Multiple Nodes Research Articles

Osteosarcoma Metastasis to the Thorax: A Pictorial Review of Chest Computed Tomography Findings.

Background: Osteosarcoma, a primary bone malignancy in children and adolescents, frequently metastasizes to the lungs, contributing significantly to morbidity and mortality. Lung Metastases: At diagnosis, 15-20% of patients present with detectable lung metastases. Chest computed tomography (CT) is vital for the early detection and monitoring of these metastases. Lung involvement typically presents as multiple nodules of varying sizes and can include atypical features such as cavitation, cystic lesions, ground-glass halos, intravascular tumor thrombi, and endobronchial disease. Additional Findings: Pleural metastasis often occurs alongside pulmonary disease, and complications like spontaneous pneumothorax may arise. Additional findings may include thoracic lymphadenopathy, cardiac tumor thrombus, and chest wall deposits. Conclusion: Familiarity with these imaging patterns is essential for radiologists to ensure timely diagnosis and effective management. This review highlights the critical role of chest CT in detecting and characterizing osteosarcoma metastasis.

Surgical Management of Canine Transmissible Venereal Tumor with Adjunctive Chemotherapy Combination

One male and a female 2-year-old Neapolitan Mastiff dogs weighing 41 and 40 kg, respectively, were presented to the University Veterinary Teaching Hospital, Jos, Nigeria, with complaints of continuous bleeding from the prepuce and vulva which were first observed about 2 months before presentation. Clinical examination revealed preputial bleeding of the male dog and serosanguinous vulva discharges with the presence of palpable masses with multiple nodules that were cauliflower-like. Cytological evaluations of smears from preputial and vaginal swabs revealed characteristic round cells with large eccentric nuclei, large nuclear-cytoplasmic ratio, and intracytoplasmic vacuolations as well as anisocytosis and anisokaryosis. Based on these cytological features, a diagnosis of canine transmissible venereal tumor (CTVT) was made in each case. Blood samples were collected for hematology and clinical chemistry. Debulking of the masses was carried out under general anesthesia using atropine sulfate and chlorpromazine as pre-anesthetics and Ketamine hydrochloride as induction and maintenance anesthetics. Following debulking, the resulting defect was reconstructed and the surgical wound was managed by daily wound cleaning and the administration of vincristine sulfate (0.025 mg/kg) slowly intravenously, ivermectin (0.3 mg/kg) subcutaneously, and piroxicam (0.3 mg/kg) and 20% amoxicillin (20 mg/kg) intramuscularly. There were no observed known vincristine sulfate-associated adverse effects as the dogs recovered and were discharged 3-week post-tumor debulking with no recurrence within 6 months of follow-up examinations. The debulked mass histologically showed several sheets of round neoplastic cells with prominent nuclei, mitotic figures, and infiltrating lymphocytes. Based on these findings, a diagnosis of CTVT was further confirmed. The hemogram showed moderate leukocytosis at day 1 in the bitch with a mild left shift at days 1 and 14 while clinical chemistry showed a marked increase in alkaline phosphatase, moderate increase in serum creatinine, and mild hypoproteinemia. In conclusion, management of CTVT with adjunctive vincristine sulfate and ivermectin combination resulted in complete healing with amelioration of known associated adverse effects.

Explainable fault diagnosis method for process flow based on data augmentation with system graph relationship

The main objective of process industrial systems is to ensure safe operation. Signal fluctuations resulting from equipment failures and variable abnormalities can progressively propagate over the entire system, potentially resulting in fault conditions. However, the sporadic occurrence of fault conditions leads to a scarcity of data samples for diagnosis models. A fault diagnosis method for process flow based on data augmentation with system graph relationships is proposed. Propagation paths of fault signals are generated through graph relationships of systems. Simultaneously, the data of multiple nodes in paths is transformed to generate augmentation samples. To ensure comprehensive learning of the information included in augmentation samples, a large number of samples are generated for pre-training based on real-world data from a system. The explainability analysis method is used to analyze the focus area of diagnostic networks on samples with and without augmentation. The experimental results demonstrate that the proposed method can substantially improve diagnostic accuracy and explainability, hence offering advantages for diagnosis tasks in small sample cases.

