Interesting Class Research Articles

Analysis of the Meso‐Mechanical Deformation Behavior of Al Foams and Ni/Al Hybrid Foams Using Time‐Lapse Micro‐Computed Tomography Measurements and 3D Optical Flow

Metal foams are an interesting class of bio‐inspired materials for lightweight construction and energy absorption. Commonly, aluminum (Al) foams are used. However, the mechanical properties have been improved by coating pure Al foams with a nanocrystalline nickel (Ni) coating resulting in Ni/Al hybrid foams. Herein, the meso‐mechanical deformation mechanisms in Al foams and the changes in the mechanisms in Ni/Al hybrid foams are studied using time‐lapse micro‐computed tomography measurements in comparison between numerical modeling using voxel finite element models and evaluation of displacement fields using 3D optical flow. This gives never‐seen insights into the highly localized 3D deformation mechanisms within the entire volume of the foams and not only on the surfaces as given by conventional digital image correlation methods. Displacements calculated by the 3D optical flow algorithm demonstrate its possibility to reveal a significant concentration of plastic deformation, particularly evident when deformation occurs within a distinct, slightly inclined band in the central region. Common numerical simulations using standard plasticity models do not accurately capture this localized deformation, underscoring the need to integrate damage and softening models into the simulation.

A novel multi-scale violence and public gathering dataset for crowd behavior classification

Dependable utilization of computer vision applications, such as smart surveillance, requires training deep learning networks on datasets that sufficiently represent the classes of interest. However, the bottleneck in many computer vision applications lies in the limited availability of adequate datasets. One particular application that is of great importance for the safety of cities and crowded areas is smart surveillance. Conventional surveillance methods are reactive and often ineffective in enable real-time action. However, smart surveillance is a key component of smart and proactive security in a smart city. Motivated by a smart city application which aims at the automatic identification of concerning events for alerting law-enforcement and governmental agencies, we craft a large video dataset that focuses on the distinction between small-scale violence, large-scale violence, peaceful gatherings, and natural events. This dataset classifies public events along two axes, the size of the crowd observed and the level of perceived violence in the crowd. We name this newly-built dataset the Multi-Scale Violence and Public Gathering (MSV-PG) dataset. The videos in the dataset go through several pre-processing steps to prepare them to be fed into a deep learning architecture. We conduct several experiments on the MSV-PG datasets using a ResNet3D, a Swin Transformer and an R(2 + 1)D architecture. The results achieved by these models when trained on the MSV-PG dataset, 88.37%, 89.76%, and 89.3%, respectively, indicate that the dataset is well-labeled and is rich enough to train deep learning models for automatic smart surveillance for diverse scenarios.

Synthesis, characterization, crystal structures and biological activity of 4-chloro-2-(2-hydroxy-3-ethoxybenzylideneamino)phenol and complexes

Molecular nanoclusters comprising a heterometallic 3d-4f stoichiometry are an interesting class of compounds. In this paper, a chloro-containing Schiff base 4-chloro-2-(2-hydroxy-3-ethoxybenzylideneamino)phenol (H2L) was prepared by condensation reaction of 3-ethoxysalicylaldehyde with 2-amino-4-chlorophenol in methanol. The Schiff base is predisposed to chelate within a cluster core both 3d and 4f metal ions. Reaction of H2L with nickel salts afforded a tetranuclear NiII complex [Ni4L4(MeOH)4] (1), while with nickel salts and lanthanide(III) nitrate three tetranuclear NiII-LnIII complexes [Ni2Eu2L4(NO3)2(DMF)2] (2), [Ni2Er2L4(NO3)2(DMF)2] (3), [Ni2Tb2L4(CH3COO)2(DMF)2]·4DMF (4) were obtained. H2L and the complexes have been characterized by elemental analysis, IR, UV–Vis spectra. H2L has also been characterized by 1H and 13C NMR spectra. Molecular structures of the compounds have been determined by single crystal X-ray determination. In the complexes, the Schiff base ligands coordinate to the metal atoms via ether oxygen, imino nitrogen and two phenolate oxygen atoms. All the Ni atoms in the complexes are in octahedral coordination. The rare earth metal atoms are eight coordinated by the phenolate and ether oxygen of the Schiff base ligands, and anionic and solvent oxygen atoms, forming square antiprismatic coordination. The compounds were evaluated for their antibacterial activities on Bacillus subtilis, Staphylococcus aureus, Escherichia coli and Pseudomonas fluorescens, and antifungal activities on Candida albicans and Aspergillus niger, and gave interesting results.

