Structural basis of human monocarboxylate transporter 1 inhibition by anti-cancer drug candidates.

Proton-coupled monocarboxylate transporters MCT1-4 catalyze the transmembrane movement of metabolically essential monocarboxylates and have been targeted for cancer treatment because of their enhanced expression in various tumors. Here, we report five cryo-EM structures, at resolutions of 3.0-3.3 Å, of human MCT1 bound to lactate or inhibitors in the presence of Basigin-2, a single transmembrane segment (TM)-containing chaperon. MCT1 exhibits similar outward-open conformations when complexed with lactate or the inhibitors BAY-8002 and AZD3965. In the presence of the inhibitor 7ACC2 or with the neutralization of the proton-coupling residue Asp309 by Asn, similar inward-open structures were captured. Complemented by structural-guided biochemical analyses, our studies reveal the substrate binding and transport mechanism of MCTs, elucidate the mode of action of three anti-cancer drug candidates, and identify the determinants for subtype-specific sensitivities to AZD3965 by MCT1 and MCT4. These findings lay out an important framework for structure-guided drug discovery targeting MCTs.

Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes.

Most patients diagnosed with resected pancreatic adenocarcinoma (PDAC) survive less than 5 years, but a minor subset survives longer. Here, we dissect the role of the tumor microbiota and the immune system in influencing long-term survival. Using 16S rRNA gene sequencing, we analyzed the tumor microbiome composition in PDAC patients with short-term survival (STS) and long-term survival (LTS). We found higher alpha-diversity in the tumor microbiome of LTS patients and identified an intra-tumoral microbiome signature (Pseudoxanthomonas-Streptomyces-Saccharopolyspora-Bacillus clausii) highly predictive of long-term survivorship in both discovery and validation cohorts. Through human-into-mice fecal microbiota transplantation (FMT) experiments from STS, LTS, or control donors, we were able to differentially modulate the tumor microbiome and affect tumor growth as well as tumor immune infiltration. Our study demonstrates that PDAC microbiome composition, which cross-talks to the gut microbiome, influences the host immune response and natural history of the disease.

Molecular basis of human noradrenaline transporter reuptake and inhibition.

Noradrenaline, also known as norepinephrine, has a wide range of activities and effects on most brain cell types1. Its reuptake from the synaptic cleft heavily relies on the noradrenaline transporter (NET) located in the presynaptic membrane2. Here we report the cryo-electron microscopy (cryo-EM) structures of the human NET in both its apo state and when bound to substrates or antidepressant drugs, with resolutions ranging from 2.5 Å to 3.5 Å. The two substrates, noradrenaline and dopamine, display a similar binding mode within the central substrate binding site (S1) and within a newly identified extracellular allosteric site (S2). Four distinct antidepressants, namely, atomoxetine, desipramine, bupropion and escitalopram, occupy the S1 site to obstruct substrate transport in distinct conformations. Moreover, a potassium ion was observed within sodium-binding site 1 in the structure of the NET bound to desipramine under the KCl condition. Complemented by structural-guided biochemical analyses, our studies reveal the mechanism of substrate recognition, the alternating access of NET, and elucidate the mode of action of the four antidepressants.

A whole-slide foundation model for digital pathology from real-world data.

Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles1-3. Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing the important slide-level context4. Here we present Prov-GigaPath, a whole-slide pathology foundation model pretrained on 1.3 billion 256 × 256 pathology image tiles in 171,189 whole slides from Providence, a large US health network comprising 28 cancer centres. The slides originated from more than 30,000 patients covering 31 major tissue types. To pretrain Prov-GigaPath, we propose GigaPath, a novel vision transformer architecture for pretraining gigapixel pathology slides. To scale GigaPath for slide-level learning with tens of thousands of image tiles, GigaPath adapts the newly developed LongNet5 method to digital pathology. To evaluate Prov-GigaPath, we construct a digital pathology benchmark comprising 9 cancer subtyping tasks and 17 pathomics tasks, using both Providence and TCGA data6. With large-scale pretraining and ultra-large-context modelling, Prov-GigaPath attains state-of-the-art performance on 25 out of 26 tasks, with significant improvement over the second-best method on 18 tasks. We further demonstrate the potential of Prov-GigaPath on vision-language pretraining for pathology7,8 by incorporating the pathology reports. In sum, Prov-GigaPath is an open-weight foundation model that achieves state-of-the-art performance on various digital pathology tasks, demonstrating the importance of real-world data and whole-slide modelling.

