A multi-iron enzyme installs copper-binding oxazolone/thioamide pairs on a nontypeable Haemophilus influenzae virulence factor.

The multinuclear nonheme iron-dependent oxidases (MNIOs) are a rapidly growing family of enzymes involved in the biosynthesis of ribosomally synthesized, posttranslationally modified peptide natural products (RiPPs). Recently, a secreted virulence factor from nontypeable Haemophilus influenzae (NTHi) was found to be expressed from an operon, which we designate the hvf operon, that also encodes an MNIO. Here, we show by Mössbauer spectroscopy that the MNIO HvfB contains a triiron cofactor. We demonstrate that HvfB works together with HvfC [a RiPP recognition element (RRE)-containing partner protein] to perform six posttranslational modifications of cysteine residues on the virulence factor precursor peptide HvfA. Structural characterization by tandem mass spectrometry and NMR shows that these six cysteine residues are converted to oxazolone and thioamide pairs, similar to those found in the RiPP methanobactin. Like methanobactin, the mature virulence factor, which we name oxazolin, uses these modified residues to coordinate Cu(I) ions. Considering the necessity of oxazolin for host cell invasion by NTHi, these findings point to a key role for copper during NTHi infection. Furthermore, oxazolin and its biosynthetic pathway represent a potential therapeutic target for NTHi.

Landmark-based auto-contouring of clinical target volumes for radiotherapy of nasopharyngeal cancer.

BACKGROUND: The delineation of clinical target volumes (CTVs) for radiotherapy for nasopharyngeal cancer is complex and varies based on the location and extent of disease. PURPOSE: The current study aimed to develop an auto-contouring solution following one protocol guidelines (NRG-HN001) that can be adjusted to meet other guidelines, such as RTOG-0225 and the 2018 International guidelines. METHODS: The study used 2-channel 3-dimensional U-Net and nnU-Net framework to auto-contour 27 normal structures in the head and neck (H&N) region that are used to define CTVs in the protocol. To define the CTV-Expansion (CTV1 and CTV2) and CTV-Overall (the outer envelope of all the CTV contours), we used adjustable morphological geometric landmarks and mimicked physician interpretation of the protocol rules by partially or fully including select anatomic structures. The results were evaluated quantitatively using the dice similarity coefficient (DSC) and mean surface distance (MSD) and qualitatively by independent reviews by two H&N radiation oncologists. RESULTS: The auto-contouring tool showed high accuracy for nasopharyngeal CTVs. Comparison between auto-contours and clinical contours for 19 patients with cancers of various stages showed a DSC of 0.94 ± 0.02 and MSD of 0.4 ± 0.4 mm for CTV-Expansion and a DSC of 0.83 ± 0.02 and MSD of 2.4 ± 0.5 mm for CTV-Overall. Upon independent review, two H&N physicians found the auto-contours to be usable without edits in 85% and 75% of cases. In 15% of cases, minor edits were required by both physicians. Thus, one physician rated 100% of the auto-contours as usable (use as is, or after minor edits), while the other physician rated 90% as usable. The second physician required major edits in 10% of cases. CONCLUSIONS: The study demonstrates the ability of an auto-contouring tool to reliably delineate nasopharyngeal CTVs based on protocol guidelines. The tool was found to be clinically acceptable by two H&N radiation oncology physicians in at least 90% of the cases.

Artificial Intelligence Uncertainty Quantification in Radiotherapy Applications - A Scoping Review.

Background/purpose: The use of artificial intelligence (AI) in radiotherapy (RT) is expanding rapidly. However, there exists a notable lack of clinician trust in AI models, underscoring the need for effective uncertainty quantification (UQ) methods. The purpose of this study was to scope existing literature related to UQ in RT, identify areas of improvement, and determine future directions. Methods: We followed the PRISMA-ScR scoping review reporting guidelines. We utilized the population (human cancer patients), concept (utilization of AI UQ), context (radiotherapy applications) framework to structure our search and screening process. We conducted a systematic search spanning seven databases, supplemented by manual curation, up to January 2024. Our search yielded a total of 8980 articles for initial review. Manuscript screening and data extraction was performed in Covidence. Data extraction categories included general study characteristics, RT characteristics, AI characteristics, and UQ characteristics. Results: We identified 56 articles published from 2015-2024. 10 domains of RT applications were represented; most studies evaluated auto-contouring (50%), followed by image-synthesis (13%), and multiple applications simultaneously (11%). 12 disease sites were represented, with head and neck cancer being the most common disease site independent of application space (32%). Imaging data was used in 91% of studies, while only 13% incorporated RT dose information. Most studies focused on failure detection as the main application of UQ (60%), with Monte Carlo dropout being the most commonly implemented UQ method (32%) followed by ensembling (16%). 55% of studies did not share code or datasets. Conclusion: Our review revealed a lack of diversity in UQ for RT applications beyond auto-contouring. Moreover, there was a clear need to study additional UQ methods, such as conformal prediction. Our results may incentivize the development of guidelines for reporting and implementation of UQ in RT.

