A hybrid demultiplexing strategy that improves performance and robustness of cell hashing.

Cell hashing, a nucleotide barcode-based method that allows users to pool multiple samples and demultiplex in downstream analysis, has gained widespread popularity in single-cell sequencing due to its compatibility, simplicity, and cost-effectiveness. Despite these advantages, the performance of this method remains unsatisfactory under certain circumstances, especially in experiments that have imbalanced sample sizes or use many hashtag antibodies. Here, we introduce a hybrid demultiplexing strategy that increases accuracy and cell recovery in multi-sample single-cell experiments. This approach correlates the results of cell hashing and genetic variant clustering, enabling precise and efficient cell identity determination without additional experimental costs or efforts. In addition, we developed HTOreader, a demultiplexing tool for cell hashing that improves the accuracy of cut-off calling by avoiding the dominance of negative signals in experiments with many hashtags or imbalanced sample sizes. When compared to existing methods using real-world datasets, this hybrid approach and HTOreader consistently generate reliable results with increased accuracy and cell recovery.

The polyextremophile Natranaerobius thermophilus adopts a dual adaptive strategy to long-term salinity stress, simultaneously accumulating compatible solutes and K.

The bacterium Natranaerobius thermophilus is an extremely halophilic alkalithermophile that can thrive under conditions of high salinity (3.3-3.9 M Na+), alkaline pH (9.5), and elevated temperature (53°C). To understand the molecular mechanisms of salt adaptation in N. thermophilus, it is essential to investigate the protein, mRNA, and key metabolite levels on a molecular basis. Based on proteome profiling of N. thermophilus under 3.1, 3.7, and 4.3 M Na+ conditions compared to 2.5 M Na+ condition, we discovered that a hybrid strategy, combining the "compatible solute" and "salt-in" mechanisms, was utilized for osmotic adjustment dur ing the long-term salinity adaptation of N. thermophilus. The mRNA level of key proteins and the intracellular content of compatible solutes and K+ support this conclusion. Specifically, N. thermophilus employs the glycine betaine ABC transporters (Opu and ProU families), Na+/solute symporters (SSS family), and glutamate and proline synthesis pathways to adapt to high salinity. The intracellular content of compatible solutes, including glycine betaine, glutamate, and proline, increases with rising salinity levels in N. thermophilus. Additionally, the upregulation of Na+/ K+/ H+ transporters facilitates the maintenance of intracellular K+ concentration, ensuring cellular ion homeostasis under varying salinities. Furthermore, N. thermophilus exhibits cytoplasmic acidification in response to high Na+ concentrations. The median isoelectric points of the upregulated proteins decrease with increasing salinity. Amino acid metabolism, carbohydrate and energy metabolism, membrane transport, and bacterial chemotaxis activities contribute to the adaptability of N. thermophilus under high salt stress. This study provides new data that support further elucidating the complex adaptation mechanisms of N. thermophilus under multiple extremes.IMPORTANCEThis study represents the first report of simultaneous utilization of two salt adaptation mechanisms within the Clostridia class in response to long-term salinity stress.

A Novel Hydrophobic Polyimide Film with Sag Structure Derived from Multi-Hybrid Strategy.

Polyimide (PI) film with hydrophilic greatly limits their application in the field of microelectronic device packaging. A novel hydrophobic PI film with sag structure and improved mechanical properties is prepared relying on the reaction between anhydride-terminated isocyanate-based polyimide (PIY) containing a seven-membered ring structure and the amino-terminated polyamide acid (PAA) via multi-hybrid strategy, this work named it as hybrid PI film and marked it as PI-PIY-X. PI-PIY-30 showed excellent hydrophobic properties, and the water contact angle could reach to 102°, which is 20% and 55% higher than simply PI film and PIY film, respectively. The water absorption is only 1.02%, with a decrease of 49% and 53% compared with PI and PIY. Due to that the degradation of seven-membered ring and generation of carbon dioxide led to the formation of sag structure, the size of sag structures is ≈16.84 and 534.55 nm for in-plane and out-plane direction, which are observed on surface of PI-PIY-30. Meanwhile, PI-PIY-30 possessed improved mechanical properties, and the tensile strength is 109.08 MPa, with 5% and more than 56% higher than that of pure PI and PIY film, showing greatly application prospects in the field of integrated circuit.

