants derived from the ancestral isolate, A1016L (16L) and A1016V/A1020I (VI) elicited neutralizing antibody with 50%-inhibitory dilutions (ID50s) in the range 2,700-5,110 for ancestral and Delta-derived viruses, and 210-1,744 for Omicron BA.1. The antigens elicited antibody specificities directed to the receptor-binding domain (RBD), N-terminal domain (NTD), fusion peptide and stem region of S2. The VI mutation enabled the production of intrinsically stable Omicron BA.1 and Omicron BA.4/5 S2P-FHA-like ectodomain oligomers in the absence of an external trimerization motif (T4 foldon), thus representing an alternative approach for stabilizing oligomeric S glycoprotein vaccines.

A Dominant-Negative Mutant of ANXA7 Impairs Calcium Signaling and Enhances the Proliferation of Prostate Cancer Cells by Downregulating the IP3 Receptor and the PI3K/mTOR Pathway.

Annexin A7/ANXA7 is a calcium-dependent membrane fusion protein with tumor suppressor gene (TSG) properties, which is located on chromosome 10q21 and is thought to function in the regulation of calcium homeostasis and tumorigenesis. However, whether the molecular mechanisms for tumor suppression are also involved in the calcium- and phospholipid-binding properties of ANXA7 remain to be elucidated. We hypothesized that the 4 C-terminal endonexin-fold repeats in ANXA7 (GX(X)GT), which are contained within each of the 4 annexin repeats with 70 amino acids, are responsible for both calcium- and GTP-dependent membrane fusion and the tumor suppressor function. Here, we identified a dominant-negative triple mutant (DNTM/DN-ANXA7J) that dramatically suppressed the ability of ANXA7 to fuse with artificial membranes while also inhibiting tumor cell proliferation and sensitizing cells to cell death. We also found that the [DNTM]ANA7 mutation altered the membrane fusion rate and the ability to bind calcium and phospholipids. In addition, in prostate cancer cells, our data revealed that variations in phosphatidylserine exposure, membrane permeabilization, and cellular apoptosis were associated with differential IP3 receptor expression and PI3K/AKT/mTOR modulation. In conclusion, we discovered a triple mutant of ANXA7, associated with calcium and phospholipid binding, which leads to the loss of several essential functions of ANXA7 pertinent to tumor protection and highlights the importance of the calcium signaling and membrane fusion functions of ANXA7 for preventing tumorigenesis.

IPos-5G: Indoor Positioning via Commercial 5G NR CSI

The fifth-generation (5G) networks have been massively deployed in commerce. The new features introduced by 5G networks are beneficial to wireless positioning. In this study, the performance of indoor positioning with commercial 5G new radio (NR) signals is investigated, and the channel state information (CSI) extracted from the downlink synchronization signal block is utilized. Considering the limited 5G NR base station (known as gNodeB) is hearable indoors, the fingerprint method is used, and an indoor positioning system termed iPos-5G is developed. The system consists of four components. Firstly, a module of quality control is applied for CSI preprocessing. Secondly, an unsupervised deep-autoencoder network is utilized to reconstruct CSI features. Thirdly, by supervised learning, a radial basis function is improved to optimize the probability model for similarity calculations. Finally, an amplitude-phase probability fusion function is proposed for positioning by weighting the coordinates of reference points. To verify the effectiveness of iPos-5G, indoor field tests are carried out in the scenarios of an office and a corridor. The test results show that iPos-5G achieves mean absolute errors of 2.14 and 2.81 m and standard deviation of the errors of 1.07 and 1.66 m, which outperforms the compared CSI fingerprint methods in terms of positioning accuracy and stability.