GPro: generative AI-empowered toolkit for promoter design.

MOTIVATION: Promoters with desirable properties are crucial in biotechnological applications. Generative AI (GenAI) has demonstrated potential in creating novel synthetic promoters with significantly enhanced functionality. However, these methods' reliance on various programming frameworks and specific task-oriented contexts limits their flexibilities. Overcoming these limitations is essential for researchers to fully leverage the power of GenAI to design promoters for their tasks. RESULTS: Here, we introduce GPro (Generative AI-empowered toolkit for promoter design), a user-friendly toolkit that integrates a collection of cutting-edge GenAI-empowered approaches for promoter design. This toolkit provides a standardized pipeline covering essential promoter design processes, including training, optimization, and evaluation. Several detailed demos are provided to reproduce state-of-the-art promoter design pipelines. GPro's user-friendly interface makes it accessible to a wide range of users including non-AI experts. It also offers a variety of optional algorithms for each design process, and gives users the flexibility to compare methods and create customized pipelines. AVAILABILITY AND IMPLEMENTATION: GPro is released as an open-source software under the MIT license. The source code for GPro is available on GitHub for Linux, macOS, and Windows: https://github.com/WangLabTHU/GPro, and is available for download via Zenodo repository at https://zenodo.org/doi/10.5281/zenodo.10681733.

Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model.

MOTIVATION: High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to generate. Simultaneously, chromatin structure analyses struggle with extremely sparse contact matrices. To address this problem, computational methods to enhance low-coverage contact matrices have been developed, but existing methods are largely based on resolution enhancement methods for natural images and hence often employ models that do not distinguish between biologically meaningful contacts, such as loops and other stochastic contacts. RESULTS: We present Capricorn, a machine learning model for Hi-C resolution enhancement that incorporates small-scale chromatin features as additional views of the input Hi-C contact matrix and leverages a diffusion probability model backbone to generate a high-coverage matrix. We show that Capricorn outperforms the state of the art in a cross-cell-line setting, improving on existing methods by 17% in mean squared error and 26% in F1 score for chromatin loop identification from the generated high-coverage data. We also demonstrate that Capricorn performs well in the cross-chromosome setting and cross-chromosome, cross-cell-line setting, improving the downstream loop F1 score by 14% relative to existing methods. We further show that our multiview idea can also be used to improve several existing methods, HiCARN and HiCNN, indicating the wide applicability of this approach. Finally, we use DNA sequence to validate discovered loops and find that the fraction of CTCF-supported loops from Capricorn is similar to those identified from the high-coverage data. Capricorn is a powerful Hi-C resolution enhancement method that enables scientists to find chromatin features that cannot be identified in the low-coverage contact matrix. AVAILABILITY AND IMPLEMENTATION: Implementation of Capricorn and source code for reproducing all figures in this paper are available at https://github.com/CHNFTQ/Capricorn.

A Portable Integrated Electrochemical Sensing System for On-Site Nitrite Detection in Food.

Ensuring an appropriate nitrite level in food is essential to keep the body healthy. However, it still remains a huge challenge to offer a portable and low-cost on-site food nitrite analysis without any expensive equipment. Herein, a portable integrated electrochemical sensing system (IESS) is developed to achieve rapid on-site nitrite detection in food, which is composed of a low-cost disposable microfluidic electrochemical patch for few-shot nitrite detection, and a reusable smartphone-assisted electronic device based on self-designed circuit board for signal processing and wireless transmission. The electrochemical patch based on MXene-Ti3C2Tx/multiwalled carbon nanotubes-cyanocobalamin (MXene/MWCNTs-VB12)-modified working electrode achieves high sensitivity of 10.533 µA mm-1 and low nitrite detection limit of 4.22 µm owing to strong electron transfer ability of hybrid MXene/MWCNTs conductive matrix and high nitrite selectivity of VB12 bionic enzyme-based ion-selective layer. Moreover, the portable IESS can rapidly collect pending testing samples through a microfluidic electrochemical patch within 1.0 s to conduct immediate nitrite analysis, and then wirelessly transmit data from a signal-processing electronic device to a smartphone via Bluetooth module. Consequently, this proposed portable IESS demonstrates rapid on-site nitrite analysis and wireless data transmission within one palm-sized electronic device, which would pave a new avenue in food safety and personal bespoke therapy.