Synthesis of Oleanolic Acid-Dithiocarbamate Conjugates and Evaluation of Their Broad-Spectrum Antitumor Activities.

Efficient and mild synthetic routes for bioactive natural product derivatives are of current interest for drug discovery. Herein, on the basis of the pharmacophore hybrid strategy, we report a two-step protocol to obtain a series of structurally novel oleanolic acid (OA)-dithiocarbamate conjugates in mild conditions with high yields. Moreover, biological evaluations indicated that representative compound 3e exhibited the most potent and broad-spectrum antiproliferative effects against Panc1, A549, Hep3B, Huh-7, HT-29, and Hela cells with low cytotoxicity on normal cells. In terms of the IC50 values, these OA-dithiocarbamate conjugates were up to 30-fold more potent than the natural product OA. These compounds may be promising hit compounds for the development of novel anti-cancer drugs.

Hybrid imidazo[1,2-a]pyridine analogs as potent ATX inhibitors with concrete in vivo antifibrosis effect.

In recent years, small-molecule inhibitors targeting the autotaxin (ATX)/lysophosphatidic acid axis gradually brought excellent disease management benefits. Herein, a series of imidazo[1,2-a]pyridine compounds (1-11) were designed as ATX inhibitors through a hybrid strategy by combining the imidazo[1,2-a]pyridine skeleton in GLPG1690 and the benzyl carbamate moiety in PF-8380. As indicated by FS-3-based enzymatic assay, the carbamate derivatives revealed moderate to satisfying ATX inhibitory potency (IC50 = 23-343 nM). Subsequently, the carbamate linker was altered to a urea moiety (12-19) with the aim of retaining ATX inhibition and improving the druglikeness profile. The binding mode analysis all over the modification process well rationalized the leading activity of urea derivatives in an enzymatic assay. Following further structural optimization, the diethanolamine derivative 19 exerted an amazing inhibitory activity (IC50 = 3.98 nM) similar to the positive control GLPG1690 (IC50 = 3.72 nM) and PF-8380 (IC50 = 4.23 nM). Accordingly, 19 was tested directly for in vivo antifibrotic effects through a bleomycin model (H&E staining), in which 19 effectively alleviated lung structural damage and fibrosis at an oral dose of 20 and 60 mg/kg. Collectively, 19 qualified as a promising ATX inhibitor for potential application in fibrosis-relevant disease treatment.

Design, synthesis and anti-fibrosis evaluation of imidazo[1,2-a]pyridine derivatives as potent ATX inhibitors.

A series of imidazo[1,2-a]pyridine compounds bearing urea moiety (8-27) were designed, synthesized and evaluated for their ATX inhibitory activities in vitro by FS-3 based enzymatic assay. Delightfully, benzylamine derivatives (14-27) exhibited higher ATX inhibitory potency with IC50 value ranging from 1.72 to 497 nM superior to benzamide analogues (8-13). Remarkably, benzylamine derivative 20 bearing 4-hydroxypiperidine exerted an amazing inhibitory activity (IC50 = 1.72 nM) which exceeded the positive control GLPG1690 (IC50 = 2.90 nM). Simultaneously, the binding model of 20 with ATX was established which rationalized the well performance of 20 in enzymatic assay. Accordingly, further in vivo studies were carried out to evaluate direct anti-fibrotic effects of 20 through Masson staining. Notably, 20 effectively alleviated lung structural damage with fewer fibrotic lesions at an oral dose of 60 mg/kg, qualifying 20 as a promising ATX inhibitor for IPF treatment.

Beating the curse of dimension with accurate statistics for the Fokker-Planck equation in complex turbulent systems.