A framework for generalized monotonicity of fusion functions

The relaxation of the property of monotonicity is a trend in the theory of aggregation and fusion functions and several generalized forms of monotonicity have been introduced, most of which are based on the notion of directional monotonicity. In this paper, we propose a general framework for generalized monotonicity that encompasses the different forms of monotonicity that we can find in the literature. Additionally, we introduce various new forms of monotonicity that are not based on directional monotonicity. Specifically, we introduce dilative monotonicity, which requires that the function increases when the inputs have increased by a common factor, and a general form of monotonicity that is dependent on a function T and a subset of the domain Z. This two new generalized monotonicities are the basis to propose a set of different forms of monotonicity. We study the particularities of each of the new proposals and their links to the previous relaxed forms of monotonicity. We conclude that the introduction of dilative monotonicity complements the conditions of weak monotonicity for fusion functions and that (T,Z)-monotonicity yields a condition that is slightly stronger than weak monotonicity. Finally, we present an application of the introduced notions of monotonicity in sentiment analysis.

Event-triggered distributed cooperative extended Kalman filter based on formation estimation

The existing distributed filters focus on the consensus estimation of multi-sensor information, but ignore the cooperative characteristics of formation targets, resulting in information loss and limited estimation accuracy. To address the problem of formation target tracking, a novel event-triggered distributed cooperative extended Kalman filter based on formation estimation is proposed, which improves the estimation performance without additional information by mining formation characteristics. Different from previous works, the formation estimation based on the best-matched model is developed to establish the connection between different targets, by which the unknown formation evolution can be modeled in the sense of minimum innovation. Considering the communication topology and the formation detection, an event-triggered mechanism is constructed to detect formation status reliably and reduce the communication burden. Afterwards, a novel distributed filter is designed by employing the multi-agent consensus theory. The cooperative term can be considered as a pseudo measurement, which forces local estimations to reach a consensus. By virtue of the variance-constrained approach, the filter gain is derived by minimizing the upper bound of the estimation error covariance. The proposed filter can also realize optimal estimation in the absence of cooperation and the multi-sensor fusion function for a single target. Moreover, the exponential stochastic boundness of the proposed filter is analyzed based on the stochastic stability theory. Finally, numerical experiments are carried out in three different simulation scenarios, and simulation results show that estimation errors of the proposed filter are significantly decreased compared to existing filters. The advantages of the proposed filter become more apparent as the number of formation members increases or the information quality of formation members improves.

Transcriptomic Differences Underlying the Activin-A Induced Large Osteoclast Formation in Both Healthy Control and Fibrodysplasia Ossificans Progressiva Osteoclasts

Fibrodysplasia Ossificans Progressiva (FOP) is a very rare genetic disease characterized by progressive heterotopic ossification (HO) of soft tissues, leading to immobility and premature death. FOP is caused by a mutation in the Activin receptor Type 1 (ACVR1) gene, resulting in altered responsiveness to Activin-A. We recently revealed that Activin-A induces fewer, but larger and more active, osteoclasts regardless of the presence of the mutated ACVR1 receptor. The underlying mechanism of Activin-A-induced changes in osteoclastogenesis at the gene expression level remains unknown. Transcriptomic changes induced by Activin-A during osteoclast formation from healthy controls and patient-derived CD14-positive monocytes were studied using RNA sequencing. CD14-positive monocytes from six FOP patients and six age- and sex-matched healthy controls were differentiated into osteoclasts in the absence or presence of Activin-A. RNA samples were isolated after 14 days of culturing and analyzed by RNA sequencing. Non-supervised principal component analysis (PCA) showed that samples from the same culture conditions (e.g., without or with Activin-A) tended to cluster, indicating that the variability induced by Activin-A treatment was larger than the variability between the control and FOP samples. RNA sequencing analysis revealed 1480 differentially expressed genes induced by Activin-A in healthy control and FOP osteoclasts with p(adj) < 0.01 and a Log2 fold change of ≥±2. Pathway and gene ontology enrichment analysis revealed several significantly enriched pathways for genes upregulated by Activin-A that could be linked to the differentiation or function of osteoclasts, cell fusion or inflammation. Our data showed that Activin-A has a substantial effect on gene expression during osteoclast formation and that this effect occurred regardless of the presence of the mutated ACVR1 receptor causing FOP.