Distributed enhanced multi-objective evolutionary algorithm based on decomposition for cluster analysis in wireless sensor network

Conventional clustering algorithms do not recognize patterns and structures with contradicting objectives in large, distributed datasets. Distributed clustering leverages rapid processing capabilities to allow multiple nodes to work together. This paper proposes a Distributed clustering based on Multiobjective Evolutionary Algorithm by Decomposition (D-MOEA/d) to solve various multiobjective optimization problems in wireless sensor networks (WSNs). In MOEA/d, a multiobjective optimization problem decomposes into several scalar optimization subproblems, each focusing on a distinct objective. Each subproblem is expressed as a clustering problem that uses local data to perform distributed clustering. The proposed method has been extended to achieve improved accuracy in less time by using a smaller feature subset with less redundancy. The Distributed Enhanced MOEA/d (DE-MOEA/d) avoids local optima by achieving diversity in the population using fuzzy-based nearest neighbor selection, sparse population initialization, and evolved mutation operator. This integration improves the accuracy of the clustering process at WSN nodes, ensuring the attainment of well-balanced solutions across multiple optimization criteria in the distributed environment. Average Euclidean and total symmetrical deviations are the two cost functions used to minimize while clustering on the MOEA/d framework. Six real-life WSN datasets are used to assess the performance of the proposed technique: (1) the Delhi air pollution dataset, (2) the Canada weather station dataset, (3) the Thames River water quality dataset, (4) the Narragansett Bay water quality dataset, (5) the Cook Agricultural land dataset and 6) Gordon Soil dataset. The simulation results of both proposed algorithms are compared with Multiobjective distributed particle swarm optimization (DMOPSO) and Distributed K-means (DK-Means). The proposed algorithm DE-MOEA/d performs better in terms of the Silhouette index (SI), Dunn index (DI), Davies–Bouldin index (DBI), and Kruskal–Wallis (KW) statistical test.

Survey of federated learning in intrusion detection

Intrusion detection methods are crucial means to mitigate network security issues. However, the challenges posed by large-scale complex network environments include local information islands, regional privacy leaks, communication burdens, difficulties in handling heterogeneous data, and storage resource bottlenecks. Federated learning has the potential to address these challenges by leveraging widely distributed and heterogeneous data, achieving load balancing of storage and computing resources across multiple nodes, and reducing the risks of privacy leaks and bandwidth resource demands. This paper reviews the process of constructing federated learning based intrusion detection system from the perspective of intrusion detection. Specifically, it outlines six main aspects: application scenario analysis, federated learning methods, privacy and security protection, selection of classification models, data sources and client data distribution, and evaluation metrics, establishing them as key research content. Subsequently, six research topics are extracted based on these aspects. These topics include expanding application scenarios, enhancing aggregation algorithm, enhancing security, enhancing classification models, personalizing model and utilizing unlabeled data. Furthermore, the paper delves into research content related to each of these topics through in-depth investigation and analysis. Finally, the paper discusses the current challenges faced by research, and suggests promising directions for future exploration.

A case of grade1 follicular lymphoma diagnosed by laparoscopic lymph node resection: differentiating from late lymph node recurrence of endometrial cancer.

Follicular lymphoma is a common hematologic malignancy; however, it is less common among all malignant diseases and is difficult to suspect in advance due to the lack of specific clinical findings. Here, we report a case in which a late recurrence of corpus cancer was first suspected and finally diagnosed as follicular lymphoma. A 67-year-old female presented to our department with enlarged pelvic lymph nodes. She was diagnosed with breast cancer (HER2-posiotive with lymph node metastasis) and corpus cancer (endometrioid carcinoma grade 2, stage IA) 16years prior, received definitive therapy and was followed up. A positron emission tomography scan was performed, and an accumulation of 18F-fluorodeoxyglucose (FDG) was detected in multiple lymph nodes, including the lymph nodes with no change in size or enlargement. We performed laparoscopic resection of the enlarged and FDG-accumulated lymph nodes and a pathological examination. The patient was diagnosed with follicular lymphoma (FL) grade 1 and is currently under observation at the Department of Hematology. FL can be considered when there is a discrepancy between the change in lymph node size and the degree of FDG accumulation. A pathological examination is useful for accurate diagnosis. Therefore, it is important to consider tissue collection; however, care must be taken to minimize the invasiveness of the procedure for the patient.