Solving the Fokker-Planck equation for high-dimensional complex dynamical systems is an important issue. Recently, the authors developed efficient statistically accurate algorithms for solving the Fokker-Planck equations associated with high-dimensional nonlinear turbulent dynamical systems with conditional Gaussian structures, which contain many strong non-Gaussian features such as intermittency and fat-tailed probability density functions (PDFs). The algorithms involve a hybrid strategy with a small number of samples [Formula: see text], where a conditional Gaussian mixture in a high-dimensional subspace via an extremely efficient parametric method is combined with a judicious Gaussian kernel density estimation in the remaining low-dimensional subspace. In this article, two effective strategies are developed and incorporated into these algorithms. The first strategy involves a judicious block decomposition of the conditional covariance matrix such that the evolutions of different blocks have no interactions, which allows an extremely efficient parallel computation due to the small size of each individual block. The second strategy exploits statistical symmetry for a further reduction of [Formula: see text] The resulting algorithms can efficiently solve the Fokker-Planck equation with strongly non-Gaussian PDFs in much higher dimensions even with orders in the millions and thus beat the curse of dimension. The algorithms are applied to a [Formula: see text]-dimensional stochastic coupled FitzHugh-Nagumo model for excitable media. An accurate recovery of both the transient and equilibrium non-Gaussian PDFs requires only [Formula: see text] samples! In addition, the block decomposition facilitates the algorithms to efficiently capture the distinct non-Gaussian features at different locations in a [Formula: see text]-dimensional two-layer inhomogeneous Lorenz 96 model, using only [Formula: see text] samples.

A Hybrid Path-Planning Strategy for Mobile Robots with Limited Sensor Capabilities.

This paper introduces a strategy for the path planning problem for platforms with limited sensor and processing capabilities. The proposed algorithm does not require any prior information but assumes that a mapping algorithm is used. If enough information is available, a global path planner finds sub-optimal collision-free paths within the known map. For the real time obstacle avoidance task, a simple and cost-efficient local planner is used, making the algorithm a hybrid global and local planning solution. The strategy was tested in a real, cluttered environment experiment using the Pioneer P3-DX and the Xbox 360 kinect sensor, to validate and evaluate the algorithm efficiency.

Conditional Gaussian Systems for Multiscale Nonlinear Stochastic Systems: Prediction, State Estimation and Uncertainty Quantification.

A conditional Gaussian framework for understanding and predicting complex multiscale nonlinear stochastic systems is developed. Despite the conditional Gaussianity, such systems are nevertheless highly nonlinear and are able to capture the non-Gaussian features of nature. The special structure of the system allows closed analytical formulae for solving the conditional statistics and is thus computationally efficient. A rich gallery of examples of conditional Gaussian systems are illustrated here, which includes data-driven physics-constrained nonlinear stochastic models, stochastically coupled reaction-diffusion models in neuroscience and ecology, and large-scale dynamical models in turbulence, fluids and geophysical flows. Making use of the conditional Gaussian structure, efficient statistically accurate algorithms involving a novel hybrid strategy for different subspaces, a judicious block decomposition and statistical symmetry are developed for solving the Fokker-Planck equation in large dimensions. The conditional Gaussian framework is also applied to develop extremely cheap multiscale data assimilation schemes, such as the stochastic superparameterization, which use particle filters to capture the non-Gaussian statistics on the large-scale part whose dimension is small whereas the statistics of the small-scale part are conditional Gaussian given the large-scale part. Other topics of the conditional Gaussian systems studied here include designing new parameter estimation schemes and understanding model errors.

Pathway and network analysis in proteomics.

Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics.

Endovascular embolization combined with anterior cervical corpectomy for treatment of cervical spinal dural arteriovenous fistula.

Background: The two main treatments for spinal dural arteriovenous fistula (SDAVF) include microsurgical occlusion or endovascular embolization (i.e., the latter alone has high recurrence rates). Here, we combined both strategies to treat/obliterate a cervical SDAVF more effectively. Case Description: A 34-year-old male presented with a marked decline in mental status attributed to an infratentorial subarachnoid hemorrhage. The left vertebral angiogram revealed a ruptured, low cervical SDAVF. He underwent successful occlusion of the spinal fistula utilizing super selective catheterization and endovascular embolization (i.e., utilizing Onyx-18 for the obliteration of target arteries). Due to significant SDAVF accompanying vessel recruitment/complex angioarchitecture, we additionally performed a C5 anterior corpectomy/fusion to afford direct access and complete surgical SDAVF occlusion. Three and 6 months later, repeated angiograms confirmed no recurrent or residual SDAVF. Conclusion: We successfully treated a low cervical SDAVF using a combination of endovascular embolization and direct surgical occlusion through an anterior C5 corpectomy with a fusion approach.

