Multi-Scale Analysis of the Damage Evolution of Coal Gangue Coarse Aggregate Concrete after Freeze-Thaw Cycle Based on CT Technology.

This study utilized X-ray computed tomography (CT) technology to analyze the meso-structure of concrete at different replacement rates, using a coal gangue coarse aggregate, after experiencing various freeze-thaw cycles (F-Ts). A predictive model for the degradation of the elastic modulus of Coal Gangue coarse aggregate Concrete (CGC), based on mesoscopic damage, was established to provide an interpretation of the macroscopic mechanical behavior of CGC after F-Ts damage at a mesoscopic scale. It was found that after F-Ts, the compressive strength of concrete, with coal gangue replacement rates of 30%, 60%, and 100%, respectively, decreased by 33.76%, 34.89%, and 42.05% compared with unfrozen specimens. The results indicate that an increase in the coal gangue replacement rate exacerbates the degradation of concrete performance during the F-Ts process. Furthermore, the established predictive formula for elastic modulus degradation closely matches the experimental data, offering a reliable theoretical basis for the durability design of CGC in F-Ts environments.

Cross-scale multi-instance learning for pathological image diagnosis.

Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20× magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.

Performance of an Artificial Intelligence Model for Recognition and Quantitation of Histologic Features of Eosinophilic Esophagitis on Biopsy Samples.

We have developed an artificial intelligence (AI)-based digital pathology model for the evaluation of histologic features related to eosinophilic esophagitis (EoE). In this study, we evaluated the performance of our AI model in a cohort of pediatric and adult patients for histologic features included in the Eosinophilic Esophagitis Histologic Scoring System (EoEHSS). We collected a total of 203 esophageal biopsy samples from patients with mucosal eosinophilia of any degree (91 adult and 112 pediatric patients) and 10 normal controls from a prospectively maintained database. All cases were assessed by a specialized gastrointestinal (GI) pathologist for features in the EoEHSS at the time of original diagnosis and rescored by a central GI pathologist (R.K.M.). We subsequently analyzed whole-slide image digital slides using a supervised AI model operating in a cloud-based, deep learning AI platform (Aiforia Technologies) for peak eosinophil count (PEC) and several histopathologic features in the EoEHSS. The correlation and interobserver agreement between the AI model and pathologists (Pearson correlation coefficient [rs] = 0.89 and intraclass correlation coefficient [ICC] = 0.87 vs original pathologist; rs = 0.91 and ICC = 0.83 vs central pathologist) were similar to the correlation and interobserver agreement between pathologists for PEC (rs = 0.88 and ICC = 0.91) and broadly similar to those for most other histologic features in the EoEHSS. The AI model also accurately identified PEC of >15 eosinophils/high-power field by the original pathologist (area under the curve [AUC] = 0.98) and central pathologist (AUC = 0.98) and had similar AUCs for the presence of EoE-related endoscopic features to pathologists' assessment. Average eosinophils per epithelial unit area had similar performance compared to AI high-power field-based analysis. Our newly developed AI model can accurately identify, quantify, and score several of the main histopathologic features in the EoE spectrum, with agreement regarding EoEHSS scoring which was similar to that seen among GI pathologists.

Deep multimodal fusion of image and non-image data in disease diagnosis and prognosis: a review.

The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the personalized diagnosis and treatment planning for a single cancer patient relies on various images (e.g. radiology, pathology and camera images) and non-image data (e.g. clinical data and genomic data). However, such decision-making procedures can be subjective, qualitative, and have large inter-subject variabilities. With the recent advances in multimodal deep learning technologies, an increasingly large number of efforts have been devoted to a key question: how do we extract and aggregate multimodal information to ultimately provide more objective, quantitative computer-aided clinical decision making? This paper reviews the recent studies on dealing with such a question. Briefly, this review will include the (a) overview of current multimodal learning workflows, (b) summarization of multimodal fusion methods, (c) discussion of the performance, (d) applications in disease diagnosis and prognosis, and (e) challenges and future directions.

Influence of Cell-type Ratio on Spatially Resolved Single-cell Transcriptomes using the Tangram Algorithm: Based on Implementation on Single-Cell and MxIF Data.