Phaeohyphomycosis caused by Trematosphaeria grisea in an immunocompromised English springer spaniel

AbstractA 10‐year‐old, male, farm‐based springer spaniel that presented with subacute progressive left‐sided central vestibular signs was diagnosed with meningoencephalitis of unknown origin. The patient received prednisolone (2 mg/kg/day orally), ciclosporin (5 mg/kg orally twice a day) and a single infusion of cytarabine (200 mg/m2). Six weeks later, neurological signs had resolved, but multiple cutaneous nodules developed on the limbs, trunk and face. Lesions discharged a dark purulent material without tissue grains. A ‘filamentous fungus’ was isolated, but discarded without identification. Aspirates from nodules showed pigmented, septate fungal hyphae, and culture of aspirates yielded Trematosphaeria grisea. Itraconazole (5 mg/kg once a day) and gradual tapering of immunosuppressive drugs led to resolution of nodular lesions over 4 months without neurological relapse. A fungal granuloma developed transiently 8 months after starting itraconazole. Immunosuppressive drugs were stopped 15 months after introduction without neurological relapse. Itraconazole was withdrawn 8 weeks later, 5 months after last relapse of skin disease.

Model for detecting metastatic deposits in lymph nodes of colorectal carcinoma on digital/ non-WSI images

IntroductionColorectal cancer (CRC) constitutes around 10% of global cancer diagnoses and death due to cancer. Treatment involves the surgical resection of the tumor and regional lymph nodes. Assessment of multiple lymph node demands meticulous examination by skilled pathologists, which can be arduous, prompting consideration for an artificial intelligence (AI)-supported workflow due to the growing number of slides to be examined, demanding heightened precision and the global shortage of pathologists.MethodThis was a retrospective cross-sectional study including digital images of glass slides containing sections of positive and negative lymph nodes obtained from radical resection of primary CRC. Lymph nodes from 165 previously diagnosed cases were selected from Agha Khan University Hospital, from Jan 2021 to Jan 2022. The images were prepared at 10X and uploaded into an open source software, Q path and deep learning model Ensemble was applied for the identification of tumor deposits in lymph node.ResultsOut of the 87 positive lymph nodes detected by AI, 73(84%) were true positive and 14(16%) were false positive. The total number of negative lymph nodes detected by AI was 78. Out of these, 69(88.5%) were true negative and 9 (11.5%) were false negative. The sensitivity was 89% and specificity 83.1%. The odds ratio was 40 with a confidence interval of 16.26–98.3. P-value was < 0.05 (< 0.0001).ConclusionThough it was a small study but its results were really appreciating and we encourage more such studies with big sample data in future.

Abstract C036: Vertical inhibition of the autophagy pathway enhances sensitization to RAS MAPK pathway inhibition in pancreatic ductal adenocarcinoma