Anatomical Flow Diversion by Hybrid Strategy for Intractable Large Cerebral Aneurysms.

BACKGROUND AND IMPORTANCE: Flow diverters (FDs) provide curative endovascular treatment for wide-necked sidewall aneurysms. The efficacy of FDs for bifurcation or branching sidewall aneurysms is probably limited. We used anatomical flow diversion (AFD) for intractable large cerebral aneurysms. We report our experiences with AFD. METHODS: The concept of AFD is the transformation from the bifurcation or branching sidewall type to the nonbranching sidewall type. Linearization of the parent artery by stenting, intentional branch occlusion, and aneurysmal coil embolization were performed. Furthermore, bypass surgery is performed for patients intolerant to branch occlusions. We evaluated the clinical outcomes of intractable aneurysms treated with AFD. RESULTS: AFD was performed in seven unruptured large aneurysms. Aneurysmal locations were the top of the basilar artery (BA), BA-superior cerebellar artery (SCA), internal carotid artery (IC)-posterior communicating artery (PcomA), and IC terminal. The mean dome diameter was 17.0 ± 4.6 mm. Six patients underwent bypass surgery. The occluded branches were the PCA + SCA, PcomA, and anterior cerebral artery (ACA) A1. An FD was used in three patients and a neck bridge stent in four patients. No intraprocedural complications occurred. Two postprocedural ischemic complications occurred in one patient. Six (86%) patients demonstrated a modified Rankin Scale (mRS) 0 at the 3-month follow-up, and one with an ischemic complication showed an mRS 5. Complete occlusion of all aneurysms was maintained with a median follow-up duration of 60 months. CONCLUSION: AFD is useful for intractable large cerebral aneurysms with high curability, although safety verification is required.

Research on hybrid strategy Particle Swarm Optimization algorithm and its applications.

The increasing complexity and high-dimensional nature of real-world optimization problems necessitate the development of advanced optimization algorithms. Traditional Particle Swarm Optimization (PSO) often faces challenges such as local optima entrapment and slow convergence, limiting its effectiveness in complex tasks. This paper introduces a novel Hybrid Strategy Particle Swarm Optimization (HSPSO) algorithm, which integrates adaptive weight adjustment, reverse learning, Cauchy mutation, and the Hook-Jeeves strategy to enhance both global and local search capabilities. HSPSO is evaluated using CEC-2005 and CEC-2014 benchmark functions, demonstrating superior performance over standard PSO, Dynamic Adaptive Inertia Weight PSO (DAIW-PSO), Hummingbird Flight patterns PSO (HBF-PSO), Butterfly Optimization Algorithm (BOA), Ant Colony Optimization (ACO), and Firefly Algorithm (FA). Experimental results show that HSPSO achieves optimal results in terms of best fitness, average fitness, and stability. Additionally, HSPSO is applied to feature selection for the UCI Arrhythmia dataset, resulting in a high-accuracy classification model that outperforms traditional methods. These findings establish HSPSO as an effective solution for complex optimization and feature selection tasks.

The Influence of the Barriers of Hybrid Strategy on Strategic Competitive Priorities: Evidence from Oil Companies.

This study examines the impact of the barrier of adopting hybrid strategy on strategic performance using the oil sector in Iraq as a case. International oil companies consider various strategies in order to achieve superior performance. The procedure needs to overcome certain essential barriers for the adoption of the hybrid strategy that combines the cost leadership and differentiation strategy. The questionnaire was distributed online due to the COVID-19 pandemic that led to the closure of companies in the country. Out of the 537 questionnaires answered, 483 were used for further analysis which yielded usable response rate of 90%. The structural equation modeling results confirmed that the high costs of technologies, the priority of other external matters, inadequate industry regulation, insufficient supply, organizational capabilities, strategic capabilities, and financial capabilities are significantly related to strategic performance. The researchers recommend conducting an in-depth study of the phenomenon based on theoretical and empirical foundations, especially considering the relationship between the barriers of a hybrid strategy and strategic performance based on linear and non-compensatory relationships. This research sheds light on the barriers to adopting the hybrid strategy required by the oil sector as it relies on continuous production.

