Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Tasks

Transfer learning is a crucial technique for handling a small amount of data that is potentially related to other abundant data. However, most of the existing methods are focused on classification tasks using images and language datasets. Therefore, in order to expand the transfer learning scheme to regression tasks, we propose a novel transfer technique based on differential geometry, namely the Geometrically Aligned Transfer Encoder (GATE). In this method, we interpret the latent vectors from the model to exist on a Riemannian curved manifold. We find a proper diffeomorphism between pairs of tasks to ensure that every arbitrary point maps to a locally flat coordinate in the overlapping region, allowing the transfer of knowledge from the source to the target data. This also serves as an effective regularizer for the model to behave in extrapolation regions. In this article, we demonstrate that GATE outperforms conventional methods and exhibits stable behavior in both the latent space and extrapolation regions for various molecular graph datasets.Comment: 12+11 pages, 6+1 figures, 0+7 table

Grouping-matrix based Graph Pooling with Adaptive Number of Clusters

Graph pooling is a crucial operation for encoding hierarchical structures within graphs. Most existing graph pooling approaches formulate the problem as a node clustering task which effectively captures the graph topology. Conventional methods ask users to specify an appropriate number of clusters as a hyperparameter, then assume that all input graphs share the same number of clusters. In inductive settings where the number of clusters can vary, however, the model should be able to represent this variation in its pooling layers in order to learn suitable clusters. Thus we propose GMPool, a novel differentiable graph pooling architecture that automatically determines the appropriate number of clusters based on the input data. The main intuition involves a grouping matrix defined as a quadratic form of the pooling operator, which induces use of binary classification probabilities of pairwise combinations of nodes. GMPool obtains the pooling operator by first computing the grouping matrix, then decomposing it. Extensive evaluations on molecular property prediction tasks demonstrate that our method outperforms conventional methods.Comment: 10 pages, 3 figure

3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation

Pretraining molecular representations from large unlabeled data is essential for molecular property prediction due to the high cost of obtaining ground-truth labels. While there exist various 2D graph-based molecular pretraining approaches, these methods struggle to show statistically significant gains in predictive performance. Recent work have thus instead proposed 3D conformer-based pretraining under the task of denoising, which led to promising results. During downstream finetuning, however, models trained with 3D conformers require accurate atom-coordinates of previously unseen molecules, which are computationally expensive to acquire at scale. In light of this limitation, we propose D&D, a self-supervised molecular representation learning framework that pretrains a 2D graph encoder by distilling representations from a 3D denoiser. With denoising followed by cross-modal knowledge distillation, our approach enjoys use of knowledge obtained from denoising as well as painless application to downstream tasks with no access to accurate conformers. Experiments on real-world molecular property prediction datasets show that the graph encoder trained via D&D can infer 3D information based on the 2D graph and shows superior performance and label-efficiency against other baselines.Comment: 16 pages, 5 figure

Stimulated penetrating keratoplasty using real-time virtual intraoperative surgical optical coherence tomography

An intraoperative surgical microscope is an essential tool in a neuro-or ophthalmological surgical environment. Yet, it has an inherent limitation to classify subsurface information because it only provides the surface images. To compensate for and assist in this problem, combining the surgical microscope with optical coherence tomography (OCT) has been adapted. We developed a real-time virtual intraoperative surgical OCT (VISOCT) system by adapting a spectral-domain OCT scanner with a commercial surgical microscope. Thanks to our custommade beam splitting and image display subsystems, the OCT images and microscopic images are simultaneously visualized through an ocular lens or the eyepiece of the microscope. This improvement helps surgeons to focus on the operation without distraction to view OCT images on another separate display. Moreover, displaying the OCT live images on the eyepiece helps surgeon's depth perception during the surgeries. Finally, we successfully processed stimulated penetrating keratoplasty in live rabbits. We believe that these technical achievements are crucial to enhance the usability of the VISOCT system in a real surgical operating condition.open0

Simultaneous imaging using combined optical coherence tomography (OCT) and photoacoustic microscopy (PAM)

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Prognostic Impact of the Immunoscore Based on Whole-Slide Image Analysis of CD3+Tumor-Infiltrating Lymphocytes in Diffuse Large B-Cell Lymphoma