Abstract PDAC growth is characterized by dependencies on both mutant KRAS signaling and autophagy. Our group and others previously demonstrated that inhibition of the RAF/MEK/ERK pathway in PDAC induces an increased dependence on autophagy, a metabolic nutrient scavenging process by which cellular components are recycled in times of nutrient stress. Combined inhibition of the ERK MAPK pathway and autophagy inhibited the growth of multiple preclinical models of PDAC. Based on these findings, combined ERK/MEK inhibition and hydroxychloroquine (HCQ) is currently under clinical evaluation (NCT03825289, NCT04386057). However, resistance to this combination has already been described. To identify sensitizers to CQ treatment, our lab performed a CRISPR-Cas9 mediated loss-of- function screen using an sgRNA library targeting genes associated with cancer signaling pathways. Surprisingly, we identified multiple autophagy related genes, indicating that concurrent inhibition of two nodes of the autophagy pathway may be a more effective method of inhibiting autophagy in PDAC. For example, we identified, PIK3C3, the gene that encodes for VPS34, a protein essential for the nucleation of autophagosomes, as a potential sensitizer to CQ treatment. Inhibition of VPS34 with the compound SAR405, resulted in both decreased autophagic flux and impaired proliferation. Additionally, VPS34 inhibition sensitized cells to CQ treatment, leading to further reduced proliferation. These results prompted us to hypothesize that inhibition of ULK1, a serine/threonine kinase critical for the initiation of autophagy, could also sensitize PDAC cells to CQ treatment. We found that ULK1 inhibition further reduced CQ-mediated anti-proliferative effects. Additionally, both ULK1 and VPS34 inhibitor treatment sensitized cells to PIKfyve inhibition with apilimod, a chemically and mechanistically distinct inhibitor of the termination phase of the autophagic pathway. Previous studies have indicated that autophagy inhibition alone is not a viable therapeutic avenue for the treatment of PDAC. Thus, we next determined whether vertical inhibition of the autophagy pathway improves the efficacy of combined inhibition of the RAS and autophagy pathway. We found that both VPS34 inhibition and ULK1 inhibition can decrease RAS inhibitor induced autophagic flux. Finally, vertical inhibition of the autophagic pathway via VPS34 inhibition and CQ sensitized PDAC cells to RAS inhibition to further reduce PDAC proliferation. Ongoing and future studies are aimed at elucidating the mechanism leading to decreased proliferation of combined anti-autophagy and RAS inhibitor therapies, as well as mechanistically understanding the effect of autophagy inhibition at multiple nodes on autophagic flux. Citation Format: Mallory K Roach, Jonathan M DeLiberty, Elyse G Schechter, Runying Yang, Noah L Pieper, Clint A Stalnecker, Kirsten L Bryant. Vertical inhibition of the autophagy pathway enhances sensitization to RAS MAPK pathway inhibition in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C036.

Abstract C018: Targeting KRAS inhibitor resistance by ferroptosis induction in PDAC

Abstract KRAS inhibitors (KRASi) have shown promising antitumor activity in PDAC patients; however, intrinsic and acquired resistance will likely preclude durable monotherapy responses in the clinic. Genetic and pharmacologic targeting of the KRAS pathway is associated with transcriptional and metabolic adaptations, including epithelial-to-mesenchymal transition (EMT) and dysregulated iron homeostasis that contribute to resistance. Therapy resistant mesenchymal cancer cells have a high reliance on iron that increases their susceptibility to ferroptosis, an iron-dependent form of cell death characterized by generation of lipid reactive oxygen species (ROS). We hypothesize that KRASi resistance characterized by EMT can be overcome by ferroptosis induction in PDAC. Here, we use in vitro and ex vivo PDAC models and transcriptomic and proteomic profiling to define critical ferroptotic liabilities in KRASi-treated PDAC. First, we evaluated the effect of inhibiting different ferroptosis defense mechanisms (e.g. SLC7A11, GPX4, FSP1) using newly developed ferroptosis inducers (FINs) in a panel of epithelial and mesenchymal PDAC cells. Mesenchymal cells were more sensitive to ferroptosis induction compared to epithelial cells. Since ferroptotic death is regulated by multiple ferroptosis defense pathways, and in general PDAC are relatively resistant to ferroptosis induction, it is unlikely that a single ferroptosis inducer will be successful. Therefore, targeting multiple nodes with FINs in a combinatorial approach may lead to a more pronounced effect. Combination GPX4 and SLC7A11 inhibition and GPX4 and FSP1 inhibition were the most effective combinations compared to respective monotherapies. Next, we evaluated if combination of KRASi and ferroptosis induction is synergistic. Transcriptomic analysis of KRAS-mutant PDAC cell lines sensitive or with a priori resistance to KRASi revealed that EMT-related gene sets were upregulated in KRASi resistant cell lines. Quantitative proteomics revealed that PDAC cells treated long-term with KRASi undergo EMT and likewise long-term KRASi in in vivo PDAC models induces EMT. A KRASi-anchored CRISPR/Cas9 screen showed that sgRNAs targeting GPX4 dropped out in multiple cell lines indicating that ferroptosis induction may be synergistic upon KRAS inhibition. Remarkably, KRASi induced an increase of lipid peroxidation levels in PDAC cells which was rescued with antioxidant ferrostatin-1. We also observed that previously generated KRASi resistant cells were more sensitive to GPX4 inhibition. Combination of GPX4 inhibitor with KRASi led to a synergistic effect on cell viability and a significantly higher level of lipid peroxidation compared to monotherapy. These findings highlight the importance of investigating ferroptosis induction as a novel strategy to overcome therapeutic resistance in KRAS-mutant PDAC. We aim to further evaluate these combinations in vivo to determine the best combinatorial strategies in PDAC to prevent or circumvent KRASi resistance. Citation Format: Aparna Padhye, Qijia Yu, Nicole Sindoni, Huan Zhang, Julien Dilly, Hanrong Feng, Andrew Aguirre, Joseph Mancias. Targeting KRAS inhibitor resistance by ferroptosis induction in PDAC [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C018.