The Tangram algorithm is a benchmarking method of aligning single-cell (sc/snRNA-seq) data to various forms of spatial data collected from the same region. With this data alignment, the annotation of the single-cell data can be projected to spatial data. However, the cell composition (cell-type ratio) of the single-cell data and spatial data might be different because of heterogeneous cell distribution. Whether the Tangram algorithm can be adapted when the two data have different cell-type ratios has not been discussed in previous works. In our practical application that maps the cell-type classification results of single-cell data to the Multiplex immunofluorescence (MxIF) spatial data, cell-type ratios were different, though they were sampled from adjacent areas. In this work, both simulation and empirical validation were conducted to quantitatively explore the impact of the mismatched cell-type ratio on the Tangram mapping in different situations. Results show that the cell-type difference has a negative influence on classification accuracy.

Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images.

Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training. Unfortunately, many prior anomaly detection methods were optimized for a specific "known" abnormality (e.g., brain tumor, bone fraction, cell types). Moreover, even though only the normal images were used in the training process, the abnormal images were often employed during the validation process (e.g., epoch selection, hyper-parameter tuning), which might leak the supposed "unknown" abnormality unintentionally. In this study, we investigated these two essential aspects regarding universal anomaly detection in medical images by (1) comparing various anomaly detection methods across four medical datasets, (2) investigating the inevitable but often neglected issues on how to unbiasedly select the optimal anomaly detection model during the validation phase using only normal images, and (3) proposing a simple decision-level ensemble method to leverage the advantage of different kinds of anomaly detection without knowing the abnormality. The results of our experiments indicate that none of the evaluated methods consistently achieved the best performance across all datasets. Our proposed method enhanced the robustness of performance in general (average AUC 0.956).

Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning.

Multi-class cell segmentation in high-resolution Giga-pixel whole slide images (WSI) is critical for various clinical applications. Training such an AI model typically requires labor-intensive pixel-wise manual annotation from experienced domain experts (e.g., pathologists). Moreover, such annotation is error-prone when differentiating fine-grained cell types (e.g., podocyte and mesangial cells) via the naked human eye. In this study, we assess the feasibility of democratizing pathological AI deployment by only using lay annotators (annotators without medical domain knowledge). The contribution of this paper is threefold: (1) We proposed a molecular-empowered learning scheme for multi-class cell segmentation using partial labels from lay annotators; (2) The proposed method integrated Giga-pixel level molecular-morphology cross-modality registration, molecular-informed annotation, and molecular-oriented segmentation model, so as to achieve significantly superior performance via 3 lay annotators as compared with 2 experienced pathologists; (3) A deep corrective learning (learning with imperfect label) method is proposed to further improve the segmentation performance using partially annotated noisy data. From the experimental results, our learning method achieved F1 = 0.8496 using molecular-informed annotations from lay annotators, which is better than conventional morphology-based annotations (F1 = 0.7015) from experienced pathologists. Our method democratizes the development of a pathological segmentation deep model to the lay annotator level, which consequently scales up the learning process similar to a non-medical computer vision task. The official implementation and cell annotations are publicly available at https://github.com/hrlblab/MolecularEL.

Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images.

Multi-instance learning (MIL) is widely used in the computer-aided interpretation of pathological Whole Slide Images (WSIs) to solve the lack of pixel-wise or patch-wise annotations. Often, this approach directly applies "natural image driven" MIL algorithms which overlook the multi-scale (i.e. pyramidal) nature of WSIs. Off-the-shelf MIL algorithms are typically deployed on a single-scale of WSIs (e.g., 20× magnification), while human pathologists usually aggregate the global and local patterns in a multi-scale manner (e.g., by zooming in and out between different magnifications). In this study, we propose a novel cross-scale attention mechanism to explicitly aggregate inter-scale interactions into a single MIL network for Crohn's Disease (CD), which is a form of inflammatory bowel disease. The contribution of this paper is two-fold: (1) a cross-scale attention mechanism is proposed to aggregate features from different resolutions with multi-scale interaction; and (2) differential multi-scale attention visualizations are generated to localize explainable lesion patterns. By training ~250,000 H&E-stained Ascending Colon (AC) patches from 20 CD patient and 30 healthy control samples at different scales, our approach achieved a superior Area under the Curve (AUC) score of 0.8924 compared with baseline models. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.