Identification of Insulator Contamination Status Based on Multi-spectral Image Fusion and Radial Basis Function Neural Network

Insulator contamination monitoring is an important way to avoid insulator contamination and maintain insulation performance. In order to ensure power supply and achieve contactless detection of insulator contamination status, a method is proposed in this paper to identify insulator contamination status by adopting infrared, ultraviolet, and visible multi-spectral image information fusion. An insulator with different contamination states in a number of substations is taken as the research object in this paper. The image segmentation is performed by using the seed region growth method to extract the infrared, ultraviolet and visible features of the insulator surface, and the radial basis function neural network learning algorithm is used to classify and decompose and fuse the images according to their different local area energies. The comparison of the recognition rates using infrared and ultraviolet features with those fused shows that the method has significant advantages and provides a new method for the detection of insulator contamination status.

Melatonin-mediated malic enzyme 2 orchestrates mitochondrial fusion and respiratory functions to promote odontoblastic differentiation during tooth development.

Tooth development is a complex process that is tightly controlled by circadian rhythm. Melatonin (MT) is a major hormonal regulator of the circadian rhythm, and influences dentin formation and odontoblastic differentiation during tooth development; however, the underlying mechanism remains elusive. This study investigated how MT regulates odontoblastic differentiation, with a special focus on its regulation of mitochondrial dynamics. In rat dental papilla cells (DPCs), we found that MT promotes odontoblastic differentiation concurrently with enhanced mitochondrial fusion, while disruption of mitochondrial fusion by depleting optic atrophy 1 (OPA1) impairs MT-mediated differentiation and mitochondrial respiratory functions. Through RNA sequencing, we discovered that MT significantly upregulated malic enzyme 2 (ME2), a mitochondrial NAD(P)+ -dependent enzyme, and identified ME2 as a critical MT downstream effector that orchestrates odontoblastic differentiation, mitochondrial fusion, and respiration functions. By detecting the spatiotemporal expression of ME2 in developing tooth germs, and using tooth germ reconstituted organoids, we also provided in vivo and ex vivo evidence that ME2 promotes dentin formation, indicating a possible involvement of ME2 in MT-modulated tooth development. Collectively, our findings offer novel understandings regarding the molecular mechanism by which MT affects cell differentiation and organogenesis, meanwhile, the critical role of ME2 in MT-regulated mitochondrial functions is also highlighted.

Directional monotonicity of multidimensional fusion functions with respect to admissible orders

The notion of directional monotonicity emerged as a relaxation of the monotonicity condition of aggregation functions. As the extension of aggregation functions to fuse more complex information than numeric data, directional monotonicity was extended to the framework of multidimensional data, with respect to the product order, which is a partial order. In this work, we present the notion of admissible order for multidimensional data and we define the concept of directional monotonicity for multidimensional fusion functions with respect to an admissible order. Moreover, we study the main properties of directionally monotone functions in this new context. We conclude that, while some of the properties are still valid (e.g. the set of directions of increasingness is still closed under convex combinations), some of the main ones no longer hold (e.g. there does not exist a finite set of directions that characterize standard monotonicity in terms of directional monotonicity).

Unsupervised Fault Detection Driven by Multivariate Time Series for Aeroengines

Aeroengine fault detection is extremely significant to ensure flight safety. However, it still poses a vast challenge due to the complexity of aeroengine systems. In this paper, an unsupervised deep autoencoder (DAE) with dimension fusion function (DFF) method (DFF-DAE) was proposed to effectively detect aeroengine faults based on multivariate time-series data. The proposed method does not rely on mathematical models and expert experience to automatically extract the hierarchical representation features of raw data. The DFF-DAE has a three-layer autoencoder with its first encoding layer incorporating a DFF, which is developed for multivariate time-series fusion before encoding. A case study of aeroengine rolling bearing locking fault was conducted on the turbofan engine ground test data composed of vibration and rotational speed time-series data in variable working states. The results indicated that DFF-DAE succeeds in efficiently employing multivariate time-series data to perform accurate fault detection. Compared with other state-of-the-art methods, DFF-DAE yields superior predictive ability and detection accuracy. Furthermore, DFF-DAE provides application potential for online fault detection because it is effective in variable working states of the aeroengine.