Regularized Loss With Hyperparameter Estimation for Weakly Supervised Single Class Segmentation.

We propose a new image level weakly supervised segmentation approach for datasets with a single object class of interest. Our approach is based on a regularized loss function inspired by the classical Conditional Random Field (CRF) modeling. Our loss models properties of generic objects, and we use it to guide CNN towards segments that are more likely to correspond to the object, thus avoiding the need for pixel precise annotations. Training CNN with regularized loss is a difficult task for gradient descent. We develop an annealing algorithm which is crucial for a successful training. Furthermore, we develop an approach for hyperparameter setting for the most important components of our regularized loss. This is far from trivial, since there is no pixel precise ground truth for guidance. The advantage of our method is that we use a standard CNN architecture and an easy to interpret loss function, derived from classical CRF models. Furthermore, we apply the same loss function for any task/dataset. We first evaluate our approach for salient object segmentation and co-segmentation. These tasks naturally involve one object class of interest. Then we adapt our approach to image level weakly supervised multi-class semantic segmentation. We obtain state-of-the-art results.

Synthesis and Characterization of Novel Cobalt Carbonyl Phosphorus and Arsenic Clusters.

Phosphorus- and arsenic-containing cobalt clusters are an interesting class of compounds that continue to provide new structures with captivating bonding patterns. Although the first members of this family were reported 45 years ago, the number of such species is still limited within the broad family of transition metal complexes bearing pnictogen atoms. Herein, we present the reaction of Co2(CO)8 as a cobalt source with a number of phosphorus- and arsenic-containing compounds under variable reaction conditions. These reactions result in various known and novel cobalt phosphorus and cobalt arsenic clusters in which different nuclearity ratios between P/As and Co exist. All those clusters were characterized by X-ray structural analysis and partly by IR, 31P{1H} NMR, EI-MS and elemental analysis. This comprehensive study is the first detailed study in this field that reveals the richness of compounds that could be obtained only by modifying the ratio of used reactants and the involved reaction conditions.

Self-sufficient primary natural killer cells engineered to express T cell receptors and interleukin-15 exhibit improved effector function and persistence.

NK cells can be genetically engineered to express a transgenic T-cell receptor (TCR). This approach offers an alternative strategy to target heterogenous tumors, as NK:TCR cells can eradicate both tumor cells with high expression of HLA class I and antigen of interest or HLA class I negative tumors. Expansion and survival of NK cells relies on the presence of IL-15. Therefore, autonomous production of IL-15 by NK:TCR cells might improve functional persistence of NK cells. Here we present an optimized NK:TCR product harnessed with a construct encoding for soluble IL-15 (NK:TCR/IL-15), to support their proliferation, persistence and cytotoxic capabilities. Expression of tumor-specific TCRs in peripheral blood derived NK-cells was achieved following retroviral transduction. NK:TCR/IL-15 cells were compared with NK:TCR cells for autonomous cytokine production, proliferation and survival. NK:BOB1-TCR/IL-15 cells, expressing a HLA-B*07:02-restricted TCR against BOB1, a B-cell lineage specific transcription factor highly expressed in all B-cell malignancies, were compared with control NK:BOB1-TCR and NK:CMV-TCR/IL-15 cells for effector function against TCR antigen positive malignant B-cell lines in vitro and in vivo. Viral incorporation of the interleukin-15 gene into engineered NK:TCR cells was feasible and high expression of the TCR was maintained, resulting in pure NK:TCR/IL-15 cell products generated from peripheral blood of multiple donors. Self-sufficient secretion of IL-15 by NK:TCR cells enables engineered NK cells to proliferate in vitro without addition of extra cytokines. NK:TCR/IL-15 demonstrated a marked enhancement of TCR-mediated cytotoxicity as well as enhanced NK-mediated cytotoxicity resulting in improved persistence and performance of NK:BOB1-TCR/IL-15 cells in an orthotopic multiple myeloma mouse model. However, in contrast to prolonged anti-tumor reactivity by NK:BOB1-TCR/IL-15, we observed in one of the experiments an accumulation of NK:BOB1-TCR/IL-15 cells in several organs of treated mice, leading to unexpected death 30 days post-NK infusion. This study showed that NK:TCR/IL-15 cells secrete low levels of IL-15 and can proliferate in an environment lacking cytokines. Repeated in vitro and in vivo experiments confirmed the effectiveness and target specificity of our product, in which addition of IL-15 supports TCR- and NK-mediated cytotoxicity.