Drug-eluting stent and drug-coated balloon for the treatment of de novo diffuse coronary artery disease lesions: A retrospective case series study.

BACKGROUND: The hybrid strategy of a combination of drug-eluting stent (DES) and drug-coated balloon (DCB) is promising for the treatment of de novo diffuse coronary artery disease (CAD). HYPOTHESIS: To investigate the efficacy and functional results of hybrid strategy. METHODS: This case series study included patients treated with a hybrid approach for de novo diffuse CAD between February 2017 and November 2021. Postprocedural quantitative flow ratio (QFR) was used to evaluate the functional results. The primary endpoint was procedural success rate. The secondary endpoints were major adverse cardiovascular events (MACE) including cardiac death, myocardial infarction (MI) (including peri-procedural MI), and target vessel revascularization. RESULTS: A total of 109 patients with 114 lesions were treated. DES and DCB were commonly used in larger proximal segments and smaller distal segments, respectively. The mean QFR value was 0.9 ± 0.1 and 105 patients (96.3%) had values >0.8 in all the treated vessels. Procedural success was achieved in 106 (97.2%) patients. No cases of cardiac death were reported at a median follow-up of 19 months. Spontaneous MI occurred in three (2.8%) patients and target vessel revascularization in six (5.5%) patients. Estimated 2-year rate of MACE excluding peri-procedural MI was higher in the group with lower QFR value (12.1 ± 5.7% vs. 5.6 ± 4.4%, log-rank p = .035) (cut-off value 0.9). CONCLUSION: Hybrid strategy is a promising approach for the treatment of de novo diffuse CAD. Postprocedural QFR has some implications for prognosis and may be helpful in guiding this approach.

A hybrid-strategy-improved butterfly optimization algorithm applied to the node coverage problem of wireless sensor networks.

To increase the node coverage of wireless sensor networks (WSN) more effectively, in this paper, we propose a hybrid-strategy-improved butterfly optimization algorithm (H-BOA). First, we introduce Kent chaotic map to initialize the population to ensure a more uniform search space. Second, a new inertial weight modified from the Sigmoid function is introduced to balance the global and local search capacities. Third, we comprehensively use elite-fusion and elite-oriented local mutation strategies to raise the population diversity. Then, we introduce a perturbation based on the standard normal distribution to reduce the possibility of the algorithm falling into premature. Finally, the simulated annealing process is introduced to evaluate the solution's quality and improve the algorithm's ability, which is helpful to jump out of the local optimal value. Through numerous experiments of the international benchmark functions, the results show the performance of H-BOA has been significantly raised. We apply it to the WSN nodes coverage problem. The results show that H-BOA improves the WSN maximum coverage and it is far more than other optimization algorithms.

Relevance of Adopting a Hybrid Strategy Mixing Single-Use and Reusable Ureteroscopes for Stones Management: An Economic Study to Support the Best Strategy.

Endoscopic procedures such as ureteroscopy (URS) have seen a recent increase in single-use devices. Despite all the advantages provided by disposable ureteroscopes (sURSs), their cost effectiveness remains questionable, leading most teams to use a hybrid strategy combining reusable (rURS) and disposable devices. Our study aimed to create an economic model that estimated the cut-off value of rURS procedures needed to support the profitability of a hybrid strategy (HS) for ureteroscopy. We used a budget impact analysis (BIA) model that estimated the financial impact of an HS compared to 100% sURS use. The model included hospital volume, sterilization costs and the private or public status of the institution. Although the hybrid strategy generally remains the best economic and clinical option, a predictive BIA model is recommended for the decision-making. We found that the minimal optimal proportion of rURS procedures in an HS was mainly impacted by the activity volume and overall number of sterilization procedures. Private and public institutions must consider these variables and models in order to adapt their HS and remain profitable.