An Immunoscore based on tumor-infiltrating T-cell density was validated as a prognostic factor in patients with solid tumors. However, the potential utility of the Immunoscore in predicting the prognosis of patients with diffuse large B-cell lymphoma (DLBCL) is unclear. Here, the prognostic value of an Immunoscore based on tumor-infiltrating CD3+ T-cell density was evaluated in 104 patients with DLBCL who underwent R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone) therapy. Digitally scanned whole-slide images were analyzed using Aperio ImageScope software. CD3+ cell densities in the whole tumor area were quantitated using 3 different methods, including number of CD3+ cells/area (mm2), ratio of CD3+ cells to total cells, and ratio of CD3+ cells to CD20+ cells. There was a high concordance among the 3 methods. Patients with low CD3+ cell density had an elevated serum lactate dehydrogenase level and a high Ki-67 proliferation index (all, P < .05). Patients with low CD3+ cell density, according to all 3 methods, had worse overall survival (OS) and worse progression-free survival (P < .05, all). They also had poor OS, independent of MYC/BCL2 double expression (DE) status, Eastern Cooperative Oncology Group performance status, or Ann Arbor stage (all, P < .05). These results were validated using 2 publicly available data sets. In both validation cohorts, patients with low CD3E mRNA expression had an elevated serum lactate dehydrogenase level, extranodal site involvement, and DE status (P < .05). They also had worse progression-free survival (P =.067 and P =.002, respectively) and OS (both P < .05). A low CD3E mRNA level was predictive of poor OS, independent of DE status. An Immunoscore based on whole-slide image analysis of CD3+ T-cell infiltration was sufficient to predict survival in patients with DLBCL. Low CD3+ cell density was a poor prognostic factor, independent of other prognostic parameters and DE status.& COPY; 2023 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights reserved.N

Immunophenotypic landscape and prognosis of diffuse large B-cell lymphoma with MYC/BCL2 double expression: An analysis of a prospectively immunoprofiled cohort

Simple Summary Diffuse large B-cell lymphoma (DLBCL) with MYC/BCL2 double-expression (DE), a recently proposed poor prognostic group, can be easily identified by immunohistochemistry in routine clinical practice. However, clinical outcomes of DE-DLBCL patients vary immensely after R-CHOP immunochemotherapy and prognostic impact of MYC/BCL2-DE was conflicting according to the cell-of-origin, i.e., between germinal center-B-cell (GCB)- and non-GCB-DLBCLs. This implies the heterogeneity within DE-DLBCLs and emphasizes a need for proper risk stratification to select the patients who require more intensive therapy. By analyzing a prospectively immunoprofiled cohort of consecutively diagnosed DLBCL patients, we confirmed the poor prognostic value of MYC/BCL2-DE in DLBCL patients treated with R-CHOP irrespective of the cell-of-origin and international prognostic index. DE-DLBCLs with a concurrent risk factor, especially, elevated serum lactate dehydrogenase (LDH), had the worst survival and DE-DLBCL patients with normal LDH had clinical outcomes similar to those of non-DE-DLBCL patients. Risk stratification of DE-DLBCL based on serum LDH may guide clinical decision-making for DE-DLBCL patients. Diffuse large B-cell lymphoma (DLBCL) patients with MYC/BCL2 double expression (DE) show poor prognosis and their clinical outcomes after R-CHOP therapy vary immensely. We investigated the prognostic value of DE in aggressive B-cell lymphoma patients (n = 461), including those with DLBCL (n = 417) and high-grade B-cell lymphoma (HGBL; n = 44), in a prospectively immunoprofiled cohort. DE was observed in 27.8% of DLBCLs and 43.2% of HGBLs (p = 0.058). DE-DLBCL patients were older (p = 0.040) and more frequently exhibited elevated serum LDH levels (p = 0.002), higher international prognostic index (IPI; p = 0.042), non-germinal-center B-cell phenotype (p < 0.001), and poor response to therapy (p = 0.042) compared to non-DE-DLBCL patients. In R-CHOP-treated DLBCL patients, DE status predicted poor PFS and OS independently of IPI (p < 0.001 for both). Additionally, in DE-DLBCL patients, older age (>60 years; p = 0.017), involvement of >= 2 extranodal sites (p = 0.021), bone marrow involvement (p = 0.001), high IPI (p = 0.017), CD10 expression (p = 0.006), poor performance status (p = 0.028), and elevated LDH levels (p < 0.001) were significantly associated with poor OS. Notably, DE-DLBCL patients with normal LDH levels exhibited similar PFS and OS to those of patients with non-DE-DLBCL. Our findings suggest that MYC/BCL2 DE predicts poor prognosis in DLBCL. Risk stratification of DE-DLBCL patients based on LDH levels may guide clinical decision-making for DE-DLBCL patients.Y