HormonomicsDB: a novel workflow for the untargeted analysis of plant growth regulators and hormones

Background Metabolomics is the simultaneous determination of all metabolites in a system. Despite significant advances in the field, compound identification remains a challenge. Prior knowledge of the compound classes of interest can improve metabolite identification. Hormones are a small signaling molecules, which function in coordination to direct all aspects of development, function and reproduction in living systems and which also pose challenges as environmental contaminants. Hormones are inherently present at low levels in tissues, stored in many forms and mobilized rapidly in response to a stimulus making them difficult to measure, identify and quantify. Methods An in-depth literature review was performed for known hormones, their precursors, metabolites and conjugates in plants to generate the database and an RShiny App developed to enable web-based searches against the database. An accompanying liquid chromatography – mass spectrometry (LC-MS) protocol was developed with retention time prediction in Retip. A meta-analysis of 14 plant metabolomics studies was used for validation. Results We developed HormonomicsDB, a tool which can be used to query an untargeted mass spectrometry (MS) dataset against a database of more than 200 known hormones, their precursors and metabolites. The protocol encompasses sample preparation, analysis, data processing and hormone annotation and is designed to minimize degradation of labile hormones. The plant system is used a model to illustrate the workflow and data acquisition and interpretation. Analytical conditions were standardized to a 30 min analysis time using a common solvent system to allow for easy transfer by a researcher with basic knowledge of MS. Incorporation of synthetic biotransformations enables prediction of novel metabolites. Conclusions HormonomicsDB is suitable for use on any LC-MS based system with compatible column and buffer system, enables the characterization of the known hormonome across a diversity of samples, and hypothesis generation to reveal knew insights into hormone signaling networks.

Semantic richness effects in isolated spoken word recognition: Evidence from massive auditory lexical decision.

While known to influence visual lexical processing, the semantic information we associate with words has recently been found to influence auditory lexical processing as well. The present work explored the influence of semantic richness in auditory lexical decision. Study 1 recreated an experiment investigating semantic richness effects in concrete nouns (Goh et al., 2016). In Study 2, we expanded the stimulus set from 442 to 8,626 items, exploring the robustness of effects observed in Study 1 against a larger data set with increased diversity in both word class and other characteristics of interest. We also utilized generalized additive mixed models to investigate potential nonlinear effects. Results indicate that semantic richness effects become more nuanced and detectable when a wider set of items belonging to different parts of speech is examined. Findings are discussed in the context of models of spoken word recognition. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Is eco-social education for trainee teachers possible using international mirror classes on landscape? Implementation of a didactic proposal in Spain and Colombia

According to the eco-social education paradigm, landscape plays a key role as an identity object of study in the interaction between nature and society. The study seeks to assess a practical experience conducted among trainee teachers consisting in the systematized analysis of local and foreign degraded landscapes in Spain and Colombia, based on environmental, sustainability and global-citizenship literacies—guarantors of eco-social education. A mirror class methodology was implemented among 127 Spanish and Colombian trainee teachers. It comprised an online videocall exchange between different teaching groups. A quasi-experimental research methodology was followed, based on the didactic and statistical analysis of the trainee teachers’ reports and a validated questionnaire. The findings reveal a global didactic acquisition of 2.9 points on a 4-scale evaluation range. The learning was higher in didactic tasks related to the landscape analysis and lower in comparative local-global analysis tasks. A statistically significant learning development of +0.8 points was found. The results suggest that the study of local landscapes captures interest and mirror classes easily bring distant realities closer, thus improving the eco-social knowledge and the feeling of an eco-social responsibility for global sustainability. Moreover, the experience in trainee teachers assures a didactic transfer to the wider population.