Development and technical validation of an artificial intelligence model for quantitative analysis of histopathologic features of eosinophilic esophagitis.

Background: In an attempt to provide quantitative, reproducible, and standardized analyses in cases of eosinophilic esophagitis (EoE), we have developed an artificial intelligence (AI) digital pathology model for the evaluation of histologic features in the EoE/esophageal eosinophilia spectrum. Here, we describe the development and technical validation of this novel AI tool. Methods: A total of 10 726 objects and 56.2 mm2 of semantic segmentation areas were annotated on whole-slide images, utilizing a cloud-based, deep learning artificial intelligence platform (Aiforia Technologies, Helsinki, Finland). Our training set consisted of 40 carefully selected digitized esophageal biopsy slides which contained the full spectrum of changes typically seen in the setting of esophageal eosinophilia, ranging from normal mucosa to severe abnormalities with regard to each specific features included in our model. A subset of cases was reserved as independent "test sets" in order to assess the validity of the AI model outside the training set. Five specialized experienced gastrointestinal pathologists scored each feature blindly and independently of each other and of AI model results. Results: The performance of the AI model for all cell type features was similar/non-inferior to that of our group of GI pathologists (F1-scores: 94.5-94.8 for AI vs human and 92.6-96.0 for human vs human). Segmentation area features were rated for accuracy using the following scale: 1. "perfect or nearly perfect" (95%-100%, no significant errors), 2. "very good" (80%-95%, only minor errors), 3. "good" (70%-80%, significant errors but still captures the feature well), 4. "insufficient" (less than 70%, significant errors compromising feature recognition). Rating scores for tissue (1.01), spongiosis (1.15), basal layer (1.05), surface layer (1.04), lamina propria (1.15), and collagen (1.11) were in the "very good" to "perfect or nearly perfect" range, while degranulation (2.23) was rated between "good" and "very good". Conclusion: Our newly developed AI-based tool showed an excellent performance (non-inferior to a group of experienced GI pathologists) for the recognition of various histologic features in the EoE/esophageal mucosal eosinophilia spectrum. This tool represents an important step in creating an accurate and reproducible method for semi-automated quantitative analysis to be used in the evaluation of esophageal biopsies in this clinical context.

Cryptotanshinone inhibits ovarian tumor growth and metastasis by degrading c-Myc and attenuating the FAK signaling pathway.

Cryptotanshinone (CT), a natural compound derived from Salvia miltiorrhiza Bunge that is also known as the traditional Chinese medicine Danshen, exhibits antitumor activity in various cancers. However, it remains unclear whether CT has a potential therapeutic benefit against ovarian cancers. The aim of this study was to test the efficacy of CT in ovarian cancer cells in vitro and using a xenograft model in NSG mice orthotopically implanted with HEY A8 human ovarian cancer cells and to explore the molecular mechanism(s) underlying CT's antitumor effects. We found that CT inhibited the proliferation, migration, and invasion of OVCAR3 and HEY A8 cells, while sensitizing the cell responses to the chemotherapy drugs paclitaxel and cisplatin. CT also suppressed ovarian tumor growth and metastasis in immunocompromised mice orthotopically inoculated with HEY A8 cells. Mechanistically, CT degraded the protein encoded by the oncogene c-Myc by promoting its ubiquitination and disrupting the interaction with its partner protein Max. CT also attenuated signaling via the nuclear focal adhesion kinase (FAK) pathway and degraded FAK protein in both cell lines. Knockdown of c-Myc using lentiviral CRISPR/Cas9 nickase resulted in reduction of FAK expression, which phenocopies the effects of CT and the c-Myc/Max inhibitor 10058-F4. Taken together, our studies demonstrate that CT inhibits primary ovarian tumor growth and metastasis by degrading c-Myc and FAK and attenuating the FAK signaling pathway.

Tumor microenvironment in pancreatic ductal adenocarcinoma: Implications in immunotherapy.