METRIC-GUIDED IMAGE RECONSTRUCTION BOUNDS VIA CONFORMAL PREDICTION.

Recent advancements in machine learning have led to the development of novel medical imaging systems and algorithms that address ill-posed problems. Assessing their trustworthiness and understanding how to deploy them safely at test time remains an important and open problem. In this work, we propose using conformal prediction to compute valid and distribution-free bounds on downstream metrics given reconstructions generated by one algorithm, and retrieve upper/lower bounds and inlier/outlier reconstructions according to the adjusted bounds. Our work offers 1) test time image reconstruction evaluation without ground truth, 2) downstream performance guarantees, 3) meaningful upper/lower bound reconstructions, and 4) meaningful statistical inliers/outlier reconstructions. We demonstrate our method on post-mastectomy radiotherapy planning using 3D breast CT reconstructions, and show 1) that metric-guided bounds have valid coverage for downstream metrics while conventional pixel-wise bounds do not and 2) anatomical differences of upper/lower bounds between metric-guided and pixel-wise methods. Our work paves way for more meaningful and trustworthy test-time evaluation of medical image reconstructions. Code available at https://github.com/matthewyccheung/conformal-metric.

Functional analysis of the zinc finger modules of the Saccharomyces cerevisiae splicing factor Luc7.

Identification of splice sites is a critical step in pre-messenger RNA (pre-mRNA) splicing because the definition of the exon/intron boundaries controls what nucleotides are incorporated into mature mRNAs. The intron boundary with the upstream exon is initially identified through interactions with the U1 small nuclear ribonucleoprotein (snRNP). This involves both base-pairing between the U1 snRNA and the pre-mRNA as well as snRNP proteins interacting with the 5' splice site (5'ss)/snRNA duplex. In yeast, this duplex is buttressed by two conserved protein factors, Yhc1 and Luc7. Luc7 has three human paralogs (LUC7L, LUC7L2, and LUC7L3), which play roles in alternative splicing. What domains of these paralogs promote splicing at particular sites is not yet clear. Here, we humanized the zinc finger (ZnF) domains of the yeast Luc7 protein in order to understand their roles in splice site selection using reporter assays, transcriptome analysis, and genetic interactions. Although we were unable to determine a function for the first ZnF domain, humanization of the second ZnF domain to mirror that found in LUC7L or LUC7L2 resulted in altered usage of nonconsensus 5'ss. In contrast, the corresponding ZnF domain of LUC7L3 could not support yeast viability. Further, humanization of Luc7 can suppress mutation of the ATPase Prp28, which is involved in U1 release and exchange for U6 at the 5'ss. Our work reveals a role for the second ZnF of Luc7 in splice site selection and suggests that different ZnF domains may have different ATPase requirements for release by Prp28.

Functional Analysis of the Zinc Finger Modules of the S. cerevisiae Splicing Factor Luc7.

Identification of splice sites is a critical step in pre-mRNA splicing since definition of the exon/intron boundaries controls what nucleotides are incorporated into mature mRNAs. The intron boundary with the upstream exon is initially identified through interactions with the U1 snRNP. This involves both base pairing between the U1 snRNA and the pre-mRNA as well as snRNP proteins interacting with the 5' splice site/snRNA duplex. In yeast, this duplex is buttressed by two conserved protein factors, Yhc1 and Luc7. Luc7 has three human paralogs (LUC7L, LUC7L2, and LUC7L3) which play roles in alternative splicing. What domains of these paralogs promote splicing at particular sites is not yet clear. Here, we humanized the zinc finger domains of the yeast Luc7 protein in order to understand their roles in splice site selection using reporter assays, transcriptome analysis, and genetic interactions. While we were unable to determine a function for the first zinc finger domain, humanization of the second zinc finger domain to mirror that found in LUC7L or LUC7L2 resulted in altered usage of nonconsensus 5' splice sites. In contrast, the corresponding zinc finger domain of LUC7L3 could not support yeast viability. Further, humanization of Luc7 can suppress mutation of the ATPase Prp28, which is involved in U1 release and exchange for U6 at the 5' splice site. Our work reveals a role for the second zinc finger of Luc7 in splice site selection and suggests that different zinc finger domains may have different ATPase requirements for release by Prp28.