High Internal Phase Emulsion for Constructing Chiral Helical Polymer-Based Circularly Polarized Luminescent Porous Materials.

Polymerized high internal phase emulsions (polyHIPEs) with circularly polarized luminescence (CPL), as an interesting class of porous materials, are of great significance for the development of CPL porous materials but have not been reported so far. Herein, we report the construction of polyHIPE-based CPL porous materials, taking advantage of an adsorption strategy. The pristine polyHIPEs constructed by chiral helical polymers, which acted as a chiral microenvironment, were fabricated by coordination polymerization of chiral acetylene monomers (R/S-SA) using HIPEs as templates. Achiral fluorescent small molecules were dispersed in the pores of the 3D porous organic chiral polymer matrix provided by polyHIPEs through the adsorption strategy, and CPL-active porous materials with blue, cyan, and green emissions were constructed using a fluorescence-selective absorption mechanism that does not rely on chirality transfer at the molecular level. The maximum luminescence dissymmetry factor (glum) value was -2.6 × 10-2. This work establishes a new and simple way for developing CPL porous materials.

Poly(silyl ether)s as Degradable and Sustainable Materials: Synthesis and Applications.

Polymer research is currently focused on sustainable and degradable polymers which are cheap, easy to synthesize, and environmentally friendly. Silicon-based polymers are thermally stable and can be utilized in various applications, such as columns and coatings. Poly(silyl ether)s (PSEs) are an interesting class of silicon-based polymers that are easily hydrolyzed in either acidic or basic conditions due to the presence of the silyl ether Si-O-C bond. Synthetically, these polymers can be formed in several different ways, and the most effective and environmentally friendly synthesis is dehydrogenative cross coupling, where the byproduct is H2 gas. These polymers have a lot of promise in the polymeric materials field due to their sustainability, thermal stability, hydrolytic degradability, and ease of synthesis, with nontoxic byproducts. In this review, we will summarize the synthetic approaches for the PSEs in the recent literature, followed by the properties and applications of these materials. A conclusion and perspective will be provided at the end.

Count What You Want: Exemplar Identification and Few-Shot Counting of Human Actions in the Wild

This paper addresses the task of counting human actions of interest using sensor data from wearable devices. We propose a novel exemplar-based framework, allowing users to provide exemplars of the actions they want to count by vocalizing predefined sounds ``one'', ``two'', and ``three''. Our method first localizes temporal positions of these utterances from the audio sequence. These positions serve as the basis for identifying exemplars representing the action class of interest. A similarity map is then computed between the exemplars and the entire sensor data sequence, which is further fed into a density estimation module to generate a sequence of estimated density values. Summing these density values provides the final count. To develop and evaluate our approach, we introduce a diverse and realistic dataset consisting of real-world data from 37 subjects and 50 action categories, encompassing both sensor and audio data. The experiments on this dataset demonstrate the viability of the proposed method in counting instances of actions from new classes and subjects that were not part of the training data. On average, the discrepancy between the predicted count and the ground truth value is 7.47, significantly lower than the errors of the frequency-based and transformer-based methods. Our project, code and dataset can be found at https://github.com/cvlab-stonybrook/ExRAC.

Designing a New Class of Mechanophoric Polymer Based on Epoxy-Functionalized Rhodamine Derivative.

Mechanophoric polymers are an interesting class of smart polymers which contains a special force-sensitive molecular motif that can lead to a chemical change within the polymer network in response to mechanical force. This investigation reports the design of a mechanophoric polymer based on epoxy-functionalized rhodamine via a monomeric approach. In this case, rhodamine (Rh) is modified with glycidyl methacrylate (GMA) through an epoxy-amine reaction to design a vinyl-functionalized multi-armed macromonomer (Rh-GMA), which is reacted with butyl acrylate (BA) to prepare the crosslinked polymeric film. The crosslinked polymeric film demonstrates mechanophoric properties under UV and stretching conditions.