Pancreatic ductal adenocarcinoma is one of the most aggressive and lethal cancers. Surgical resection is the only curable treatment option, but it is available for only a small fraction of patients at the time of diagnosis. With current therapeutic regimens, the average 5-year survival rate is less than 10% in pancreatic cancer patients. Immunotherapy has emerged as one of the most promising treatment options for multiple solid tumors of advanced stage. However, its clinical efficacy is suboptimal in most clinical trials on pancreatic cancer. Current studies have suggested that the tumor microenvironment is likely the underlying barrier affecting immunotherapy drug efficacy in pancreatic cancer. In this review, we discuss the role of the tumor microenvironment in pancreatic cancer and the latest advances in immunotherapy on pancreatic cancer.

Histological assessment of new cholangioscopy-guided forceps in ERCP biliary stricture sampling: a blinded comparative study.

Background and study aims Obtaining quality tissue during ERCP biliary stricture sampling is of paramount importance for a timely diagnosis. While single-operator cholangioscopy (SOC)-guided biopsies have been suggested to be the superior biliary tissue acquisition modality given direct tissue visualization, less is known about the specimen histological quality. We aimed to analyze the specimen quality of SOC biopsies and compare the new generation forceps with prior "legacy" forceps. Patients and methods Patients who underwent SOC from January 2017-August 2021 for biliary sampling were reviewed. In February 2020, the SOC-guided biopsy forceps were changed from legacy SpyBite to the SpyBite Max forceps (max). Specimens were assessed by blinded pathologists for crush artifact (none, mild, or severe) and gross size (greatest dimension in mm). Crush artifact and gross size were compared between the two groups. The diagnostic performance characteristics for cholangiocarcinoma (CCA), were assessed in an exploratory fashion. Results Eighty-one patients (max = 27, legacy = 54) with similar baseline characteristics were included in this study. On blinded pathological assessment, 58 % had crush artifact, without significant differences between the two groups (Max 63 % vs. Legacy 56 %; P  = 0.64). A similar mean specimen size was found (max 3 mm vs. legacy 3.2 mm; P  = 0.24). The overall prevalence of CCA was 40 %. The sensitivity, specificity, positive predictive value, and negative predictive value of the entire cohort using a combination of cytology, fluorescence in situ hybridization, and SOC-guided biopsies were 78.1 %, 91.8 %, 86.2 %, and 86.5 %, respectively. No difference between legacy or max groups was found. Conclusions A high rate of crush artifact was found in SOC-guided biopsy specimens. Further investigation regarding proper biopsy technique and handling is necessary to increase the diagnostic yield with SOC-guided biopsies.

Adipose-Derived Stem Cells Facilitate Ovarian Tumor Growth and Metastasis by Promoting Epithelial to Mesenchymal Transition Through Activating the TGF-ß Pathway.

Adipose-derived stem cells (ADSC) are multipotent mesenchymal stem cells derived from adipose tissues and are capable of differentiating into multiple cell types in the tumor microenvironment (TME). The roles of ADSC in ovarian cancer (OC) metastasis are still not well defined. To understand whether ADSC contributes to ovarian tumor metastasis, we examined epithelial to mesenchymal transition (EMT) markers in OC cells following the treatment of the ADSC-conditioned medium (ADSC-CM). ADSC-CM promotes EMT in OC cells. Functionally, ADSC-CM promotes OC cell proliferation, survival, migration, and invasion. We further demonstrated that ADSC-CM induced EMT via TGF-ß growth factor secretion from ADSC and the ensuing activation of the TGF-ß pathway. ADSC-CM-induced EMT in OC cells was reversible by the TGF-ß inhibitor SB431542 treatment. Using an orthotopic OC mouse model, we also provide the experimental evidence that ADSC contributes to ovarian tumor growth and metastasis by promoting EMT through activating the TGF-ß pathway. Taken together, our data indicate that targeting ADSC using the TGF-ß inhibitor has the therapeutic potential in blocking the EMT and OC metastasis.

An Adolescent with Chanarin-Dorfman Syndrome Presenting with Ichthyosis and Hepatic Steatosis.