ARIC: accurate and robust inference of cell type proportions from bulk gene expression or DNA methylation data.

Quantifying cell proportions, especially for rare cell types in some scenarios, is of great value in tracking signals associated with certain phenotypes or diseases. Although some methods have been proposed to infer cell proportions from multicomponent bulk data, they are substantially less effective for estimating the proportions of rare cell types which are highly sensitive to feature outliers and collinearity. Here we proposed a new deconvolution algorithm named ARIC to estimate cell type proportions from gene expression or DNA methylation data. ARIC employs a novel two-step marker selection strategy, including collinear feature elimination based on the component-wise condition number and adaptive removal of outlier markers. This strategy can systematically obtain effective markers for weighted $\upsilon$-support vector regression to ensure a robust and precise rare proportion prediction. We showed that ARIC can accurately estimate fractions in both DNA methylation and gene expression data from different experiments. We further applied ARIC to the survival prediction of ovarian cancer and the condition monitoring of chronic kidney disease, and the results demonstrate the high accuracy and robustness as well as clinical potentials of ARIC. Taken together, ARIC is a promising tool to solve the deconvolution problem of bulk data where rare components are of vital importance.

Gemini: memory-efficient integration of hundreds of gene networks with high-order pooling.

MOTIVATION: The exponential growth of genomic sequencing data has created ever-expanding repositories of gene networks. Unsupervised network integration methods are critical to learn informative representations for each gene, which are later used as features for downstream applications. However, these network integration methods must be scalable to account for the increasing number of networks and robust to an uneven distribution of network types within hundreds of gene networks. RESULTS: To address these needs, we present Gemini, a novel network integration method that uses memory-efficient high-order pooling to represent and weight each network according to its uniqueness. Gemini then mitigates the uneven network distribution through mixing up existing networks to create many new networks. We find that Gemini leads to more than a 10% improvement in F1 score, 15% improvement in micro-AUPRC, and 63% improvement in macro-AUPRC for human protein function prediction by integrating hundreds of networks from BioGRID, and that Gemini's performance significantly improves when more networks are added to the input network collection, while Mashup and BIONIC embeddings' performance deteriorates. Gemini thereby enables memory-efficient and informative network integration for large gene networks and can be used to massively integrate and analyze networks in other domains. AVAILABILITY AND IMPLEMENTATION: Gemini can be accessed at: https://github.com/MinxZ/Gemini.

Structural characterization of the Plasmodium falciparum lactate transporter PfFNT alone and in complex with antimalarial compound MMV007839 reveals its inhibition mechanism.

Plasmodium falciparum, the deadliest causal agent of malaria, caused more than half of the 229 million malaria cases worldwide in 2019. The emergence and spreading of frontline drug-resistant Plasmodium strains are challenging to overcome in the battle against malaria and raise urgent demands for novel antimalarial agents. The P. falciparum formate-nitrite transporter (PfFNT) is a potential drug target due to its housekeeping role in lactate efflux during the intraerythrocytic stage. Targeting PfFNT, MMV007839 was identified as a lead compound that kills parasites at submicromolar concentrations. Here, we present 2 cryogenic-electron microscopy (cryo-EM) structures of PfFNT, one with the protein in its apo form and one with it in complex with MMV007839, both at 2.3 Å resolution. Benefiting from the high-resolution structures, our study provides the molecular basis for both the lactate transport of PfFNT and the inhibition mechanism of MMV007839, which facilitates further antimalarial drug design.

Individual and combined effects of high-temperature stress at booting and flowering stages on rice grain quality.