Clinical acceptability of automatically generated lymph node levels and structures of deglutition and mastication for head and neck radiation therapy.

Background and Purpose: Auto-contouring of complex anatomy in computed tomography (CT) scans is a highly anticipated solution to many problems in radiotherapy. In this study, artificial intelligence (AI)-based auto-contouring models were clinically validated for lymph node levels and structures of swallowing and chewing in the head and neck. Materials and Methods: CT scans of 145 head and neck radiotherapy patients were retrospectively curated. One cohort (n = 47) was used to analyze seven lymph node levels and the other (n = 98) used to analyze 17 swallowing and chewing structures. Separate nnUnet models were trained and validated using the separate cohorts. For the lymph node levels, preference and clinical acceptability of AI vs human contours were scored. For the swallowing and chewing structures, clinical acceptability was scored. Quantitative analyses of the test sets were performed for AI vs human contours for all structures using overlap and distance metrics. Results: Median Dice Similarity Coefficient ranged from 0.77 to 0.89 for lymph node levels and 0.86 to 0.96 for chewing and swallowing structures. The AI contours were superior to or equally preferred to the manual contours at rates ranging from 75% to 91%; there was not a significant difference in clinical acceptability for nodal levels I-V for manual versus AI contours. Across all AI-generated lymph node level contours, 92% were rated as usable with stylistic to no edits. Of the 340 contours in the chewing and swallowing cohort, 4% required minor edits. Conclusions: An accurate approach was developed to auto-contour lymph node levels and chewing and swallowing structures on CT images for patients with intact nodal anatomy. Only a small portion of test set auto-contours required minor edits.

Polydimethylsiloxane Polymerized Emulsions for Acoustic Materials Prepared Using Reactive Triblock Copolymer Surfactants.

Porous polymers have interesting acoustic properties including wave dampening and acoustic impedance matching and may be used in numerous acoustic applications, e.g., waveguiding or acoustic cloaking. These materials can be prepared by the inclusion of gas-filled voids, or pores, within an elastic polymer network; therefore, porous polymers that have controlled porosity values and a wide range of possible mechanical properties are needed, as these are key factors that impact the sound-dampening properties. Here, the synthesis of acoustic materials with varying porosities and mechanical properties that could be controlled independent of the pore morphology using emulsion-templated polymerizations is described. Polydimethylsiloxane-based ABA triblock copolymer surfactants were prepared using reversible addition-fragmentation chain transfer polymerizations to control the emulsion template and act as an additional cross-linker in the polymerization. Acoustic materials prepared with reactive surfactants possessed a storage modulus of ∼300 kPa at a total porosity of 71% compared to materials prepared using analogous nonreactive surfactants that possessed storage modulus values of ∼150 kPa at similar porosities. These materials display very low longitudinal sound speeds of ∼35 m/s at ultrasonic frequencies, making them excellent candidates in the preparation of acoustic devices such as metasurfaces or lenses.

Fully-automated, CT-only GTV contouring for palliative head and neck radiotherapy.