Chanarin-Dorfman syndrome also known as neutral lipid storage disease is a rare multisystemic autosomal recessive disorder. It is mostly encountered in patients of Mediterranean and Middle Eastern origin. Most patients are brought to medical attention secondary to dermatological manifestations namely ichthyosis. Here, we report a 10-year-old Kurdish male patient with ichthyosis, who was referred to pediatric liver clinic for transaminase elevation of unknown etiology despite elaborate workup. Histological findings on liver biopsy were consistent with nonalcoholic steatohepatitis. Genetic testing identified homozygous mutation C.776G>A (p.G259D) in the Abhydrolase domain containing 5 gene on chromosome 3 described in patients with Chanarin-Dorfman syndrome. After the initiation of a diet with high medium chain triglycerides/long chain triglycerides ratio, aerobic exercise, and vitamin E, the patient liver enzymes improved. Due to debilitating ichthyosis, he was started on acitretin therapy that was discontinued due to transaminases elevation. Patient is currently stable and doing well.

Villin-1 and Gelsolin Regulate Changes in Actin Dynamics That Affect Cell Survival Signaling Pathways and Intestinal Inflammation.

BACKGROUND & AIMS: Cell stress signaling pathways result in phosphorylation of the eukaryotic translation initiation factor 2 subunit alpha (EIF2S1 or EIF2A), which affects regulation of protein translation. Translation reprogramming mitigates stress by activating pathways that result in autophagy and cell death, to eliminate damaged cells. Actin is modified during stress and EIF2A is dephosphorylated to restore homeostasis. It is not clear how actin affects EIF2A signaling. We studied the actin-binding proteins villin 1 (VIL1) and gelsolin (GSN) in intestinal epithelial cells (IECs) to determine whether they respond to cell stress response and affect signaling pathways. METHODS: We performed studies with mice with disruptions in Vil1 and Gsn (double-knockout mice). Wild-type (WT) mice either were or were not (controls) exposed to cell stressors such as tumor necrosis factor and adherent-invasive Escherichia coli. Distal ileum tissues were collected from mice; IECs and enteroids were cultured and analyzed by histology, immunoblots, phalloidin staining, immunohistochemistry, electron microscopy, and flow cytometry. HT-29 cells were incubated with cell stressors such as DTT, IFN, and adherent-invasive E coli or control agents; cells were analyzed by immunoblots and quantitative polymerase chain reaction. Green fluorescent protein and green fluorescent protein tagged mutant EIF2A were expressed from a lentiviral vector. The mouse immunity-related GTPase (IRGM1) was overexpressed in embryonic fibroblasts from dynamin1 like (DNM1L) protein-knockout mice or their WT littermates. IRGM1 was overexpressed in embryonic fibroblasts from receptor interacting serine/threonine kinase 1-knockout mice or their WT littermates. Human IRGM was overexpressed in human epithelial cell lines incubated with the DNM1L-specific inhibitor Mdivi-1. Mitochondria were analyzed by semi-quantitative confocal imaging. We performed immunohistochemical analyses of distal ileum tissues from 6-8 patients with Crohn's disease (CD) and 6-8 individuals without CD (controls). RESULTS: In IECs exposed to cell stressors, EIF2A signaling reduced expression of VIL1 and GSN. However, VIL1 and GSN were required for dephosphorylation of EIF2A and recovery from cell stress. In mouse and human IECs, prolonged, unresolved stress was accompanied by continued down-regulation of VIL1 and GSN, resulting in constitutive phosphorylation of EIF2A and overexpression of IRGM1 (or IRGM), which regulates autophagy. Overexpression of IRGM1 (or IRGM) induced cell death by necroptosis, accompanied by release of damage-associated molecular patterns (DAMPs). In double-knockout mice, constitutive phosphorylation of EIF2A and over-expression of IRGM1 resulted in spontaneous ileitis that resembled human CD in symptoms and histology. Distal ileum tissues from patients with CD had lower levels of VIL1 and GSN, increased phosphorylation of EIF2A, increased levels of IRGM and necroptosis, and increased release of nuclear DAMPs compared with controls. CONCLUSIONS: In studies of intestinal epithelial tissues from patients with CD and embryonic fibroblasts from mice, along with enteroids and human IEC lines, we found that induction of cell stress alters the cytoskeleton in IECs via changes in the actin-binding proteins VIL1 and GSN. Acute changes in actin dynamics increase IEC survival, whereas long-term changes in actin dynamics lead to IEC death and intestinal inflammation. IRGM regulates necroptosis and release of DAMPs to induce gastrointestinal inflammation, linking IRGM activity with CD.