Pentamodes: Effect of unit cell topology on mechanical properties

Pentamodes (first conceived theoretically by Milton and Cherkaev) are a very interesting class of mechanical metamaterials that can be used as building blocks of structures withdecoupled bulk and shear moduli. The pentamodes usually are composed of double cone-shaped struts with the middle diameter being large and the end diameters being tiny (ideally approaching zero). The cubic diamond geometry was proposed by Milton and Cherkaev as a suitable geometry for the unit cell and has since been used in the majority of the works on pentamodes. In this work, we aim to evaluate the degree to which the base unit cell design contributes to high bulk to shear modulus ratio, also known as Figure Of Merit(FOM). In addition to the diamond unit cell, three other well-known unit cell types are considered, and the effect of small diameter size and the ratio of large-to-small diameter, α, on the FOM is evaluated. The results showed that regardless of the base unit cell shape, the FOM value is highly dependent on the d (the smaller diameter size of double-cone) value, while its dependence on the D (the greater diameter of double-cone) value is very weak. For d/h∝0.05 (h representing the linkage length), figures of merit in the range of 103 could be reached for all the studied topologies.

Comparative Analysis of The Differences Between Music Education in Kindergarten and Extra-Curricular Interest Classes.

Currently, most parents are interested in early childhood music enlightenment and want their children to learn music. In addition to the music initiation that is generally available in kindergartens, there are many extracurricular music classes. So, many parents enroll their young children in extracurricular music classes. This paper compares the educational models in kindergartens and extracurricular interest classes in the music enlightenment education of young children. This research can be used to better design music instruction that is better suited to the varying needs of young children. The methodology of this research is a case study. Through comparative analyses, it can be seen that the teaching modes of kindergarten teachers and private interest classes teachers are completely different, including teaching objectives, teaching atmosphere and parents’ perception requirements, and so on. At the same time, this paper also puts forward some suggestions on the current teaching mode of early childhood music.

The Effect of Agility Balance and Coordination Warm-Up on Children’s Tennis Performance

Background and Aims: Tennis is also a sophisticated action, intense confrontation, technical and tactical use of complex and varied network antagonistic sport, on physical and mental intelligence aspects of the higher requirements, emphasizing skills and physical dominance. The purpose of this study is to select 40 male children aged 6-10 years old from a tennis club in Xi'an for a group experiment based on the selection conditions of age, height, weight, training time no more than 6 months, and training twice a week, and to find out the theoretical basis of agility, balance, and coordination intervention training on the improvement of children's tennis hitting ability. Materials and Methods: This study adopts the literature method, expert interview method, experiment method, and mathematical statistics method. Five expert professors were interviewed, and the validity of expert interviews and experimental results was measured by IOC, t-test, and other data. Through 8 weeks of agility, balance, and coordination training in the experimental group and the control group, and 90 minutes of intervention training twice a week, it is concluded that the experimental group and the control group have significant differences, and the experimental group has significantly improved its ability in batting and other aspects. Results: The results showed that: (1) 8-week agility, balance, and coordination intervention training applied in the warm-up part significantly improved the agility, balance, and coordination ability of children's tennis in the experimental group, and the intervention program was feasible to improve the agility, balance and coordination ability of children's tennis students in the interest class. (2) After 8 weeks of agility, balance, and coordination training, the number of goals scored by the students in the experimental group in the scoring area of 1, 2, and 3 points of forehand stroke showed a significant difference compared with before training, while the difference was only significant in the scoring area of 1 point of the backhand stroke. It shows that 8 weeks of agility, balance, and coordination training can improve the stability and accuracy of forehand strokes more obviously. Conclusion: (1) Stability and accuracy are based on good agility, balance, and coordination. Agility, balance, and coordination can improve the accuracy and stability of forehand and backhand in tennis training for children. (2) 6-10 years old is the best period for children to conduct agility, balance, and coordination intervention training, so strengthening agility, balance, and coordination training in tennis warm-up training can not only improve the stability and accuracy of children's tennis shots but also increase children's interest and concentration.