BACKGROUND: High temperature stress (HTS) has become a serious threat to rice grain quality and few studies have examined the effects of HTS across multiple stages on rice grain quality. In the present study, we conducted 2 years of HTS treatments under three temperature regimes (32/22 °C, 40/30 °C and 44/34 °C) and HTS durations of 2 days and 4 days at three critical stages: booting, flowering, and a combination of booting and flowering. We employed the heat degree days (HDD) metric, which accounts for both the level and duration of HTS, to quantify the relationships between grain quality traits and HTS. RESULTS: The results revealed the diverse effects of HTS on rice grain quality at different stages, durations and temperature levels. HTS significantly (P < 0.05) reduced grain quality, with the highest sensitivities (reduction per 1 °C day-1 increase in HDD) observed at the flowering stage, followed by the combined and booting stages treatments under mild HTS treatment (40/30 °C). However, under extreme HTS treatments (44/34 °C) for 4 days, rice grains subjected to combined HTS treatment experienced complete mortality. CONCLUSION: Pre-exposed to HTS at the booting stage within a certain intensity can alleviate the adverse effects of post-flowering HTS on grain quality. This provides valuable insights for assessing the potential impact of multiple HTS events on the grain quality under future climate warming. © 2024 Society of Chemical Industry.

Bismuth-Based Electrocatalysts for Identical Value-Added Formic Acid Through Coupling CO2 Reduction and Methanol Oxidation.

It is an effective way to reduce atmospheric CO2 via electrochemical CO2 reduction reaction (CO2 RR), while the slow oxygen evolution reaction (OER) occurs at the anode with huge energy consumption. Herein, methanol oxidation reaction (MOR) is used to replace OER, coupling CO2 RR to achieve co-production of formate. Through enhancing OCHO* adsorption by oxygen vacancies engineering and synergistic effect by heteroatom doping, Bi/Bi2 O3 and Ni─Bi(OH)3 are synthesized for efficient production of formate via simultaneous CO2 RR and methanol oxidation reaction (MOR), achieving that the coupling of CO2 RR//MOR only required 7.26 kWh gformate -1 power input, much lower than that of CO2 RR//OER (13.67 kWh gformate -1 ). Bi/Bi2 O3 exhibits excellent electrocatalytic CO2 RR performance, achieving FEformate >80% in a wide potential range from -0.7 to -1.2 V (vs RHE). For MOR, Ni─Bi(OH)3 exhibits efficient MOR catalytic performance with the FEformate >98% in the potential range of 1.35-1.6 V (vs RHE). Not only demonstrates the two-electrode systems exceptional stability, working continuously for over 250 h under a cell voltage of 3.0 V, but the cathode and anode can maintain a FE of over 80%. DFT calculation results reveal that the oxygen vacancies of Bi/Bi2 O3 enhance the adsorption of OCHO* intermediate, and Ni─Bi(OH)3 reduce the energy barrier for the rate determining step, leading to high catalytic activity.

Normal-weight visceral obesity promotes a higher 10-year atherosclerotic cardiovascular disease risk in patients with type 2 diabetes mellitus-a multicenter study in China.

BACKGROUND: Visceral obesity is associated with high cardiovascular events risk in type 2 diabetes mellitus (T2DM). Whether normal-weight visceral obesity will pose a higher atherosclerotic cardiovascular disease (ASCVD) risk than body mass index (BMI)-defined overweight or obese counterparts with or without visceral obesity remains unclear. We aimed to explore the relationship between general obesity and visceral obesity and 10-year ASCVD risk in patients with T2DM. METHODS: Patients with T2DM (6997) who satisfied the requirements for inclusion were enrolled. Patients were considered to have normal weight when 18.5 kg/m2 ≤ BMI < 24 kg/m2; overweight when 24 kg/m2 ≤ BMI < 28 kg/m2; and obesity when BMI ≥ 28 kg/m2. Visceral obesity was defined as a visceral fat area (VFA) ≥ 100 cm2. Patients were separated into six groups based on BMI and VFA. The odd ratios (OR) for a high 10-year ASCVD risk for different combinations of BMI and VFA were analysed using stepwise logistic regression. Receiver operating characteristic (ROC) curves for diagnosing the high 10-year ASCVD risk were constructed, and areas under the ROC curves were estimated. Potential non-linear relationships between VFA levels and high 10-year ASCVD risk were examined using restricted cubic splines (knot = 4). Multilinear regression was used to identify factors affecting VFA in patients with T2DM. RESULTS: In patients with T2DM, subjects with normal-weight visceral obesity had the highest 10-year ASCVD risk among the six groups, which had more than a 2-fold or 3-fold higher OR than those who were overweight or obese according to BMI but did not have visceral obesity (all P < 0.05). The VFA threshold for high 10-year ASCVD risk was 90 cm2. Multilinear regression showed significant differences in the effect of age, hypertension, drinking, fasting serum insulin, fasting plasma glucose, 2 h postprandial C-peptide, triglyceride, total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol on VFA in patients with T2DM (all P < 0.05). CONCLUSIONS: T2DM patients with normal-weight visceral obesity had a higher 10-year ASCVD risk than BMI-defined overweight or obese counterparts with or without visceral obesity, which should initiate standardised management for ASCVD primary prevention.