Both the anti- and pro-apoptotic functions of villin regulate cell turnover and intestinal homeostasis.

In the small intestine, epithelial cells are derived from stem cells in the crypts, migrate up the villus as they differentiate and are ultimately shed from the villus tips. This process of proliferation and shedding is tightly regulated to maintain the intestinal architecture and tissue homeostasis. Apoptosis regulates both the number of stem cells in the crypts as well as the sloughing of cells from the villus tips. Previously, we have shown that villin, an epithelial cell-specific actin-binding protein functions as an anti-apoptotic protein in the gastrointestinal epithelium. The expression of villin is highest in the apoptosis-resistant villus cells and lowest in the apoptosis-sensitive crypts. In this study we report that villin is cleaved in the intestinal mucosa to generate a pro-apoptotic fragment that is spatially restricted to the villus tips. This cleaved villin fragment severs actin in an unregulated fashion to initiate the extrusion and subsequent apoptosis of effete cells from the villus tips. Using villin knockout mice, we validate the physiological role of villin in apoptosis and cell extrusion from the gastrointestinal epithelium. Our study also highlights the potential role of villin's pro-apoptotic function in the pathogenesis of inflammatory bowel disease, ischemia-reperfusion injury, enteroinvasive bacterial and parasitic infections.

Autotaxin and LPA1 and LPA5 receptors exert disparate functions in tumor cells versus the host tissue microenvironment in melanoma invasion and metastasis.

UNLABELLED: Autotaxin (ENPP2/ATX) and lysophosphatidic acid (LPA) receptors represent two key players in regulating cancer progression. The present study sought to understand the mechanistic role of LPA G protein-coupled receptors (GPCR), not only in the tumor cells but also in stromal cells of the tumor microenvironment. B16F10 melanoma cells predominantly express LPA5 and LPA2 receptors but lack LPA1. LPA dose dependently inhibited invasion of cells across a Matrigel layer. RNAi-mediated knockdown of LPA5 relieved the inhibitory effect of LPA on invasion without affecting basal invasion. This suggests that LPA5 exerts an anti-invasive action in melanoma cells in response to LPA. In addition, both siRNA-mediated knockdown and pharmacologic inhibition of LPA2 reduced the basal rate invasion. Unexpectedly, when probing the role of this GPCR in host tissues, it was found that the incidence of melanoma-derived lung metastasis was greatly reduced in LPA5 knockout (KO) mice compared with wild-type (WT) mice. LPA1-KO but not LPA2-KO mice also showed diminished melanoma-derived lung metastasis, suggesting that host LPA1 and LPA5 receptors play critical roles in the seeding of metastasis. The decrease in tumor cell residence in the lungs of LPA1-KO and LPA5-KO animals was apparent 24 hours after injection. However, KO of LPA1, LPA2, or LPA5 did not affect the subcutaneous growth of melanoma tumors. IMPLICATIONS: These findings suggest that tumor and stromal LPA receptors, in particular LPA1 and LPA5, play different roles in invasion and the seeding of metastasis.

Dynamic implicit-solvent coarse-grained models of lipid bilayer membranes: fluctuating hydrodynamics thermostat.

We introduce a thermostat based on fluctuating hydrodynamics for dynamic simulations of implicit-solvent coarse-grained models of lipid bilayer membranes. We show our fluctuating hydrodynamics approach captures interesting correlations in the dynamics of lipid bilayer membranes that are missing in simulations performed using standard Langevin dynamics. Our momentum conserving thermostat accounts for solvent-mediated momentum transfer by coupling coarse-grained degrees of freedom to stochastic continuum fields that account for both the solvent hydrodynamics and thermal fluctuations. We present both a general framework and specific methods to couple the particle and continuum degrees of freedom in a manner consistent with statistical mechanics and amenable to efficient computational simulation. For self-assembled vesicles, we study the diffusivity of lipids and their spatial correlations. We find the hydrodynamic coupling yields within the bilayer interesting correlations between diffusing lipids that manifest as a vortex-like structure similar to those observed in explicit-solvent simulations. We expect the introduced fluctuating hydrodynamics methods to provide a way to extend implicit-solvent models for use in a wide variety of dynamic studies.