Planning for palliative radiotherapy is performed without the advantage of MR or PET imaging in many clinics. Here, we investigated CT-only GTV delineation for palliative treatment of head and neck cancer. Two multi-institutional datasets of palliative-intent treatment plans were retrospectively acquired: a set of 102 non-contrast-enhanced CTs and a set of 96 contrast-enhanced CTs. The nnU-Net auto-segmentation network was chosen for its strength in medical image segmentation, and five approaches separately trained: (1) heuristic-cropped, non-contrast images with a single GTV channel, (2) cropping around a manually-placed point in the tumor center for non-contrast images with a single GTV channel, (3) contrast-enhanced images with a single GTV channel, (4) contrast-enhanced images with separate primary and nodal GTV channels, and (5) contrast-enhanced images along with synthetic MR images with separate primary and nodal GTV channels. Median Dice similarity coefficient ranged from 0.6 to 0.7, surface Dice from 0.30 to 0.56, and 95th Hausdorff distance from 14.7 to 19.7 mm across the five approaches. Only surface Dice exhibited statistically-significant difference across these five approaches using a two-tailed Wilcoxon Rank-Sum test (p ≤ 0.05). Our CT-only results met or exceeded published values for head and neck GTV autocontouring using multi-modality images. However, significant edits would be necessary before clinical use in palliative radiotherapy.

Deep learning-based dose prediction to improve the plan quality of volumetric modulated arc therapy for gynecologic cancers.

BACKGROUND: In recent years, deep-learning models have been used to predict entire three-dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated. PURPOSE: To develop a deep-learning model to predict high-quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements. METHODS: A total of 79 VMAT plans for the female pelvis were used to train (47 plans), validate (16 plans), and test (16 plans) 3D dense dilated U-Net models to predict 3D dose distributions. The models received the normalized CT scan, dose prescription, and target and normal tissue contours as inputs. Three models were used to predict the dose distributions for plans in the test set. A radiation oncologist specializing in the treatment of gynecologic cancers scored the test set predictions using a 5-point scale (5, acceptable as-is; 4, prefer minor edits; 3, minor edits needed; 2, major edits needed; and 1, unacceptable). The clinical plans for which the dose predictions indicated that improvements could be made were reoptimized with constraints extracted from the predictions. RESULTS: The predicted dose distributions in the test set were of comparable quality to the clinical plans. The mean voxel-wise dose difference was -0.14 ± 0.46 Gy. The percentage dose differences in the predicted target metrics of D 1 % ${D}_{1{\mathrm{\% }}}$ and D 98 % ${D}_{98{\mathrm{\% }}}$ were -1.05% ± 0.59% and 0.21% ± 0.28%, respectively. The dose differences in the predicted organ at risk mean and maximum doses were -0.30 ± 1.66 Gy and -0.42 ± 2.07 Gy, respectively. A radiation oncologist deemed all of the predicted dose distributions clinically acceptable; 12 received a score of 5, and four received a score of 4. Replanning of flagged plans (five plans) showed that the original plans could be further optimized to give dose distributions close to the predicted dose distributions. CONCLUSIONS: Deep-learning dose prediction can be used to predict high-quality and clinically acceptable dose distributions for VMAT female pelvis plans, which can then be used to identify plans that can be improved with additional optimization.

A real-time contouring feedback tool for consensus-based contour training.

Purpose: Variability in contouring structures of interest for radiotherapy continues to be challenging. Although training can reduce such variability, having radiation oncologists provide feedback can be impractical. We developed a contour training tool to provide real-time feedback to trainees, thereby reducing variability in contouring. Methods: We developed a novel metric termed localized signed square distance (LSSD) to provide feedback to the trainee on how their contour compares with a reference contour, which is generated real-time by combining trainee contour and multiple expert radiation oncologist contours. Nine trainees performed contour training by using six randomly assigned training cases that included one test case of the heart and left ventricle (LV). The test case was repeated 30 days later to assess retention. The distribution of LSSD maps of the initial contour for the training cases was combined and compared with the distribution of LSSD maps of the final contours for all training cases. The difference in standard deviations from the initial to final LSSD maps, ΔLSSD, was computed both on a per-case basis and for the entire group. Results: For every training case, statistically significant ΔLSSD were observed for both the heart and LV. When all initial and final LSSD maps were aggregated for the training cases, before training, the mean LSSD ([range], standard deviation) was -0.8 mm ([-37.9, 34.9], 4.2) and 0.3 mm ([-25.1, 32.7], 4.8) for heart and LV, respectively. These were reduced to -0.1 mm ([-16.2, 7.3], 0.8) and 0.1 mm ([-6.6, 8.3], 0.7) for the final LSSD maps during the contour training sessions. For the retention case, the initial and final LSSD maps of the retention case were aggregated and were -1.5 mm ([-22.9, 19.9], 3.4) and -0.2 mm ([-4.5, 1.5], 0.7) for the heart and 1.8 mm ([-16.7, 34.5], 5.1) and 0.2 mm ([-3.9, 1.6],0.7) for the LV. Conclusions: A tool that uses real-time contouring feedback was developed and successfully used for contour training of nine trainees. In all cases, the utility was able to guide the trainee and ultimately reduce the variability of the trainee's contouring.