Meta-path fusion based neural recommendation in heterogeneous information networks

As a powerful data modeling tool, Heterogeneous Information Network (HIN) has been successfully used in auxiliary information exploitation to boost recommendation performance. For HIN based recommendation, it is challenging to extract and fuse useful features of user preferences and item attributes under different semantic paths in HINs. Existing methods leverage a pre-defined fusion function to integrate different semantics for recommendation, which cannot characterize the complex nonlinear interactions between users and items. In this paper, we present a general framework named MNRec, short for Meta-path fusion based Neural Recommendation, to extract and fuse user and item embeddings under different meta-paths for recommendation. Under the framework, we propose an instantiation of MNRec with Multi-Layer Perceptron (MLP) structure. It consists of two major steps, i.e., meta-path based heterogeneous network embedding and deep learning based rating prediction. Concretely, appropriate meta-paths are first designed according to domain knowledge. Then the embeddings of users and items are obtained through a meta-path and commuting matrix based heterogeneous network embedding method. Finally, in light of the powerful nonlinear modeling capabilities of deep neural networks, the learned embeddings under different meta-paths are integrated into a two-pathway MLP structure for rating prediction. Experimental results on three real-world datasets demonstrate the superiority and effectiveness of MNRec compared with state-of-the-art baselines in rating prediction.

Lipid-mediated interplay between host restriction factor Serinc5 and HIV-1 ENV glycoprotein.

Human immunodeficiency virus (HIV-1) is a pathogenic retrovirus that continues to be a severe threat to public health. HIV-1 is an enveloped virus that enters and infects cells through cell-virus membrane fusion, mediated by HIV-1 envelope glycoprotein, Env.The host restriction factor, Serinc5, impairs this process. It reduces HIV infection rates by incorporating itself into the viral membrane and blocking fusion of the virus with the plasma membrane of uninfected cells. The exact restriction mechanism of Serinc5 remains unknown. Studies suggest that Serinc5 disrupts Env distribution at the membrane and inhibits the protein's fusion function. We hypothesize that Serinc5 acts on Env by altering the physical properties of the surrounding membrane. In this study, we evaluate the nature of the membrane properties affected by Serinc5. Applying the fluorescent probe FLIPPER-TR to measure membrane order in viral particles with and without Serinc5, we discovered that Serinc5 slightly increases lipid order. HIV-1 bearing Env with a truncated cytoplasmic tail (∆CT) avoids infection restriction by Serinc5. We used isolated giant plasma membrane vesicles (GPMVs) to quantify Serinc5 and Env partitioning into specific lipid domains. Our results indicate that Serinc5 and Env partition into the liquid-disordered lipid domains of the plasma membrane. Interestingly, mutant Env ∆CT showed different lipid phase preferences than Serinc5 and wild-type Env. We discuss how these different lipid interactions might affect entry of progeny viruses into cells by membrane fusion.

Single-Image Dehazing Based on Improved Bright Channel Prior and Dark Channel Prior

Single-image dehazing plays a significant preprocessing role in machine vision tasks. As the dark-channel-prior method will fail in the sky region of the image, resulting in inaccurately estimated parameters, and given the failure of many methods to address a large band of haze, we propose a simple yet effective method for single-image dehazing based on an improved bright prior and dark channel prior. First, we use the Otsu method by particle swarm optimization to divide the hazy image into sky regions and non-sky regions. Then, we use the improved bright channel prior and dark channel prior to estimate the parameters in the physical model. Second, we propose a weighted fusion function to efficiently fuse the parameters estimated by two priors. Finally, the clear image is restored through the physical model. Experiments illustrate that our method can solve the problem of the invalidation of the dark channel prior in the sky region well and achieve high-quality image restoration, especially for images with limited haze.