Pinball-OCSVM for Early-Stage COVID-19 Diagnosis with Limited Posteroanterior Chest X-Ray Images

The conventional way of respiratory coronavirus disease 2019 (COVID-19) diagnosis is reverse transcription polymerase chain reaction (RT-PCR), which is less sensitive during early stages; especially if the patient is asymptomatic, which may further cause more severe pneumonia. In this context, several deep learning models have been proposed to identify pulmonary infections using publicly available chest X-ray (CXR) image datasets for early diagnosis, better treatment and quick cure. In these datasets, the presence of less number of COVID-19 positive samples compared to other classes (normal, pneumonia and Tuberculosis) raises the challenge for unbiased learning of deep learning models. This learning problem can be considered as one-class classification problem where the target class samples are present and other classes are absent or ill-defined. All deep learning models opted class balancing techniques to solve this issue; which however should be avoided in any medical diagnosis process. Moreover, the deep learning models are also data hungry and need massive computation resources. Therefore, for quicker diagnosis, this research proposes a novel pinball loss function based one-class support vector machine (PB-OCSVM), that can work in presence of limited COVID-19 positive CXR samples (target class or class-of-interest (CoI) samples) with objectives to maximize the learning efficiency and to minimize the false predictions. The performance of the proposed model is compared with conventional OCSVM and recent deep learning models, and the experimental results prove that the proposed model outperformed state-of-the-art methods. To validate the robustness of the proposed model, experiments are also performed with noisy CXR images and UCI benchmark datasets.

Multifractal Analysis of Measures Arising from Random Substitutions

We study regularity properties of frequency measures arising from random substitutions, which are a generalisation of (deterministic) substitutions where the substituted image of each letter is chosen independently from a fixed finite set. In particular, for a natural class of such measures, we derive a closed-form analytic formula for the Lq\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L^q$$\\end{document}-spectrum and prove that the multifractal formalism holds. This provides an interesting new class of measures satisfying the multifractal formalism. More generally, we establish results concerning the Lq\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L^q$$\\end{document}-spectrum of a broad class of frequency measures. We introduce a new notion called the inflation wordLq\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L^q$$\\end{document}-spectrum of a random substitution and show that this coincides with the Lq\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L^q$$\\end{document}-spectrum of the corresponding frequency measure for all q≥0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$q \\ge 0$$\\end{document}. As an application, we obtain closed-form formulas under separation conditions and recover known results for topological and measure theoretic entropy.

The Weird and the Wonderful in Our Solar System: Searching for Serendipity in the Legacy Survey of Space and Time

We present a novel method for anomaly detection in solar system object data in preparation for the Legacy Survey of Space and Time. We train a deep autoencoder for anomaly detection and use the learned latent space to search for other interesting objects. We demonstrate the efficacy of the autoencoder approach by finding interesting examples, such as interstellar objects, and show that by using the autoencoder, further examples of interesting classes can be found. We also investigate the limits of classic unsupervised approaches to anomaly detection through the generation of synthetic anomalies and evaluate the feasibility of using a supervised learning approach. Future work should consider expanding the feature space to increase the variety of anomalies that can be uncovered during the survey using an autoencoder.

Green chemistry route to chitosan hydrogels and investigation of the materials as efficient dye adsorbents

Abstract Biopolymer-based materials for the adsorption of toxic dyes represent an interesting class of materials for environmental applications. Here we report on chitosan as the starting material for synthesizing dye adsorbents. In particular, the synthesis, characterization, and cationic dye adsorption properties of chitosan hydrogel adsorbents are reported. Polyanionic itaconated chitosan derivatives were synthesized in solvent-less conditions for the first time. Itaconated chitosan was cross-linked using thiol-ene chemistry to obtain hydrogels. The influence of the incorporated carboxylate groups and the cross-linker fraction on the adsorption of Methylene Blue (MB) was investigated. In addition, the impact of pH, adsorbent dose, initial concentration, and ionic strength were investigated to determine the optimum conditions for MB uptake, and the dye uptake kinetics, adsorption isotherms, selectivity, and reusability of the adsorbents were unveiled. A maximum adsorption capacity of 556 mg/g could be achieved, outperforming commercial activated charcoal and ion exchange resins. Furthermore the chitosan hydrogel adsorbents were shown to capture >90 % of cationic MB from a binary equimolar mixture with the anionic dye Methyl Orange. Since the adsorbents can be regenerated and re-used afterwards at least 20 times, retaining a high dye adsorption fraction of >95 %, these materials are promising candidates for environmental applications.