Hemangioendothelioma of the spleen and liver in a 1-year-old child.

Concurrent occurrence of hemangioendothelioma in the spleen and liver of a 1-year-old child is a rare clinical case.She had a history of good health. Laboratory tests showed elevated inflammatory markers, but all other parameters were within normal range.In this article, we exhibit the typical clinical presentation and Pathology imaging features of this disease.CT imaging revealed a 9.8 cm×8.6 cm mass in the spleen, with areas of non-enhancing liquefactive necrosis. The mass exhibited heterogeneous enhancement during the arterial phase and progressive enhancement during the venous phase. Multiple nodules were observed in liver segments S2 and S4, the largest being approximately 3.7 cm×3.5 mm, with enhancement patterns similar to the splenic lesion. The patient underwent splenectomy, left hemihepatectomy, and cholecystectomy. Intraoperatively, the splenic mass exhibited some areas appearing spongy. Multiple nodules were observed in the left liver, presenting as gray-brown solid masses with a spongy texture. Pathology confirmed a diagnosis of hemangioendothelioma, supported by positive immunohistochemical staining for CD31, CD34, ERG, and FLI-1.

Generalized hierarchical coded caching

Optimizing data traffic is a key concern in content distribution networks to reduce the bandwidth needed to serve the requested content to the final users. In this context, hierarchical coded caching has been proposed as an effective method for reducing traffic. The typical two-level scenario consists in a tree-like structure: on the first layer, a set of intermediate nodes or helpers with local caches store and serve content to a subset of users at the second layer, who also have their local caches. A central server partially stores its content between the first- and second-layer caches, in uncoded form, and subsequently transmits coded messages to satisfy the files demanded by the users. This topology has been thoroughly analyzed in the literature over the past years; however, little attention has been put in arbitrary topologies, where users might be connected to multiple helper nodes, which is especially interesting for scenarios such as wireless networks. Thus, in this paper we study a hierarchical two-level coded caching system with an arbitrary topology and propose a novel content distribution method for this generalized model that leverages the multiple connections between users and helpers to improve the performance of the distribution process. We give achievability results for this system and compare its performance with other classical single-layer and two-layer coded caching systems. Our results show that further gains are possible by using two levels of caching in different operating regimes.

CT and MR imaging characteristics of primary hepatic lymphoma

BackgroundPrimary hepatic lymphoma (PHL) is a rare primary liver tumor. The purpose of this study was to investigate the crucial imaging characteristics of PHL.MethodsCT/MR data and clinical features of 16 patients with pathologically proven PHL were retrospectively reviewed. The assessed imaging characteristics included lesion distribution, growth appearance, density or signal characteristics, and typical signs during the dynamic enhancement.ResultsA total of sixteen patients were included in this study, ranging in age from 27 to 86 years (nine men and seven women; mean age, 62 years). Right upper abdominal pain, malaise, or first detection during physical examination were the chief complaint. Dynamic contrast-enhanced CT and MRI demonstrated focal masses in thirteen patients (13/16, 81%), and multiple nodules in three patients (3/16, 19%). The lesions showed vascular floating sign in 10 patients (10/16, 63%) and biliary pass-through sign in 6 patients (6/16, 37%). Necrosis in PHLs were observed in 10 patients (10/16, 63%) and presented target sign on T2WI in 5 out of 11 patients (5/11, 45%) undergoing MRI. After enhancement, PHLs had multinodular sign in the arterial phase in 10 patients (10/16, 63%), and presented washout of contrast medium in the portal-venous or delayed phase in 12 patients (12/16, 75%), pseudocapsule in the delayed phase in 15 patients (15/16, 94%), and double-ring sign in the delayed phase in 8 patients (8/16, 50%).ConclusionPHL is generally seen in elderly patients with elevated beta-2-microglobulin levels. Its distinctive imaging features including obviously low ADC value, biliary/vascular floating sign, multinodular sign and double-ring sign in the delayed phase, may help to diagnose and differentiate PHL.