Three-step hybrid strategy towards efficiently selecting variables in multivariate calibration of near-infrared spectra.

Variable (feature or wavelength) selection is a critical step in multivariate calibration of near-infrared (NIR) spectra. The high-resolution NIR or its imaging instruments usually generate hundreds or thousands of wavelengths, which make the variable selection methods tend to appear a high risk of overfitting, low efficiency, or requiring large computational abilities. Thus, it is a great challenge to efficiently select informative variables and obtain an optimal variable combination in a huge variable space. We propose a hybrid strategy for efficiently selecting variables based on three steps including rough selection, fine selection and optimal selection. The strong interpretability method like wavelength interval selection method (interval partial least squares, iPLS) was first used to roughly select informative intervals and shrink the variable space. Wavelength point selection methods such as variable importance in projection (VIP) and modified variable combination population analysis (mVCPA) were used to continuingly shrink the variable space from large to small in order to remain the very important variables. In the third step, applying some optimization methods such as iteratively retaining informative variables (IRIV) and genetic algorithm (GA) is to find an optimal variable combination from the remaining variables. It makes full use of the advantages of various involved methods and makes up for their disadvantages when facing high dimensional data. Two NIR datasets were employed to investigate the performance of the three-step hybrid strategy. It can significantly improve the prediction performance of the models built when compared with other single or hybrid methods (iPLS, VIP, iPLS-VIP, iPLS-VCPA, iPLS-mVCPA, VIP-GA, VIP-IRIV, mVCPA-GA, mVCPA-IRIV), indicating that the three-step hybrid strategy, including iPLS-VIP-IRIV, iPLS-VIP-GA, iPLS-mVCPA-GA and iPLS-mVCPA-IRIV, could efficiently select informative variables. Therefore, the three-step hybrid strategy is a good alternative for variable selection methods in the face of high dimensional NIR spectral data.

The hybrid bacterial foraging algorithm based on many-objective optimizer.

A new multi-objective optimized bacterial foraging algorithm - Hybrid Multi-Objective Optimized Bacterial Foraging Algorithm (HMOBFA) is presented in this article. The proposed algorithm combines the crossover-archives strategy and the life-cycle optimization strategy, look for the best method through research area. The crossover-archive strategy with an external archive and internal archive is assigned to different selection principles to focus on diversity and convergence separately. Additionally, according to the local landscape to satisfy population diversity and variability as well as avoiding redundant local searches, individuals can switch their states periodically throughout the colony lifecycle with the life-cycle optimization strategy. all of which may perform significantly well. The performance of the algorithm was examined with several standard criterion functions and compared with other classical multi-objective majorization methods. The examiner results show that the HMOBFA algorithm can achieve a significant enhancement in performance compare with other method and handles many-objective issues with solid complexity, convergence as well as diversity. The HMOBFA algorithm has been proven to be an excellent alternative to past methods for solving the improvement of many-objective problems.

Novel Hybrids of Podophyllotoxin and Coumarin Inhibit the Growth and Migration of Human Oral Squamous Carcinoma Cells.

Oral squamous cell carcinoma is the most common malignancy of oral tumor. In this study, two novel hybrids of podophyllotoxin and coumarin were designed using molecular hybridization strategy and synthesized. Pharmacological evaluation showed that the potent compound 12b inhibited the proliferation of three human oral squamous carcinoma cell lines with nanomolar IC50 values, as well as displayed less toxicity on normal cells. Mechanistic studies indicated that 12b triggered HSC-2 cell apoptosis, induced cell cycle arrest, and inhibited cell migration. Moreover, 12b could disturb the microtubule network via binding into the tubulin. It was noteworthy that induction of autophagy by 12b was associated with the upregulation of Beclin1, as well as LC3-II. Furthermore, 12b significantly stimulated the AMPK pathway and restrained the AKT/mTOR pathway in HSC-2 cells. These results indicated that compound 12b was a promising candidate for further investigation.