Cellular profiling of a recently-evolved social behavior in cichlid fishes.

Social behaviors are diverse in nature, but it is unclear how conserved genes, brain regions, and cell populations generate this diversity. Here we investigate bower-building, a recently-evolved social behavior in cichlid fishes. We use single nucleus RNA-sequencing in 38 individuals to show signatures of recent behavior in specific neuronal populations, and building-associated rebalancing of neuronal proportions in the putative homolog of the hippocampal formation. Using comparative genomics across 27 species, we trace bower-associated genome evolution to a subpopulation of glia lining the dorsal telencephalon. We show evidence that building-associated neural activity and a departure from quiescence in this glial subpopulation together regulate hippocampal-like neuronal rebalancing. Our work links behavior-associated genomic variation to specific brain cell types and their functions, and suggests a social behavior has evolved through changes in glia.

Synthesis and Applications of Elastomeric Polymerized High Internal Phase Emulsions (PolyHIPEs).

Polymer foams (PFs) are among the most industrially produced polymeric materials, and they are found in applications including aerospace, packaging, textiles, and biomaterials. PFs are predominantly prepared using gas-blowing techniques, but PFs can also be prepared from templating techniques such as polymerized high internal phase emulsions (polyHIPEs). PolyHIPEs have many experimental design variables which control the physical, mechanical, and chemical properties of the resulting PFs. Both rigid and elastic polyHIPEs can be prepared, but while elastomeric polyHIPEs are less commonly reported than hard polyHIPEs, elastomeric polyHIPEs are instrumental in the realization of new materials in applications including flexible separation membranes, energy storage in soft robotics, and 3D-printed soft tissue engineering scaffolds. Furthermore, there are few limitations to the types of polymers and polymerization methods that have been used to prepare elastic polyHIPEs due to the wide range of polymerization conditions that are compatible with the polyHIPE method. In this review, an overview of the chemistry used to prepare elastic polyHIPEs from early reports to modern polymerization methods is provided, focusing on the applications that flexible polyHIPEs are used in. The review consists of four sections organized around polymer classes used in the preparation of polyHIPEs: (meth)acrylics and (meth)acrylamides, silicones, polyesters and polyurethanes, and naturally occurring polymers. Within each section, the common properties, current challenges, and an outlook is suggested on where elastomeric polyHIPEs can be expected to continue to make broad, positive impacts on materials and technology for the future.

Lanthanide Squarate Complexes Containing Mono- and Trivalent Thallium.

The synthesis and structural characterization of 16 new thallium lanthanide squarate complexes and 1 new cerium squarate oxalate complex are presented. These new complexes─Tl[Ln(C4O4)(H2O)5]·C4O4 (Ln = La-Nd) (1), Tl3[Ln3(C4O4)6(H2O)6]·8H2O (Ln = Sm-Lu, Y) (2), Tl[Ce(C4O4)2(H2O)6]·C4O4 (3), and [Ce2(C4O4)2(C2O4)(H2O)8]·2H2O (4)─all contain the squarate ligand bound to the trivalent lanthanides with varying coordination modes and denticities. Of the four new groups of complexes prepared in this work, two groupings contain monovalent thallium and trivalent lanthanides, the most common oxidation states for these metals. One complex (3), however, contains trivalent thallium, which is an unusual and challenging oxidation state to stabilize. The Tl3+ cation is formed from in situ oxidation by way of tetravalent cerium (Ce4+/Ce3+, E° = 1.72 V; Tl3+/Tl+ = 1.252 V), leading to the formation of a Tl3+-Ce3+-squarate complex. Additionally, one complex (4) is unique in this work in that it contains both the squarate and oxalate ligands, the latter of which was formed in situ from squarate. Single-crystal X-ray diffraction analysis reveals that 1 and 2 have a 2D structure constructed from either LnO4(H2O)5 monocapped square antiprismatic (CN = 9) metal centers (for 1) or LnO4(H2O)4 square antiprismatic (CN = 8) metal centers (for 2), 3 is a 1D chain structure constructed from CeO3(H2O)6 monocapped square antiprismatic (CN = 9) cerium centers, and 4 is a 3D framework structure constructed from CeO5(H2O)4 monocapped square antiprismatic (CN = 9) cerium centers. 2 and 4 display rare coordination modes for the squarate ligand. Herein, the synthesis, characterization, and structural descriptions of these new complexes are presented.