A Novel TEK::GAB2 Gene Fusion in Pediatric Angiosarcoma of Pelvic soft Tissue: A Case Report and Literature Review.

Pediatric angiosarcoma of soft tissue, an extremely rare entity, remains poorly understood from a genetic standpoint. Herein, we present the case of a previously healthy 17-year-old girl with acute left hip pain. Subsequent magnetic resonance imaging revealed a 21.8 cm left pelvic sidewall mass with heterogeneous enhancement and multiple lung nodules. Biopsy of the tumor showed an infiltrative, hemorrhagic neoplasm composed primarily of atypical spindle to epithelioid cells. Focal vasoformative architecture was appreciated. Immunohistochemically, the tumor cells were strongly positive for CD31, ERG, and FLI-1, supporting the diagnosis of angiosarcoma. Genetic analysis identified a novel TEK::GAB2 gene fusion. TEK belongs to the angiopoietin receptor family, and its fusion with GAB2 is predicted to mediate tumorigenesis. This report expands the current knowledge on the spectrum of gene rearrangements of angiosarcoma.

Hypergraph-Based Influence Maximization in Online Social Networks

Influence maximization in online social networks is used to select a set of influential seed nodes to maximize the influence spread under a given diffusion model. However, most existing proposals have huge computational costs and only consider the dyadic influence relationship between two nodes, ignoring the higher-order influence relationships among multiple nodes. It limits the applicability and accuracy of existing influence diffusion models in real complex online social networks. To this end, in this paper, we present a novel information diffusion model by introducing hypergraph theory to determine the most influential nodes by jointly considering adjacent influence and higher-order influence relationships to improve diffusion efficiency. We mathematically formulate the influence maximization problem under higher-order influence relationships in online social networks. We further propose a hypergraph sampling greedy algorithm (HSGA) to effectively select the most influential seed nodes. In the HSGA, a random walk-based influence diffusion method and a Monte Carlo-based influence approximation method are devised to achieve fast approximation and calculation of node influences. We conduct simulation experiments on six real datasets for performance evaluations. Simulation results demonstrate the effectiveness and efficiency of the HSGA, and the HSGA has a lower computational cost and higher seed selection accuracy than comparison mechanisms.

Unmasking the Face of a Rare Disease – Progressive Nodular Histiocytosis

Abstract Non-Langerhans cell histiocytoses (NLCHs) are a complex group of disorders which can be difficult to diagnose due to their confounding clinical and microscopic features. Progressive nodular histiocytosis (PNH) is a rare subtype of NLCH characterized by benign, xanthogranulomatous lesions on the skin and mucous membranes. Clinical features of PNH involve the gradual emergence of multiple yellow-brown papules and nodules, with histological examination demonstrating the presence of foamy tissue macrophages, spindle-shaped cells within a fibrocollagenous matrix, and Touton cells. We present a rare case of a 55-year-old female patient with progressively increasing facial nodules, where clinical, histopathological, and immunohistochemical findings clinched the diagnosis of PNH.

Dynamic category-sensitive hypergraph inferring and homo-heterogeneous neighbor feature learning for drug-related microbe prediction.