Distinct and overlapping features of nodal peripheral T-cell lymphomas exhibiting a follicular helper T-cell phenotype: a multicenter study emphasizing the clinicopathological significance of follicular helper T-cell marker expression

Nodal peripheral T-cell lymphoma (PTCL) is a heterogeneous category including angioim-munoblastic T-cell lymphoma (AITL), PTCL of follicular helper T-cell (Tfh) phenotype (PTCL-Tfh), and PTCL, not otherwise specified (PTCL-NOS). We explored Tfh marker profiles in nodal PTCL. Nodal PTCLs (n = 129) were reclassified into AITL (58%; 75/129), PTCL-Tfh (26%; 34/129), and PTCL-NOS (16%; 20/129). Histologically, clear cell clusters, high endothelial venules, follicular den-dritic cell proliferation, EBV+ cells, and Hodgkin-Reed-Sternberg (HRS)-like cells were more com-mon in AITL than PTCL-Tfh (HRS-like cells, P = .005; otherwise, P < .001) and PTCL-NOS (HRS-like cells, P = .028; otherwise, P < .001). PTCL-NOS had a higher Ki-67 index than AITL (P = .001) and PTCL-Tfh (P = .002). Clinically, AITL had frequent B symptoms (versus PTCL-Tfh, P = .010), while PTCL-NOS exhibited low stage (versus AITL + PTCL-Tfh, P = .036). Positive Tfh markers were greater in AITL (3.5 +/- 1.1) than PTCL-Tfh (2.9 +/- 0.9; P = .006) and PTCL-NOS (0.5 +/- 0.5; P < .001). Tfh markers showed close correlations among them and AITL-defining histol-ogy. By clustering analysis, AITL and PTCL-NOS were relatively exclusively clustered, while PTCL-Tfh overlapped with them. Survival was not different among the PTCL entities. By Cox regression, sex and ECOG performance status (PS) independently predicted shorter progression-free survival in the whole cohort (male, P = .001, HR = 2.5; PS > 2, P = .010, HR = 1.9) and in 'Tfh-lymphomas' (ie, AITL + PTCL-Tfh) (male, P = .001, HR = 2.6; PS > 2, P = .016, HR = 2.1), while only PS predicted shorter overall survival (OS) in the whole cohort (P = .012, HR = 2.7) and in 'Tfh-lym-phomas' (P = .001; HR = 3.2). ICOS predicted favorable OS in 'Tfh-lymphomas' (log-rank; P = .016). Despite the overlapping features, nodal PTCL entities could be characterized by Tfh markers revealing clinicopathologic implications.(c) 2022 Elsevier Inc. All rights reserved.N

Attention-based solubility prediction of polysulfide and electrolyte analysis for lithium–sulfur batteries

Abstract During the continuous charge and discharge process in lithium-sulfur batteries, one of the next-generation batteries, polysulfides are generated in the battery’s electrolyte, and impact its performance in terms of power and capacity by involving the process. The amount of polysulfides in the electrolyte could be estimated by the change of the Gibbs free energy of the electrolyte, $$\Delta _{mix}\textrm{G}$$ Δ mix G in the presence of polysulfide. However, obtaining $$\Delta _{mix}\textrm{G}$$ Δ mix G of the diverse mixtures of components in the electrolyte is a complex and expensive task that shows itself as a bottleneck in optimization of electrolytes. In this work, we present a machine-learning approach for predicting $$\Delta _{mix}\textrm{G}$$ Δ mix G of electrolytes. The proposed architecture utilizes (1) an attention-based model (Attentive FP), a contrastive learning model (MolCLR) or morgan fingerprints to represent chemical components, and (2) transformers to account for the interactions between chemicals in the electrolyte. This architecture was not only capable of predicting electrolyte properties, including those of chemicals not used during training, but also providing insights into chemical interactions within electrolytes. It revealed that interactions with other chemicals relate to the logP and molecular weight of the chemicals