A deep-learning-based dose verification tool utilizing fluence maps for a cobalt-60 compensator-based intensity-modulated radiation therapy system.

Background and purpose: A novel cobalt-60 compensator-based intensity-modulated radiation therapy (IMRT) system was developed for a resource-limited environment but lacked an efficient dose verification algorithm. The aim of this study was to develop a deep-learning-based dose verification algorithm for accurate and rapid dose predictions. Materials and methods: A deep-learning network was employed to predict the doses from static fields related to beam commissioning. Inputs were a cube-shaped phantom, a beam binary mask, and an intersecting volume of the phantom and beam binary mask, while output was a 3-dimensional (3D) dose. The same network was extended to predict patient-specific doses for head and neck cancers using two different approaches. A field-based method predicted doses for each field and combined all calculated doses into a plan, while the plan-based method combined all nine fluences into a plan to predict doses. Inputs included patient computed tomography (CT) scans, binary beam masks, and fluence maps truncated to the patient's CT in 3D. Results: For static fields, predictions agreed well with ground truths with average deviations of less than 0.5% for percent depth doses and profiles. Even though the field-based method showed excellent prediction performance for each field, the plan-based method showed better agreement between clinical and predicted dose distributions. The distributed dose deviations for all planned target volumes and organs at risk were within 1.3 Gy. The calculation speed for each case was within two seconds. Conclusions: A deep-learning-based dose verification tool can accurately and rapidly predict doses for a novel cobalt-60 compensator-based IMRT system.

Acute inflammation alters lung lymphocytes and potentiates innate-like behavior in young mouse lung CD8 T cells, resembling lung CD8 T cells from old mice.

Inflammation plays a significant role in lung infection including that caused by Mycobacterium tuberculosis, in which both adaptive and innate lymphocytes can affect infection control. How inflammation affects infection is understood in a broad sense, including inflammaging (chronic inflammation) seen in the elderly, but the explicit role that inflammation can play in regulation of lymphocyte function is not known. To fill this knowledge gap, we used an acute lipopolysaccharide (LPS) treatment in young mice and studied lymphocyte responses, focusing on CD8 T cell subsets. LPS treatment decreased the total numbers of T cells in the lungs of LPS mice while also increasing the number of activated T cells. We demonstrate that lung CD8 T cells from LPS mice became capable of an antigen independent innate-like IFN-γ secretion, dependent on IL-12p70 stimulation, paralleling innate-like IFN-γ secretion of lung CD8 T cells from old mice. Overall, this study provides information on how acute inflammation can affect lymphocytes, particularly CD8 T cells, which could potentially affect immune control of various disease states.

Synthesis of patterned polyHIPE-hydrogel composite materials using thiol-ene chemistry.

Elastomeric materials combining multiple properties within a single composite are highly desired in applications including biomaterials interfaces, actuators, and soft robotics. High spatial resolution is required to impart different properties across the composite for the intended application, but many techniques used to prepare these composites rely on multistep and complex methods. There is a need for the development of simple and efficient platforms to design layered composite materials. Here, we report the synthesis of horizontally- and vertically-patterned composites consisting of PDMS-based polymerized high internal phase emulsion (polyHIPE) porous elastomers and PDMS/PEG hydrogels. Composites with defined interfaces that were mechanically robust were prepared, and rheological analysis of the polyHIPE and hydrogel layers showed storage moduli values of âˆ¼ 35 kPa and 45 kPa respectively. The compressive Young's Modulus and maximum strain of the polyHIPEs were dependent on the thiol to ene ratio in the formulation and obtained values ranging from 6 to 25 kPa and 50-65% respectively. The mechanical properties, total porosity of the polyHIPE, and swelling ratio of the hydrogel were unaffected by the patterning technique compared to non-patterned controls. PolyHIPE-hydrogel composite materials having up to 7-different horizontally pattered layers could be prepared that could expand and contract up hydration and drying.