The microbes in human body play a crucial role in influencing the functions of drugs, as they can regulate the activities and toxicities of drugs. Most recent methods for predicting drug-microbe associations are based on graph learning. However, the relationships among multiple drugs and microbes are complex, diverse, and heterogeneous. Existing methods often fail to fully model the relationships. In addition, the attributes of drug-microbe pairs exhibit long-distance spatial correlations, which previous methods have not integrated effectively. We propose a new prediction method named DHDMP which is designed to encode the relationships among multiple drugs and microbes and integrate the attributes of various neighbor nodes along with the pairwise long-distance correlations. First, we construct a hypergraph with dynamic topology, where each hyperedge represents a specific relationship among multiple drug nodes and microbe nodes. Considering the heterogeneity of node attributes across different categories, we developed a node category-sensitive hypergraph convolution network to encode these diverse relationships. Second, we construct homogeneous graphs for drugs and microbes respectively, as well as drug-microbe heterogeneous graph, facilitating the integration of features from both homogeneous and heterogeneous neighbors of each target node. Third, we introduce a graph convolutional network with cross-graph feature propagation ability to transfer node features from homogeneous to heterogeneous graphs for enhanced neighbor feature representation learning. The propagation strategy aids in the deep fusion of features from both types of neighbors. Finally, we design spatial cross-attention to encode the attributes of drug-microbe pairs, revealing long-distance correlations among multiple pairwise attribute patches. The comprehensive comparison experiments showed our method outperformed state-of-the-art methods for drug-microbe association prediction. The ablation studies demonstrated the effectiveness of node category-sensitive hypergraph convolution network, graph convolutional network with cross-graph feature propagation, and spatial cross-attention. Case studies on three drugs further showed DHDMP's potential application in discovering the reliable candidate microbes for the interested drugs. Source codes and supplementary materials are available at https://github.com/pingxuan-hlju/DHDMP.

Parallel isogeometric boundary element analysis with T-splines on CUDA

We present a framework for parallel isogeometric boundary element analysis (BEA) of elastic solids on CUDA. To deal with traction discontinuities, we propose a BEA model that supports multiple nodes and semi-discontinuous elements. The multiplicity of a node is defined by the number of regions containing any element influenced by the node. A region is a group of connected elements delimited by a closed crease curve. The default shape function of an element is determined by a linear operator applied to a set of basis functions. A BEA model is supposed to be generated from a watertight boundary representation of a solid. In this paper, we employ bicubic analysis-suitable T-splines. In this case, the shape of an element is defined by its Bézier extraction operator applied to the tensor product of Bernstein polynomials of degree 3. We describe the data structures of the BEA model and the main algorithms of the analysis pipeline on CUDA. In particular, we describe two strategies for parallel assembling of the linear system of equations. We also introduce a novel approach for inside integration based on the subdivision of the singular region in triangles with constant aspect ratio. In the T-splines context, we extend the Bézier extraction to handle unstructured T-meshes with crease edges. Moreover, we propose a scheme for embedding the influence of linked tangency handles on the shape of an element directly into the Bézier extraction operator. Such an embedding enables the removal of the corresponding nodes from the BEA model and the application of an alternative collocation method we discuss in the paper. We present several experiments to evaluate the accuracy and efficiency of the proposed framework. The results demonstrate that the GPU can be advantageously employed for parallelizing T-spline-based isogeometric analysis using boundary elements, achieving a speedup of up to 29x compared to the sequential code on a current laptop. We make the BEA code available in a prototype in MATLAB with a graphical interface that allows users to apply boundary conditions and visualize analysis results on the boundary.

Pulmonary Langerhans cell histiocytosis with multiple cavitary nodules after lung cancer surgery

AbstractPulmonary Langerhans cell histiocytosis (PLCH) is a subtype of Langerhans cell histiocytosis, a rare neoplastic disease characterized by lung involvement. Here, we present a case involving a patient with multiple cavitary nodules who was diagnosed with PLCH during surveillance after lung cancer surgery. A 74‐year‐old woman underwent right upper lobe resection surgery for right upper lobe lung adenocarcinoma, pStage IIA, 5 years ago. The patient underwent surveillance without adjuvant chemotherapy. During the fifth year of follow‐up, multiple nodules with cavitation were observed on computed tomography in both lung fields. Chemotherapy was considered to address the suspected recurrence of lung cancer; however, video‐assisted thoracoscopic surgery was performed due to the need for biomarker testing. Pathological examination led to the diagnosis of PLCH. This case emphasizes the importance of a proactive histological diagnosis to determine the appropriate treatment strategy, even in situations where lung cancer recurrence is clinically suspected.

The role of microwave ablation in combination with surgery in the management of multiple high-risk pulmonary nodules

The role of microwave ablation in combination with surgery in the management of multiple high-risk pulmonary nodules