Deep nonlocal low-rank regularization for complex-domain pixel super-resolution.

Pixel super-resolution (PSR) has emerged as a promising technique to break the sampling limit for phase imaging systems. However, due to the inherent nonconvexity of phase retrieval problem and super-resolution process, PSR algorithms are sensitive to noise, leading to reconstruction quality inevitably deteriorating. Following the plug-and-play framework, we introduce the nonlocal low-rank (NLR) regularization for accurate and robust PSR, achieving a state-of-the-art performance. Inspired by the NLR prior, we further develop the complex-domain nonlocal low-rank network (CNLNet) regularization to perform nonlocal similarity matching and low-rank approximation in the deep feature domain rather than the spatial domain of conventional NLR. Through visual and quantitative comparisons, CNLNet-based reconstruction shows an average 1.4 dB PSNR improvement over conventional NLR, outperforming existing algorithms under various scenarios.

Urban stormwater management at the meso-level: A review of trends, challenges and approaches.

Cities worldwide are facing a significant threat of stormwater hazards caused by the increase in extreme downpours and urbanization. Meso-level urban stormwater management focuses on alleviating the detrimental impacts of urban flooding and enhancing water resource utilization at the block or community scale, typically through 1) specific policies and management rules; 2) catchment-scale scenario simulation, optimization and evaluation; 3) the group of stormwater control measures implementation. It may effectively coordinate macro-level urban stormwater management planning and micro-level distributed stormwater control facilities. This study conducts a review of Urban Stormwater Management at Meso-level (USM-M) with a view to research publication trends, citation analysis, geographic spread and subject category, as well as content analysis, including temporal progression and research gaps. The Web of Science database and CiteSpace are used for the bibliometric analysis of 66 articles from 2006 to 2021. The results show that the number of USM-M topic articles generally has an upward trend over the years. Whilst the United States and China are leading research on this topic, the European countries have diverse local research and dense cooperation. Research foci have generally shifted from theoretical frameworks to multi-element subdivided topics and specific technical roadmaps. Moreover, the spatial layout optimization and multi-functional integration are, or will be, potential research directions in terms of enhancing stormwater utilization and co-benefits of USM-M. This systematic review concludes trends, challenges and potential approaches of USM-M, and aims to provide recommendations for researchers and policymakers on the development of a more advanced and comprehensive USM-M.

Dual-Atom Catalysts Derived from a Preorganized Covalent Organic Framework for Enhanced Electrochemical Oxygen Reduction.

Dual-atom catalysts (DAC) are deemed as promising electrocatalysts due to the abundant active sites and adjustable electronic structure, but the fabrication of well-defined DAC is still full of challenges. Herein, bonded Fe dual-atom catalysts (Fe2 DAC) with Fe2 N6 C8 O2 configuration were developed through one-step carbonization of a preorganized covalent organic framework with bimetallic Fe chelation sites (Fe2 COF). The transition from Fe2 COF to Fe2 DAC involved the dissociation of the nanoparticles and the capture of atoms by carbon defects. Benefitting from the optimized d-band center and enhanced adsorption of OOH* intermediates, Fe2 DAC exhibited outstanding oxygen reduction activity with a half-wave potential of 0.898 V vs. RHE. This work will guide more fabrication of dual-atom and even cluster catalysts from preorganized COF in the future.

A Hexagonal Nut-Like Metal-Organic Framework and Its Conformal Transformation.

Hollow structured metal-organic frameworks (MOFs) and their derivatives are desired in catalysis, energy storage, etc. However, fabrication of novel hollow MOFs and revelation of their formation mechanisms remain challenging. Herein, open hollow 2D MOFs in the form of hexagonal nut are prepared through self-template method, which can be readily scaled up at gram scale in a one-pot preparation. The evolution from the initial superstructure to the final stable MOFs is tracked by wide-angle X-ray scattering, transforming from solid hexagon to open hollow hexagon. More importantly, this protocol can be extended to synthesizing a series of open hollow structured MOFs with sizes ranging from ≈120 to ≈1200 nm. Further, open hollow structured cobalt/N-doped porous carbon composites are realized through conformal transformation of the as-prepared MOFs, which demonstrates promising applications in sustainable energy conversion technologies. This study sheds light on the kinetically controlled synthesis of novel 2D MOFs for their extended utilizations.

Ru-Incorporated Nickel Diselenide Nanosheet Arrays with Accelerated Adsorption Kinetics toward Overall Water Splitting.

Developing high-efficiency electrocatalysts toward overall water splitting is an increasingly important area for sustainable energy evolution. Theoretical calculation results demonstrate that the incorporation of Ru optimizes the Gibbs free energy of adsorption of H2 O molecules and intermediates for the hydrogen/oxygen evolution reactions (HER/OER) on metal selenide sites, thus boosting electrocatalytic overall water splitting. Accordingly, ruthenium modified nickel diselenide nanosheet arrays are designed and construct on nickel foam (Ru-NiSe2 /NF). The obtained Ru-NiSe2 /NF electrode with a stable 3D structure shows greatly improved OER and HER activity in alkaline solution. Particularly, toward OER, it only requires 210 mV to obtain a current density of 10 mA cm-2 , and the formation of the intermediate nickel oxyhydroxide as active center during the OER process is captured by in situ Raman. Moreover, the overall water splitting can be driven by a voltage of merely 1.537 V to obtain 10 mA cm-2 . This work provides an available strategy for selenides to enhance electrochemical properties and inspires more studies to explore highly efficient electrocatalysts toward full water splitting.

cfDNApipe: a comprehensive quality control and analysis pipeline for cell-free DNA high-throughput sequencing data.

MOTIVATION: Cell-free DNA (cfDNA) is gaining substantial attention from both biological and clinical fields as a promising marker for liquid biopsy. Many aspects of disease-related features have been discovered from cfDNA high-throughput sequencing (HTS) data. However, there is still a lack of integrative and systematic tools for cfDNA HTS data analysis and quality control (QC). RESULTS: Here, we propose cfDNApipe, an easy-to-use and systematic python package for cfDNA whole-genome sequencing (WGS) and whole-genome bisulfite sequencing (WGBS) data analysis. It covers the entire analysis pipeline for the cfDNA data, including raw sequencing data processing, QC and sophisticated statistical analysis such as detecting copy number variations (CNVs), differentially methylated regions and DNA fragment size alterations. cfDNApipe provides one-command-line-execution pipelines and flexible application programming interfaces for customized analysis. AVAILABILITY AND IMPLEMENTATION: https://xwanglabthu.github.io/cfDNApipe/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An Efficient and Accurate Iris Recognition Algorithm Based on a Novel Condensed 2-ch Deep Convolutional Neural Network.

Recently, deep learning approaches, especially convolutional neural networks (CNNs), have attracted extensive attention in iris recognition. Though CNN-based approaches realize automatic feature extraction and achieve outstanding performance, they usually require more training samples and higher computational complexity than the classic methods. This work focuses on training a novel condensed 2-channel (2-ch) CNN with few training samples for efficient and accurate iris identification and verification. A multi-branch CNN with three well-designed online augmentation schemes and radial attention layers is first proposed as a high-performance basic iris classifier. Then, both branch pruning and channel pruning are achieved by analyzing the weight distribution of the model. Finally, fast finetuning is optionally applied, which can significantly improve the performance of the pruned CNN while alleviating the computational burden. In addition, we further investigate the encoding ability of 2-ch CNN and propose an efficient iris recognition scheme suitable for large database application scenarios. Moreover, the gradient-based analysis results indicate that the proposed algorithm is robust to various image contaminations. We comprehensively evaluated our algorithm on three publicly available iris databases for which the results proved satisfactory for real-time iris recognition.